ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.     

Twenty Years After the 1988 Yellowstone Fires: Lessons About Disturbance and Ecosystems

The 1988 Yellowstone fires were among the first in what has proven to be an upsurge in large severe fires in the western USA during the past 20 years. At the time of the fires, little was known about the impacts of such a large severe disturbance because scientists had had few previous opportunities to study such an event. Ecologists predicted short- and long-term effects of the 1988 fires on vegetation, biogeochemistry, primary productivity, wildlife, and aquatic ecosystems based on scientific understanding of the time. Twenty-plus years of subsequent study allow these early predictions to be evaluated. Most of the original predictions were at least partially supported, but some predictions were refuted, others nuanced, and a few postfire phenomena were entirely unexpected. Post-1988 Yellowstone studies catalyzed advances in ecology focused on the importance of spatial and temporal heterogeneity, contingent influences, and multiple interacting drivers. Post-1988 research in Yellowstone also has changed public perceptions of fire as an ecological process and attitudes towards fire management. Looking ahead to projected climate change and more frequent large fires, the well-documented ecological responses to the 1988 Yellowstone fires provide a foundation for detecting and evaluating potential changes in fire regimes of temperate mountainous regions.     

The requirement of uncoordinated 51-like kinase 1 (ULK1) and ULK2 in the regulation of autophagy

Autophagy is an evolutionarily conserved physiological process of self-digestion by a cell to adapt to various stresses, including starvation. Its molecular basis involves the concerted activation of proteins encoded by the family of autophagy-related (Atg) genes. The best characterized is the serine/threonine protein kinase Atg1 in yeast which appears to be essential at the early stage of autophagy. In mammals, five Atg1 homologues have been identified as uncoordinated (UNC) 51-like kinase 1 to 4 and STK36. ULK1 and ULK2 are the most closely related members of the family, sharing 78% homology within their protein kinase domains. However, the specific function of ULK1 and ULK2 in mammalian autophagy is not fully understood. Here, we demonstrate that ULK1 and ULK2 are functionally redundant protein kinases required to mediate autophagy under nutrient-deprived conditions in fibroblasts. In contrast, ULK1, but not ULK2, is critical to induce the autophagic response of cerebellar granule neurons (CGN) to low potassium concentration in serum-free conditions. Furthermore, we found that ULK1 has a cytoprotective function in neurons. Together, these results provide strong genetic evidence that ULK1 is an essential component of the autophagic signaling pathway. The ability of ULK2 to compensate for the loss of ULK1 function is cell-type specific.     

Endothelial expression of human ABCA1 in mice increases plasma HDL cholesterol and reduces diet-induced atherosclerosis

The role of endothelial ABCA1 expression in reverse cholesterol transport (RCT) was examined in transgenic mice, using the endothelial-specific Tie2 promoter. Human ABCA1 (hABCA1) was significantly expressed in endothelial cells (EC) of most tissues except the liver. Increased expression of ABCA1 was not observed in resident peritoneal macrophages. ApoA-I-mediated cholesterol efflux from aortic EC was 2.6-fold higher (P < 0.0001) for cells from transgenic versus control mice. On normal chow diet, Tie2 hABCA1 transgenic mice had a 25% (P < 0.0001) increase in HDL-cholesterol (HDL-C) and more than a 2-fold increase of eNOS mRNA in the aorta (P < 0.04). After 6 months on a high-fat, high-cholesterol (HFHC) diet, transgenic mice compared with controls had a 40% increase in plasma HDL-C (P < 0.003) and close to 40% decrease in aortic lesions (P < 0.02). Aortas from HFHC-fed transgenic mice also showed gene expression changes consistent with decreased inflammation and apoptosis. Beneficial effects of the ABCA1 transgene on HDL-C levels or on atherosclerosis were absent when the transgene was transferred onto ApoE or Abca1 knockout mice. In summary, expression of hABCA1 in EC appears to play a role in decreasing diet-induced atherosclerosis in mice and is associated with increased plasma HDL-C levels and beneficial gene expression changes in EC.     

Hydrogen ion buffers for biological research

Five new zwitterionic hydrogen ion buffers are described for the first time. These buffers, all N-substituted 3-amino-2-hydroxypropanesulfonic acids, with pK_a's between 6.9 and 7.9 have been subjected to a number of stringent tests in mammalian tissue culture, plant pathology, and virology to detect inhibitory effects. They appear to be equivalent to or better than any other buffers heretofore available.     

Elimination of laboratory ozone leads to a dramatic improvement in the reproducibility of microarray gene expression measurements

Background  

Environmental ozone can rapidly degrade cyanine 5 (Cy5), a fluorescent dye commonly used in microarray gene expression studies. Cyanine 3 (Cy3) is much less affected by atmospheric ozone. Degradation of the Cy5 signal relative to the Cy3 signal in 2-color microarrays will adversely reduce the Cy5/Cy3 ratio resulting in unreliable microarray data.