Palatability, glycemic, and insulinemic responses to various carbohydrate formulations: Alternatives for the diagnosis of insulin dysregulation in horses?

BACKGROUND: Oral glycemic challenge (GC) tests are recommended for diagnosis of insulin dysregulation (ID). Various protocols are used, but all have limitations in terms of palatability, ease of use, variable composition, geographic availability, or some combination of these. HYPOTHESIS/OBJECTIVE: To evaluate newly developed formulations with defined carbohydrate composition for use as oral GCs. ANIMALS: Thirty-four horses and ponies in various metabolic states. METHODS: Our objectives were carried out in 2 separate cross-over experiments. First, the palatability and acceptance of various GCs (2 syrups, 1 granulate) offered for free intake were compared to glucose mixed in a chaff-based diet. Subsequently, syrups were administered by syringe and compared to an oral glucose test using naso-gastric tubing (tube OGT) to investigate the glycemic and insulinemic responses. Second, these variables were compared in the best performing GC-formulations (granulate further optimized to pelleted formulation and 1 syrup) and a tube OGT. All GCs were administered with equivalent amounts of 0.5 g glycemic carbohydrates per kg body weight. RESULTS: Only the GC pellets were consumed completely by all horses (consumption time 5 ± 2 min). When administered by syringe, the GC syrup also was well accepted. The insulin concentrations at 120 min correlated significantly between tube OGT and GC pellets (r = .717; P < .001) or GC syrup (r = .913; P < .001). The new GC syrup and GC pellets discriminate between healthy and ID horses. CONCLUSIONS AND CLINICAL SIGNIFICANCE: The GC pellets (DysChEq)™ and GC syrup can be used as palatable and well-accepted oral GC tests for assessment of ID in horses.

Association between insulin dysregulation and adrenocorticotropic hormone in aged horses and ponies with no clinical signs of pituitary pars intermedia dysfunction.

BACKGROUND: High concentrations of adrenocorticotropic hormone (ACTH) are used as an indicator of pituitary pars intermedia dysfunction (PPID), but other factors that may influence ACTH need to be understood, if diagnostic reference ranges for ACTH are to be used with confidence. Insulin dysregulation (ID) could be one such factor, as insulin affects pituitary hormones in other species. OBJECTIVES: To test the hypothesis that a relationship exists between high insulin and high ACTH in aged (>15-year-old) animals with no clinical signs of PPID. STUDY DESIGN: A cohort study. METHODS: Thirteen horses and eleven ponies (17-25 years-old; mares and geldings) were clinically examined for signs of PPID in the spring (November 2020) and autumn (April 2021). On the same day, blood samples were taken before and 2 h after an oral glucose test (OGT). Concentrations of insulin, glucose, ACTH and cortisol were measured. RESULTS: There was no association between ACTH and cortisol. However, there was a positive linear correlation between ACTH and post-OGT (insulin in the autumn (r = 0.427, p = 0.04). Two horses and six ponies had ACTH above the cut-off value for PPID diagnosis, and of these eight animals, six also had insulin concentrations above the cut-off value for ID. MAIN LIMITATIONS: The cohort was small and thyrotropin-releasing hormone (TRH) stimulation tests were not performed. CONCLUSIONS: In autumn, high ACTH was associated with ID, when no clinical signs of PPID were present. Because ACTH is used in PPID diagnosis, further work is required to understand this interaction.

Carbohydrate pellets to assess insulin dysregulation in horses.

BACKGROUND: A glycemic challenge test is used for the diagnosis of insulin dysregulation (ID) in horses and ponies. Different forms of the test exist where the administrative route and dose of glucose vary, which makes interpretation of results challenging. HYPOTHESIS/OBJECTIVES: To evaluate the palatability of, and blood glucose and insulin responses to, carbohydrate pellets fed as an oral glucose test (OGT), and to establish the diagnostic threshold for ID when using the pellets. ANIMALS: University and privately-owned horses and ponies (n = 157) comprised of 31 breeds and both sexes. METHODS: Multicenter cohort study. A custom-produced glycemic pellet was offered for free intake at 0.5 g/kg BW soluble carbohydrate and serum insulin and blood glucose concentrations measured before and after (60, 120, and 180 minutes) the pellets were offered. Pellet acceptance and intake time (those that finished within 10 minutes) were determined to assess palatability. RESULTS: The pellets were palatable to 132/157 animals, and ponies found the pellets more (P = .004) palatable than horses. The median intake time (4 [3-6] minutes) was positively correlated with acceptance grade (r = .51; P < .0001). Consumption of the pellets elicited peak blood glucose (6.6 [5.8-7.8] mmol/L) and serum insulin (40.5 [19-99.8] µIU/mL) responses at 120 minutes. At 120 minutes the optimal cut-off was 83 µIU/mL (95% CI: 70-99 µIU/mL) for the IMMULITE 2000XPi assay. CONCLUSIONS AND CLINICAL IMPORTANCE: The pellets were palatable and a suitable, novel carbohydrate source for the OGT.

Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis.

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand-drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.

A genome-wide library of MADM mice for single-cell genetic mosaic analysis.

Mosaic analysis with double markers (MADM) offers one approach to visualize and concomitantly manipulate genetically defined cells in mice with single-cell resolution. MADM applications include the analysis of lineage, single-cell morphology and physiology, genomic imprinting phenotypes, and dissection of cell-autonomous gene functions in vivo in health and disease. Yet, MADM can only be applied to <25% of all mouse genes on select chromosomes to date. To overcome this limitation, we generate transgenic mice with knocked-in MADM cassettes near the centromeres of all 19 autosomes and validate their use across organs. With this resource, >96% of the entire mouse genome can now be subjected to single-cell genetic mosaic analysis. Beyond a proof of principle, we apply our MADM library to systematically trace sister chromatid segregation in distinct mitotic cell lineages. We find striking chromosome-specific biases in segregation patterns, reflecting a putative mechanism for the asymmetric segregation of genetic determinants in somatic stem cell division.

Coordinated Pulses of mRNA and of Protein Translation or Degradation Produce EGF-Induced Protein Bursts.

Protein responses to extracellular cues are governed by gene transcription, mRNA degradation and translation, and protein degradation. In order to understand how these time-dependent processes cooperate to generate dynamic responses, we analyzed the response of human mammary cells to the epidermal growth factor (EGF). Integrating time-dependent transcript and protein data into a mathematical model, we inferred for several proteins their pre-and post-stimulus translation and degradation coefficients and found that they exhibit complex, time-dependent variation. Specifically, we identified strategies of protein production and degradation acting in concert to generate rapid, transient protein bursts in response to EGF. Remarkably, for some proteins, for which the response necessitates rapidly decreased abundance, cells exhibit a transient increase in the corresponding degradation coefficient. Our model and analysis allow inference of the kinetics of mRNA translation and protein degradation, without perturbing cells, and open a way to understanding the fundamental processes governing time-dependent protein abundance profiles.

Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data.

In this chapter, we describe an approach to reconstruct cellular signaling networks based on measurements of protein activation after different stimulation experiments. As experimental platform reverse-phase protein arrays (RPPA) are used. RPPA allow the measurement of proteins and phosphoproteins across many samples in parallel with minimal sample consumption using a panel of highly target protein-specific antibodies. Functional interactions of proteins are modeled using a Boolean network. We describe the Boolean network reconstruction approach ddepn (dynamic deterministic effects propagation networks), which uses time course data to derive protein interactions based on perturbation experiments. We explain how the method works, give a practical application example, and describe how the results can be interpreted. Furthermore prior knowledge on signaling pathways is essential for network reconstruction. Here we describe the use of our software rBiopaxParser to integrate prior knowledge on protein signaling available in public databases. All applied methods are freely available as open-source R software packages. We describe the preparation of RPPA data as well as all relevant programming steps to format the RPPA data, to infer the prior knowledge, and to reconstruct and analyze the protein signaling networks.

Actual Isothermal Effects of Water-Filtered Infrared A-Irradiation.

In this study, the athermal effects of water-filtered infrared A (wIRA)-irradiation (780-1400 nm) on human dermal fibroblasts were investigated. For this purpose, cells were exposed to wIRA-irradiation (178 mW cm(-2) for 1 h), while a sophisticated experimental setup prevented warming of the samples exceeding 0.1°C. The investigated parameters were the formation of reactive oxygen species (ROS), mitochondrial membrane potential and superoxide release, protein oxidation, proliferation rate, as well as intracellular Ca(2+) -release in single cells, most of them quantified via fluorescence microscopy and fluorimetric techniques. The existence of actual athermal wIRA-effects is still intensively discussed, since their detection requires a careful experimental setup and both efficient and powerful temperature regulation of the exposed samples. Here, we can definitively show that some of the supposed athermal wIRA-effects may be rather artifacts, since wIRA did not reveal any impact on the above mentioned parameters-as long as the temperature of the exposed cells was carefully maintained. Though, we were able to identify an athermal DNA-protective wIRA-effect, since the induced DNA damage (quantified via 8-Oxo-G-formation) was significantly decreased after a subsequent UVB-exposure. These results suggest that many of the supposed athermal wIRA-effects can be induced by pure warming of the samples, independent from any wIRA-irradiation.

Subtyping of breast cancer using reverse phase protein arrays.

Reverse phase protein arrays (RPPAs) present a robust and sensitive high capacity platform for targeted proteomics that relies on highly specific antibodies to obtain a quantitative readout regarding phosphorylation state and abundance of proteins of interest. This review summarizes the current state of RPPA-based proteomic profiling of breast cancer in the context of existing preanalytical strategies and sample preparation protocols. RPPA-based subtypes identified so far are compared to those obtained by other approaches such as immunohistochemistry, genomics and transcriptomics. Special attention is given to discussing the potential of RPPA for biomarker discovery and biomarker validation.

RPPanalyzer toolbox: an improved R package for analysis of reverse phase protein array data.

Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new time course plotting function. We demonstrate the robustness of our enhanced RPPanalyzer platform by analyzing longitudinal RPPA data of MET receptor signaling upon stimulation with different hepatocyte growth factor concentrations.

Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.

BACKGROUND: Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. RESULTS: The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionally analysed RB and RPS6 in the long-term networks. CONCLUSIONS: We derived protein interaction models for three breast cancer cell lines. Changes compared to the common reference network hint towards individual characteristics and potential drug resistance mechanisms. Simulation of perturbations were consistent with the experimental data, confirming our combined reverse and forward engineering approach as valuable for drug discovery and personalised medicine.

Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery.

The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Synergism between Hedgehog-GLI and EGFR signaling in Hedgehog-responsive human medulloblastoma cells induces downregulation of canonical Hedgehog-target genes and stabilized expression of GLI1.

Aberrant activation of Hedgehog (HH) signaling has been identified as a key etiologic factor in many human malignancies. Signal strength, target gene specificity, and oncogenic activity of HH signaling depend profoundly on interactions with other pathways, such as epidermal growth factor receptor-mediated signaling, which has been shown to cooperate with HH/GLI in basal cell carcinoma and pancreatic cancer. Our experimental data demonstrated that the Daoy human medulloblastoma cell line possesses a fully inducible endogenous HH pathway. Treatment of Daoy cells with Sonic HH or Smoothened agonist induced expression of GLI1 protein and simultaneously prevented the processing of GLI3 to its repressor form. To study interactions between HH- and EGF-induced signaling in greater detail, time-resolved measurements were carried out and analyzed at the transcriptomic and proteomic levels. The Daoy cells responded to the HH/EGF co-treatment by downregulating GLI1, PTCH, and HHIP at the transcript level; this was also observed when Amphiregulin (AREG) was used instead of EGF. We identified a novel crosstalk mechanism whereby EGFR signaling silences proteins acting as negative regulators of HH signaling, as AKT- and ERK-signaling independent process. EGFR/HH signaling maintained high GLI1 protein levels which contrasted the GLI1 downregulation on the transcript level. Conversely, a high-level synergism was also observed, due to a strong and significant upregulation of numerous canonical EGF-targets with putative tumor-promoting properties such as MMP7, VEGFA, and IL-8. In conclusion, synergistic effects between EGFR and HH signaling can selectively induce a switch from a canonical HH/GLI profile to a modulated specific target gene profile. This suggests that there are more wide-spread, yet context-dependent interactions, between HH/GLI and growth factor receptor signaling in human malignancies.

Microspot immunoassay-based analysis of plasma protein profiles for biomarker discovery strategies.

To expedite the development of personalized medicine, new and reliable biomarkers are required to facilitate early diagnosis, to determine prognosis, predict response or resistance to different therapies, and to monitor disease progression or recurrence. Human body fluids, such as blood, present a promising resource for biomarker discovery, in every sense. Microspot immunoassays allow the simultaneous quantification of multiple analytes from a minute amount of samples in a single measurement. The experimental design of microspot immunoassays is based on antibody pairs recognizing different epitopes of the analyte. The first antibody is used to capture the analyte from the complex sample, and the second antibody is used for detection. As with traditional enzyme-linked immunosorbent assays, highly reliable and reproducible results are obtained.

QuantProReloaded: quantitative analysis of microspot immunoassays.

UNLABELLED: Protein microarrays are well-established as sensitive tools for proteomics. Particularly, the microspot immunoassay (MIA) platform enables a quantitative analysis of (phospho-) proteins in complex solutions (e.g. cell lysates or blood plasma) and with low consumption of samples and reagents. Despite numerous biological and clinical applications of MIAs there is currently no user-friendly open source data analysis software available with versatile options for data analysis and data visualization. Here, we introduce the open source software QuantProReloaded that is specifically designed for the analysis of data from MIA experiments. AVAILABILITY AND IMPLEMENTATION: QuantProReloaded is written in R and Java and is open for download under the BSB license at http://code.google.com/p/quantproreloaded/.