Izabela Sokolowska

Investigation of stable and transient protein–protein interactions: Past, present, and future

This article presents an overview of the literature and a review of recent advances in the analysis of stable and transient protein–protein interactions (PPIs) with a focus on their function within cells, organs, and organisms. The significance of PTMs within the PPIs is also discussed. We focus on methods to study PPIs and methods of detecting PPIs, with particular emphasis on electrophoresis‐based and MS‐based investigation of PPIs, including specific examples. The validation of PPIs is emphasized and the limitations of the current methods for studying stable and transient PPIs are discussed. Perspectives regarding PPIs, with focus on bioinformatics and transient PPIs are also provided.

Protein–protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein–protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Potential biomarkers in psychiatry: focus on the cholesterol system.

● Introduction ● Methods for proteomic analysis – Sample fractionation/biochemical fractionation – MS ● The cholesterol system – Cholesterol and apolipoproteins – Cholesterol – Apos – ApoE – ApoB – ApoA1 and ApoA4 ● The cholesterol system and specific disorders – Alzheimers disease – Schizophrenia – Depression – Developmental disorders: ASDs ● Discussion – Proteomic considerations for analysis of Apos – Considering diet and lifestyle effects on the cholesterol system – Consequences of disturbed cholesterol and Apos in the CNS ● Conclusion

Proteomic analysis of plasma membranes isolated from undifferentiated and differentiated HepaRG cells

Liver infection with hepatitis B virus (HBV), a DNA virus of the Hepadnaviridae family, leads to severe disease, such as fibrosis, cirrhosis and hepatocellular carcinoma. The early steps of the viral life cycle are largely obscure and the host cell plasma membrane receptors are not known. HepaRG is the only proliferating cell line supporting HBV infection in vitro, following specific differentiation, allowing for investigation of new host host-cell factors involved in viral entry, within a more robust and reproducible environment. Viral infection generally begins with receptor recognition at the host cell surface, following highly specific cell-virus interactions. Most of these interactions are expected to take place at the plasma membrane of the HepaRG cells. In the present study, we used this cell line to explore changes between the plasma membrane of undifferentiated (−) and differentiated (+) cells and to identify differentially-regulated proteins or signaling networks that might potentially be involved in HBV entry. Our initial study identified a series of proteins that are differentially expressed in the plasma membrane of (−) and (+) cells and are good candidates for potential cell-virus interactions. To our knowledge, this is the first study using functional proteomics to study plasma membrane proteins from HepaRG cells, providing a platform for future experiments that will allow us to understand the cell-virus interaction and mechanism of HBV viral infection.

Identification of Potential Tumor Differentiation Factor (TDF) Receptor from Steroid-responsive and Steroid-resistant Breast Cancer Cells

Background: Tumor differentiation factor (TDF) is a newly identified pituitary protein with no known receptor. Results: Heat shock 70-kDa proteins are potential TDF receptor candidates. Conclusion: TDF acts on breast cells through a novel pathway. Significance: These data may help to elucidate the role of TDF. Tumor differentiation factor (TDF) is a recently discovered protein, produced by the pituitary gland and secreted into the bloodstream. TDF and TDF-P1, a 20-amino acid peptide selected from the open reading frame of TDF, induce differentiation in human breast and prostate cancer cells but not in other cells. TDF protein has no identified site of action or receptor, and its mechanism of action is unknown. Here, we used TDF-P1 to purify and identify potential TDF receptor (TDF-R) candidates from MCF7 steroid-responsive breast cancer cells and non-breast HeLa cancerous cells using affinity purification chromatography (AP), and mass spectrometry (MS). We identified four candidate proteins from the 70-kDa heat shock protein (HSP70) family in MCF7 cells. Experiments in non-breast HeLa cancerous cells did not identify any TDF-R candidates. AP and MS experiments were validated by AP and Western blotting (WB). We additionally looked for TDF-R in steroid-resistant BT-549 cells and human dermal fibroblasts (HDF-a) using AP and WB. TDF-P1 interacts with potential TDF-R candidates from MCF7 and BT-549 breast cells but not from HeLa or HDF-a cells. Immunofluorescence (IF) experiments identified GRP78, a TDF-R candidate, at the cell surface of MCF7, BT-549 breast cells, and HeLa cells but not HDF-a cells. IF of other HSP70 proteins demonstrated labeling on all four cell types. These results point toward GRP78 and HSP70 proteins as strong TDF-R candidates and suggest that TDF interacts with its receptor, exclusively on breast cells, through a steroid-independent pathway.

Disulfide proteomics for identification of extracellular or secreted proteins.

The combination of SDS‐PAGE and MS is one of the most powerful and perhaps most frequently used gel‐based proteomics approaches in protein identification. However, one drawback of this method is that separation takes place under denaturing and reducing (R) conditions and as a consequence, all proteins with identical apparent molecular mass (Mr) will run together. Therefore, low‐abundant proteins may not be easily identified. Another way of investigating proteins by proteomics is by analyzing subproteomes from a total proteome such as phosphoproteomics, glycoproteomics, or disulfide proteomics. Here, we took advantage of the property of secreted proteins to form disulfide bridges and investigated disulfide‐linked proteins, using SDS‐PAGE under nonreducing (NR) conditions. We separated sera from normal subjects and from patients with various diseases by SDS‐PAGE (NR) and (R) conditions, followed by LC‐MS/MS analysis. Although we did not see any detectable difference between the sera separated by SDS‐PAGE(R), we could easily identify the disulfide‐linked proteins separated by SDS‐PAGE (NR). LC‐MS/MS analysis of the disulfide‐linked proteins correctly identified haptoglobin (Hp), a disulfide‐linked protein usually found as a heterotetramer or as a disulfide‐linked heteropolymer. Western blotting under NR and R conditions using anti‐Hp antibodies confirmed the LC‐MS/MS experiments and further confirmed that upon reduction, the disulfide‐linked Hp heterotetramers and polymers were no longer disulfide‐linked polymers. These data suggest that simply by separating samples on SDS‐PAGEunder NR conditions, a different, new proteomics subset can be revealed and then identified.

Identification of consistent alkylation of cysteine-less peptides in a proteomics experiment.

In a proteomics experiment, reduction and alkylation of proteins prior to enzymatic digestion ensures high sequence coverage of that protein during a database search. However, the alkylation procedure uses an excess of an alkylating agent such as iodoacetamide (IAA). Therefore, although other amino acids are alkylated, these modified peptides are not identified in a database search. Here we show that a large proportion of peptides are mono- and di-alkylated by IAA and therefore not identified via a database search. The first alkylation consistently takes place at the N-terminal amino acid. Therefore, we propose that during the database search conducted during a proteomics experiment, one should have the option of searching for any alkylated peptide at the N-terminal amino acid.

Automated Mass Spectrometry-Based Functional Assay for the Routine Analysis of the Secretome

The secretome represents the set of proteins secreted into the extracellular space of cells. These proteins have been shown to play a major role in cell-cell communication. For example, recent observations revealed the presence of diffusible factors with proliferative properties in the secretome of cancer cells. Thus, a qualitative and quantitative analysis of the secretome could lead to the identification of these factors and consequently to the development of new therapeutic strategies. Here, we provide an automated simple and effective strategy to identify novel targets in the secretome of specifically treated cells using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Furthermore, we explore the supportive role of mass spectrometry (MS) in the development of functional assays of identified secreted target molecules. Simplicity is achieved by growing cells in medium free of serum, which eliminates the need to remove the most abundant serum proteins and at the same time reduces disturbing matrix effects. Upon identification of these factors, their validation and characterization will follow. Moreover, this approach can also lead to the identification of proteins abnormally secreted, shed, or oversecreted by cells as response to a stimulus. Furthermore, we also discuss the problems that one may encounter. Finally, we discuss the broad application of automated MS-based proteomics, particularly in cancer research, highlighting new horizons for the use of MS.

Identification of a potential tumor differentiation factor receptor candidate in prostate cancer cells

Tumor differentiation factor (TDF) is a pituitary protein that is secreted into the bloodstream and has an endocrine function. TDF and TDF‐P1, a 20‐residue peptide selected from the ORF of TDF, induce differentiation in human breast and prostate cancer cells, but not in other cells. TDF has no known mechanism of action. In our recent study, we identified heat shock 70 kDa proteins (HSP70s) as TDF receptors (TDF‐Rs) in breast cancer cells. Therefore, we sought to investigate whether TDF‐R candidates from prostate cancer cells are the same as those identified in breast cancer cells. Here, we used TDF‐P1 to purify the potential TDF‐R candidates by affinity purification chromatography from DU145 and PC3 steroid‐resistant prostate cancer cells, LNCaP steroid‐responsive prostate cancer cells, and nonprostate NG108 neuroblastoma and BLK CL.4 fibroblast‐like cells. We identified the purified proteins by MS, and validated them by western blotting, immunofluorescence microscopy, immunoaffinity purification chromatography, and structural biology. We identified seven candidate proteins, of which three were from the HSP70 family. These three proteins were validated as potential TDF‐R candidates in LNCaP steroid‐responsive and in DU145 and PC3 steroid‐resistant prostate cancer cells, but not in NG108 neuroblastoma and BLK CL.4 fibroblast‐like cells. Our previous study and the current study suggest that GRP78, and perhaps HSP70s, are strong TDF‐R candidates, and further suggest that TDF interacts with its receptors exclusively in breast and prostate cells, inducing cell differentiation through a novel, steroid‐independent pathway.

Automatic Determination of Disulfide Bridges in Proteins

Precise determination of disulfide linkages between cysteine (Cys) residues in proteins is essential in the determination of protein structure. Therefore, a reliable automated method for the identification of disulfide bridges can serve as an important tool in the analysis of the tertiary structure of proteins of interest. Here, we describe the current and past methods used to identify disulfide bridges in proteins, with a focus on mass spectrometry (MS)–based methods and a particular emphasis on nanoliquid chromatography–tandem mass spectrometry (nanoLC-MS/MS)–based methods. We also show the development of an easy method based on the separation of disulfide-linked proteins by sodium dodecyl sulfate–polyacrylamide gel electrophoresis under denaturing and nonreducing conditions and selective in-gel digestion of proteins using reducing and nonreducing conditions, followed by analysis of the resulting peptide mixture by nanoACQUITY UPLC coupled to a quadrupole time-of-flight (QTOF) Micro mass spectrometer (nanoLC-MS/MS). Data-dependent analysis (DDA) nanoLC-MS/MS and information-dependent analysis (IDA) nanoLC-MS/MS were used for random and targeted identification of disulfide-linked peptides. Finally, an example of electrospray-MS (ESI-MS) and ESI-MS/MS–based determination of disulfide-linked peptides is shown.