6-Sulphated Chondroitins Have a Positive Influence on Axonal Regeneration

Chondroitin sulphate proteoglycans (CSPGs) upregulated in the glial scar inhibit axon regeneration via their sulphated glycosaminoglycans (GAGs). Chondroitin 6-sulphotransferase-1 (C6ST-1) is upregulated after injury leading to an increase in 6-sulphated GAG. In this study, we ask if this increase in 6-sulphated GAG is responsible for the increased inhibition within the glial scar, or whether it represents a partial reversion to the permissive embryonic state dominated by 6-sulphated glycosaminoglycans (GAGs). Using C6ST-1 knockout mice (KO), we studied post-injury changes in chondroitin sulphotransferase (CSST) expression and the effect of chondroitin 6-sulphates on both central and peripheral axon regeneration. After CNS injury, wild-type animals (WT) showed an increase in mRNA for C6ST-1, C6ST-2 and C4ST-1, but KO did not upregulate any CSSTs. After PNS injury, while WT upregulated C6ST-1, KO showed an upregulation of C6ST-2. We examined regeneration of nigrostriatal axons, which demonstrate mild spontaneous axon regeneration in the WT. KO showed many fewer regenerating axons and more axonal retraction than WT. However, in the PNS, repair of the median and ulnar nerves led to similar and normal levels of axon regeneration in both WT and KO. Functional tests on plasticity after the repair also showed no evidence of enhanced plasticity in the KO. Our results suggest that the upregulation of 6-sulphated GAG after injury makes the extracellular matrix more permissive for axon regeneration, and that the balance of different CSs in the microenvironment around the lesion site is an important factor in determining the outcome of nervous system injury

Models of Traumatic Cerebellar Injury

Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. Studies of human TBI demonstrate that the cerebellum is sometimes affected even when the initial mechanical insult is directed to the cerebral cortex. Some of the components of TBI, including ataxia, postural instability, tremor, impairments in balance and fine motor skills, and even cognitive deficits, may be attributed in part to cerebellar damage. Animal models of TBI have begun to explore the vulnerability of the cerebellum. In this paper, we review the clinical presentation, pathogenesis, and putative mechanisms underlying cerebellar damage with an emphasis on experimental models that have been used to further elucidate this poorly understood but important aspect of TBI. Animal models of indirect (supratentorial) trauma to the cerebellum, including fluid percussion, controlled cortical impact, weight drop impact acceleration, and rotational acceleration injuries, are considered. In addition, we describe models that produce direct trauma to the cerebellum as well as those that reproduce specific components of TBI including axotomy, stab injury, in vitro stretch injury, and excitotoxicity. Overall, these models reveal robust characteristics of cerebellar damage including regionally specific Purkinje cell injury or loss, activation of glia in a distinct spatial pattern, and traumatic axonal injury. Further research is needed to better understand the mechanisms underlying the pathogenesis of cerebellar trauma, and the experimental models discussed here offer an important first step toward achieving that objective

Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases

Microorganisms that cause infectious diseases present critical issues of national security, public health, and economic welfare.  For example, in recent years, highly pathogenic strains of avian influenza have emerged in Asia, spread through Eastern Europe and threaten to become pandemic. As demonstrated by the coordinated response to Severe Acute Respiratory Syndrome (SARS) and influenza, agents of infectious disease are being addressed via large-scale genomic sequencing.  The goal of genomic sequencing projects are to rapidly put large amounts of data in the public domain to accelerate research on disease surveillance, treatment, and prevention. However, our ability to derive information from large comparative genomic datasets lags far behind acquisition.  Here we review the computational challenges of comparative genomic analyses, specifically sequence alignment and reconstruction of phylogenetic trees.  We present novel analytical results on from two important infectious diseases, Severe Acute Respiratory Syndrome (SARS) and influenza.SARS and influenza have similarities and important differences both as biological and comparative genomic analysis problems.  Influenza viruses (Orthymxyoviridae) are RNA based.  Current evidence indicates that influenza viruses originate in aquatic birds from wild populations. Influenza has been studied for decades via well-coordinated international efforts.  These efforts center on surveillance via antibody characterization of the hemagglutinin (HA) and neuraminidase (N) proteins of the circulating strains to inform vaccine design. However we still do not have a clear understanding of: 1) various transmission pathways such as the role of intermediate hosts such as swine and domestic birds and 2) the key mutation and genomic recombination events that underlie periodic pandemics of influenza.  In the past 30 years, sequence data from HA and N loci has become an important data type. In the past year, full genomic data has become prominent.  These data present exciting opportunities to address unanswered questions in influenza pandemics.SARS is caused by a previously unrecognized lineage of coronavirus, SARS-CoV, which like influenza has an RNA based genome.  Although SARS-CoV is widely believed to have originated in animals there remains disagreement over the candidate animal source that lead to the original outbreak of SARS.  In contrast to the long history of the study of influenza, SARS was only recognized in late 2002 and the virus that causes SARS has been documented primarily by genomic sequencing.In the past, most studies of influenza were performed on a limited number of isolates and genes suited to a particular problem.  Major goals in science today are to understand emerging diseases in broad geographic, environmental, societal, biological, and genomic contexts. Synthesizing diverse information brought together by various researchers is important to find out what can be done to prevent future outbreaks {JON03}.  Thus comprehensive means to organize and analyze large amounts of diverse information are critical.  For example, the relationships of isolates and patterns of genomic change observed in large datasets might not be consistent with hypotheses formed on partial data.  Moreover when researchers rely on partial datasets, they restrict the range of possible discoveries.Phylogenetics is well suited to the complex task of understanding emerging infectious disease. Phylogenetic analyses can test many hypotheses by comparing diverse isolates collected from various hosts, environments, and points in time and organizing these data into various evolutionary scenarios.  The products of a phylogenetic analysis are a graphical tree of ancestor-descendent relationships and an inferred summary of mutations, recombination events, host shifts, geographic, and temporal spread of the viruses.  However, this synthesis comes at a price.  The cost of computation of phylogenetic analysis expands combinatorially as the number of isolates considered increases. Thus, large datasets like those currently produced are commonly considered intractable.  We address this problem with synergistic development of heuristics tree search strategies and parallel computing.Fil: Janies, D.. Ohio State University; Estados UnidosFil: Pol, Diego. Ohio State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Sequencing and Analysis of JC Virus DNA From Natalizumab-Treated PML Patients

BACKGROUND: Progressive multifocal leukoencephalopathy (PML) in natalizumab-treated MS patients is linked to JC virus (JCV) infection. JCV sequence variation and rearrangements influence viral pathogenicity and tropism. To better understand PML development, we analyzed viral DNA sequences in blood, CSF and/or urine of natalizumab-treated PML patients. METHODS: Using biofluid samples from 17 natalizumab-treated PML patients, we sequenced multiple isolates of the JCV noncoding control region (NCCR), VP1 capsid coding region, and the entire 5 kb viral genome. RESULTS: Analysis of JCV from multiple biofluids revealed that individuals were infected with a single genotype. Across our patient cohort, multiple PML-associated NCCR rearrangements and VP1 mutations were present in CSF and blood, but absent from urine-derived virus. NCCR rearrangements occurred in CSF of 100% of our cohort. VP1 mutations were observed in blood or CSF in 81% of patients. Sequencing of complete JCV genomes demonstrated that NCCR rearrangements could occur without VP1 mutations, but VP1 mutations were not observed without NCCR rearrangement. CONCLUSIONS: These data confirm that JCV in natalizumab-PML patients is similar to that observed in other PML patient groups, multiple genotypes are associated with PML, individual patients appear to be infected with a single genotype, and PML-associated mutations arise in patients during PML development

Development of a multi-biomarker disease activity test for rheumatoid arthritis

BACKGROUND: Disease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment. OBJECTIVES: To develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis. METHODS: Candidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing. RESULTS: 130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities. CONCLUSION: We followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels

Olanzapine metabolic side effects: a weight gain issue?

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