A statistical framework for integrating two microarray data sets in differential expression analysis

<p>Abstract</p> <p>Background</p> <p>Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis.</p> <p>Results</p> <p>Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models.</p> <p>Conclusion</p> <p>The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.</p

Alterations of renal phenotype and gene expression profiles due to protein overload in NOD-related mouse strains

BACKGROUND: Despite multiple causes, Chronic Kidney Disease is commonly associated with proteinuria. A previous study on Non Obese Diabetic mice (NOD), which spontaneously develop type 1 diabetes, described histological and gene expression changes incurred by diabetes in the kidney. Because proteinuria is coincident to diabetes, the effects of proteinuria are difficult to distinguish from those of other factors such as hyperglycemia. Proteinuria can nevertheless be induced in mice by peritoneal injection of Bovine Serum Albumin (BSA). To gain more information on the specific effects of proteinuria, this study addresses renal changes in diabetes resistant NOD-related mouse strains (NON and NOD.B10) that were made to develop proteinuria by BSA overload. METHODS: Proteinuria was induced by protein overload on NON and NOD.B10 mouse strains and histology and microarray technology were used to follow the kidney response. The effects of proteinuria were assessed and subsequently compared to changes that were observed in a prior study on NOD diabetic nephropathy. RESULTS: Overload treatment significantly modified the renal phenotype and out of 5760 clones screened, 21 and 7 kidney transcripts were respectively altered in the NON and NOD.B10. Upregulated transcripts encoded signal transduction genes, as well as markers for inflammation (Calmodulin kinase beta). Down-regulated transcripts included FKBP52 which was also down-regulated in diabetic NOD kidney. Comparison of transcripts altered by proteinuria to those altered by diabetes identified mannosidase 2 alpha 1 as being more specifically induced by proteinuria. CONCLUSION: By simulating a component of diabetes, and looking at the global response on mice resistant to the disease, by virtue of a small genetic difference, we were able to identify key factors in disease progression. This suggests the power of this approach in unraveling multifactorial disease processes

Cloning of livestock by somatic cell nuclear transfer

© Springer International Publishing AG, part of Springer Nature 2018. Since the cloning of “Dolly” by somatic cell nuclear transfer in 1996, numerous articles have been published concerning the application of this technology to a large variety of mammalian species including all the major livestock species. While live births have been obtained for many species, the efficiency of cellular reprogramming essential for success has not been significantly improved. This chapter will attempt to address the inputs utilized for this procedure and the major manipulation steps with the objective of identifying the major factors which might affect this efficiency of reprogramming and some of the studies addressing these factors. Finally, the challenging task of setting optimum endpoints for experiments involving domestic species will be discussed

Large-scale identification of genes implicated in kidney glomerulus development and function

To advance our understanding of development, function and diseases in the kidney glomerulus, we have established and large-scale sequenced cDNA libraries from mouse glomeruli at different stages of development, resulting in a catalogue of 6053 different genes. The glomerular cDNA clones were arrayed and hybridized against a series of labeled targets from isolated glomeruli, non-glomerular kidney tissue, FACS-sorted podocytes and brain capillaries, which identified over 300 glomerular cell-enriched transcripts, some of which were further sublocalized to podocytes, mesangial cells and juxtaglomerular cells by in situ hybridization. For the earliest podocyte marker identified, Foxc2, knockout mice were used to analyze the role of this protein during glomerular development. We show that Foxc2 controls the expression of a distinct set of podocyte genes involved in podocyte differentiation and glomerular basement membrane maturation. The primary podocyte defects also cause abnormal differentiation and organization of the glomerular vascular cells. We surmise that studies on the other novel glomerulus-enriched transcripts identified in this study will provide new insight into glomerular development and pathomechanisms of disease

Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) is an advanced imaging technique used to map certain brain functions preoperatively—the information obtained is used to assess risk and operative feasibility while helping to guide the surgical approach. fMRI has been validated for motor mapping, language lateralization, and to a lesser extent language localization (due, in part, to the evolution of our understanding of language function). This chapter aims to provide an insight into preoperative fMRI beginning with brain physiology to a discussion of recent advancements while providing tips on how to approach post-processing and image assessment

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease