Grb7 SH2 domain structure and interactions with a cyclic peptide inhibitor of cancer cell migration and proliferation

Background

Human growth factor receptor bound protein 7 (Grb7) is an adapter protein that mediates the coupling of tyrosine kinases with their downstream signaling pathways. Grb7 is frequently overexpressed in invasive and metastatic human cancers and is implicated in cancer progression via its interaction with the ErbB2 receptor and focal adhesion kinase (FAK) that play critical roles in cell proliferation and migration. It is thus a prime target for the development of novel anti-cancer therapies. Recently, an inhibitory peptide (G7-18NATE) has been developed which binds specifically to the Grb7 SH2 domain and is able to attenuate cancer cell proliferation and migration in various cancer cell lines.

Results

As a first step towards understanding how Grb7 may be inhibited by G7-18NATE, we solved the crystal structure of the Grb7 SH2 domain to 2.1 Å resolution. We describe the details of the peptide binding site underlying target specificity, as well as the dimer interface of Grb 7 SH2. Dimer formation of Grb7 was determined to be in the μM range using analytical ultracentrifugation for both full-length Grb7 and the SH2 domain alone, suggesting the SH2 domain forms the basis of a physiological dimer. ITC measurements of the interaction of the G7-18NATE peptide with the Grb7 SH2 domain revealed that it binds with a binding affinity of K_d = ~35.7 μM and NMR spectroscopy titration experiments revealed that peptide binding causes perturbations to both the ligand binding surface of the Grb7 SH2 domain as well as to the dimer interface, suggesting that dimerisation of Grb7 is impacted on by peptide binding.

Conclusion

Together the data allow us to propose a model of the Grb7 SH2 domain/G7-18NATE interaction and to rationalize the basis for the observed binding specificity and affinity. We propose that the current study will assist with the development of second generation Grb7 SH2 domain inhibitors, potentially leading to novel inhibitors of cancer cell migration and invasion.     

Quirks of dye nomenclature. 9. Fluorescein

Adolf Baeyer announced the discovery of fluorescein in 1871 and named it after its most striking property, i.e., fluorescence. I describe here the synthesis of fluorescein. There are seven molecular species in both the solid state or in solution. I also summarize some of the diverse applications of the dye, both medical and nonmedical, which depend mostly on the facile detection of fluorescein at low concentration. Both animal and human toxicity are examined.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

The small regulatory RNA molecule MicA is involved in <Emphasis Type="Italic">Salmonella enterica</Emphasis> serovar Typhimurium biofilm formation

Background  

LuxS is the synthase enzyme of the quorum sensing signal AI-2. In Salmonella Typhimurium, it was previously shown that a luxS deletion mutant is impaired in biofilm formation. However, this phenotype could not be complemented by extracellular addition of quorum sensing signal molecules.     

The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.     

Functional characterisation of the regulation of CAAT enhancer binding protein alpha by GSK-3 phosphorylation of Threonines 222/226

Background  

Glycogen Synthase Kinase-3 (GSK3) activity is repressed following insulin treatment of cells. Pharmacological inhibition of GSK3 mimics the effect of insulin on Phosphoenolpyruvate Carboxykinase (PEPCK), Glucose-6 Phosphatase (G6Pase) and IGF binding protein-1 (IGFBP1) gene expression. CAAT/enhancer binding protein alpha (C/EBPα) regulates these gene promoters in liver and is phosphorylated on two residues (T222/T226) by GSK3, although the functional outcome of the phosphorylation has not been established. We aimed to establish whether CEBPα is a link between GSK3 and these gene promoters.     

ALS/FTD‐associated FUS activates GSK‐3β to disrupt the VAPB–PTPIP51 interaction and ER–mitochondria associations

Defective FUS metabolism is strongly associated with amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD), but the mechanisms linking FUS to disease are not properly understood. However, many of the functions disrupted in ALS/FTD are regulated by signalling between the endoplasmic reticulum (ER) and mitochondria. This signalling is facilitated by close physical associations between the two organelles that are mediated by binding of the integral ER protein VAPB to the outer mitochondrial membrane protein PTPIP51, which act as molecular scaffolds to tether the two organelles. Here, we show that FUS disrupts the VAPB–PTPIP51 interaction and ER–mitochondria associations. These disruptions are accompanied by perturbation of Ca²⁺ uptake by mitochondria following its release from ER stores, which is a physiological read‐out of ER–mitochondria contacts. We also demonstrate that mitochondrial ATP production is impaired in FUS‐expressing cells; mitochondrial ATP production is linked to Ca²⁺ levels. Finally, we demonstrate that the FUS‐induced reductions to ER–mitochondria associations and are linked to activation of glycogen synthase kinase‐3β (GSK‐3β), a kinase already strongly associated with ALS/FTD.     

Turmeric: old spice,new spice

The history of chemical investigations into the yellow components of turmeric can be traced from 1815. Although the major yellow component of turmeric, curcumin, often is represented as a 1,3-diketone in the solid state and in nonaqueous solution, it exists in the enol form. The struggle to identify the chemical structure of curcumin continued for nearly a century and was complicated by the difficult purification of curcumin and by the presence of two additional yellow components. Food remains the main use for turmeric (old spice); its use as a dye has diminished since the 19th century, but it may have pharmaceutical uses (new spice).