Population-based RNA profiling in Add Health finds social disparities in inflammatory and antiviral gene regulation to emerge by young adulthood.

Health in later life varies significantly by individual demographic characteristics such as age, sex, and race/ethnicity, as well as by social factors including socioeconomic status and geographic region. This study examined whether sociodemographic variations in the immune and inflammatory molecular underpinnings of chronic disease might emerge decades earlier in young adulthood. Using data from 1,069 young adults from the National Longitudinal Study of Adolescent to Adult Health (Add Health)-the largest nationally representative and ethnically diverse sample with peripheral blood transcriptome profiles-we analyzed variation in the expression of genes involved in inflammation and type I interferon (IFN) response as a function of individual demographic factors, sociodemographic conditions, and biobehavioral factors (smoking, drinking, and body mass index). Differential gene expression was most pronounced by sex, race/ethnicity, and body mass index (BMI), but transcriptome correlates were identified for every demographic dimension analyzed. Inflammation-related gene expression showed the most pronounced variation as a function of biobehavioral factors (BMI and smoking) whereas type I IFN-related transcripts varied most strongly as a function of individual demographic characteristics (sex and race/ethnicity). Bioinformatic analyses of transcription factor and immune-cell activation based on transcriptome-wide empirical differences identified additional effects of family poverty and geographic region. These results identify pervasive sociodemographic differences in immune-cell gene regulation that emerge by young adulthood and may help explain social disparities in the development of chronic illness and premature mortality at older ages.

Interferon stimulated genes as peripheral diagnostic markers of early pregnancy in sheep: a critical assessment.

We investigated the diagnostic reliability of pregnancy detection using changes in interferon stimulated gene (ISG) messenger RNA (mRNA) levels in circulating immune cells in ewes. Two different groups of ewes (an experimental group, experiment 1 and a farm group, experiment 2) were oestrus-synchronized and blood sampled on day 14 (D0=day of insemination in control animals, experiment 1) and day 15 (experiment 2). Real-time PCR were performed to evaluate the abundance of different ISG mRNAs. In the experimental group, peripheral blood mononuclear cells of 29 ewes born and bred in experimental facilities were isolated using a Percoll gradient method. Gene expression for Chemokine (C-X-C motif) ligand 10 (CXCL10), Myxovirus (influenza virus) resistance 1 (MX1) and Signal transducer and activator of transcription 1 (STAT1) mRNA were, respectively, 8.3-fold, 6.1-fold and 2.7-fold higher (P0.10) in CXCL10, STAT1, MX1, Myxovirus (influenza virus) resistance 2 (MX2) and ISG15 ubiquitin-like modifier (ISG15) mRNA expression were found between pregnant and non-pregnant ewes. The ROC curves and the hierarchical classification generated from the real-time PCR data failed to discriminate between pregnant and non-pregnant animals. In this group of animals, our results show a strong variability in ISG expression patterns: 17% of animals identified as non-pregnant by the five tests were in fact pregnant, only 52% of pregnant animals had at least two positive results (two genes above threshold), whereas up to five positive results (five genes above threshold) were needed to avoid misclassification. In conclusion, this study illustrates the high variability in ISG expression levels in immune circulating cells during early pregnancy and, therefore, highlights the limits of using ISG expression levels in blood samples, collected on PAXgene® tubes on farms, for early pregnancy detection in sheep.

Assessment of the translational value of mouse lupus models using clinically relevant biomarkers.

Lupus is an autoimmune disease with a poorly understood etiology that manifests with a diverse pathology. This heterogeneity has been a challenge to clinical drug development efforts. A related difficulty is the uncertain translational power of animal models used for evaluating potential drug targets and candidate therapeutics, because it is unlikely that any 1 preclinical model will recapitulate the spectrum of human disease. Therefore, multiple models, along with an understanding of the immune mechanisms that drive them, are necessary if we are to use them to identify valid drug targets and evaluate candidate therapies successfully. To this end, we have characterized several different mouse lupus models and report their differences with respect to biomarkers and symptoms that are representative of the human disease. We compared the pristane-induced mouse lupus disease model using 3 different strains (DBA/1, SJL, BALB/c), and the spontaneous NZB x NZW F1(NZB/W) mouse model. We show that the models differ significantly in their autoantibody profiles, disease manifestations such as nephritis and arthritis, and expression of type I interferon-regulated genes. Similar to the NZB/W model, pristane-induced disease in SJL mice manifests with nephritis and proteinuria, whereas the pristane-treated DBA/1 mice develop arthritis and an interferon-driven gene signature that closely resembles that in human patients. The elucidation of each model's strengths and the identification of translatable biomarkers yields insight for basic lupus research and drug development, and should assist in the proper selection of models for evaluating candidate targets and therapeutic strategies.

Evaluation of inter- and intramolecular primary structure homologies of interferons by a Monte Carlo method.

Using Sellers TT algorithm, primary structure repeats have been described for interferon (IFN)-alpha, -beta 1, and gamma. To reevaluate these results and to extend them to IFN-beta 2 (interleukin-6), a modified algorithm was developed that uses a metric to define the "best" partial homology of two peptide sequences and to compare it to those detected in random permutations of the peptide. Using this approach, the known structural homologies of IFN-alpha with IFN-beta 1 and of human (Hu) IFN-gamma with murine (Mu) IFN-gamma were identified correctly. However, the primary structure repeats in the amino acid sequences of IFN-alpha, -beta 1, and -gamma turned out to be no better than those detectable in random permutations of these sequences. These results were confirmed using a different, nonlinear metric. A previously used approach to demonstrate significance was shown to produce false-positive results. No significant primary structure homologies were detected among IFN-beta 1, -beta 2, and -gamma. In contrast to the amino acid sequence analysis, the DNA sequence of HuIFN-beta 1 contained a significant repeat that had no significant counterpart in MuIFN-beta or in IFN-alpha. In conclusion, some previously reported results obtained with Sellers TT algorithm on amino acid sequences are easily explained as random similarities, and it is therefore strongly recommended that a method like ours should be used to control significance.