CCL2 Accelerates Microglia-Mediated Aβ Oligomer Formation and Progression of Neurocognitive Dysfunction

The linkages between neuroinflammation and Alzheimer's disease (AD) pathogenesis are well established. What is not, however, is how specific immune pathways and proteins affect the disease. To this end, we previously demonstrated that transgenic over-expression of CCL2 enhanced microgliosis and induced diffuse amyloid plaque deposition in Tg2576 mice. This rodent model of AD expresses a Swedish beta-amyloid (Abeta) precursor protein mutant.We now report that CCL2 transgene expression accelerates deficits in spatial and working memory and hippocampal synaptic transmission in beta-amyloid precursor protein (APP) mice as early as 2-3 months of age. This is followed by increased numbers of microglia that are seen surrounding Abeta oligomers. CCL2 does not suppress Abeta degradation. Rather, CCL2 and tumor necrosis factor-alpha directly facilitated Abeta uptake, intracellular Abeta oligomerization, and protein secretion.We posit that CCL2 facilitates Abeta oligomer formation in microglia and propose that such events accelerate memory dysfunction by affecting Abeta seeding in the brain

Short-Term Immunosuppression Promotes Engraftment of Embryonic and Induced Pluripotent Stem Cells

SummaryEmbryonic stem cells (ESCs) are an attractive source for tissue regeneration and repair therapies because they can be differentiated into virtually any cell type in the adult body. However, for this approach to succeed, the transplanted ESCs must survive long enough to generate a therapeutic benefit. A major obstacle facing the engraftment of ESCs is transplant rejection by the immune system. Here we show that blocking leukocyte costimulatory molecules permits ESC engraftment. We demonstrate the success of this immunosuppressive therapy for mouse ESCs, human ESCs, mouse induced pluripotent stem cells (iPSCs), human induced pluripotent stem cells, and more differentiated ESC/(iPSCs) derivatives. Additionally, we provide evidence describing the mechanism by which inhibition of costimulatory molecules suppresses T cell activation. This report describes a short-term immunosuppressive approach capable of inducing engraftment of transplanted ESCs and iPSCs, providing a significant improvement in our mechanistic understanding of the critical role costimulatory molecules play in leukocyte activation

Loss of CX3CR1 increases accumulation of inflammatory monocytes and promotes gliomagenesis

The most abundant populations of non-neoplastic cells in the glioblastoma (GBM) microenvironment are resident microglia, macrophages and infiltrating monocytes from the blood circulation. The mechanisms by which monocytes infiltrate into GBM, their fate following infiltration, and their role in GBM growth are not known. Here we tested the hypothesis that loss of the fractalkine receptor CX3CR1 in microglia and monocytes would affect gliomagenesis. Deletion of Cx3cr1 from the microenvironment resulted in increased tumor incidence and shorter survival times in glioma-bearing mice. Loss of Cx3cr1 did not affect accumulation of microglia/macrophages in peri-tumoral areas, but instead indirectly promoted the trafficking of CD11b+CD45hiCX3CR1lowLy-6ChiLy-6G-F4/80-/low circulating inflammatory monocytes into the CNS, resulting in their increased accumulation in the perivascular area. Cx3cr1-deficient microglia/macrophages and monocytes demonstrated upregulation of IL1{beta} expression that was inversely proportional to Cx3cr1 gene dosage. The Proneural subgroup of the TCGA GBM patient dataset with high IL1{beta} expression showed shorter survival compared to patients with low IL1{beta}. IL1{beta} promoted tumor growth and increased the cancer stem cell phenotype in murine and human Proneural glioma stem cells (GSCs). IL1{beta} activated the p38 MAPK signaling pathway and expression of monocyte chemoattractant protein (MCP-1/CCL2) by tumor cells. Loss of Cx3cr1 in microglia in a monocyte-free environment had no impact on tumor growth and did not alter microglial migration. These data suggest that enhancing signaling to CX3CR1 or inhibiting IL1{beta} signaling in intra-tumoral macrophages can be considered as potential strategies to decrease the tumor-promoting effects of monocytes in Proneural GBM

Expression and Differential Responsiveness of Central Nervous System Glial Cell Populations to the Acute Phase Protein Serum Amyloid A

Acute-phase response is a systemic reaction to environmental/inflammatory insults and involves hepatic production of acute-phase proteins, including serum amyloid A (SAA). Extrahepatically, SAA immunoreactivity is found in axonal myelin sheaths of cortex in Alzheimer's disease and multiple sclerosis (MS), although its cellular origin is unclear. We examined the responses of cultured rat cortical astrocytes, microglia and oligodendrocyte precursor cells (OPCs) to master pro-inflammatory cytokine tumour necrosis factor (TNF)-\u3b1 and lipopolysaccaride (LPS). TNF-\u3b1 time-dependently increased Saa1 (but not Saa3) mRNA expression in purified microglia, enriched astrocytes, and OPCs (as did LPS for microglia and astrocytes). Astrocytes depleted of microglia were markedly less responsive to TNF-\u3b1 and LPS, even after re-addition of microglia. Microglia and enriched astrocytes showed complementary Saa1 expression profiles following TNF-\u3b1 or LPS challenge, being higher in microglia with TNF-\u3b1 and higher in astrocytes with LPS. Recombinant human apo-SAA stimulated production of both inflammatory mediators and its own mRNA in microglia and enriched, but not microglia-depleted astrocytes. Co-ultramicronized palmitoylethanolamide/luteolin, an established anti-inflammatory/neuroprotective agent, reduced Saa1 expression in OPCs subjected to TNF-\u3b1 treatment. These last data, together with past findings suggest that co-ultramicronized palmitoylethanolamide/luteolin may be a novel approach in the treatment of inflammatory demyelinating disorders like MS

Making sense out of massive data by going beyond differential expression

With the rapid growth of publicly available high-throughput transcriptomic data, there is increasing recognition that large sets of such data can be mined to better understand disease states and mechanisms. Prior gene expression analyses, both large and small, have been dichotomous in nature, in which phenotypes are compared using clearly defined controls. Such approaches may require arbitrary decisions about what are considered “normal” phenotypes, and what each phenotype should be compared to. Instead, we adopt a holistic approach in which we characterize phenotypes in the context of a myriad of tissues and diseases. We introduce scalable methods that associate expression patterns to phenotypes in order both to assign phenotype labels to new expression samples and to select phenotypically meaningful gene signatures. By using a nonparametric statistical approach, we identify signatures that are more precise than those from existing approaches and accurately reveal biological processes that are hidden in case vs. control studies. Employing a comprehensive perspective on expression, we show how metastasized tumor samples localize in the vicinity of the primary site counterparts and are overenriched for those phenotype labels. We find that our approach provides insights into the biological processes that underlie differences between tissues and diseases beyond those identified by traditional differential expression analyses. Finally, we provide an online resource (http://concordia.csail.mit.edu) for mapping users’ gene expression samples onto the expression landscape of tissue and disease

Does It Matter Who Writes Medical News Stories?

David Henry and colleagues review Australian news stories over a five-year period to assess whether quality is associated with who wrote the story: a specialist health journalist or a non-specialist

Approaches to working in high-dimensional data spaces: gene expression microarrays

This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification

Haptoglobin phenotype is not a predictor of recurrence free survival in high-risk primary breast cancer patients

Contains fulltext : 70104tjan-heijnen.pdf (publisher's version ) (Open Access)BACKGROUND: Better breast cancer prognostication may improve selection of patients for adjuvant therapy. We conducted a retrospective follow-up study in which we investigated sera of high-risk primary breast cancer patients, to search for proteins predictive of recurrence free survival. METHODS: Two sample sets of high-risk primary breast cancer patients participating in a randomised national trial investigating the effectiveness of high-dose chemotherapy were analysed. Sera in set I (n = 63) were analysed by surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker finding. Initial results were validated by analysis of sample set II (n = 371), using one-dimensional gel-electrophoresis. RESULTS: In sample set I, the expression of a peak at mass-to-charge ratio 9198 (relative intensity 20), identified as haptoglobin (Hp) alpha-1 chain, was strongly associated with recurrence free survival (global Log-rank test; p = 0.0014). Haptoglobin is present in three distinct phenotypes (Hp 1-1, Hp 2-1, and Hp 2-2), of which only individuals with phenotype Hp 1-1 or Hp 2-1 express the haptoglobin alpha-1 chain. As the expression of the haptoglobin alpha-1 chain, determined by SELDI-TOF MS, corresponds to the phenotype, initial results were validated by haptoglobin phenotyping of the independent sample set II by native one-dimensional gel-electrophoresis. With the Hp 1-1 phenotype as the reference category, the univariate hazard ratio for recurrence was 0.87 (95% CI: 0.56 - 1.34, p = 0.5221) and 1.03 (95% CI: 0.65 - 1.64, p = 0.8966) for the Hp 2-1 and Hp 2-2 phenotypes, respectively, in sample set II. CONCLUSION: In contrast to our initial results, the haptoglobin phenotype was not identified as a predictor of recurrence free survival in high-risk primary breast cancer in our validation set. Our initial observation in the discovery set was probably the result of a type I error (i.e. false positive). This study illustrates the importance of validation in obtaining the true clinical applicability of a potential biomarker

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses