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Background: Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems., Methods: We apply the Software Development Lifecycle Framework-conceived by Winston Boyce in 1970-to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building., Results: We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time., Conclusions: Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: The murder of George Floyd created national outcry that echoed down to national institutions, including universities and academic systems to take a hard look at systematic and systemic racism in higher education. This motivated the creation of a fear and tension-minimizing, curricular offering, " Courageous Conversations," collaboratively engaging students, staff, and faculty in matters of diversity, equity, and inclusion (DEI) in the Department of Health Outcomes and Biomedical Informatics at the University of Florida., Methods: A qualitative design was employed assessing narrative feedback from participants during the Fall semester of 2020. Additionally, the ten-factor model implementation framework was applied and assessed. Data collection included two focus groups and document analysis with member-checking. Thematic analysis (i.e., organizing, coding, synthesizing) was used to analyze a priori themes based on the four agreements of the courageous conversations framework, stay engaged, expect to experience discomfort, speak your truth, and expect and accept non-closure., Results: A total of 41 participants of which 20 (48.78%) were department staff members, 11 (26.83%) were department faculty members, and 10 (24.30%) were graduate students. The thematic analysis revealed 1) that many participants credited their learning experiences to what their peers had said about their own personal lived experiences during group sessions, and 2) several participants said they would either retake the course or recommend it to a colleague., Conclusion: With structured implementation, courageous conversations can be an effective approach to create more diverse, equitable, and inclusive spaces in training programs with similar DEI ecosystems., (© The Author(s) 2022.)

Background: Social and behavioral aspects of our lives significantly impact our health, yet minimal social determinants of health (SDOH) data elements are collected in the healthcare system., Methods: In this proof-of-concept study we developed a repeatable SDOH enrichment and integration process to incorporate dynamically evolving SDOH domain concepts from consumers into clinical data. This process included SDOH mapping, linking compiled consumer data to patient records in Electronic Health Records, data quality analysis and preprocessing, and storage., Results: Consumer compilers data coverage ranged from ~90 to ~54% and the percentage match rate between compilers was between ~21 and 64%. Our preliminary analysis showed that apart from demographic factors, several SDOH factors like home-ownership, marital-status, presence of children, number of members per household, economic stability and education were significantly different between the COVID-19 positive and negative patient groups while estimated family-income and home market-value were not., Conclusion: Our preliminary analysis shows commercial consumer data can be a viable source of SDOH factor at an individual-level for clinical data thus providing a path for clinicians to improve patient treatment and care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Greer, Zayas and Bhattacharyya.)

To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.

Src kinase activity is controlled by various mechanisms involving a coordinated movement of kinase and regulatory domains. Notwithstanding the extensive knowledge related to the backbone dynamics, little is known about the more subtle side-chain dynamics within the regulatory domains and their role in the activation process. Here, we show through experimental methyl dynamic results and predicted changes in side-chain conformational couplings that the SH2 structure of Fyn contains a dynamic network capable of propagating binding information. We reveal that binding the phosphorylated tail of Fyn perturbs a residue cluster near the linker connecting the SH2 and SH3 domains of Fyn, which is known to be relevant in the regulation of the activity of Fyn. Biochemical perturbation experiments validate that those residues are essential for inhibition of Fyn, leading to a gain of function upon mutation. These findings reveal how side-chain dynamics may facilitate the allosteric regulation of the different members of the Src kinase family., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Src Homology 3 domains are ubiquitous small interaction modules known to act as docking sites and regulatory elements in a wide range of proteins. Prior experimental NMR work on the SH3 domain of Src showed that ligand binding induces long-range dynamic changes consistent with an induced fit mechanism. The identification of the residues that participate in this mechanism produces a chart that allows for the exploration of the regulatory role of such domains in the activity of the encompassing protein. Here we show that a computational approach focusing on the changes in side chain dynamics through ligand binding identifies equivalent long-range effects in the Src SH3 domain. Mutation of a subset of the predicted residues elicits long-range effects on the binding energetics, emphasizing the relevance of these positions in the definition of intramolecular cooperative networks of signal transduction in this domain. We find further support for this mechanism through the analysis of seven other publically available SH3 domain structures of which the sequences represent diverse SH3 classes. By comparing the eight predictions, we find that, in addition to a dynamic pathway that is relatively conserved throughout all SH3 domains, there are dynamic aspects specific to each domain and homologous subgroups. Our work shows for the first time from a structural perspective, which transduction mechanisms are common between a subset of closely related and distal SH3 domains, while at the same time highlighting the differences in signal transduction that make each family member unique. These results resolve the missing link between structural predictions of dynamic changes and the domain sectors recently identified for SH3 domains through sequence analysis.

Elderly patients, aged 65 or older, make up 13.5% of the U.S. population, but represent 45.2% of the top 10% of healthcare utilizers, in terms of expenditures. Middle-aged Americans, aged 45 to 64 make up another 37.0% of that category. Given the high demand for healthcare services by the aforementioned population, it is important to identify high-cost users of healthcare systems and, more importantly, ineffective utilization patterns to highlight where targeted interventions could be placed to improve care delivery. In this work, we present a novel multi-level framework applying machine learning (ML) methods (i.e., random forest regression and hierarchical clustering) to group patients with similar utilization profiles into clusters. We use a vector space model to characterize a patient's utilization profile as the number of visits to different care providers and prescribed medications. We applied the proposed methods using the 2013 Medical Expenditures Panel Survey (MEPS) dataset. We identified clusters of healthcare utilization patterns of elderly and middle-aged adults in the United States, and assessed the general and clinical characteristics associated with these utilization patterns. Our results demonstrate the effectiveness of the proposed framework to model healthcare utilization patterns. Understanding of these patterns can be used to guide healthcare policy-making and practice.

Protein folding is in its early stages largely determined by the protein sequence and complex local interactions between amino acids, resulting in lower energy conformations that provide the context for further folding into the native state. We compiled a comprehensive data set of early folding residues based on pulsed labeling hydrogen deuterium exchange experiments. These early folding residues have corresponding higher backbone rigidity as predicted by DynaMine from sequence, an effect also present when accounting for the secondary structures in the folded protein. We then show that the amino acids involved in early folding events are not more conserved than others, but rather, early folding fragments and the secondary structure elements they are part of show a clear trend toward conserving a rigid backbone. We therefore propose that backbone rigidity is a fundamental physical feature conserved by proteins that can provide important insights into their folding mechanisms and stability., (Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

DIDA (DIgenic diseases DAtabase) is a novel database that provides for the first time detailed information on genes and associated genetic variants involved in digenic diseases, the simplest form of oligogenic inheritance. The database is accessible via http://dida.ibsquare.be and currently includes 213 digenic combinations involved in 44 different digenic diseases. These combinations are composed of 364 distinct variants, which are distributed over 136 distinct genes. The web interface provides browsing and search functionalities, as well as documentation and help pages, general database statistics and references to the original publications from which the data have been collected. The possibility to submit novel digenic data to DIDA is also provided. Creating this new repository was essential as current databases do not allow one to retrieve detailed records regarding digenic combinations. Genes, variants, diseases and digenic combinations in DIDA are annotated with manually curated information and information mined from other online resources. Next to providing a unique resource for the development of new analysis methods, DIDA gives clinical and molecular geneticists a tool to find the most comprehensive information on the digenic nature of their diseases of interest., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Trust guidelines and policies outline recommendations for the management of common clinical and non-clinical situations, serving to standardise best practice. Prior to this project, there was no consolidated location for these documents. Lack of organisational structure and inadequate search functionality within the trust intranet led to time wasted locating information, acting outside of recognised best practice, and ultimately potentially compromising patient safety. We surveyed 55 junior doctors, 95% of respondents were dependent on guidelines on a daily basis. 20% spending greater than 5 minutes to locate protocols and 38% unable to locate some relevant documents at all. We analysed the time taken for junior doctors to locate six randomly selected protocols. Pre-intervention mean time was 133 seconds (on six occasions doctors were unable to locate the guideline). All trust guidelines and protocols currently available on the intranet were collated, consolidated, and renamed according to content. These were then re-alphabetised and new search terms linked to each document. Existing links were then uploaded and a single web page made available via the trust intranet homepage. The new page was publicised by email, posters and interdepartmental presentations. In our post intervention survey, 97% of respondents were aware of the project and had made use of the page. All protocols were located during re-testing with 90% of those resurveyed stating it was easier to locate protocols. Overall, a reduction in the time and number of clicks required to locate protocols was demonstrated: mean time 16 seconds vs 133 seconds pre-intervention (n=60). 53% of guidelines located in <30s and 86% <2 minutes.

Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants., Results: We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones., Conclusions: Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.

Protein dynamics are important for understanding protein function. Unfortunately, accurate protein dynamics information is difficult to obtain: here we present the DynaMine webserver, which provides predictions for the fast backbone movements of proteins directly from their amino-acid sequence. DynaMine rapidly produces a profile describing the statistical potential for such movements at residue-level resolution. The predicted values have meaning on an absolute scale and go beyond the traditional binary classification of residues as ordered or disordered, thus allowing for direct dynamics comparisons between protein regions. Through this webserver, we provide molecular biologists with an efficient and easy to use tool for predicting the dynamical characteristics of any protein of interest, even in the absence of experimental observations. The prediction results are visualized and can be directly downloaded. The DynaMine webserver, including instructive examples describing the meaning of the profiles, is available at http://dynamine.ibsquare.be., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Objective: Prediabetes is a high-risk state for developing diabetes and associated complications. The purpose of this paper was to report trends in prevalence of prediabetes for individuals aged 16 and older in England without previously diagnosed diabetes., Setting: Data collected by the Health Survey for England (HSE) in England in the years 2003, 2006, 2009 and 2011., Participants: Individuals aged 16 and older who participated in the HSE and provided a blood sample., Primary Outcome Variable: Individuals were classified as having prediabetes if glycated haemoglobin was between 5.7% and 6.4% and were not previously diagnosed with diabetes., Results: The prevalence rate of prediabetes increased from 11.6% to 35.3% from 2003 to 2011. By 2011, 50.6% of the population who were overweight (body mass index (BMI)>25) and ≥40 years of age had prediabetes. In bivariate relationships, individuals with greater socioeconomic deprivation were more likely to have prediabetes in 2003 (p=0.0008) and 2006 (p=0.0246), but the relationship was not significant in 2009 (p=0.213) and 2011 (p=0.3153). In logistic regressions controlling for age, sex, race/ethnicity, BMI and high blood pressure, the second most socioeconomically deprived had a significantly elevated risk of having prediabetes (2011, OR=1.45; 95% CI 1.26 to 1.88)., Conclusions: There has been a marked increase in the proportion of adults in England with prediabetes. The socioeconomically deprived are at substantial risk. In the absence of concerted and effective efforts to reduce risk, the number of people with diabetes is likely to increase steeply in coming years., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Mutations in the PINK1 gene are a frequent cause of autosomal recessive Parkinson's disease (PD). PINK1 encodes a mitochondrial kinase with neuroprotective activity, implicated in maintaining mitochondrial homeostasis and function. In concurrence with Parkin, PINK1 regulates mitochondrial trafficking and degradation of damaged mitochondria through mitophagy. Moreover, PINK1 can activate autophagy by interacting with the pro-autophagic protein Beclin-1. Here, we report that, upon mitochondrial depolarization, PINK1 interacts with and phosphorylates Bcl-xL, an anti-apoptotic protein also known to inhibit autophagy through its binding to Beclin-1. PINK1-Bcl-xL interaction does not interfere either with Beclin-1 release from Bcl-xL or the mitophagy pathway; rather it protects against cell death by hindering the pro-apoptotic cleavage of Bcl-xL. Our data provide a functional link between PINK1, Bcl-xL and apoptosis, suggesting a novel mechanism through which PINK1 regulates cell survival. This pathway could be relevant for the pathogenesis of PD as well as other diseases including cancer.

Protein function and dynamics are closely related; however, accurate dynamics information is difficult to obtain. Here based on a carefully assembled data set derived from experimental data for proteins in solution, we quantify backbone dynamics properties on the amino-acid level and develop DynaMine--a fast, high-quality predictor of protein backbone dynamics. DynaMine uses only protein sequence information as input and shows great potential in distinguishing regions of different structural organization, such as folded domains, disordered linkers, molten globules and pre-structured binding motifs of different sizes. It also identifies disordered regions within proteins with an accuracy comparable to the most sophisticated existing predictors, without depending on prior disorder knowledge or three-dimensional structural information. DynaMine provides molecular biologists with an important new method that grasps the dynamical characteristics of any protein of interest, as we show here for human p53 and E1A from human adenovirus 5.

Background: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods., Results: Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes., Conclusions: The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2.

Experimental NMR relaxation studies have shown that peptide binding induces dynamical changes at the side-chain level throughout the second PDZ domain of PTP1e, identifying as such the collection of residues involved in long-range communication. Even though different computational approaches have identified subsets of residues that were qualitatively comparable, no quantitative analysis of the accuracy of these predictions was thus far determined. Here, we show that our information theoretical method produces quantitatively better results with respect to the experimental data than some of these earlier methods. Moreover, it provides a global network perspective on the effect experienced by the different residues involved in the process. We also show that these predictions are consistent within both the human and mouse variants of this domain. Together, these results improve the understanding of intra-protein communication and allostery in PDZ domains, underlining at the same time the necessity of producing similar data sets for further validation of thses kinds of methods.

Bone marrow mesenchymal cells (MSCs) can protect leukemic cells from chemotherapy, thus increasing their survival rate. We studied the potential molecular mechanisms underlying this effect in acute lymphoblastic leukemia (ALL) cells. Coculture of ALL cells with MSCs induced on the lymphoblast plasma membrane the expression of a signaling complex formed by hERG1 (human ether-à-go-go-related gene 1) channels, the β(1)-integrin subunit, and the chemokine receptor CXC chemokine receptor-4. The assembly of such a protein complex activated both the extracellular signal-related kinase 1/2 (ERK1/2) and the phosphoinositide 3-kinase (PI3K)/Akt prosurvival signaling pathways. At the same time, ALL cells became markedly resistant to chemotherapy-induced apoptosis. hERG1 channel function appeared to be important for both the initiation of prosurvival signals and the development of drug resistance, because specific channel blockers decreased the protective effect of MSCs. NOD/SCID mice engrafted with ALL cells and treated with channel blockers showed reduced leukemic infiltration and had higher survival rates. Moreover, hERG1 blockade enhanced the therapeutic effect produced by corticosteroids. Our findings provide a rationale for clinical testing of hERG1 blockers in the context of antileukemic therapy for patients with ALL.

Background: Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues., Results: We developed an effective representation of structural information by modeling spherical regions around candidate residues, and extracting statistics on the properties of their content such as physico-chemical properties, atomic density, flexibility, presence of water molecules. We trained an SVM classifier combining our features with sequence-based information and previously developed 3D features, and compared its performance with the most recent state-of-the-art approaches on different benchmark datasets. We further analyzed the discriminant power of the information provided by the presence of heterogens in the residue neighborhood., Conclusions: Our structure-based method achieves consistent improvements on all tested datasets over both sequence-based and structure-based state-of-the-art approaches. Structural neighborhood information is shown to be responsible for such results, and predicting the presence of nearby heterogens seems to be a promising direction for further improvements.

Carbon nanotubes have been applied in several areas of nerve tissue engineering to probe and augment cell behaviour, to label and track subcellular components, and to study the growth and organization of neural networks. Recent reports show that nanotubes can sustain and promote neuronal electrical activity in networks of cultured cells, but the ways in which they affect cellular function are still poorly understood. Here, we show, using single-cell electrophysiology techniques, electron microscopy analysis and theoretical modelling, that nanotubes improve the responsiveness of neurons by forming tight contacts with the cell membranes that might favour electrical shortcuts between the proximal and distal compartments of the neuron. We propose the 'electrotonic hypothesis' to explain the physical interactions between the cell and nanotube, and the mechanisms of how carbon nanotubes might affect the collective electrical activity of cultured neuronal networks. These considerations offer a perspective that would allow us to predict or engineer interactions between neurons and carbon nanotubes.

Voltage-dependent K+ channels are the main determinants in controlling cellular excitability within the central nervous system. Among voltage-dependent K+ channels, the ERG subfamily is deeply involved in the control of cellular excitability, both in mammals and in invertebrates. ERG channels are encoded by different genes: the erg1 gene, which can generate two alternative transcripts (erg1a and erg1b), erg2 and erg3. The aim of the present study was to determine the expression pattern and cellular localization of ERG proteins (ERG1, ERG2, and ERG3) in the mouse CNS, differentiating, for the first time, the ERG1A and ERG1B isoforms. To this purpose, novel specific antibodies were raised against the various channel proteins and their specificity and immunoreactivity tested. It emerged that: 1) all the erg genes were indeed translated in neuronal tissue; 2) ERG proteins distribution in the mouse CNS often overlapped, and only in specific areas each ERG protein showed a distinct pattern of expression; and 3) ERG proteins were generally expressed in neuronal soma, but dendritic and/or white matter labeling could be detected in specific areas. The finding that ERG proteins often have an overlapping expression suggests that neuronal ERG currents could be determined, at least in part, by heterotetrameric ERG channels. This suggestion is demonstrated to occur for ERG1A/ERG1B by showing that the two isoforms coassemble in mouse brain., ((c) 2005 Wiley-Liss, Inc.)

The signature amidase from the extremophile archeum Sulfolobus solfataricus is an enantioselective enzyme that cleaves S-amides. We report here that this enzyme also converts nitriles in the corresponding organic acid, similarly to the well characterized amidase from Rhodococcus rhodochrous J1. The archaeal and rhodococcal enzymes belong to the signature amidases and contain the typical serine-glycine rich motif. They work at different optimal temperature, share a high sequence similarity and both contain an additional CX3C motif. To explain their dual specificity, we built a 3D model of the structure of the S. solfataricus enzyme, which suggests that, in addition to the classical catalytic Ser-cisSer-Lys, a putative additional Cys-cisSer-Lys catalytic site, likely to be responsible for nitrile hydrolysis, is present in these proteins. The results of random and site-directed mutagenesis experiments, as well as inhibition studies support our hypothesis.

Adhesive receptors of the integrin family are primarily involved in cell-extracellular matrix adhesion. Additionally, integrins trigger multiple signaling pathways that are involved in cell migration, proliferation, survival, and differentiation. We previously demonstrated that the activation of integrins containing the beta(1) subunit leads to a selective increase in potassium currents carried by the human ether-a-go-go-related gene (hERG) channels in neuroblastoma and leukemia cells; this current activation modulates adhesion-dependent differentiation in these cells. We hypothesized that the cross-talk between integrins and hERG channels could be traced back to the assembly of a macromolecular signaling complex comprising the two proteins. We tested this hypothesis in both SH-SY5Y neuroblastoma cells and in human embryonic kidney 293 cells stably transfected with hERG1 and, therefore, expressing only the full-length hERG1 protein on the plasma membrane. The beta(1) integrin and hERG1 coprecipitate in these cells and colocalize in both intracellular and surface membrane compartments. The two proteins also coprecipitate with caveolin-1, suggesting the localization of the complex in lipid rafts/caveolae. hERG1-transfected cells undergo an activation of hERG currents after beta(1) integrin-mediated adhesion to fibronectin; concomitant with this activation, the focal adhesion kinase associates with the hERG1 protein and becomes tyrosine phosphorylated. Using hERG1-specific inhibitors, we show that the tyrosine phosphorylation of focal adhesion kinase is strictly dependent on hERG channel activity. Similarly, the activity of the small GTPase Rac1 turned out to be dependent on hERG currents. On the whole, these data indicate that the hERG1 protein associates with beta(1) integrins and modulates adhesion receptor signaling.

During spinal cord maturation neuronal excitability gradually differentiates to meet different functional demands. Spontaneous activity, appearing early during spinal development, is regulated by the expression pattern of ion channels in individual neurons. While emerging excitability of embryonic motoneurons has been widely investigated, little is known about that of spinal interneurons. Voltage-dependent K+ channels are a heterogeneous class of ion channels that accomplish several functions. Recently voltage-dependent K+ channels encoded by erg subfamily genes (ERG channels) were shown to modulate excitability in immature neurons of mouse and quail. We investigated the expression of ERG channels in immature spinal interneurons, using organotypic embryonic cultures of mouse spinal cord after 1 and 2 weeks of development in vitro. We report here that all the genes of the erg family known so far (erg1a, erg1b, erg2, erg3) are expressed in embryonic spinal cultures. We demonstrate for the first time that three ERG proteins (ERG1A, ERG2 and ERG3) are co-expressed in the same neuronal population, and display a spatio-temporal distribution in the spinal slices. ERG immuno-positive cells, representing mainly GABAergic interneurons, were present in large numbers at early stages of development, while declining later, with a ventral to dorsal gradient. Patch clamp recordings confirmed these data, showing that ventral interneurons expressed functional ERG currents only transiently. Similar expression of the erg genes was observed at comparable ages in vivo. The role of ERG currents in regulating neuronal excitability during the earliest phases of spinal circuitry development will be examined in future studies.