Stem Cell Transplantation in Traumatic Spinal Cord Injury: A Systematic Review and Meta-Analysis of Animal Studies

Spinal cord injury (SCI) is a devastating condition that causes substantial morbidity and mortality and for which no treatments are available. Stem cells offer some promise in the restoration of neurological function. We used systematic review, meta-analysis, and meta-regression to study the impact of stem cell biology and experimental design on motor and sensory outcomes following stem cell treatments in animal models of SCI. One hundred and fifty-six publications using 45 different stem cell preparations met our prespecified inclusion criteria. Only one publication used autologous stem cells. Overall, allogeneic stem cell treatment appears to improve both motor (effect size, 27.2%; 95% Confidence Interval [CI], 25.0%–29.4%; 312 comparisons in 5,628 animals) and sensory (effect size, 26.3%; 95% CI, 7.9%–44.7%; 23 comparisons in 473 animals) outcome. For sensory outcome, most heterogeneity between experiments was accounted for by facets of stem cell biology. Differentiation before implantation and intravenous route of delivery favoured better outcome. Stem cell implantation did not appear to improve sensory outcome in female animals and appeared to be enhanced by isoflurane anaesthesia. Biological plausibility was supported by the presence of a dose–response relationship. For motor outcome, facets of stem cell biology had little detectable effect. Instead most heterogeneity could be explained by the experimental modelling and the outcome measure used. The location of injury, method of injury induction, and presence of immunosuppression all had an impact. Reporting of measures to reduce bias was higher than has been seen in other neuroscience domains but were still suboptimal. Motor outcomes studies that did not report the blinded assessment of outcome gave inflated estimates of efficacy. Extensive recent preclinical literature suggests that stem-cell–based therapies may offer promise, however the impact of compromised internal validity and publication bias mean that efficacy is likely to be somewhat lower than reported here.     

Temporal Analysis of Hepatitis C Virus Cell Entry with Occludin Directed Blocking Antibodies

Hepatitis C virus (HCV) is a major cause of liver disease worldwide. A better understanding of its life cycle, including the process of host cell entry, is important for the development of HCV therapies and model systems. Based on the requirement for numerous host factors, including the two tight junction proteins claudin-1 (CLDN1) and occludin (OCLN), HCV cell entry has been proposed to be a multi-step process. The lack of OCLN-specific inhibitors has prevented a comprehensive analysis of this process. To study the role of OCLN in HCV cell entry, we created OCLN mutants whose HCV cell entry activities could be inhibited by antibodies. These mutants were expressed in polarized HepG2 cells engineered to support the complete HCV life cycle by CD81 and miR-122 expression and synchronized infection assays were performed to define the kinetics of HCV cell entry. During these studies, OCLN utilization differences between HCV isolates were observed, supporting a model that HCV directly interacts with OCLN. In HepG2 cells, both HCV cell entry and tight junction formation were impaired by OCLN silencing and restored by expression of antibody regulatable OCLN mutant. Synchronized infection assays showed that glycosaminoglycans and SR-BI mediated host cell binding, while CD81, CLDN1 and OCLN all acted sequentially at a post-binding stage prior to endosomal acidification. These results fit a model where the tight junction region is the last to be encountered by the virion prior to internalization.     

Dengue Virus Co-opts UBR4 to Degrade STAT2 and Antagonize Type I Interferon Signaling

An estimated 50 million dengue virus (DENV) infections occur annually and more than forty percent of the human population is currently at risk of developing dengue fever (DF) or dengue hemorrhagic fever (DHF). Despite the prevalence and potential severity of DF and DHF, there are no approved vaccines or antiviral therapeutics available. An improved understanding of DENV immune evasion is pivotal for the rational development of anti-DENV therapeutics. Antagonism of type I interferon (IFN-I) signaling is a crucial mechanism of DENV immune evasion. DENV NS5 protein inhibits IFN-I signaling by mediating proteasome-dependent STAT2 degradation. Only proteolytically-processed NS5 can efficiently mediate STAT2 degradation, though both unprocessed and processed NS5 bind STAT2. Here we identify UBR4, a 600-kDa member of the N-recognin family, as an interacting partner of DENV NS5 that preferentially binds to processed NS5. Our results also demonstrate that DENV NS5 bridges STAT2 and UBR4. Furthermore, we show that UBR4 promotes DENV-mediated STAT2 degradation, and most importantly, that UBR4 is necessary for efficient viral replication in IFN-I competent cells. Our data underscore the importance of NS5-mediated STAT2 degradation in DENV replication and identify UBR4 as a host protein that is specifically exploited by DENV to inhibit IFN-I signaling via STAT2 degradation.     

The Genetic Basis of Escherichia coli Pathoadaptation to Macrophages

Antagonistic interactions are likely important driving forces of the evolutionary process underlying bacterial genome complexity and diversity. We hypothesized that the ability of evolved bacteria to escape specific components of host innate immunity, such as phagocytosis and killing by macrophages (MΦ), is a critical trait relevant in the acquisition of bacterial virulence. Here, we used a combination of experimental evolution, phenotypic characterization, genome sequencing and mathematical modeling to address how fast, and through how many adaptive steps, a commensal Escherichia coli (E. coli) acquire this virulence trait. We show that when maintained in vitro under the selective pressure of host MΦ commensal E. coli can evolve, in less than 500 generations, virulent clones that escape phagocytosis and MΦ killing in vitro, while increasing their pathogenicity in vivo, as assessed in mice. This pathoadaptive process is driven by a mechanism involving the insertion of a single transposable element into the promoter region of the E. coli yrfF gene. Moreover, transposition of the IS186 element into the promoter of Lon gene, encoding an ATP-dependent serine protease, is likely to accelerate this pathoadaptive process. Competition between clones carrying distinct beneficial mutations dominates the dynamics of the pathoadaptive process, as suggested from a mathematical model, which reproduces the observed experimental dynamics of E. coli evolution towards virulence. In conclusion, we reveal a molecular mechanism explaining how a specific component of host innate immunity can modulate microbial evolution towards pathogenicity.     

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R²) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R² based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)², where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R². However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R². Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R² may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.     

Meiotic Recombination in Arabidopsis Is Catalysed by DMC1, with RAD51 Playing a Supporting Role

Recombination establishes the chiasmata that physically link pairs of homologous chromosomes in meiosis, ensuring their balanced segregation at the first meiotic division and generating genetic variation. The visible manifestation of genetic crossing-overs, chiasmata are the result of an intricate and tightly regulated process involving induction of DNA double-strand breaks and their repair through invasion of a homologous template DNA duplex, catalysed by RAD51 and DMC1 in most eukaryotes. We describe here a RAD51-GFP fusion protein that retains the ability to assemble at DNA breaks but has lost its DNA break repair capacity. This protein fully complements the meiotic chromosomal fragmentation and sterility of Arabidopsis rad51, but not rad51 dmc1 mutants. Even though DMC1 is the only active meiotic strand transfer protein in the absence of RAD51 catalytic activity, no effect on genetic map distance was observed in complemented rad51 plants. The presence of inactive RAD51 nucleofilaments is thus able to fully support meiotic DSB repair and normal levels of crossing-over by DMC1. Our data demonstrate that RAD51 plays a supporting role for DMC1 in meiotic recombination in the flowering plant, Arabidopsis.