Sandy D. Der

The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease

The recycling of the amyloid precursor protein (APP) from the cell surface via the endocytic pathways plays a key role in the generation of amyloid β peptide (Aβ) in Alzheimer disease. We report here that inherited variants in the SORL1 neuronal sorting receptor are associated with late-onset Alzheimer disease. These variants, which occur in at least two different clusters of intronic sequences within the SORL1 gene (also known as LR11 or SORLA) may regulate tissue-specific expression of SORL1. We also show that SORL1 directs trafficking of APP into recycling pathways and that when SORL1 is underexpressed, APP is sorted into Aβ-generating compartments. These data suggest that inherited or acquired changes in SORL1 expression or function are mechanistically involved in causing Alzheimer disease.

Functional classification of interferon-stimulated genes identified using microarrays

Interferons (IFNs) are a family of multifunctional cytokines thatactivate transcription of subsets of genes. The gene products inducedby IFNs are responsible for IFN antiviral, antiproliferative, andimmunomodulatory properties. To obtain a more comprehensive list and abetter understanding of the genes regulated by IFNs, we compiled datafrom many experiments, using two different microarray formats. Thecombined data sets identified >300 IFN‐stimulated genes (ISGs). Toprovide new insight into IFN‐induced cellular phenotypes, we assignedthese ISGs to functional categories. The data are accessible on the World Wide Web at http://www.lerner.ccf.org/labs/williams, including functional categories and individual genes listed in asearchable database. The entries are linked to GenBank and Unigenesequence information and other resources. The goal is to eventuallycompile a comprehensive list of all ISGs. Recognition of the functionsof the ISGs and their specific roles in the biological effects of IFNsis leading to a greater appreciation of the many facets of theseintriguing and essential cytokines. This review focuses on thefunctions of the ISGs identified by analyzing the microarray data andfocuses particularly on new insights into the protein kinaseRNA‐regulated (PRKR) protein, which have been made possible with theavailability of PRKR‐null mice.

Deficient cytokine signaling in mouse embryo fibroblasts with a targeted deletion in the PKR gene: role of IRF‐1 and NF‐κB

The interferon (IFN)‐induced double‐stranded RNA (dsRNA)‐activated Ser/Thr protein kinase (PKR) plays a role in the antiviral and antiproliferative effects of IFN. PKR phosphorylates initiation factor eIF2α, thereby inhibiting protein synthesis, and also activates the transcription factor, nuclear factor‐κB (NF‐κB), by phosphorylating the inhibitor of NF‐κB, IκB. Mice devoid of functional PKR (Pkr°/°) derived by targeted gene disruption exhibit a diminished response to IFN‐γ and poly(rI:rC) (pIC). In embryo fibroblasts derived from Pkr°/° mice, interferon regulatory factor 1 (IRF‐1) or guanylate binding protein (Gbp) promoter–reporter constructs were unresponsive to IFN‐γ or pIC but response could be restored by co‐transfection with PKR. The lack of responsiveness could be attributed to a diminished activation of IRF‐1 and/or NF‐κB in response to IFN‐γ or pIC. Thus, PKR acts as a signal transducer for IFN‐stimulated genes dependent on the transcription factors IRF‐1 and NF‐κB.

Prognostic and Predictive Gene Signature for Adjuvant Chemotherapy in Resected Non–Small-Cell Lung Cancer

PURPOSE The JBR.10 trial demonstrated benefit from adjuvant cisplatin/vinorelbine (ACT) in early-stage non-small-cell lung cancer (NSCLC). We hypothesized that expression profiling may identify stage-independent subgroups who might benefit from ACT. PATIENTS AND METHODS Gene expression profiling was conducted on mRNA from 133 frozen JBR.10 tumor samples (62 observation [OBS], 71 ACT). The minimum gene set that was selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified. The prognostic value of this gene signature was tested in four independent published microarray data sets and by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR). RESULTS A 15-gene signature separated OBS patients into high-risk and low-risk subgroups with significantly different survival (hazard ratio [HR], 15.02; 95% CI, 5.12 to 44.04; P < .001; stage I HR, 13.31; P < .001; stage II HR, 13.47; P < .001). The prognostic effect was verified in the same 62 OBS patients where gene expression was assessed by qPCR. Furthermore, it was validated consistently in four separate microarray data sets (total 356 stage IB to II patients without adjuvant treatment) and additional JBR.10 OBS patients by qPCR (n = 19). The signature was also predictive of improved survival after ACT in JBR.10 high-risk patients (HR, 0.33; 95% CI, 0.17 to 0.63; P = .0005), but not in low-risk patients (HR, 3.67; 95% CI, 1.22 to 11.06; P = .0133; interaction P < .001). Significant interaction between risk groups and ACT was verified by qPCR. CONCLUSION This 15-gene expression signature is an independent prognostic marker in early-stage, completely resected NSCLC, and to our knowledge, is the first signature that has demonstrated the potential to select patients with stage IB to II NSCLC most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine.

Three-gene prognostic classifier for early-stage non small-cell lung cancer.

PURPOSE Several microarray studies have reported gene expression signatures that classify non-small-cell lung carcinoma (NSCLC) patients into different prognostic groups. However, the prognostic gene lists reported to date overlap poorly across studies, and few have been validated independently using more quantitative assay methods. PATIENTS AND METHODS The expression of 158 putative prognostic genes identified in previous microarray studies was analyzed by reverse transcription quantitative polymerase chain reaction in the tumors of 147 NSCLC patients. Concordance indices and risk scores were used to identify a stage-independent set of genes that could classify patients with significantly different prognoses. RESULTS We have identified a three-gene classifier (STX1A, HIF1A, and CCR7) for overall survival (hazard ratio = 3.8; 95% CI, 1.7 to 8.2; P < .001). The classifier was also able to stratify stage I and II patients and further improved the predictive ability of clinical factors such as histology and tumor stage. The predictive value of this three-gene classifier was validated in two large independent microarray data sets from Harvard and Duke Universities. CONCLUSION We have identified a new three-gene classifier that is independent of and improves on stage to stratify early-stage NSCLC patients with significantly different prognoses. This classifier may be tested further for its potential value to improve the selection of resected NSCLC patients in adjuvant therapy.

CD4+ regulatory T cells require CTLA-4 for the maintenance of systemic tolerance

Cytotoxic T lymphocyte antigen-4 (CTLA-4) plays a critical role in negatively regulating T cell responses and has also been implicated in the development and function of natural FOXP3+ regulatory T cells. CTLA-4–deficient mice develop fatal, early onset lymphoproliferative disease. However, chimeric mice containing both CTLA-4–deficient and –sufficient bone marrow (BM)–derived cells do not develop disease, indicating that CTLA-4 can act in trans to maintain T cell self-tolerance. Using genetically mixed blastocyst and BM chimaeras as well as in vivo T cell transfer systems, we demonstrate that in vivo regulation of Ctla4−/− T cells in trans by CTLA-4–sufficient T cells is a reversible process that requires the persistent presence of FOXP3+ regulatory T cells with a diverse TCR repertoire. Based on gene expression studies, the regulatory T cells do not appear to act directly on T cells, suggesting they may instead modulate the stimulatory activities of antigen-presenting cells. These results demonstrate that CTLA-4 is absolutely required for FOXP3+ regulatory T cell function in vivo.

Prognostic gene-expression signature of carcinoma-associated fibroblasts in non-small cell lung cancer

The tumor microenvironment strongly influences cancer development, progression, and metastasis. The role of carcinoma-associated fibroblasts (CAFs) in these processes and their clinical impact has not been studied systematically in non-small cell lung carcinoma (NSCLC). We established primary cultures of CAFs and matched normal fibroblasts (NFs) from 15 resected NSCLC. We demonstrate that CAFs have greater ability than NFs to enhance the tumorigenicity of lung cancer cell lines. Microarray gene-expression analysis of the 15 matched CAF and NF cell lines identified 46 differentially expressed genes, encoding for proteins that are significantly enriched for extracellular proteins regulated by the TGF-β signaling pathway. We have identified a subset of 11 genes (13 probe sets) that formed a prognostic gene-expression signature, which was validated in multiple independent NSCLC microarray datasets. Functional annotation using protein–protein interaction analyses of these and published cancer stroma-associated gene-expression changes revealed prominent involvement of the focal adhesion and MAPK signaling pathways. Fourteen (30%) of the 46 genes also were differentially expressed in laser-capture–microdissected corresponding primary tumor stroma compared with the matched normal lung. Six of these 14 genes could be induced by TGF-β1 in NF. The results establish the prognostic impact of CAF-associated gene-expression changes in NSCLC patients.

Distinct Transcriptional Profiles in Ex Vivo CD4+ and CD8+ T Cells Are Established Early in Human Immunodeficiency Virus Type 1 Infection and Are Characterized by a Chronic Interferon Response as Well as Extensive Transcriptional Changes in CD8+ T Cells

ABSTRACT Changes in T-cell function are a hallmark of human immunodeficiency virus type 1 (HIV-1) infection, but the pathogenic mechanisms leading to these changes are unclear. We examined the gene expression profiles in ex vivo human CD4+ and CD8+ T cells from untreated HIV-1-infected individuals at different clinical stages and rates of disease progression. Profiles of pure CD4+ and CD8+ T-cell subsets from HIV-1-infected nonprogressors with controlled viremia were indistinguishable from those of individuals not infected with HIV-1. Similarly, no gene clusters could distinguish T cells from individuals with early infection from those seen in chronic progressive HIV-1 infection, whereas differences were observed between uninfected individuals or nonprogressors versus early or chronic progressors. In early and chronic HIV-1 infection, three characteristic gene expression signatures were observed. (i) CD4+ and CD8+ T cells showed increased expression of interferon-stimulated genes (ISGs). However, some ISGs, including CXCL9, CXCL10, and CXCL11, and the interleukin-15 alpha receptor were not upregulated. (ii) CD4+ and CD8+ T cells showed a cluster similar to that observed in thymocytes. (iii) More genes were differentially regulated in CD8+ T cells than in CD4+ T cells, including a cluster of genes downregulated exclusively in CD8+ T cells. In conclusion, HIV-1 infection induces a persistent T-cell transcriptional profile, early in infection, characterized by a dramatic but potentially aberrant interferon response and a profile suggesting an active thymic output. These findings highlight the complexity of the host-virus relationship in HIV-1 infection.

Genomic DNA functions as a universal external standard in quantitative real-time PCR

Real-time quantitative PCR (qPCR) is a powerful tool for quantifying specific DNA target sequences. Although determination of relative quantity is widely accepted as a reliable means of measuring differences between samples, there are advantages to being able to determine the absolute copy numbers of a given target. One approach to absolute quantification relies on construction of an accurate standard curve using appropriate external standards of known concentration. We have validated the use of tissue genomic DNA as a universal external standard to facilitate quantification of any target sequence contained in the genome of a given species, addressing several key technical issues regarding its use. This approach was applied to validate mRNA expression of gene candidates identified from microarray data and to determine gene copies in transgenic mice. A simple method that can assist achieving absolute quantification of gene expression would broadly enhance the uses of real-time qPCR and in particular, augment the evaluation of global gene expression studies.

Heterogeneity in control of mRNA stability by AU-rich elements.

AU-rich elements (AREs), located in the 3′-untranslated region of unstable cytokine and chemokine mRNAs, promote rapid decay of otherwise stable mRNAs and may mediate selective mRNA stabilization in response to stimulation with interleukin-1 (IL-1). AREs vary considerably, however, in both size and sequence context. To assess the heterogeneity involved in control of mRNA stability by ARE motifs, human mRNA sequences from IL-1α-stimulated HEK293 cells and T98G cells were screened for either instability or stability using both cDNA (950 ARE containing sequences) and Affymetrix oligonucleotide (U95Av2 GeneChip) array analysis. Although ARE-containing mRNAs exhibited a broad range of stability, IL-1α promoted stability in a subset of mRNAs that were unstable when transcriptionally induced by tumor necrosis factor α. Stabilization of granulocyte/macrophage-colony stimulating factor and IL-8 mRNAs by IL-1α was achieved only after 2 h of stimulation, required ongoing protein synthesis, and depended on the activation of p38 MAPK. In contrast, stabilization of Gro3 mRNA in response to IL-1α was achieved immediately and was insensitive to inhibitors of protein synthesis and p38 MAPK activation. In concert, these findings demonstrate that ARE sequences are functionally heterogeneous; only a subset of unstable mRNAs is sensitive to stabilization by IL-1α. Moreover, IL-1α promotes stabilization of unstable mRNAs through distinct mechanistic pathways that distinguish between specific mRNA sequences.