FGF2 induces breast cancer growth through ligand-independent activation and recruitment of ERα and PRBΔ4 isoform to MYC regulatory sequences.

Progression to hormone-independent growth leading to endocrine therapy resistance occurs in a high proportion of patients with estrogen receptor alpha (ERα) and progesterone receptors (PR) positive breast cancer. We and others have previously shown that estrogen- and progestin-induced tumor growth requires ERα and PR interaction at their target genes. Here, we show that fibroblast growth factor 2 (FGF2)-induces cell proliferation and tumor growth through hormone-independent ERα and PR activation and their interaction at the MYC enhancer and proximal promoter. MYC inhibitors, antiestrogens or antiprogestins reverted FGF2-induced effects. LC-MS/MS identified 700 canonical proteins recruited to MYC regulatory sequences after FGF2 stimulation, 397 of which required active ERα (ERα-dependent). We identified ERα-dependent proteins regulating transcription that, after FGF2 treatment, were recruited to the enhancer as well as proteins involved in transcription initiation that were recruited to the proximal promoter. Also, among the ERα-dependent and independent proteins detected at both sites, PR isoforms A and B as well as the novel protein product PRBΔ4 were found. PRBΔ4 lacks the hormone-binding domain and was able to induce reporter gene expression from estrogen-regulated elements and to increase cell proliferation when cells were stimulated with FGF2 but not by progestins. Analysis of the Cancer Genome Atlas data set revealed that PRBΔ4 expression is associated with worse overall survival in luminal breast cancer patients. This discovery provides a new mechanism by which growth factor signaling can engage nonclassical hormone receptor isoforms such as PRBΔ4, which interacts with growth-factor activated ERα and PR to stimulate MYC gene expression and hence progression to endocrine resistance.

Daily rhythms in the cyanobacterium synechococcus elongatus probed by high-resolution mass spectrometry-based proteomics reveals a small defined set of cyclic proteins.

Circadian rhythms are self-sustained and adjustable cycles, typically entrained with light/dark and/or temperature cycles. These rhythms are present in animals, plants, fungi, and several bacteria. The central mechanism behind these "pacemakers" and the connection to the circadian regulated pathways are still poorly understood. The circadian rhythm of the cyanobacterium Synechococcus elongatus PCC 7942 (S. elongatus) is highly robust and controlled by only three proteins, KaiA, KaiB, and KaiC. This central clock system has been extensively studied functionally and structurally and can be reconstituted in vitro. These characteristics, together with a relatively small genome (2.7 Mbp), make S. elongatus an ideal model system for the study of circadian rhythms. Different approaches have been used to reveal the influence of the central S. elongatus clock on rhythmic gene expression, rhythmic mRNA abundance, rhythmic DNA topology changes, and cell division. However, a global analysis of its proteome dynamics has not been reported yet. To uncover the variation in protein abundances during 48 h under light and dark cycles (12:12 h), we used quantitative proteomics, with TMT 6-plex isobaric labeling. We queried the S. elongatus proteome at 10 different time points spanning a single 24-h period, leading to 20 time points over the full 48-h period. Employing multidimensional separation and high-resolution mass spectrometry, we were able to find evidence for a total of 82% of the S. elongatus proteome. Of the 1537 proteins quantified over the time course of the experiment, only 77 underwent significant cyclic variations. Interestingly, our data provide evidence for in- and out-of-phase correlation between mRNA and protein levels for a set of specific genes and proteins. As a range of cyclic proteins are functionally not well annotated, this work provides a resource for further studies to explore the role of these proteins in the cyanobacterial circadian rhythm.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer.

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Assessing Multi-Attribute Characterization of Enveloped and Non-Enveloped Viral Particles by Capillary Electrophoresis.

Virus-based biopharmaceutical products are used in clinical applications such as vaccines, gene therapy, and immunotherapy. However, their manufacturing remains a challenge, hampered by the lack of appropriate analytical tools for purification monitoring or characterization of the final product. This paper describes the implementation of a highly sensitive method, capillary electrophoresis (CE)-sodium dodecyl sulfate (SDS) combined with a laser-induced fluorescence (LIF) detector to monitor the impact of various bioprocess steps on the quality of different viral vectors. The fluorescence labelling procedure uses the (3-(2-furoyl) quinoline-2-carboxaldehyde dye, and the CE-SDS LIF method enables the evaluation of in-process besides final product samples. This method outperforms other analytical methods, such as SDS-polyacrylamide gel electrophoresis with Sypro Ruby staining, in terms of sensitivity, resolution, and high-throughput capability. Notably, this CE-SDS LIF method was also successfully implemented to characterize enveloped viruses such as Maraba virus and lentivirus, whose development as biopharmaceuticals is now restricted by the lack of suitable analytical tools. This method was also qualified for quantification of rAAV2 according to the International Council for Harmonisation guidelines. Overall, our work shows that CE-SDS LIF is a precise and sensitive analytical platform for in-process sample analysis and quantification of different virus-based targets, with a great potential for application in biomanufacturing.

Metabolomics profile responses to changing environments in a common bean (Phaseolus vulgaris L.) germplasm collection.

Metabolomics is one of the most powerful -omics to assist plant breeding. Despite the recognized genetic diversity in Portuguese common bean germplasm, details on its metabolomics profiles are still missing. Aiming to promote their use and to understand the environment's effect in bean metabolomics profiles, 107 Portuguese common bean accessions, cropped under contrasting environments, were analyzed using spectrophotometric, untargeted and targeted mass spectrometry approaches. Although genotype was the most relevant factor on bean metabolomics profile, a clear genotype × environment interaction was also detected. Multivariate analysis highlighted, on the heat-stress environment, the existence of higher levels of salicylic acid, and lower levels of triterpene saponins. Three clusters were defined within each environment. White accessions presented the lowest content and the colored ones the highest levels of prenol lipids and flavonoids. Sources of interesting metabolomics profiles are now identified for bean breeding, focusing either on local or on broad adaptation.

Cerebrospinal Fluid Chitinases as Biomarkers for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative neuromuscular disease that affects motor neurons controlling voluntary muscles. Survival is usually 2-5 years after onset, and death occurs due to respiratory failure. The identification of biomarkers would be very useful to help in disease diagnosis and for patient stratification based on, e.g., progression rate, with implications in therapeutic trials. Neurofilaments constitute already-promising markers for ALS and, recently, chitinases have emerged as novel marker targets for the disease. Here, we investigated cerebrospinal fluid (CSF) chitinases as potential markers for ALS. Chitotriosidase (CHIT1), chitinase-3-like protein 1 (CHI3L1), chitinase-3-like protein 2 (CHI3L2) and the benchmark marker phosphoneurofilament heavy chain (pNFH) were quantified by an enzyme-linked immunosorbent assay (ELISA) from the CSF of 34 ALS patients and 24 control patients with other neurological diseases. CSF was also analyzed by UHPLC-mass spectrometry. All three chitinases, as well as pNFH, were found to correlate with disease progression rate. Furthermore, CHIT1 was elevated in ALS patients with high diagnostic performance, as was pNFH. On the other hand, CHIT1 correlated with forced vital capacity (FVC). The three chitinases correlated with pNFH, indicating a relation between degeneration and neuroinflammation. In conclusion, our results supported the value of CHIT1 as a diagnostic and progression rate biomarker, and its potential as respiratory function marker. The results opened novel perspectives to explore chitinases as biomarkers and their functional relevance in ALS.

Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients.

Separation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets are first corrected for baseline drift and background noise before the MS scans are converted from profile to centroid. A new alignment strategy that includes purity control is introduced, and features are quantified using the original data with scans recorded as profile, not the extracted features. All the algorithms used in this work are part of the Finnee Matlab toolbox that is freely available. The approach was validated using metabolites in exhaled breath condensates to differentiate individuals diagnosed with asthma from patients with chronic obstructive pulmonary disease. With this new pipeline, twice as many markers were found with Finnee in comparison to XCMS-online, and nearly 50% more than with MS-Dial, two of the most popular freeware for untargeted metabolomics analysis.

Targeting TNFα in severe psoriasis-mass spectrometry reveals a time-dependent specific inhibition of Factor H in responding patients.

Drugs targeting TNFα (eg, Etanercept®) provide effective control of severe psoriasis. In absence of validated biological parameters of inflammation in psoriasis most decisions on therapeutics have relied mostly on clinical criteria, namely the "Psoriasis Area and Severity Index" (PASI). The purpose of this study was to assess by mass spectrometry alterations in concentrations of serum proteins that specifically correlated with effectiveness of Etanercept treatment. This prospective study enrolled 10 patients suffering from moderate to severe psoriasis (PASI score > 10 and < 17) and treated with Etanercept over a period of 24 weeks; 10 healthy, age-matched volunteers provided controls. Serum proteins sensitive to Etanercept treatment were identified using SELDI-TOF (surface-enhanced laser desorption and ionization - time of flight) coupled to nano LC-ESI/MS (nano liquid chromatography-electrospray ionization/tandem mass spectrometry) technologies. For comparisons between groups of individuals p-values (considered significant when < 0.01) were estimated with non-parametric tests, namely Mann-Whitney (for unpaired data) and Wilcoxon signed-rank (for paired data). In responding patients it could be shown using SELDI-TOF spectrometry that two proteins (134 kDa and 4.3 kDa) return to control levels by 24 weeks of treatment. Using nano LC-ESI/MS the 134 kDa species was identified as complement Factor H. These observations deserve further analyses utilizing larger cohorts of patients. Determination of Factor H levels may become a complementary tool to follow remission or predict the onset of relapse in the follow-up of patients under treatment with Etanercept.

Temperature Modulates the Secretome of the Phytopathogenic Fungus Lasiodiplodia theobromae.

Environmental alterations modulate host-microorganism interactions. Little is known about how climate changes can trigger pathogenic features on symbiont or mutualistic microorganisms. Current climate models predict increased environmental temperatures. The exposing of phytopathogens to these changing conditions can have particularly relevant consequences for economically important species and for humans. The impact on pathogen/host interaction and the shift on their biogeographical range can induce different levels of virulence in new hosts, allowing massive losses in agricultural and health fields. Lasiodiplodia theobromae is a phytopathogenic fungus responsible for a number of diseases in various plants. It has also been described as an opportunist pathogen in humans, causing infections with different levels of severity. L. theobromae has a high capacity of adaptation to different environments, such as woody plants, moist argillaceous soils, or even humans, being able to grow and infect hosts in a wide range of temperatures (9-39°C). Nonetheless, the effect of an increase of temperature, as predicted in climate change models, on L. theobromae is unknown. Here we explore the effect of temperature on two strains of L. theobromae - an environmental strain, CAA019, and a clinical strain, CBS339.90. We show that both strains are cytotoxic to mammalian cells but while the environmental strain is cytotoxic mainly at 25°C, the clinical strain is cytotoxic mainly at 30 and 37°C. Extracellular gelatinolytic, xylanolytic, amylolytic, and cellulolytic activities at 25 and 37°C were characterized by zymography and the secretome of both strains grown at 25, 30, and 37°C were characterized by electrophoresis and by Orbitrap LC-MS/MS. More than 75% of the proteins were identified, mostly enzymes (glycosyl hydrolases and proteases). The strains showed different protein profiles, which were affected by growth temperature. Also, strain specific proteins were identified, such as a putative f5/8 type c domain protein - known for being involved in pathogenesis - by strain CAA019 and a putative tripeptidyl-peptidase 1 protein, by strain CBS339.90. We showed that temperature modulates the secretome of L. theobromae. This modulation may be associated with host-specificity requirements. We show that the study of abiotic factors, such as temperature, is crucial to understand host/pathogen interactions and its impact on disease.

Monitoring light/dark association dynamics of multi-protein complexes in cyanobacteria using size exclusion chromatography-based proteomics.

UNLABELLED: Diurnal rhythms are recurring 24h patterns such as light/dark cycles that affect many natural environmental and biological processes. The cyanobacterium Synechococcus elongatus PCC 7942 (S. elongatus) produces its energy through photosynthesis and therefore its internal molecular machinery is strongly influenced by these diurnal rhythms. Moreover, it has one of the simplest, self-sustained, circadian rhythms, extensively studied functionally and structurally. These characteristics together with the relatively small genome of S. elongatus, make it an ideal model system for the study of diurnal and circadian rhythms. Although expression of many gene transcripts has been shown to fluctuate in phase with the circadian rhythm, fluctuations at the protein level were less pronounced. This led us to hypothesize that the diurnal adaptation occurs at the level of higher organization of protein complexes. Therefore, we probed the abundance and constituency of S. elongatus protein complexes during the day and night. Following several well-known complexes such as the RNA polymerase, the ribosome and photosynthetic protein complexes, we observe for the first time that these complexes change not only in abundance but also in constituency. Therefore, we conclude that the dynamic assembly of protein complexes is indeed also a key-player in the processes governing the diurnal rhythm. SIGNIFICANCE: The succession of day and night periods imposes drastic changes in all living organisms. Cyanobacteria produce their energy through photosynthesis and are therefore strongly influenced by diurnal rhythms. The cyanobacteria, Synechococcus elongatus PCC 7942 (S. elongatus), also exhibit a self-sustained biological clock. The connection between the central circadian oscillator and its output to the rest of the cell is not completely known. It has been shown that the expression of many gene transcripts heavily fluctuates in phase with the circadian rhythm; however, our recent global proteomics investigation revealed that the diurnal fluctuations seemed to be less pronounced at the protein level. As many known regulatory functions depend on protein-protein interactions (PPIs) and/or protein assemblies and the fact that so few fluctuations in protein abundances were observed earlier, here we investigated the diurnal adaptation at the level of dynamic changes in protein assembly. The paper demonstrates that the combination of native protein complex fractionation and high-resolution proteomics provides insight in the regulation of megadalton protein assemblies in cyanobacteria, including the ribosomal and photosynthetic complexes. The differences observed between the light and dark conditions in these complexes indicate a cyclic regulation of essential cellular processes.