Immediate-Early Genes as Influencers in Genetic Networks and their Role in Alzheimer's Disease

Immediate-early genes (IEGs) are a class of activity-regulated genes (ARGs) that are transiently and rapidly activated in the absence of de novo protein synthesis in response to neuronal activity. We explored the role of IEGs in genetic networks to pinpoint potential drug targets for Alzheimer's disease (AD). Using a combination of network analysis and genome-wide association study (GWAS) summary statistics we show that (1) IEGs exert greater topological influence across different human and mouse gene networks compared to other ARGs, (2) ARGs are sparsely involved in diseases and significantly more mutational constrained compared to non-ARGs, (3) Many AD-linked variants are in ARGs gene regions, mainly in MARK4 near FOSB, with an AD risk eQTL that increases MARK4 expression in cortical areas, (4) MARK4 holds an influential place in a dense AD multi-omic network and a high AD druggability score. Our work on IEGs' influential network role is a valuable contribution to guiding interventions for diseases marked by dysregulation of their downstream targets and highlights MARK4 as a promising underexplored AD-target.

Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures

Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.

Preliminary In Vitro and In Vivo Insights of In Silico Candidate Repurposed Drugs for Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease and is the most common type of dementia. Although a considerably large amount of money has been invested in drug development for AD, no disease modifying treatment has been detected so far. In our previous work, we developed a computational method to highlight stage-specific candidate repurposed drugs against AD. In this study, we tested the effect of the top 13 candidate repurposed drugs that we proposed in our previous work in a severity stage-specific manner using an in vitro BACE1 assay and the effect of a top-ranked drug from the list of our previous work, tetrabenazine (TBZ), in the 5XFAD as an AD mouse model. From our in vitro screening, we detected 2 compounds (clomiphene citrate and Pik-90) that showed statistically significant inhibition against the activity of the BACE1 enzyme. The administration of TBZ at the selected dose and therapeutic regimen in 5XFAD in male and female mice showed no significant effect in behavioral tests using the Y-maze and the ELISA immunoassay of Aβ40. To our knowledge, this is the first time the drug tetrabenazine has been tested in the 5XFAD mouse model of AD in a sex-stratified manner. Our results highlight 2 drugs (clomiphene citrate and Pik-90) from our previous computational work for further investigation.

Stbd1-deficient mice display insulin resistance associated with enhanced hepatic ER-mitochondria contact

Starch binding domain-containing protein 1 (STBD1) is an endoplasmic reticulum (ER)-resident, glycogen-binding protein. In addition to glycogen, STBD1 has been shown to interact with several proteins implicated in glycogen synthesis and degradation, yet its function in glycogen metabolism remains largely unknown. In addition to the bulk of the ER, STBD1 has been reported to localize at regions of physical contact between mitochondria and the ER, known as Mitochondria-ER Contact sites (MERCs). Given the emerging correlation between distortions in the integrity of hepatic MERCs and insulin resistance, our study aimed to delineate the role of STBD1 in vivo by addressing potential abnormalities in glucose metabolism and ER-mitochondria communication associated with insulin resistance in mice with targeted inactivation of Stbd1 (Stbd1KO). We show that Stbd1KO mice at the age of 24 weeks displayed reduced hepatic glycogen content and aberrant control of glucose homeostasis, compatible with insulin resistance. In line with the above, Stbd1-deficient mice presented with increased fasting blood glucose and insulin levels, attenuated activation of insulin signaling in the liver and skeletal muscle and elevated liver sphingomyelin content, in the absence of hepatic steatosis. Furthermore, Stbd1KO mice were found to exhibit enhanced ER-mitochondria association and increased mitochondrial fragmentation in the liver. Nevertheless, the enzymatic activity of hepatic respiratory chain complexes and ER stress levels in the liver were not altered. Our findings identify a novel important role for STBD1 in the control of glucose metabolism, associated with the integrity of hepatic MERCs.

Age-Related Exosomal and Endogenous Expression Patterns of miR-1, miR-133a, miR-133b, and miR-206 in Skeletal Muscles

Skeletal muscle growth and maintenance depend on two tightly regulated processes, myogenesis and muscle regeneration. Both processes involve a series of crucial regulatory molecules including muscle-specific microRNAs, known as myomiRs. We recently showed that four myomiRs, miR-1, miR-133a, miR-133b, and miR-206, are encapsulated within muscle-derived exosomes and participate in local skeletal muscle communication. Although these four myomiRs have been extensively studied for their function in muscles, no information exists regarding their endogenous and exosomal levels across age. Here we aimed to identify any age-related changes in the endogenous and muscle-derived exosomal myomiR levels during acute skeletal muscle growth. The four endogenous and muscle-derived myomiRs were investigated in five skeletal muscles (extensor digitorum longus, soleus, tibialis anterior, gastrocnemius, and quadriceps) of 2-week–1-year-old wild-type male mice. The expression of miR-1, miR-133a, and miR-133b was found to increase rapidly until adolescence in all skeletal muscles, whereas during adulthood it remained relatively stable. By contrast, endogenous miR-206 levels were observed to decrease with age in all muscles, except for soleus. Differential expression of the four myomiRs is also inversely reflected on the production of two protein targets; serum response factor and connexin 43. Muscle-derived exosomal miR-1, miR-133a, and miR-133b levels were found to increase until the early adolescence, before reaching a plateau phase. Soleus was found to be the only skeletal muscle to release exosomes enriched in miR-206. In this study, we showed for the first time an in-depth longitudinal analysis of the endogenous and exosomal levels of the four myomiRs during skeletal muscle development. We showed that the endogenous expression and extracellular secretion of the four myomiRs are associated to the function and size of skeletal muscles as the mice age. Overall, our findings provide new insights for the myomiRs’ significant role in the first year of life in mice.

Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective

The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter ("Die" score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.

Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.

PathWalks: identifying pathway communities using a disease-related map of integrated information

MOTIVATION

Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways.

RESULTS

We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vagkaratzas/PathWalks.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington's Disease Status Based on Omics Data

Huntington’s disease is a rare neurodegenerative disease caused by a cytosine–adenine–guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington’s disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-β) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca²⁺) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca²⁺, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.

PathwayConnector: finding complementary pathways to enhance functional analysis

SUMMARY

PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases.

AVAILABILITY AND IMPLEMENTATION

http://bioinformatics.cing.ac.cy/PathwayConnector.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Revealing Clusters of Connected Pathways Through Multisource Data Integration in Huntington's Disease and Spastic Ataxia

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary "connectors" to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.

Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms

The abundance of available information for each disease from multiple sources (e.g. as genetic, regulatory, metabolic, and protein-protein interaction) constitutes both an advantage and a challenge in identifying disease-specific underlying mechanisms. Integration of multi-source data is a rising topic and a great challenge in precision medicine and is crucial in enhancing disease understanding, identifying meaningful clusters of molecular mechanisms and increasing precision and personalisation towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The overall aim of this work was to develop a novel network-based integration methodology with the following characteristics: (i) maximise the number of data sources, (ii) utilise holistic approaches to integrate these sources (iii) be simple, flexible and extendable, (iv) be conclusive. Here, we present the case of Alzheimer's disease as a paradigm for illustrating our novel approach.

SIGNIFICANCE

In this work we present an integration methodology, which aggregates a large number of the available data sources and types by exploiting the holistic nature of network approaches. It is simple, flexible and extendable generating solid conclusions regarding the molecular mechanisms that underlie the input data. We have illustrated the strength of our proposed methodology using Alzheimer's disease as a paradigm. This method is expected to serve as a stepping-stone for further development of integration methods of multi-source omic-data and to contribute to progress towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The output of this methodology may act as a reference map of implicated pathways in the disease under investigation, where pathways related to additional omics data from any kind of experiment may be projected. This will increase the precision in the understanding of the disease and may contribute to personalised approaches for patients with different disease-related pathway profile, leading to a more precise, personalised and ideally preventive management of the disease.

A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition

Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synap–tic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI), a prominent form of short-term synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of endocannabinoids in synaptic transmission as a feedback mechanism both at synaptic and network level.

Virtual physiological human: training challenges

The virtual physiological human (VPH) initiative encompasses a wide range of activities, including structural and functional imaging, data mining, knowledge discovery tool and database development, biomedical modelling, simulation and visualization. The VPH community is developing from a multitude of relatively focused, but disparate, research endeavours into an integrated effort to bring together, develop and translate emerging technologies for application, from academia to industry and medicine. This process initially builds on the evolution of multi-disciplinary interactions and abilities, but addressing the challenges associated with the implementation of the VPH will require, in the very near future, a translation of quantitative changes into a new quality of highly trained multi-disciplinary personnel. Current strategies for undergraduate and on-the-job training may soon prove insufficient for this. The European Commission seventh framework VPH network of excellence is exploring this emerging need, and is developing a framework of novel training initiatives to address the predicted shortfall in suitably skilled VPH-aware professionals. This paper reports first steps in the implementation of a coherent VPH training portfolio.

Auditory gating in rat hippocampus and medial prefrontal cortex: effect of the cannabinoid agonist WIN55,212-2

Sensory gating can be assessed in rodents and humans using an auditory conditioning (C)-test (T) paradigm, with schizophrenic patients exhibiting a loss of gating. Dysregulation of the endocannabinoid system has been proposed to be involved in the pathogenesis of schizophrenia. We studied auditory gating and the effects of the cannabinoid agonist WIN55,212-22 on gating in CA3 and dentate gyrus (DG) of the hippocampus and medial prefrontal cortex (mPFC) in male Lister hooded rats using in vivo electrophysiology. The effects of a single dose of WIN55,212-2 on the N2 local field potential (LFP) test/conditioning amplitude ratios (T/C ratio) and response latencies were examined. In rats that demonstrated gating of N2, mPFC showed higher T/C ratios and shorter conditioning response latencies compared to DG and CA3. WIN55,212-2 disrupted auditory gating in all three areas with a significant increase in test amplitudes in the gating rats. A group of non-gating rats demonstrated higher test amplitudes and higher T/C ratios compared to gating rats. WIN55,212-2 had no effect on T/C ratios in the non-gating rats. The cannabinoid receptor (CB1) antagonist SR141716A prevented WIN55,212-2 induced disruption of gating. This study demonstrates gated auditory-evoked responses in CA3, DG and mPFC. The mPFC showed an early phase of gating which may later be modulated by CA3 and DG activity. Furthermore, cannabinoid receptor activation disrupted auditory gating in CA3, DG and mPFC, an effect which was prevented by CB1 receptor antagonism. The results further demonstrate the presence of a non-gating rat population which responded differently to cannabinoid agonists.

Sensory gating and its modulation by cannabinoids: electrophysiological, computational and mathematical analysis

Gating of sensory information can be assessed using an auditory conditioning-test paradigm which measures the reduction in the auditory evoked response to a test stimulus following an initial conditioning stimulus. Recording brainwaves from specific areas of the brain using multiple electrodes is helpful in the study of the neurobiology of sensory gating. In this paper, we use such technology to investigate the role of cannabinoids in sensory gating in the CA3 region of the rat hippocampus. Our experimental results show that application of the exogenous cannabinoid agonist WIN55,212-2 can abolish sensory gating. We have developed a phenomenological model of cannabinoid dynamics incorporated within a spiking neural network model of CA3 with synaptically interacting pyramidal and basket cells. Direct numerical simulations of this model suggest that the basic mechanism for this effect can be traced to the suppression of inhibition of slow GABA(B) synapses. Furthermore, by working with a simpler mathematical firing rate model we are able to show the robustness of this mechanism for the abolition of sensory gating.

Adsorption and selectivity characteristics of several human serum proteins with immobilised hard Lewis metal ion-chelate adsorbents

In this investigation, human serum has been used as an example of a crude protein mixture to define the protein binding characteristics and selectivity of several immobilised hard Lewis metal ion affinity chromatographic (IMAC) adsorbents. Specifically, the binding properties of immobilised O-phosphoserine (im-OPS) and 8-hydroxyquinoline (im-8-HQ), with immobilised iminodiacetic acid as a control system, have been investigated in combination with the hard Lewis metal ions, Al3+, Ca2+, Fe3+, Yb3+, and the borderline metal ion, Cu2+, over the pH range pH 5.5 to pH 8.0 with buffers of 0.5 M ionic strength. The same IMAC adsorbents were also investigated for their protein binding capabilities with buffers of an ionic strength of 0.06 M at pH 5.5 and pH 8.0. The binding behaviour of four "marker" proteins, namely transferrin, alpha2-macroglobulin, gammaglobulin and human serum albumin have furthermore been employed to monitor the differences in protein selectivity exhibited by these IMAC systems. The experimental findings confirm that these hard Lewis metal ion IMAC adsorbents function in a "mixed" binding mode with both coordination and electrostatic characteristics evident, depending on the ionic strength and pH of the equilibration or elution buffers. Based on a screening protocol, several members of the im-Mn+-8-HQ and im-Mn+-OPS adsorbent series have been identified with high selectivity for transferrin and alpha2-macroglobulin. These hard Lewis metal ion IMAC adsorbents thus provide attractive alternatives for selective fractionation of human serum proteins.

Application of immobilized metal ion chelate complexes as pseudocation exchange adsorbents for protein separation

The interactions of horse muscle myoglobin (MYO), tuna heart cytochrome c (CYT), and hen egg white lysozyme (LYS) with three different immobilized metal ion affinity (IMAC) adsorbents involving the chelated complexes of the hard Lewis metal ions Al3+, Ca2+, Fe3+, and Yb3+ and the borderline Lewis metal ion Cu2+ have been investigated in the presence of low- and high-ionic strength buffers and at two different pH values. In contrast to the selectivity behavior noted with buffers of high ionic strength, with low-ionic strength buffers, these three proteins interact with the hard metal ion IMAC adsorbents in a manner more characteristic of cation exchange behavior, although in contrast to the cation exchange chromatography of these proteins, as the pH value of the elution buffer was increased, the retention also increased. The selectivity differences observed under these conditions appear to be due to the formation of hydrolytic complexes of these immobilized metal ion chelate systems involving a change in the coordination geometry of the im-M(n+)-chelate at higher pH values. The experimental observations have been evaluated in terms of the effective charge on the immobilized metal ion chelate complex and the charge characteristics of the specific proteins.

Protein selectivity in immobilized metal affinity chromatography based on the surface accessibility of aspartic and glutamic acid residues

The interaction of different species variants of cytochrome c and myoglobin, as well as hen egg white lysozyme, with the hard Lewis metal ions Al3+, Ca2+, Fe3+, and Yb3+ and the borderline metal ion Cu2+, immobilized to iminodiacetic acid (IDA)-Sepharose CL-4B, has been investigated over the range pH 5.5-8.0. With appropriately chosen buffer and metal ion conditions, these proteins can be bound to the immobilized Mn+-IDA adsorbents via negatively charged amino acid residues accessible on the protein surface. For example, tuna heart cytochrome c, which lacks surface-accessible histidine residues, readily bound to the Fe3+-IDA adsorbent, while the other proteins also showed affinity toward immobilized Fe3+-IDA adsorbents when buffers containing 30 mM of imidazole were used. These studies document that protein selectivity can be achieved with hard-metal-ion immobilized metal ion affinity chromatography (IMAC) systems through the interaction of surface-exposed aspartic and glutamic acid residues on the protein with the immobilized Mn+-IDA complex. These investigations have also documented that the so-called soft or borderline immobilized metal ions such as the Cu2+-IDA adsorbent can also interact with surface-accessible aspartic and glutamic acid residues in a protein-dependent manner. A relationship is evident between the number of clustering of the surface-accessible aspartic and glutamic residues and protein selectivity with these IMAC systems. The use of elution buffers which contain organic compound modifiers which replicate the carboxyl group moieties of these amino acids on the surface of proteins is also described.

High-performance liquid chromatography of amino acids, peptides and proteins. CXXXI. O-phosphoserine as a new chelating ligand for use with hard Lewis metal ions in the immobilized-metal affinity chromatography of proteins

Conditions for the immobilization of O-phosphoserine (OPS) to epoxy-activated Sepharose CL-4B are described. The binding behaviour of OPS and iminodiacetic acid (IDA) immobilized onto Sepharose CL-4B, toward the hard Lewis metal ions Al3+, Fe3+, Ca2+ and Yb3+, and Cu2+ ion as a borderline metal ion control, over the pH range pH 4.0 to pH 8.0, was examined. Immobilized OPS shows a stronger affinity for Fe3+ and Al3+ ions but a lower affinity for Cu2+ and Yb3+ ions, compared to immobilized iminodiacetic acid (IDA), over the equilibrating range examined. Immobilized OPS-Mn+ was screened for protein binding using as model proteins tuna heart cytochrome c (THCC), horse myoglobin (HMYO) and hen egg while lysozyme (HEWL) over the pH range 5.5 to 8.0. Immobilized OPS-Fe3+ bound THCC under all the examined equilibrating conditions, bound HMYO between pH 5.5 and pH 7.0 and did not bind HEWL under any condition examined. Immobilized OPS thus presents an additional mode of metal ion and protein selectivity in immobilized-metal affinity chromatography.

Development of an enzyme immunoassay for the detection of Clostridium novyi type B alpha toxin

Clostridium novyi type B alpha toxin was purified to homogeneity and shown to have a molecular weight of 200 kD by SDS polyacrylamide gel electrophoresis. The toxin was toxoided and used to produce a pair of non-interfering monoclonal antibodies. Their specificity was confirmed by immunoblotting and bioassay. The monoclonal antibodies were used to develop an enzyme immunoassay which was more sensitive than bioassay, and permitted less than 1 ng/ml toxin to be detected in a rapid 10 min assay format. Use of the assay can eliminate the requirement for in vivo testing of novyi toxin and toxoid, provided measurements of biological activity are not required. Because of its speed and sensitivity, the assay can be used to monitor toxin production during fermentation and as an alternative to bioassay to measure antigen content during toxoiding and vaccine formulation.

Glucose-fructose oxidoreductase, a new enzyme isolated from Zymomonas mobilis that is responsible for sorbitol production

The enzymes responsible for sorbitol formation in Zymomonas mobilis were investigated. A previously undescribed enzyme catalyzes the intermolecular oxidation-reduction of glucose and fructose to form gluconolactone and sorbitol. This enzyme has been purified; it had a subunit size of 40,000 daltons and is probably tetrameric at low pH. It contained tightly bound NADP as the hydrogen carrier and did not require any added cofactor for activity. In addition, a gluconolactonase has been isolated, although not completely purified. Together these two enzymes were capable of completely converting a 54% (wt/vol) equimolar mixture of glucose and fructose to sorbitol and sodium gluconate at the optimum pH of close to 6.2. The oxidoreductase had low affinities for its substrates, but natural environmental conditions would expose it to high concentrations of sugars. The amount of the enzyme in Z. mobilis cells was sufficient to account for the rate of sorbitol formation in vivo. However, the enzyme was present in the highest amounts when the cells were grown on glucose alone, and it was repressed by the presence of fructose; this was not the case with the gluconolactonase.

Gluconate kinase from Zymomonas mobilis: isolation and characteristics

The enzyme gluconate kinase EC 2.7.1.12 has been found at high levels in glucose-grown Zymomonas mobilis cells. A simple procedure, based on differential dye-ligand chromatography, has been used to isolate the enzyme, purifying it some 600-fold. The purified enzyme is a monomer of molecular weight 18,000 Da, which is much smaller than other gluconate kinases reported. It has a relatively low affinity for ATP. (Km = 1.5 mM), but high for gluconate (Km = 0.33 mM), and has little activity with any other potential substrates.