Genome-Wide Association Study and Gene Expression Analysis Identifies CD84 as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10-8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10-11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry. © 2013 Cui et al

Coordinate up-regulation of TMEM97 and cholesterol biosynthesis genes in normal ovarian surface epithelial cells treated with progesterone: implications for pathogenesis of ovarian cancer

<p>Abstract</p> <p>Background</p> <p>Ovarian cancer (OvCa) most often derives from ovarian surface epithelial (OSE) cells. Several lines of evidence strongly suggest that increased exposure to progesterone (P4) protects women against developing OvCa. However, the underlying mechanisms of this protection are incompletely understood.</p> <p>Methods</p> <p>To determine downstream gene targets of P4, we established short term <it>in vitro </it>cultures of non-neoplastic OSE cells from six subjects, exposed the cells to P4 (10^-6M) for five days and performed transcriptional profiling with oligonucleotide microarrays containing over 22,000 transcripts.</p> <p>Results</p> <p>We identified concordant but modest gene expression changes in cholesterol/lipid homeostasis genes in three of six samples (responders), whereas the other three samples (non-responders) showed no expressional response to P4. The most up-regulated gene was <it>TMEM97 </it>which encodes a transmembrane protein of unknown function (MAC30). Analyses of outlier transcripts, whose expression levels changed most significantly upon P4 exposure, uncovered coordinate up-regulation of 14 cholesterol biosynthesis enzymes, insulin-induced gene 1, low density lipoprotein receptor, <it>ABCG1</it>, endothelial lipase, stearoyl- CoA and fatty acid desaturases, long-chain fatty-acyl elongase, and down-regulation of steroidogenic acute regulatory protein and <it>ABCC6</it>. Highly correlated tissue-specific expression patterns of <it>TMEM97 </it>and the cholesterol biosynthesis genes were confirmed by analysis of the GNF Atlas 2 universal gene expression database. Real-time quantitative RT-PCR analyses revealed 2.4-fold suppression of the <it>TMEM97 </it>gene expression in short-term cultures of OvCa relative to the normal OSE cells.</p> <p>Conclusion</p> <p>These findings suggest that a co-regulated transcript network of cholesterol/lipid homeostasis genes and <it>TMEM97 </it>are downstream targets of P4 in normal OSE cells and that <it>TMEM97 </it>plays a role in cholesterol and lipid metabolism. The P4-induced alterations in cholesterol and lipid metabolism in OSE cells might play a role in conferring protection against OvCa.</p

A review on experimental and clinical genetic associations studies on fear conditioning, extinction and cognitive-behavioral treatment

Fear conditioning and extinction represent basic forms of associative learning with considerable clinical relevance and have been implicated in the pathogenesis of anxiety disorders. There is considerable inter-individual variation in the ability to acquire and extinguish conditioned fear reactions and the study of genetic variants has recently become a focus of research. In this review, we give an overview of the existing genetic association studies on human fear conditioning and extinction in healthy individuals and of related studies on cognitive-behavioral treatment (CBT) and exposure, as well as pathology development after trauma. Variation in the serotonin transporter (5HTT) and the catechol-o-methyltransferase (COMT) genes has consistently been associated with effects in pre-clinical and clinical studies. Interesting new findings, which however require further replication, have been reported for genetic variation in the dopamine transporter (DAT1) and the pituitary adenylate cyclase 1 receptor (ADCYAP1R1) genes, whereas the current picture is inconsistent for variation in the brain-derived neurotrophic factor (BDNF) gene. We end with a discussion of the findings and their limitations, as well as future directions that we hope will aid the field to develop further

Bacteria-inducing legume nodules involved in the improvement of plant growth, health and nutrition

Bacteria-inducing legume nodules are known as rhizobia and belong to the class Alphaproteobacteria and Betaproteobacteria. They promote the growth and nutrition of their respective legume hosts through atmospheric nitrogen fixation which takes place in the nodules induced in their roots or stems. In addition, rhizobia have other plant growth-promoting mechanisms, mainly solubilization of phosphate and production of indoleacetic acid, ACC deaminase and siderophores. Some of these mechanisms have been reported for strains of rhizobia which are also able to promote the growth of several nonlegumes, such as cereals, oilseeds and vegetables. Less studied are the mechanisms that have the rhizobia to promote the plant health; however, these bacteria are able to exert biocontrol of some phytopathogens and to induce the plant resistance. In this chapter, we revised the available data about the ability of the legume nodule-inducing bacteria for improving the plant growth, health and nutrition of both legumes and nonlegumes. These data showed that rhizobia meet all the requirements of sustainable agriculture to be used as bio-inoculants allowing the total or partial replacement of chemicals used for fertilization or protection of crops

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Proton NMR characterization of intact primary and metastatic melanoma cells in 2D & 3D cultures

Abstract Objective To characterize the differences between the primary and metastatic melanoma cell lines grown in 2D cultures and 3D cultures. Methods Primary melanoma cells (WM115) and metastatic melanoma cells (WM266) extracted from a single donor was cultured in 2D as well as 3D cultures. These cells were characterized using proton NMR spectrometry, and the qualitative chemical shifts markers were identified and discussed. Results In monolayer culture (2D), we observed one qualitative chemical shift marker for primary melanoma cells. In spheroid cultures (3D), we observed nine significant chemical shifts, of which eight markers were specific for primary melanoma spheroids, whereas the other one marker was specific to metastatic melanoma spheroids. This study suggests that the glucose accumulation and phospholipid composition vary significantly between the primary and metastatic cells lines that are obtained from a single donor and also with the cell culturing methods. 14 qualitative chemical shift markers were obtained in the comparison between monolayer culture and spheroids cultures irrespective of the differences in the cell lines. Among which 4 were unique to monolayer cultures whereas 10 chemical shifts were unique to the spheroid cultures. This study also shows that the method of cell culture would drastically affect the phospholipid composition of the cells and also depicts that the cells in spheroid culture closely resembles the cells in vivo. Conclusion This study shows the high specificity of proton NMR spectrometry in characterizing cancer cell lines and also shows the variations in the glucose accumulation and phospholipid composition between the primary and metastatic melanoma cell lines from the same donor. Differences in the cell culture method does plays an important role in phospholipid composition of the cells