Influence of ARHGEF3 and RHOA Knockdown on ACTA2 and Other Genes in Osteoblasts and Osteoclasts

Osteoporosis is a common bone disease that has a strong genetic component. Genome-wide linkage studies have identified the chromosomal region 3p14-p22 as a quantitative trait locus for bone mineral density (BMD). We have previously identified associations between variation in two related genes located in 3p14-p22, ARHGEF3 and RHOA, and BMD in women. In this study we performed knockdown of these genes using small interfering RNA (siRNA) in human osteoblast-like and osteoclast-like cells in culture, with subsequent microarray analysis to identify genes differentially regulated from a list of 264 candidate genes. Validation of selected findings was then carried out in additional human cell lines/cultures using quantitative real-time PCR (qRT-PCR). The qRT-PCR results showed significant down-regulation of the ACTA2 gene, encoding the cytoskeletal protein alpha 2 actin, in response to RHOA knockdown in both osteoblast-like (P<0.001) and osteoclast-like cells (P = 0.002). RHOA knockdown also caused up-regulation of the PTH1R gene, encoding the parathyroid hormone 1 receptor, in Saos-2 osteoblast-like cells (P<0.001). Other findings included down-regulation of the TNFRSF11B gene, encoding osteoprotegerin, in response to ARHGEF3 knockdown in the Saos-2 and hFOB 1.19 osteoblast-like cells (P = 0.003– 0.02), and down-regulation of ARHGDIA, encoding the Rho GDP dissociation inhibitor alpha, in response to RHOA knockdown in osteoclast-like cells (P<0.001). These studies identify ARHGEF3 and RHOA as potential regulators of a number of genes in bone cells, including TNFRSF11B, ARHGDIA, PTH1R and ACTA2, with influences on the latter evident in both osteoblast-like and osteoclast-like cells. This adds further evidence to previous studies suggesting a role for the ARHGEF3 and RHOA genes in bone metabolism

Antibody Repertoires in Humanized NOD-scid-IL2Rγnull Mice and Human B Cells Reveals Human-Like Diversification and Tolerance Checkpoints in the Mouse

Immunodeficient mice reconstituted with human hematopoietic stem cells enable the in vivo study of human hematopoiesis. In particular, NOD-scid-IL2Rγnull engrafted mice have been shown to have reasonable levels of T and B cell repopulation and can mount T-cell dependent responses; however, antigen-specific B-cell responses in this model are generally poor. We explored whether developmental defects in the immunoglobulin gene repertoire might be partly responsible for the low level of antibody responses in this model. Roche 454 sequencing was used to obtain over 685,000 reads from cDNA encoding immunoglobulin heavy (IGH) and light (IGK and IGL) genes isolated from immature, naïve, or total splenic B cells in engrafted NOD-scid-IL2Rγnull mice, and compared with over 940,000 reads from peripheral B cells of two healthy volunteers. We find that while naïve B-cell repertoires in humanized mice are chiefly indistinguishable from those in human blood B cells, and display highly correlated patterns of immunoglobulin gene segment use, the complementarity-determining region H3 (CDR-H3) repertoires are nevertheless extremely diverse and are specific for each individual. Despite this diversity, preferential DH-JH pairings repeatedly occur within the CDR-H3 interval that are strikingly similar across all repertoires examined, implying a genetic constraint imposed on repertoire generation. Moreover, CDR-H3 length, charged amino-acid content, and hydropathy are indistinguishable between humans and humanized mice, with no evidence of global autoimmune signatures. Importantly, however, a statistically greater usage of the inherently autoreactive IGHV4-34 and IGKV4-1 genes was observed in the newly formed immature B cells relative to naïve B or total splenic B cells in the humanized mice, a finding consistent with the deletion of autoreactive B cells in humans. Overall, our results provide evidence that key features of the primary repertoire are shaped by genetic factors intrinsic to human B cells and are principally unaltered by differences between mouse and human stromal microenvironments

Genome-wide joint SNP and CNV analysis of aortic root diameter in African Americans: the HyperGEN study

<p>Abstract</p> <p>Background</p> <p>Aortic root diameter is a clinically relevant trait due to its known relationship with the pathogenesis of aortic regurgitation and risk for aortic dissection. African Americans are an understudied population despite a particularly high burden of cardiovascular diseases. We report a genome-wide association study on aortic root diameter among African Americans enrolled in the HyperGEN study. We invoked a two-stage, mixed model procedure to jointly identify SNP allele and copy number variation effects.</p> <p>Results</p> <p>Results suggest novel genetic contributors along a large region between the <it>CRCP </it>and <it>KCTD7 </it>genes on chromosome 7 (p = 4.26 × 10^<b>-7</b>); and the <it>SIRPA </it>and <it>PDYN </it>genes on chromosome 20 (p = 3.28 × 10^<b>-8</b>).</p> <p>Conclusions</p> <p>The regions we discovered are candidates for future studies on cardiovascular outcomes, particularly in African Americans. The methods we employed can also provide an outline for genetic researchers interested in jointly testing SNP and CNV effects and/or applying mixed model procedures on a genome-wide scale.</p

Models of epidemics: when contact repetition and clustering should be included

Background The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread. Methods We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts. Results The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model. Conclusion We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, models assuming repeating contacts will also behave similarly as random mixing models. If the number of daily contacts or the transmission probability is low, as assumed for MRSA or Ebola, particular consideration should be given to the actual structure of potentially contagious contacts when designing the model.ISSN:1742-468

Memory in Microbes: Quantifying History-Dependent Behavior in a Bacterium

Memory is usually associated with higher organisms rather than bacteria. However, evidence is mounting that many regulatory networks within bacteria are capable of complex dynamics and multi-stable behaviors that have been linked to memory in other systems. Moreover, it is recognized that bacteria that have experienced different environmental histories may respond differently to current conditions. These “memory” effects may be more than incidental to the regulatory mechanisms controlling acclimation or to the status of the metabolic stores. Rather, they may be regulated by the cell and confer fitness to the organism in the evolutionary game it participates in. Here, we propose that history-dependent behavior is a potentially important manifestation of memory, worth classifying and quantifying. To this end, we develop an information-theory based conceptual framework for measuring both the persistence of memory in microbes and the amount of information about the past encoded in history-dependent dynamics. This method produces a phenomenological measure of cellular memory without regard to the specific cellular mechanisms encoding it. We then apply this framework to a strain of Bacillus subtilis engineered to report on commitment to sporulation and degradative enzyme (AprE) synthesis and estimate the capacity of these systems and growth dynamics to ‘remember’ 10 distinct cell histories prior to application of a common stressor. The analysis suggests that B. subtilis remembers, both in short and long term, aspects of its cell history, and that this memory is distributed differently among the observables. While this study does not examine the mechanistic bases for memory, it presents a framework for quantifying memory in cellular behaviors and is thus a starting point for studying new questions about cellular regulation and evolutionary strategy

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks

<p>Abstract</p> <p>Background</p> <p>Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network.</p> <p>Methods</p> <p>We simulate transmission of a vaccine-prevetable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection.</p> <p>Results</p> <p>We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective.</p> <p>Conclusion</p> <p>For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled.</p

Suppression of Osteosarcoma Cell Invasion by Chemotherapy Is Mediated by Urokinase Plasminogen Activator Activity via Up-Regulation of EGR1

Background: The cellular and molecular mechanisms of tumour response following chemotherapy are largely unknown. We found that low dose anti-tumour agents up-regulate early growth response 1 (EGR1) expression. EGR1 is a member of the immediate-early gene group of transcription factors which modulate transcription of multiple genes involved in cell proliferation, differentiation, and development. It has been reported that EGR1 act as either tumour promoting factor or suppressor. We therefore examined the expression and function of EGR1 in osteosarcoma. Methods: We investigated the expression of EGR1 in human osteosarcoma cell lines and biopsy specimens. We next examined the expression of EGR1 following anti-tumour agents treatment. To examine the function of EGR1 in osteosarcoma, we assessed the tumour growth and invasion in vitro and in vivo. Results: Real-time PCR revealed that EGR1 was down-regulated both in osteosarcoma cell lines and osteosarcoma patients’ biopsy specimens. In addition, EGR1 was up-regulated both in osteosarcoma patient’ specimens and osteosarcoma cell lines following anti-tumour agent treatment. Although forced expression of EGR1 did not prevent osteosarcoma growth, forced expression of EGR1 prevented osteosarcoma cell invasion in vitro. In addition, forced expression of EGR1 promoted downregulation of urokinase plasminogen activator, urokinase receptor, and urokinase plasminogen activity. Xenograft mice models showed that forced expression of EGR1 prevents osteosarcoma cell migration into blood vessels. Conclusions: These findings suggest that although chemotherapy could not prevent osteosarcoma growth in chemotherapy-resistant patients, it did prevent osteosarcoma cell invasion by down-regulation of urokinase plasminogen activity via up-regulation of EGR1 during chemotherapy periods

Effects of THBS3, SPARC and SPP1 expression on biological behavior and survival in patients with osteosarcoma

BACKGROUND: Osteosarcoma is a very aggressive tumor with a propensity to metastasize and invade surrounding tissue. Identification of the molecular determinants of invasion and metastatic potential may guide the development of a rational strategy for devising specific therapies that target the pathways leading to osteosarcoma. METHODS: In this study, we used pathway-focused low density expression cDNA arrays to screen for candidate genes related to tumor progression. Expression patterns of the selected genes were validated by real time PCR on osteosarcoma patient tumor samples and correlated with clinical and pathological data. RESULTS: THBS3, SPARC and SPP1 were identified as genes differentially expressed in osteosarcoma. In particular, THBS3 was expressed at significantly high levels (p = 0.0001) in biopsies from patients with metastasis at diagnosis, which is a predictor of worse overall survival, event-free survival and relapse free survival at diagnosis. After chemotherapy, patients with tumors over-expressing THBS3 have worse relapse free survival. High SPARC expression was found in 51/55 (96.3%) osteosarcoma samples derived from 43 patients, and correlated with the worst event-free survival (p = 0.03) and relapse free survival (p = 0.07). Overexpression of SPP1 was found in 47 of 53 (89%) osteosarcomas correlating with better overall survival, event-free survival and relapse free survival at diagnosis. CONCLUSION: In this study three genes were identified with pattern of differential gene expression associated with a phenotypic role in metastasis and invasion. Interestingly all encode for proteins involved in extracellular remodeling suggesting potential roles in osteosarcoma progression. This is the first report on the THBS3 gene working as a stimulator of tumor progression. Higher levels of THBS3 maintain the capacity of angiogenesis. High levels of SPARC are not required for tumor progression but are necessary for tumor growth and maintenance. SPP1 is not necessary for tumor progression in osteosarcoma and may be associated with inflammatory response and bone remodeling, functioning as a good biomarker