Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Activation of an Olfactory Receptor Inhibits Proliferation of Prostate Cancer Cells

Olfactory receptors (ORs) are expressed not only in the sensory neurons of the olfactory epithelium, where they detect volatile substances, but also in various other tissues where their potential functions are largely unknown. Here, we report the physiological characterization of human OR51E2, also named prostate-specific G-protein-coupled receptor (PSGR) due to its reported up-regulation in prostate cancer. We identified androstenone derivatives as ligands for the recombinant receptor. PSGR can also be activated with the odorant β-ionone. Activation of the endogenous receptor in prostate cancer cells by the identified ligands evoked an intracellular Ca²⁺ increase. Exposure to β-ionone resulted in the activation of members of the MAPK family and inhibition of cell proliferation. Our data give support to the hypothesis that because PSGR signaling could reduce growth of prostate cancer cells, specific receptor ligands might therefore be potential candidates for prostate cancer treatment.Excessive signaling by G-protein-coupled receptors (GPCRs)³ such as endothelin A receptor (1), bradykinin 1 receptor (2), follicle-stimulating hormone receptor (3), and thrombin receptor (4, 5) is known to occur in prostate cancers due to strong overexpression of the respective receptors. Activation of some of these GPCRs results in androgen-independent androgen receptor activation, thus promoting the transition of prostate cancer cells from an androgen-dependent to an androgen-independent state (6, 7).The prostate-specific G-protein-coupled receptor (PSGR) is a class A GPCR that was initially identified as a prostate-specific tumor biomarker (8–10). It is specifically expressed in prostate epithelial cells, and its expression increases significantly in human prostate intraepithelial neoplasia and prostate tumors, suggesting that PSGR may play an important role in early prostate cancer development and progression (9, 11). Although expression of the human PSGR was found to be prostate-specific (10, 12), mRNA can also be detected in the olfactory zone and the medulla oblongata of the human brain (12). Human PSGR shares 93% amino acid homology to the respective mouse and rat homologues, which are also expressed in the brain (12). Interestingly, PSGR has numerous sequence motifs in common with the large superfamily of olfactory receptors (ORs), which build the largest class of human GPCRs and allow the recognition of a wide range of structurally diverse molecules in the nasal epithelium (13–15). Recently, also the steroid hormones androstenone and androstadienone were identified as OR ligands (16). In addition to their role in the sensory neurons of the nose, ORs have been found in different tissues throughout the body (17, 18). Their function(s) in these extranasal locations are questionable except for in a few cases where functional studies have been performed in spermatozoa (19, 20) and in enterochromaffin cells of the gastrointestinal tract (21).Here, we report the identification of steroid ligands of heterologously expressed PSGR and investigate the functional relevance of PSGR expression in prostate tissue. Steroid hormones elicited rapid Ca²⁺ responses in the LNCaP prostate cancer cell line and in primary human prostate epithelial cells. Moreover, activated PSGR causes phosphorylation of p38 and stress-activated protein kinase/c-Jun NH₂-terminal kinase (SAPK/JNK) mitogen-activated protein kinases (MAPKs), resulting in reduced proliferation rates in prostate cancer cells.     

Complex Lipid Requirements for SNARE- and SNARE Chaperone-dependent Membrane Fusion

Membrane fusion without lysis has been reconstituted with purified yeast vacuolar SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors), the SNARE chaperones Sec17p/Sec18p and the multifunctional HOPS complex, which includes a subunit of the SNARE-interactive Sec1-Munc18 family, and vacuolar lipids: phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), phosphatidic acid (PA), cardiolipin (CL), ergosterol (ERG), diacylglycerol (DAG), and phosphatidylinositol 3-phosphate (PI3P). We now report that many of these lipids are required for rapid and efficient fusion of the reconstituted SNARE proteoliposomes in the presence of SNARE chaperones. Omission of either PE, PA, or PI3P from the complete set of lipids strongly reduces fusion, and PC, PE, PA, and PI3P constitute a minimal set of lipids for fusion. PA could neither be replaced by other lipids with small headgroups such as DAG or ERG nor by the acidic lipids PS or PI. PA is needed for full association of HOPS and Sec18p with proteoliposomes having a minimal set of lipids. Strikingly, PA and PE are as essential for SNARE complex assembly as for fusion, suggesting that these lipids facilitate functional interactions among SNAREs and SNARE chaperones.Biological membrane fusion is the regulated rearrangement of the lipids in two apposed sealed membranes to form one bilayer while mixing lumenal contents without leakage or lysis. It is fundamental for intracellular vesicular traffic, cell growth and division, regulated secretion of hormones and other blood proteins, and neurotransmission and thus has attracted wide and sustained study (1, 2). Its fundamental mechanisms are conserved and employ a Rab-family GTPase, proteins which bind to the GTP-bound form of a Rab, termed its “effectors” (3), and SNARE³ (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) proteins (4) with their attendant chaperones. SNAREs are integral or peripheral membrane proteins with characteristic heptad-repeat domains, which can associate in 4-helical coiled-coils (5), termed “cis-SNARE complexes,” if they are all anchored to the same membrane bilayer, or “trans-SNARE complexes” if they are anchored to apposed membranes.Stable membrane proximity (docking) does not suffice for fusion. Studies in model systems have shown that fusion can be promoted by any of several agents, which promote bilayer rearrangement, such as diacylglycerol (6), high levels of calcium (7), viral-encoded fusion proteins (8, 9), or SNAREs (10, 11). These studies frequently employed liposomes or proteoliposomes of simple lipid composition, suggesting that fusion may not have stringent requirements of lipid head group species. However, each of these model fusion reactions is accompanied by substantial lysis (12–15), whereas the preservation of subcellular compartments is a hallmark of physiological membrane fusion.We have studied membrane fusion with the vacuole (lysosome) of Saccharomyces cerevisiae (reviewed in Ref. 16). The fusion of isolated vacuoles requires the Rab Ypt7p, 4 SNAREs (Vam3p, Vti1p, Vam7p, and Nyv1p), the SNARE chaperones Sec17p (α-soluble N-ethylmaleimide-sensitive factor attachment protein)/Sec18p (N-ethylmaleimide-sensitive factor) and the hexameric HOPS complex (17), and key “regulatory” lipids including ERG, phosphoinositides, and DAG (18). HOPS interacts physically or functionally with each component of this fusion system. HOPS stably associates with Ypt7p in its GTP-bound state (19). One HOPS subunit, Vps33p, is a member of the Sec1-Munc18 family of SNARE-binding proteins, and HOPS exhibits direct affinity for SNAREs (17, 20–22) and proofreads correct vacuolar SNARE pairing (23). HOPS also has direct affinity for phosphoinositides (17). The SNAREs on isolated vacuoles are in cis-complexes, which are disassembled by Sec17p, Sec18p, and ATP (24). Docking requires Ypt7p (25) and HOPS (17). During docking, vacuoles are drawn against each other until each has a substantial membrane domain tightly apposed to the other. Each of the proteins (26) and lipids (18) required for fusion becomes enriched in a ring-shaped microdomain, the “vertex ring,” which surrounds the two tightly apposed membrane domains. Not only do the proteins depend on each other, in a cascade fashion, for vertex ring enrichment, and the lipids depend on each other for their vertex ring enrichment as well, but the lipids and proteins are mutually interdependent for their enrichment at this ring-shaped microdomain (18, 27). Fusion occurs around the ring, joining the two organelles. The fusion of vacuoles bearing physiological fusion constituents does not cause measurable organelle lysis, although fusion supported exclusively by higher levels of SNARE proteins is accompanied by massive lysis (28), in accord with model liposome studies (14). Thus fusion microdomain assembly and the coordinate action of SNAREs with other proteins and lipids to promote fusion without lysis are central topics in membrane fusion studies.Reconstitution of fusion with pure components allows chemical definition of essential elements of this biologically important reaction. Although SNAREs can drive a slow fusion of PC/PS proteoliposomes (29), this was not stimulated by HOPS and Sec17p/Sec18p (30). SNARE proteoliposomes bearing all the vacuolar lipids (18, 31–33), PC, PE, PI, PS, CL, PA, ERG, DAG, PI3P, and phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂), showed rapid and efficient fusion that was fully dependent on Sec17p/Sec18p and HOPS (30). The omission of either DAG, ERG, or phosphoinositide from the liposomes caused a marked reduction in fusion (30). We now report that PE and PA are also necessary for rapid and efficient fusion, function in distinct manners, and are required for efficient assembly of newly formed SNARE complexes by the SNARE chaperones Sec17p/Sec18p and HOPS.     

Candidate Serological Biomarkers for Cancer Identified from the Secretomes of 23 Cancer Cell Lines and the Human Protein Atlas

Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6–137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.Cancer is a major cause of mortality worldwide, accounting for 10 million new cases and more than 6 million deaths per year. In developing countries, cancer is the second most common cause of death, accounting for 23–25% of the overall mortality rate (1). Notwithstanding improvements in diagnostic imaging technologies and medical treatments, the long term survival of most cancer patients is poor. Cancer therapy is often challenging because the majority of cancers are initially diagnosed in their advanced stages. For example, the 5-year survival rate for patients with HNC¹ is less than 50%. More than 50% of all HNC patients have advanced disease at the time of diagnosis (2, 3). Enormous effort has been devoted to screening and characterizing cancer markers for the early detection of cancer. Thus far, these markers include carcinoembryonic antigen, prostate-specific antigen, α-fetoprotein, CA 125, CA 15-3, and CA 19-9. Unfortunately, most biomarkers have limited specificity, sensitivity, or both (4). Thus, there is a growing consensus that marker panels, which are more sensitive and specific than individual markers, would increase the efficacy and accuracy of early stage cancer detection (4–8). The development of novel and useful biomarker panels is therefore an urgent need in the field of cancer management.Proteomics technology platforms are promising tools for the discovery of new cancer biomarkers (9). Over the past decade, serum and plasma have been the major targets of proteomics studies aimed at identifying potential cancer biomarkers (10–13). However, the progress of these studies has been hampered by the complex nature of serum/plasma samples and the large dynamic range between the concentrations of different proteins (14). As cancer biomarkers are likely to be present in low amounts in blood samples, the direct isolation of these markers from plasma and serum samples requires a labor-intensive process involving the depletion of abundant proteins and extensive protein fractionation prior to mass spectrometric analysis (15–18). Alternatively, the secretome, or group of proteins secreted by cancer cells (19), can be analyzed to identify circulating molecules present at elevated levels in serum or plasma samples from cancer patients. These proteins have the potential to act as cancer-derived marker candidates, which are distinct from host-responsive marker candidates. We, along with other groups, have demonstrated the efficacy of secretome-based strategies in a variety of cancer types, including NPC (20), breast cancer (21, 22), lung cancer (23, 24), CRC (25, 26), oral cancer (27), prostate cancer (28, 29), ovarian cancer (30), and Hodgkin lymphoma (31). In these studies, proteins secreted from cancer cells into serum-free media were resolved by one- or two-dimensional gels followed by in-gel tryptic digestion and analysis via MALDI-TOF MS or LC-MS/MS. Alternatively, the proteins were trypsin-digested in solution and analyzed by LC-MS/MS. In general, more proteins were detected in the secretome using the LC-MS/MS method than the MALDI-TOF MS method. Advanced protein separation and identification technologies have made it possible to detect more proteins in the secretomes of cancer cells, thereby facilitating the discovery of cancer biomarkers.Although the cancer cell secretomes of various tumor types have been individually analyzed by different groups using distinct protocols, few studies have used the same protocol to compare cancer cell secretomes derived from different tumor types. We previously assessed the secretomes of 21 cancer cell lines derived from 12 cancer types (i.e. consisting of 795 protein identities and 325 non-redundant proteins) by one-dimensional gel and MALDI-TOF MS (25). Our preliminary findings revealed that different cell lines have distinct secreted protein profiles and that several putative biomarkers, such as Mac-2BP (20, 26, 27, 29) and cathepsin D (21, 23, 32), present in the secretome of a given cancer cell type are commonly shared among different cancers. These observations suggest that an in-depth comparison of secretomes derived from different tumor types may identify marker candidates common to most cancers as well as markers for specific cancer types. As an increasing number of proteins are identified in the secretomes of various cancer cell lines, scientists are faced with the challenge of quickly and efficiently narrowing down the list to candidates with higher chances of success during validation testing with precious clinical specimens.In the present study, we applied one-dimensional SDS-PAGE in conjunction with nano-LC-MS/MS (GeLC-MS/MS) (33, 34) to analyze the conditioned media of 23 cancer cell lines derived from 11 cancer types, including NPC, breast cancer, bladder cancer, cervical cancer, CRC, epidermoid carcinoma, liver cancer, lung cancer, T cell lymphoma, oral cancer, and pancreatic cancer. Within this data set, 4,584 non-redundant proteins were identified from a total of 23 cell lines, yielding an average of ∼1,300 proteins per cell line. Potential marker candidates were identified via the comparative analysis of different cell line secretomes and by putative linkages to cancer-relevant pathways. The selected proteins were further compared with the HPA (35) to generate a focused data set of proteins that are secreted or released, cancer type-specific, and highly expressed in human cancer tissues. Finally, we selectively validated four proteins as potential serological cancer markers using blood samples from cancer patients.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Down-regulation of Ras-related Protein Rab 5C-dependent Endocytosis and Glycolysis in Cisplatin-resistant Ovarian Cancer Cell Lines

Drug resistance poses a major challenge to ovarian cancer treatment. Understanding mechanisms of drug resistance is important for finding new therapeutic targets. In the present work, a cisplatin-resistant ovarian cancer cell line A2780-DR was established with a resistance index of 6.64. The cellular accumulation of cisplatin was significantly reduced in A2780-DR cells as compared with A2780 cells consistent with the general character of drug resistance. Quantitative proteomic analysis identified 340 differentially expressed proteins between A2780 and A2780-DR cells, which involve in diverse cellular processes, including metabolic process, cellular component biogenesis, cellular processes, and stress responses. Expression levels of Ras-related proteins Rab 5C and Rab 11B in A2780-DR cells were lower than those in A2780 cells as confirmed by real-time quantitative PCR and Western blotting. The short hairpin (sh)RNA-mediated knockdown of Rab 5C in A2780 cells resulted in markedly increased resistance to cisplatin whereas overexpression of Rab 5C in A2780-DR cells increases sensitivity to cisplatin, demonstrating that Rab 5C-dependent endocytosis plays an important role in cisplatin resistance. Our results also showed that expressions of glycolytic enzymes pyruvate kinase, glucose-6-phosphate isomerase, fructose-bisphosphate aldolase, lactate dehydrogenase, and phosphoglycerate kinase 1 were down-regulated in drug resistant cells, indicating drug resistance in ovarian cancer is directly associated with a decrease in glycolysis. Furthermore, it was found that glutathione reductase were up-regulated in A2780-DR, whereas vimentin, HSP90, and Annexin A1 and A2 were down-regulated. Taken together, our results suggest that drug resistance in ovarian cancer cell line A2780 is caused by multifactorial traits, including the down-regulation of Rab 5C-dependent endocytosis of cisplatin, glycolytic enzymes, and vimentin, and up-regulation of antioxidant proteins, suggesting Rab 5C is a potential target for treatment of drug-resistant ovarian cancer. This constitutes a further step toward a comprehensive understanding of drug resistance in ovarian cancer.Ovarian cancer is the major cause of death in women with gynecological cancer. Early diagnosis of ovarian cancer is difficult, while its progression is fast. The standard treatment is surgical removal followed by platinum-taxane chemotherapy. However, the efficacy of the traditional surgery and chemotherapy is rather compromised and platinum resistant cancer recurs in ∼25% of patients within six months, and the overall five-year survival rate is about 31% (1–3). Virtually no efficient second line treatment is available. In order to increase survival rates from ovarian cancer and enhance patients'' quality of life, new therapeutic targets are urgently required, necessitating a deeper understanding of molecular mechanisms of drug resistance.Mechanisms of drug-resistance in ovarian cancer have been extensively studied over the last 30 years. Earlier studies have found that multiple factors are linked to drug resistance in human ovarian cancer including reduced intracellular drug accumulation, intracellular cisplatin inactivation, and increased DNA repair (4). Reduced cellular drug accumulation is mediated by the copper transporter-1 responsible for the influx of cisplatin (5–9) and MDR1, which encodes an integral membrane protein named P-glycoprotein for the active efflux of platinum drugs. Up-regulation of MDR1 has been observed in cisplatin-treated ovarian cancer cells although cisplatin is not a substrate of P-glycoprotein (10–13). A fraction of intracellular cisplatin can be converted into cisplatin-thiol conjugates by glutathione-S-transferase (GST) π, leading to inactivation of cisplatin. Up-regulation of both GSTπ and γ-glutamylcysteine synthetase has been associated with cisplatin resistance in ovarian, cervical and lung cancer cell lines (14–18). Binding of cisplatin to DNA leads to intrastrand or interstrand cross-links that alter the structure of the DNA molecule causing DNA damage. It has been amply documented that pathways for recognition and repair of damaged DNA are up-regulated in drug-resistant cancer cells (19–26). Furthermore, the secondary mutations have been identified, which restore the wild-type BRCA2 reading frame enhancing the acquired resistance to platinum-based chemotherapy (24). Alternations in other signaling pathways have also been found in drug resistant ovarian cancer (27–29). For example, DNA-PK phosphorylates RAC-alpha serine/threonine-protein kinase (AKT) and inhibits cisplatin-mediated apoptosis (28); and silencing of HDAC4 increases acetyl-STAT1 levels to prevent platinum-induced STAT1 activation and restore cisplatin sensitivity (29).Proteomics is playing an increasingly important role in identifying differentially expressed proteins between drug-resistant and drug sensitive ovarian cancer cells (30–35). An earlier study has identified 57 differentially expressed proteins in human ovarian cancer cells and their platinum-resistant sublines, including annexin A3, destrin, cofilin 1, Glutathione-S-transferase omega 1, and cytosolic NADP+-dependent isocitrate dehydrogenase using 2D gel electrophoresis (30). Employing a similar 2D gel electrophoresis approach, changes in protein expressions of capsid glycoprotein, fructose-bisphosphate aldolase C, heterogeneous nuclear ribonucleoproteins A2/B1, putative RNA-binding protein 3, Ran-specific GTPase-activating protein, ubiquitin carboxyl-terminal hydrolase isozyme L1, stathmin, ATPSH protein, chromobox protein homolog3, and phosphoglycerate kinase 1 (PGK)¹ were found in A2780 and drug-resistant A2780 cells (32). It is worth mentioning that ALDO and PGK are glycolytic enzymes, indicating that glycolysis plays a role in drug resistance. Studies have demonstrated that resistance to platinum drugs in ovarian cancer cells is linked to mitochondrial dysfunctions in oxidative phosphorylation and energy production (36–40). Mitochondrial proteomic analysis of drug-resistant cells has shown that five mitochondrial proteins (ATP-a, PRDX3, PHB, ETF, and ALDH) that participate in the electron transport respiratory chain are down-regulated in drug-resistant cell lines (41). PRDX3 is involved in redox regulation of the cell to protect radical-sensitive enzymes from oxidative damage. However, it is not clear how down-regulation of PRDX3 is associated with drug-resistance. A more recent study showed that activated leukocyte cell adhesion molecule (ALCA) and A kinase anchoring protein 12 (AKAP12) are elevated in drug-resistant A2780-CP20 cells by quantifying the mitochondrial proteins (42). Despite these efforts, the drug-resistance mechanisms are not yet well understood.In this work, we established and characterized a drug-resistant cell line A2780-DR from A2780 cells. We employed a quantitative proteomic method to identify the differentially expressed proteins between A2780 and A2780-DR cells. Expression changes of selected proteins were confirmed by qPCR and Western blotting. We also used shRNA silencing to explore functions of Rab 5C and Rab 11B proteins in drug resistance. Our data indicate that the differentially expressed proteins participate in a variety of cellular processes and enhance our understanding of the mechanisms of drug resistance in ovarian cancer cells.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]