Response projected clustering for direct association with physiological and clinical response data

<p>Abstract</p> <p>Background</p> <p>Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients' residual tumor sizes after chemotherapy or glucose levels at various stages of diabetic patients. Current clustering analysis cannot directly incorporate such quantitative metadata into the clustering heatmap of gene expression. It will be quite useful if these clinical response data can be effectively summarized in the high-dimensional clustering display so that important groups of genes can be intuitively discovered with different degrees of relevance to target disease phenotypes.</p> <p>Results</p> <p>We introduced a novel clustering analysis approach, <it>response projected clustering </it>(RPC), which uses a high-dimensional geometrical projection of response data to the gene expression space. The projected response vector, which becomes the origin in the projected space, is then clustered together with the projected gene vectors based on their different degrees of association with the response vector. A bootstrap-counting based RPC analysis is also performed to evaluate statistical tightness of identified gene clusters. Our RPC analysis was applied to the <it>in vitro </it>growth-inhibition and microarray profiling data on the NCI-60 cancer cell lines and the microarray gene expression study of macrophage differentiation in atherogenesis. These RPC applications enabled us to identify many known and novel gene factors and their potential pathway associations which are highly relevant to the drug's chemosensitivity activities and atherogenesis.</p> <p>Conclusion</p> <p>We have shown that RPC can effectively discover gene networks with different degrees of association with clinical metadata. Performed on each gene's response projected vector based on its degree of association with the response data, RPC effectively summarizes individual genes' association with metadata as well as their own expression patterns. Thus, RPC greatly enhances the utility of clustering analysis on investigating high-dimensional microarray gene expression data with quantitative metadata.</p

The statistical neuroanatomy of frontal networks in the macaque

We were interested in gaining insight into the functional properties of frontal networks based upon their anatomical inputs. We took a neuroinformatics approach, carrying out maximum likelihood hierarchical cluster analysis on 25 frontal cortical areas based upon their anatomical connections, with 68 input areas representing exterosensory, chemosensory, motor, limbic, and other frontal inputs. The analysis revealed a set of statistically robust clusters. We used these clusters to divide the frontal areas into 5 groups, including ventral-lateral, ventral-medial, dorsal-medial, dorsal-lateral, and caudal-orbital groups. Each of these groups was defined by a unique set of inputs. This organization provides insight into the differential roles of each group of areas and suggests a gradient by which orbital and ventral-medial areas may be responsible for decision-making processes based on emotion and primary reinforcers, and lateral frontal areas are more involved in integrating affective and rational information into a common framework

Endotype Discovery in Acute Respiratory Distress Syndrome

Endotype Discovery in Acute Respiratory Distress Syndrome Dr Romit Samanta Acute respiratory distress syndrome (ARDS) affects 10% of critical care patients and is characterised by acute refractory hypoxaemia and bilateral pulmonary infiltrates on thoracic imaging. Mortality from severe ARDS is approximately 40%, and has not changed in 50 years despite decades of study. Randomised controlled trials of therapies for ARDS have been unsuccessful due to the heterogeneity of the patient population. This has led repeatedly to potentially promising therapies being discarded. The primary reason for the failure is that the underlying biological processes occurring in ARDS are poorly understood. This thesis attempts to address this heterogeneity, and explores the underlying biology by using an integrated, unsupervised bioinformatics approach to describe different mechanistic subtypes (endotypes) of ARDS. The endotypes described here are derived from analysis of data collected by three UK-based studies: an observational study of sepsis (GAinS), an observational study of severe influenza (MOSAIC), and a randomised controlled trial of simvastatin in ARDS (HARP-2). A combination of automated clustering methods and network analysis tools have been used to integrate blood biomarkers and gene expression (transcriptomic) data to define distinct endotypes of ARDS. Three endotypes of ARDS were identified in each of the studies. Integration of protein biomarker and transcriptomic data from patients recruited to the GAinS study identified three endotypes, one of which was characterised by severely dysregulated cytokine release, which we termed hyper-inflammatory. Two gene modules discriminated these patients from a hypo-inflammatory endotype, consisting of patients with globally depressed cytokine concentrations. Enrichment of the genes in these two modules identified genes that were important in vesicle fusion and cytokine release. Mutations in these genes cause the familial type of haemophagocytic lymphohistiocytosis (HLH). The implication here is that these genes played a role in the severely dysregulated cytokine concentrations observed within patients with hyper-inflammatory, sepsis-associated ARDS. Analysis of the cytokines and transcriptomic data collected during the MOSAIC study identified three endotypes we named: adaptive, endothelial leak and neutrophil driven. The endothelial leak endotype was characterised by enrichment of genes associated with SLIT- ROBO signalling. SLIT-ROBO signalling is essential for maintaining pulmonary endothelial integrity and failure of this mechanism has been shown to cause alveolar oedema in murine models of sepsis and influenza infection. These patients had significantly lower albumin levels than the adaptive endotype, and 48.5% of them required mechanical ventilation. Despite the greater need for mechanical ventilation, the outcomes of these patients were similar to those of patients with the adaptive endotype, of whom only 20.4% required mechanical ventilation. Cluster analysis of patient biomarker concentrations from the HARP-2 study identified three endotypes. Two of these endotypes had elevated serum IL-6 and sTNFR-1 concentrations, consistent with a hyper-inflammatory profile. Patients with one of the hyper-inflammatory endotypes, which we termed MMP-8 dominant, demonstrated a strong therapeutic response to simvastatin compared with placebo (28-day survival, adjusted HR = 0.35, 95% CI 0.18- 0.71; p = 0.003). Patients with this endotype, who received simvastatin, had a similar 28-day survival profile to patients with a hypo-inflammatory endotype, characterised by globally depressed biomarker levels. Patients with the other hyper-inflammatory endotype, which we termed sRAGE dominant, did not show any therapeutic response to simvastatin. The endotypes described are temporally stable, and some relate to novel mechanisms not previously recognised in patients with ARDS. The endotypes are all biologically plausible, amenable to the development of further mechanistic insights using laboratory-based tech- niques, and may influence patient outcomes and response to treatments. Further development and prospective validation of these endotypes are required. If validated, they may offer the opportunity to stratify patients in future clinical trials to treatments that are more likely to improve their outcomes, whilst avoiding treatments that might cause adverse effects. The methods described in this thesis could be applied to other heterogeneous and poorly understood clinical syndromes.National Institute for Health Research Medical Research Council Cambridge Biomedical Research Centre GlaxoSmithKline PL

Of mice and men: Sparse statistical modeling in cardiovascular genomics

In high-throughput genomics, large-scale designed experiments are becoming common, and analysis approaches based on highly multivariate regression and anova concepts are key tools. Shrinkage models of one form or another can provide comprehensive approaches to the problems of simultaneous inference that involve implicit multiple comparisons over the many, many parameters representing effects of design factors and covariates. We use such approaches here in a study of cardiovascular genomics. The primary experimental context concerns a carefully designed, and rich, gene expression study focused on gene-environment interactions, with the goals of identifying genes implicated in connection with disease states and known risk factors, and in generating expression signatures as proxies for such risk factors. A coupled exploratory analysis investigates cross-species extrapolation of gene expression signatures--how these mouse-model signatures translate to humans. The latter involves exploration of sparse latent factor analysis of human observational data and of how it relates to projected risk signatures derived in the animal models. The study also highlights a range of applied statistical and genomic data analysis issues, including model specification, computational questions and model-based correction of experimental artifacts in DNA microarray data.Comment: Published at http://dx.doi.org/10.1214/07-AOAS110 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Assessment of Health-Economic Burden of Obesity Trends with Population-Based Preventive Strategies in a Developed Economy

The burden of obesity varies with age, ethnicity, socio-economic status and state economies. All new projections should hence accommodate population ageing, and other population changes such as immigration, health-care system reform, or technological advances for disease treatment for a comprehensible assessment of global burden. The unfordable and expensive nature for reversing the obesity tide arises from policies developed to combat obesity. Most of these approaches aim at bringing the problem under control, rather than affecting a cure, and obviously require a multi-disciplinary and intensive regimen. Prevention is the only feasible option and is essential for all affected countries. Yet it is not simple to have population based UK-wide strategic framework for tackling obesity. Besides existence of multiple layers of governance, there are clear demarcations between targets in diet; nutrition and physical activity level between regions some of which are not realistic. Population based approaches target policies and process, aiming for a transition towards healthy population diets, activity levels and weight status. It is essential to understand these aspects differ culturally and between and within countries. There are still no clear and appropriate answers about answer when, where, why, and, how costs accrue in obese populations, further long term commitments are required for the same. Most population-based prevention policies are cost effective, largely paying for themselves through future health gains and resulting reductions in health expenditures. Therefore these prevention programs should be high on the scientific and political agendas

Identification and Cloning of Transcripts from Triterpenoid-Induced Neuronal Outgrowth in Neuro-2a Cells from Mus musculus

Neurodegenerative diseases severely reduce quality of life and are often responsible for the death of those suffering from them. In addition to the societal toll these diseases inflict on populations, the health care costs are estimated to be hundreds of billions of dollars annually for the U.S. alone. Unfortunately, direct pharmacological treatment for some of the most common of these diseases, such as Alzheimer’s, remains elusive. Traditional medicine in China and India have recommended a variety of plants for preventing and treating these diseases. For example, Centella asiatica (L.) Urban, also knows as Gotu Kola, is believed to treat and prevent many common ailments and diseases, including Alzheimer’s disease and other dementia-related diseases. C. asiatica has been shown to contain phytochemicals, such as asiatic acid (AA) and madecassic acid (MA), which are believed to be responsible for the physiological response by humans to C. asiatica extracts. The present research examined the transcriptomes of mouse Neuro-2a cells treated with AA, MA, and ethanol (vehicle) to discover gene transcripts that were highly expressed (10X or greater) by both AA and MA treatments compared to ethanol alone. This resulted in a core set of 23 transcripts, which was used to determine a subset of transcripts for cloning into a mammalian overexpression plasmid (pcDNA-DEST40). High level expression of the five transcripts (Mpp3-204, Pak1-206, Tardbp-204, Usf1-210, and Zc3h15-204) following AA and MA treatments was verified using qPCR. The suite of plasmids containing these transcripts will be used in subsequent experiments with neurons in culture to explore whether one of these proteins or a combination of them is sufficient for significantly increased neurite outgrowth and length, as was demonstrated with direct AA and MA treatments

Transcriptional Profiling of Plasmodium falciparum Parasites from Patients with Severe Malaria Identifies Distinct Low vs. High Parasitemic Clusters

Background: In the past decade, estimates of malaria infections have dropped from 500 million to 225 million per year; likewise, mortality rates have dropped from 3 million to 791,000 per year. However, approximately 90% of these deaths continue to occur in sub-Saharan Africa, and 85% involve children less than 5 years of age. Malaria mortality in children generally results from one or more of the following clinical syndromes: severe anemia, acidosis, and cerebral malaria. Although much is known about the clinical and pathological manifestations of CM, insights into the biology of the malaria parasite, specifically transcription during this manifestation of severe infection, are lacking. Methods and Findings: We collected peripheral blood from children meeting the clinical case definition of cerebral malaria from a cohort in Malawi, examined the patients for the presence or absence of malaria retinopathy, and performed whole genome transcriptional profiling for Plasmodium falciparum using a custom designed Affymetrix array. We identified two distinct physiological states that showed highly significant association with the level of parasitemia. We compared both groups of Malawi expression profiles with our previously acquired ex vivo expression profiles of parasites derived from infected patients with mild disease; a large collection of in vitro Plasmodium falciparum life cycle gene expression profiles; and an extensively annotated compendium of expression data from Saccharomyces cerevisiae. The high parasitemia patient group demonstrated a unique biology with elevated expression of Hrd1, a member of endoplasmic reticulum-associated protein degradation system. Conclusions: The presence of a unique high parasitemia state may be indicative of the parasite biology of the clinically recognized hyperparasitemic severe disease syndrome

Visualising the cross-level relationships between pathological and physiological processes and gene expression: analyses of haematological diseases.

The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields

IDOL regulates systemic energy balance through control of neuronal VLDLR expression.

Liver X receptors limit cellular lipid uptake by stimulating the transcription of Inducible Degrader of the LDL Receptor (IDOL), an E3 ubiquitin ligase that targets lipoprotein receptors for degradation. The function of IDOL in systemic metabolism is incompletely understood. Here we show that loss of IDOL in mice protects against the development of diet-induced obesity and metabolic dysfunction by altering food intake and thermogenesis. Unexpectedly, analysis of tissue-specific knockout mice revealed that IDOL affects energy balance, not through its actions in peripheral metabolic tissues (liver, adipose, endothelium, intestine, skeletal muscle), but by controlling lipoprotein receptor abundance in neurons. Single-cell RNA sequencing of the hypothalamus demonstrated that IDOL deletion altered gene expression linked to control of metabolism. Finally, we identify VLDLR rather than LDLR as the primary mediator of IDOL effects on energy balance. These studies identify a role for the neuronal IDOL-VLDLR pathway in metabolic homeostasis and diet-induced obesity