Comparative analyses of proteins from Haemophilus influenzae biofilm and planktonic populations using metabolic labeling and mass spectrometry

BACKGROUND: Non-typeable H. influenzae (NTHi) is a nasopharyngeal commensal that can become an opportunistic pathogen causing infections such as otitis media, pneumonia, and bronchitis. NTHi is known to form biofilms. Resistance of bacterial biofilms to clearance by host defense mechanisms and antibiotic treatments is well-established. In the current study, we used stable isotope labeling by amino acids in cell culture (SILAC) to compare the proteomic profiles of NTHi biofilm and planktonic organisms. Duplicate continuous-flow growth chambers containing defined media with either “light” (L) isoleucine or “heavy” (H) (13)C(6)-labeled isoleucine were used to grow planktonic (L) and biofilm (H) samples, respectively. Bacteria were removed from the chambers, mixed based on weight, and protein extracts were generated. Liquid chromatography-mass spectrometry (LC-MS) was performed on the tryptic peptides and 814 unique proteins were identified with 99% confidence. RESULTS: Comparisons of the NTHi biofilm to planktonic samples demonstrated that 127 proteins showed differential expression with p-values ≤0.05. Pathway analysis demonstrated that proteins involved in energy metabolism, protein synthesis, and purine, pyrimidine, nucleoside, and nucleotide processes showed a general trend of downregulation in the biofilm compared to planktonic organisms. Conversely, proteins involved in transcription, DNA metabolism, and fatty acid and phospholipid metabolism showed a general trend of upregulation under biofilm conditions. Selected reaction monitoring (SRM)-MS was used to validate a subset of these proteins; among these were aerobic respiration control protein ArcA, NAD nucleotidase and heme-binding protein A. CONCLUSIONS: The present proteomic study indicates that the NTHi biofilm exists in a semi-dormant state with decreased energy metabolism and protein synthesis yet is still capable of managing oxidative stress and in acquiring necessary cofactors important for biofilm survival. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12866-014-0329-9) contains supplementary material, which is available to authorized users

Cancer-associated exportin-6 upregulation inhibits the transcriptionally repressive and anticancer effects of nuclear profilin-1

Aberrant expression of nuclear transporters and deregulated subcellular localization of their cargo proteins are emerging as drivers and therapeutic targets of cancer. Here, we present evidence that the nuclear exporter exportin-6 and its cargo profilin-1 constitute a functionally important and frequently deregulated axis in cancer. Exportin-6 upregulation occurs in numerous cancer types and is associated with poor patient survival. Reducing exportin-6 level in breast cancer cells triggers antitumor effects by accumulating nuclear profilin-1. Mechanistically, nuclear profilin-1 interacts with eleven-nineteen-leukemia protein (ENL) within the super elongation complex (SEC) and inhibits the ability of the SEC to drive transcription of numerous pro-cancer genes including MYC. XPO6 and MYC are positively correlated across diverse cancer types including breast cancer. Therapeutically, exportin-6 loss sensitizes breast cancer cells to the bromodomain and extra-terminal (BET) inhibitor JQ1. Thus, exportin-6 upregulation is a previously unrecognized cancer driver event by spatially inhibiting nuclear profilin-1 as a tumor suppressor

Phospho-Ser784-VCP is required for DNA damage response and is associated with poor prognosis of chemotherapy-treated breast cancer

Spatiotemporal protein reorganization at DNA damage sites induced by genotoxic chemotherapies is crucial for DNA damage response (DDR), which influences treatment response by directing cancer cell fate. This process is orchestrated by valosin-containing protein (VCP), an AAA+ ATPase that extracts polyubiquinated chromatin proteins and facilitates their turnover. However, because of the essential and pleiotropic effects of VCP in global proteostasis, it remains challenging practically to understand and target its DDR-specific functions. We describe a DNA-damage-induced phosphorylation event (Se

RPL24: a potential therapeutic target whose depletion or acetylation inhibits polysome assembly and cancer cell growth.

Partial loss of large ribosomal subunit protein 24 (RPL24) function is known to protect mice against Akt or Myc-driven cancers, in part via translational inhibition of a subset of cap(eIF4E)-dependently translated mRNAs. The role of RPL24 in human malignancies is unknown. By analyzing a public dataset of matched human breast cancers and normal mammary tissue, we found that breast cancers express significantly more RPL24 than matched normal breast samples. Depletion of RPL24 in breast cancer cells by \u3e70% reduced cell viability by 80% and decreased protein expression of the eIF4E-dependently translated proteins cyclin D1 (75%), survivin (46%) and NBS1 (30%) without altering GAPDH or beta-tubulin levels. RPL24 knockdown also reduced 80S subunit levels relative to 40S and 60S levels. These effects on expression of eIF4E-dependent proteins and ribosome assembly were mimicked by 2-24 h treatment with the pan-HDACi, trichostatin A (TSA), which induced acetylation of 15 different polysome-associated proteins including RPL24. Furthermore, HDAC6-selective inhibition or HDAC6 knockdown induced ribosomal protein acetylation. Via mass spectrometry, we found that 60S-associated, but not, polysome-associated, RPL24 undergoes HDACi-induced acetylation on K27. Thus, RPL24 K27 acetylation may play a role in ribosome assembly. These findings point toward a novel acetylation-dependent polysome assembly mechanism regulating tumorigenesis

Restriction landmark genomic scanning (RLGS) spot identification by second generation virtual RLGS in multiple genomes with multiple enzyme combinations.

BackgroundRestriction landmark genomic scanning (RLGS) is one of the most successfully applied methods for the identification of aberrant CpG island hypermethylation in cancer, as well as the identification of tissue specific methylation of CpG islands. However, a limitation to the utility of this method has been the ability to assign specific genomic sequences to RLGS spots, a process commonly referred to as "RLGS spot cloning."ResultsWe report the development of a virtual RLGS method (vRLGS) that allows for RLGS spot identification in any sequenced genome and with any enzyme combination. We report significant improvements in predicting DNA fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median Euclidian distance between actual and predicted spot migration of 0.18 centimeters for the most complex human RLGS pattern. We report the confirmed identification of 795 human and 530 mouse RLGS spots for the most commonly used enzyme combinations. We also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes. We demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation.ConclusionThe new vRLGS system reported here is highly robust for the identification of novel RLGS spots. The migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. The identification of hundreds of mouse and human RLGS spot loci confirms the strong bias of RLGS studies to focus on CpG islands and provides a valuable resource to rapidly study their methylation

Malic enzyme 1 absence in synovial sarcoma shifts antioxidant system dependence and increases sensitivity to ferroptosis induction with ACXT-3102

PURPOSE: To investigate the metabolism of synovial sarcoma (SS) and elucidate the effect of malic enzyme 1 absence on SS redox homeostasis. EXPERIMENTAL DESIGN: ME1 expression was measured in SS clinical samples, SS cell lines, and tumors from an SS mouse model. The effect of ME1 absence on glucose metabolism was evaluated utilizing Seahorse assays, metabolomics, and C13 tracings. The impact of ME1 absence on SS redox homeostasis was evaluated by metabolomics, cell death assays with inhibitors of antioxidant systems, and measurements of intracellular reactive oxygen species (ROS). The susceptibility of ME1-null SS to ferroptosis induction was interrogated in vitro and in vivo. RESULTS: ME1 absence in SS was confirmed in clinical samples, SS cell lines, and an SS tumor model. Investigation of SS glucose metabolism revealed that ME1-null cells exhibit higher rates of glycolysis and higher flux of glucose into the pentose phosphate pathway (PPP), which is necessary to produce NADPH. Evaluation of cellular redox homeostasis demonstrated that ME1 absence shifts dependence from the glutathione system to the thioredoxin system. Concomitantly, ME1 absence drives the accumulation of ROS and labile iron. ROS and iron accumulation enhances the susceptibility of ME1-null cells to ferroptosis induction with inhibitors of xCT (erastin and ACXT-3102). In vivo xenograft models of ME1-null SS demonstrate significantly increased tumor response to ACXT-3102 compared with ME1-expressing controls. CONCLUSIONS: These findings demonstrate the translational potential of targeting redox homeostasis in ME1-null cancers and establish the preclinical rationale for a phase I trial of ACXT-3102 in SS patients. See related commentary by Subbiah and Gan, p. 3408

Proteins in tumor-derived plasma extracellular vesicles indicate tumor origin

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor