TET2 is a component of the estrogen receptor complex and controls 5mC to 5hmC conversion at estrogen receptor cis-regulatory regions.

Estrogen receptor-α (ER) drives tumor development in ER-positive (ER+) breast cancer. The transcription factor GATA3 has been closely linked to ER function, but its precise role in this setting remains unclear. Quantitative proteomics was used to assess changes to the ER complex in response to GATA3 depletion. Unexpectedly, few proteins were lost from the ER complex in the absence of GATA3, with the only major change being depletion of the dioxygenase TET2. TET2 binding constituted a near-total subset of ER binding in multiple breast cancer models, with loss of TET2 associated with reduced activation of proliferative pathways. TET2 knockdown did not appear to change global methylated cytosine (5mC) levels; however, oxidation of 5mC to 5-hydroxymethylcytosine (5hmC) was significantly reduced, and these events occurred at ER enhancers. These findings implicate TET2 in the maintenance of 5hmC at ER sites, providing a potential mechanism for TET2-mediated regulation of ER target genes.

Proteomic Profiling and Monitoring of Training Distress and Illness in University Swimmers During a 25-Week Competitive Season.

PURPOSE: To evaluate relationships of proteomics data, athlete-reported illness, athlete training distress (TDS), and coaches' ratings of distress and performance over the course of the competitive season. METHODS: Thirty-five NCAA Division II swimmers were recruited to the study (male n = 19, female n = 16; age 19.1 ± 1.6 years). Athletes provided fingerprick dried blood spot (DBS) samples, illness symptoms, and TDS every Monday for 19 of 25 weeks in their season. Coaches monitored performance and rated visual signs of distress. DBS samples were analyzed for a targeted panel of 12 immune-related proteins using liquid chromatography/mass spectrometry (LC/MS). RESULTS: Thirty-two swimmers completed the protocol. The data were grouped in 2-3 weeks segments to facilitate interpretation and analysis of the data. TDS scores varied between athletes, and were highest during the early fall conditioning ramp up period (8.9 ± 1.6 at baseline to a peak of 22.6 ± 2.0). The percent of athletes reporting illness was high throughout the season (50-78%). Analysis of TDS using Principle Component Analysis (PCA) revealed that 40.5% of the variance (PC1) could be attributed to illness prevalence, and TDS scores for the athletes reporting illness and no illness were different across the season (P < 0.001). The coaches' ratings of swim performance and swimmer's distress, sex, and racing distance (sprinters, middle distance, long distance) were not correlated with PC1. Linear Discriminant Analysis (LDA) analysis of the data showed a separation of the baseline weeks from exam weeks with or without competitions, and with competitions alone (p < 0.001). Seven of the 12 proteins monitored over the course of training were upregulated, and the addition of the protein data to LDA analysis enhanced the separation between these groups of weeks. CONCLUSION: TDS and illness were related in this group of 32 collegiate swimmers throughout the competitive season, and expression of immune proteins improved the statistical separation of baseline weeks from the most stressful weeks. TDS data provided by the swimmers did not match their coaches' ratings of distress and swim performance. The importance of the immune system in the reaction to internal and external stress in athletes should be an area of further research.

Proteomics-Based Detection of Immune Dysfunction in an Elite Adventure Athlete Trekking Across the Antarctica.

Proteomics monitoring of an elite adventure athlete (age 33 years) was conducted over a 28-week period that culminated in the successful, solo, unassisted, and unsupported two month trek across the Antarctica (1500 km). Training distress was monitored weekly using a 19-item, validated training distress scale (TDS). Weekly dried blood spot (DBS) specimens were collected via fingerprick blood drops onto standard blood spot cards. DBS proteins were measured with nano-electrospray ionization liquid chromatography tandem mass spectrometry (nanoLC-MS/MS) in data-independent acquisition (DIA) mode, and 712 proteins were identified and quantified. The 28-week period was divided into time segments based on TDS scores, and a contrast analysis between weeks five and eight (low TDS) and between weeks 20 and 23 (high TDS, last month of Antarctica trek) showed that 31 proteins (n = 20 immune related) were upregulated and 35 (n = 17 immune related) were downregulated. Protein-protein interaction (PPI) networks supported a dichotomous immune response. Gene ontology (GO) biological process terms for the upregulated immune proteins showed an increase in regulation of the immune system process, especially inflammation, complement activation, and leukocyte mediated immunity. At the same time, GO terms for the downregulated immune-related proteins indicated a decrease in several aspects of the overall immune system process including neutrophil degranulation and the antimicrobial humoral response. These proteomics data support a dysfunctional immune response in an elite adventure athlete during a sustained period of mental and physical distress while trekking solo across the Antarctica.

Detection of Functional Overreaching in Endurance Athletes Using Proteomics.

No reliable biomarkers exist to identify athletes in various training states including functional overreaching (FOR), non-functional overreaching (NFOR), and overtraining syndrome (OTS). Participants (N = 10, age 38.3 ± 3.4 years) served as their own controls and in random, counterbalanced order either ran/cycled 2.5 h (70.0 ± 3.7% VO2max) three days in a row (FOR) or sat in the lab (rest) (separated by three weeks; 7:00⁻9:30 am, overnight fasted state). Participants provided fingerprick samples for dried blood spot samples (DBS) pre- and post-exercise/rest, and then during two recovery days. DBS proteins were measured with nanoLC-MS in data-independent acquisition (DIA) mode, and 593 proteins were identified and quantified. Proteins were considered for the FOR cluster if they were elevated during one of the two recovery days but not more than one of the exercise days (compared to rest). The generalized estimating equation (GEE) was used to identify proteins linked to FOR. A total of 13 proteins was linked to FOR and most were associated with the acute phase response and innate immune system activation. This study used a system-wide proteomics approach to define a targeted panel of blood proteins related to FOR that could form the basis of future NFOR- and OTS-based studies.

Endogenous miRNA in the green alga Chlamydomonas regulates gene expression through CDS-targeting.

MicroRNAs (miRNAs) are 21-24-nucleotide RNAs present in many eukaryotes that regulate gene expression as part of the RNA-induced silencing complex. The sequence identity of the miRNA provides the specificity to guide the silencing effector Argonaute (AGO) protein to target mRNAs via a base-pairing process 1 . The AGO complex promotes translation repression and/or accelerated decay of this target mRNA 2 . There is overwhelming evidence both in vivo and in vitro that translation repression plays a major role 3-7 . However, there has been controversy about which of these three mechanisms is more significant in vivo, especially when effects of miRNA on endogenous genes cannot be faithfully represented by reporter systems in which, at least in metazoans, the observed repression vastly exceeds that typically observed for endogenous mRNAs 8,9 . Here, we provide a comprehensive global analysis of the evolutionarily distant unicellular green alga Chlamydomonas reinhardtii to quantify the effects of miRNA on protein synthesis and RNA abundance. We show that, similar to metazoan steady-state systems, endogenous miRNAs in Chlamydomonas can regulate gene expression both by destabilization of the mRNA and by translational repression. However, unlike metazoan miRNA where target site utilization localizes mainly to 3' UTRs, in Chlamydomonas utilized target sites lie predominantly within coding regions. These results demonstrate the evolutionarily conserved mode of action for miRNAs, but details of the mechanism diverge between the plant and metazoan kingdoms.

The brassinosteroid receptor BRI1 can generate cGMP enabling cGMP-dependent downstream signaling.

The brassinosteroid receptor brassinosteroid insensitive 1 (BRI1) is a member of the leucine-rich repeat receptor-like kinase family. The intracellular kinase domain of BRI1 is an active kinase and also encapsulates a guanylate cyclase catalytic centre. Using liquid chromatography tandem mass spectrometry, we confirmed that the recombinant cytoplasmic domain of BRI1 generates pmol amounts of cGMP per µg protein with a preference for magnesium over manganese as a co-factor. Importantly, a functional BRI1 kinase is essential for optimal cGMP generation. Therefore, the guanylate cyclase activity of BRI1 is modulated by the kinase while cGMP, the product of the guanylate cyclase, in turn inhibits BRI1 kinase activity. Furthermore, we show using Arabidopsis root cell cultures that cGMP rapidly potentiates phosphorylation of the downstream substrate brassinosteroid signaling kinase 1 (BSK1). Taken together, our results suggest that cGMP acts as a modulator that enhances downstream signaling while dampening signal generation from the receptor.

Comprehensive assessment of estrogen receptor beta antibodies in cancer cell line models and tissue reveals critical limitations in reagent specificity.

Estrogen Receptor-ß (ERß) has been implicated in many cancers. In prostate and breast cancer its function is controversial, but genetic studies implicate a role in cancer progression. Much of the confusion around ERß stems from antibodies that are inadequately validated, yet have become standard tools for deciphering its role. Using an ERß-inducible cell system we assessed commonly utilized ERß antibodies and show that one of the most commonly used antibodies, NCL-ER-BETA, is non-specific for ERß. Other antibodies have limited ERß specificity or are only specific in one experimental modality. ERß is commonly studied in MCF-7 (breast) and LNCaP (prostate) cancer cell lines, but we found no ERß expression in either, using validated antibodies and independent mass spectrometry-based approaches. Our findings question conclusions made about ERß using the NCL-ER-BETA antibody, or LNCaP and MCF-7 cell lines. We describe robust reagents, which detect ERß across multiple experimental approaches and in clinical samples.

Identifying the science and technology dimensions of emerging public policy issues through horizon scanning.

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.

A foundation for reliable spatial proteomics data analysis.

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue.

Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

Proteomics of total membranes and subcellular membranes.

Membrane proteins are key molecules in the cell and are important targets for drug development. Much effort has, therefore, been directed towards research of this group of proteins, but their hydrophobic nature can make working with them challenging. Here we discuss methodologies used in the study of the membrane proteome, specifically discussing approaches that circumvent technical issues specific to the membrane. In addition, we review several techniques used for visualization, qualification, quantitation and localization of membrane proteins. The combination of the techniques we describe holds great promise to allow full characterization of the membrane proteome and to map the dynamic changes within it essential for cellular function.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions.

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Sub-cellular localization of membrane proteins.

In eukaryotes, numerous complex sub-cellular structures exist. The majority of these are delineated by membranes. Many proteins are trafficked to these in order to be able to carry out their correct physiological function. Assigning the sub-cellular location of a protein is of paramount importance to biologists in the elucidation of its role and in the refinement of knowledge of cellular processes by tracing certain activities to specific organelles. Membrane proteins are a key set of proteins as these form part of the boundary of the organelles and represent many important functions such as transporters, receptors, and trafficking. They are, however, some of the most challenging proteins to work with due to poor solubility, a wide concentration range within the cell and inaccessibility to many of the tools employed in proteomics studies. This review focuses on membrane proteins with particular emphasis on sub-cellular localization in terms of methodologies that can be used to determine the accurate location of membrane proteins to organelles. We also discuss what is known about the membrane protein cohorts of major organelles.