Alternate approach to understanding the molecular mechanisms of stroke-induced injury

Research in the area of stroke has not yielded any new treatments, besides tissue plasminogen activator. New findings are suggesting that the therapeutic window of providing neuroprotection is wider than once thought. Moreover, the role of the peripheral immune system in abetting neurodegeneration is being elucidated, but it appears this reaction occurs 2-3 days after the stroke. This mini-review examines this new evidence about the molecular mechanisms leading to stroke-induced neuronal death, which suggests new therapeutic approaches to its treatment.     

Rat Genome Database (RGD): mapping disease onto the genome

The Rat Genome Database (RGD, http://rgd.mcw.edu) is an NIH-funded project whose stated mission is ‘to collect, consolidate and integrate data generated from ongoing rat genetic and genomic research efforts and make these data widely available to the scientific community’. In a collaboration between the Bioinformatics Research Center at the Medical College of Wisconsin, the Jackson Laboratory and the National Center for Biotechnology Information, RGD has been created to meet these stated aims. The rat is uniquely suited to its role as a model of human disease and the primary focus of RGD is to aid researchers in their study of the rat and in applying their results to studies in a wider context. In support of this we have integrated a large amount of rat genetic and genomic resources in RGD and these are constantly being expanded through ongoing literature and bulk dataset curation. RGD version 2.0, released in June 2001, includes curated data on rat genes, quantitative trait loci (QTL), microsatellite markers and rat strains used in genetic and genomic research. VCMap, a dynamic sequence-based homology tool was introduced, and allows researchers of rat, mouse and human to view mapped genes and sequences and their locations in the other two organisms, an essential tool for comparative genomics. In addition, RGD provides tools for gene prediction, radiation hybrid mapping, polymorphic marker selection and more. Future developments will include the introduction of disease-based curation expanding the curated information to cover popular disease systems studied in the rat. This will be integrated with the emerging rat genomic sequence and annotation pipelines to provide a high-quality disease-centric resource, applicable to human and mouse via comparative tools such as VCMap. RGD has a defined community outreach focus with a Visiting Scientist program and the Rat Community Forum, a web-based forum for rat researchers and others interested in using the rat as an experimental model. Thus, RGD is not only a valuable resource for those working with the rat but also for researchers in other model organisms wishing to harness the existing genetic and physiological data available in the rat to complement their own work.     

Proteomics and leukocytes: an approach to understanding potential molecular mechanisms of inflammatory responses

Leukocytes play an important role in the progression of disease and leukocyte-derived proteins are associated with the pathogenesis of the disease. Leukocyte activation causes production of inflammatory mediators, over-expression of cell surface adhesion molecules, and an increase in migration and infiltration, phagocytosis, and degranulation, as well as receptor phosphorylation and signal transduction. An increasing number of studies on the application of leukocyte proteomics have appeared in mapping protein profiles of inflammatory cells, contributing to the understanding of potential mechanisms involved in leukocyte function. Together with improvements in proteomic technology in leukocyte research, leukocyte proteomic analysis becomes more simple, rapid, flexible, sensitive, and specific. This enables proteomic investigation of activated or non-activated leukocytes to be highly focused on defined suborgans or specific signaling pathways. Research in leukocyte proteomics is progressing from fingerprinting to functioning, human cell lines to primary leukocytes, non-activated cells to inflammatory mediator-stimulated cells, in vitro culture to in vivo challenge, and animal models to human disease. A number of newly identified proteins from leukocyte proteomics may offer new mechanism-orientated targets for drug discovery and development.     

A new approach to understanding the molecular mechanisms through which estrogens affect cognition

Traditional approaches to the study of hormones and cognition have been primarily observational or correlational in nature. Because this work does not permit causal relationships to be identified, very little is known about the specific molecules and cellular events through which hormones affect cognitive function. In this review, we propose a new approach to study hormones and memory, where the systematic blocking of cellular events can reveal which such events are necessary for hormones to influence memory consolidation. The discussion will focus on the modulation of the hippocampus and hippocampal memory by estrogens, given the extensive literature on this subject, and will illustrate how the application of this approach is beginning to reveal important new information about the molecular mechanisms through which estrogens modulate memory consolidation. The clinical relevance of this work will also be discussed.     

Using online tools at the Bovine Genome Database to manually annotate genes in the new reference genome

With the availability of a new highly contiguous Bos taurus reference genome assembly (ARS-UCD1.2), it is the opportune time to upgrade the bovine gene set by seeking input from researchers. Furthermore, advances in graphical genome annotation tools now make it possible for researchers to leverage sequence data generated with the latest technologies to collaboratively curate genes. For many years the Bovine Genome Database (BGD) has provided tools such as the Apollo genome annotation editor to support manual bovine gene curation. The goal of this paper is to explain the reasoning behind the decisions made in the manual gene curation process while providing examples using the existing BGD tools. We will describe the sources of gene annotation evidence provided at the BGD, including RNA-seq and Iso-Seq data. We will also explain how to interpret various data visualizations when curating gene models, and will demonstrate the value of manual gene annotation. The process described here can be applied to manual gene curation for other species with similar tools. With a better understanding of manual gene annotation, researchers will be encouraged to edit gene models and contribute to the enhancement of livestock gene sets.     

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.     

The network of time: understanding the molecular circadian system

The circadian clock provides a temporal structure that modulates biological functions from the level of gene expression to performance and behaviour. Pioneering work on the fruitfly Drosophila has provided a basis for understanding how the temporal sequence of daily events is controlled in mammals. New insights have come from work on mammals, specifically from studying the daily activity profiles of clock mutant mice; from more detailed recordings of clock gene expression under different experimental conditions and in different tissues; and from the discovery and analysis of a growing number of additional clock genes. These new results are moving the model paradigm away from a simple negative feedback loop to a molecular network. Understanding the coupling and interactions of this network will help us to understand the evolution of the circadian system, advance medical diagnosis and treatment, improve the health of shift workers and frequent travellers, and will generally enable the treatment of clock-related pathologies.     

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.     

Disease causing mutations in the TNF and TNFR superfamilies: Focus on molecular mechanisms driving disease

The tumor necrosis factor (TNF) and TNF receptor (TNFR) superfamilies comprise multidomain proteins with diverse roles in cell activation, proliferation and cell death. These proteins play pivotal roles in the initiation, maintenance and termination of immune responses and have vital roles outside the immune system. The discovery and analysis of diseases associated with mutations in these families has revealed crucial mechanistic details of their normal functions. This review focuses on mutations causing four different diseases, which represent distinct pathological mechanisms that can exist within these superfamilies: autoimmune lymphoproliferative syndrome (ALPS; FAS mutations), common variable immunodeficiency (CVID; TACI mutations), tumor necrosis factor receptor associated periodic syndrome (TRAPS; TNFR1 mutations) and hypohidrotic ectodermal dysplasia (HED; EDA1/EDAR mutations). In particular, we highlight how mutations have revealed information about normal receptor-ligand function and how such studies might direct new therapeutic approaches.     

Antibody-induced arthritis: disease mechanisms and genes involved at the effector phase of arthritis

During the development of rheumatoid arthritis (RA) autoantibodies to IgG-Fc, citrullinated proteins, collagen type II (CII), glucose 6 phosphoisomerase (G6PI) and some other self-antigens appear. Of these, a pathogenic effect of the anti-CII and anti-G6PI antibodies is well demonstrated using animal models. These new antibody mediated arthritis models have proven to be very useful for studies involved in understanding the molecular pathways of the induction of arthritis in joints. Both the complement and FcγR systems have been found to play essential roles. Neutrophils and macrophages are important inflammatory cells and the secretion of tumour necrosis factor-α and IL-1β is pathogenic. The identification of the genetic polymorphisms predisposing to arthritis is important for understanding the complexity of arthritis. Disease mechanisms and gene regions studied using the two antibody-induced arthritis mouse models (collagen antibody-induced arthritis and serum transfer-induced arthritis) are compared and discussed for their relevance in RA pathogenesis.     

Antibody-induced arthritis: disease mechanisms and genes involved at the effector phase of arthritis

During the development of rheumatoid arthritis (RA) autoantibodies to IgG-Fc, citrullinated proteins, collagen type II (CII), glucose 6 phosphoisomerase (G6PI) and some other self-antigens appear. Of these, a pathogenic effect of the anti-CII and anti-G6PI antibodies is well demonstrated using animal models. These new antibody mediated arthritis models have proven to be very useful for studies involved in understanding the molecular pathways of the induction of arthritis in joints. Both the complement and FcgammaR systems have been found to play essential roles. Neutrophils and macrophages are important inflammatory cells and the secretion of tumour necrosis factor-alpha and IL-1beta is pathogenic. The identification of the genetic polymorphisms predisposing to arthritis is important for understanding the complexity of arthritis. Disease mechanisms and gene regions studied using the two antibody-induced arthritis mouse models (collagen antibody-induced arthritis and serum transfer-induced arthritis) are compared and discussed for their relevance in RA pathogenesis.     

Neurostimulation of the Cholinergic Anti-Inflammatory Pathway Ameliorates Disease in Rat Collagen-Induced Arthritis

Introduction

The inflammatory reflex is a physiological mechanism through which the nervous system maintains immunologic homeostasis by modulating innate and adaptive immunity. We postulated that the reflex might be harnessed therapeutically to reduce pathological levels of inflammation in rheumatoid arthritis by activating its prototypical efferent arm, termed the cholinergic anti-inflammatory pathway. To explore this, we determined whether electrical neurostimulation of the cholinergic anti-inflammatory pathway reduced disease severity in the collagen-induced arthritis model.

Methods

Rats implanted with vagus nerve cuff electrodes had collagen-induced arthritis induced and were followed for 15 days. Animals underwent active or sham electrical stimulation once daily from day 9 through the conclusion of the study. Joint swelling, histology, and levels of cytokines and bone metabolism mediators were assessed.

Results

Compared with sham treatment, active neurostimulation of the cholinergic anti-inflammatory pathway resulted in a 52% reduction in ankle diameter (p = 0.02), a 57% reduction in ankle diameter (area under curve; p = 0.02) and 46% reduction overall histological arthritis score (p = 0.01) with significant improvements in inflammation, pannus formation, cartilage destruction, and bone erosion (p = 0.02), accompanied by numerical reductions in systemic cytokine levels, not reaching statistical significance. Bone erosion improvement was associated with a decrease in serum levels of receptor activator of NF-κB ligand (RANKL) from 132±13 to 6±2 pg/mL (mean±SEM, p = 0.01).

Conclusions

The severity of collagen-induced arthritis is reduced by neurostimulation of the cholinergic anti-inflammatory pathway delivered using an implanted electrical vagus nerve stimulation cuff electrode, and supports the rationale for testing this approach in human inflammatory disorders.     

Surface plasmon resonance: towards an understanding of the mechanisms of biological molecular recognition

With the introduction of new instruments and improved sensor chip chemistries, surface plasmon resonance (SPR) is finding new applications for molecular interaction studies. Easy access to high-quality kinetic and thermodynamic data for macromolecular binding events is providing insights into the fundamental mechanisms of molecular recognition. Progress is being made to allow larger-scale interaction studies. In addition, combining SPR with other analytical methods is enabling SPR-based analysis of interaction proteomics.     

Cellular and molecular mechanisms of Parkinson's disease: neurotoxins, causative genes, and inflammatory cytokines

1. Parkinson's disease (PD) is considered to be an aging-related neurodegeneration of catecholamine (CA) systems [typically A9 dopamine (DA) neurons in the substantia nigra and A6 noradrenaline (NA) neurons in the locus coeruleus]. The main symptom is movement disorder caused by a DA deficiency at the nerve terminals of fibers that project from the substantia nigra to the striatum. Most PD is sporadic (sPD) without any hereditary history. sPD is speculated to be caused by some exogenous or endogenous substances that are neurotoxic toward CA neurons, which toxicity leads to mitochondrial dysfunction and subsequent oxidative stress resulting in the programmed cell death (apoptosis or autophagy) of DA neurons. 2. Recent studies on the causative genes of rare familial PD (fPD) cases, such as alpha-synuclein and parkin, suggest that dysfunction of the ubiquitin-proteasome system (UPS) and the resultant accumulation of misfolded proteins and endoplasmic reticulum stress may cause the death of DA neurons. 3. Activated microglia, which accompany an inflammatory process, are present in the nigro-striatum of the PD brain; and they produce protective or toxic substances, such as cytokines, neurotrophins, and reactive oxygen or nitrogen species. These activated microglia may be neuroprotective at first in the initial stage, and later may become neurotoxic owing to toxic change to promote the progression toward the death of CA neurons.4. All of these accumulating evidences on sPD and fPD points to a hypothesis that multiple primary causes of PD may be ultimately linked to a final common signal-transduction pathway leading to programmed cell death, i.e., apoptosis or autophagy, of the CA neurons.     

A methodology based on molecular interactions and pathways to find candidate genes associated to diseases: its application to schizophrenia and Alzheimer's disease

Experimental techniques for the identification of genes associated with diseases are expensive and have certain limitations. In this scenario, computational methods are useful tools to identify lists of promising genes for further experimental verification. This paper describes a flexible methodology for the in silico prediction of genes associated with diseases combining the use of available tools for gene enrichment analysis, gene network generation and gene prioritization. A set of reference genes, with a known association to a disease, is used as bait to extract candidate genes from molecular interaction networks and enriched pathways. In a second step, prioritization methods are applied to evaluate the similarities between previously selected candidates and the set of reference genes. The top genes obtained by these programs are grouped into a single list sorted by the number of methods that have selected each gene. As a proof of concept, top genes reported a few years ago in SzGene and AlzGene databases were used as references to predict genes associated to schizophrenia and Alzheimer's disease, respectively. In both cases, we were able to predict a statistically significant amount of genes belonging to the updated lists.     

Database and interaction network of genes involved in oral cancer: Version II

The oral cancer gene database has been compiled to enable fast retrieval of updated information and role of the genes implicated in oral cancer. The first version of the database with 242 genes was published in Online Journal of Bioinformatics 8(1), 41-44, 2007. In the second version, the database has been enlarged to include 374 genes by adding 132 gene entries. The architecture and format of the database is similar to the earlier version, and includes updated information and external hyperlinks for all the genes. The functional gene interaction network for important biological processes and molecular functions has been rebuilt based on 374 genes using 'String 8.3'. The database is freely available at http://www.actrec.gov.in/OCDB/index.htm and provides the scientist information and external links for the genes involved in oral cancer, interactions between them, and their role in the biology of oral cancer along with clinical relevance.