Biodiversity Loss and the Taxonomic Bottleneck: Emerging Biodiversity Science

Human domination of the Earth has resulted in dramatic changes to global and local patterns of biodiversity. Biodiversity is critical to human sustainability because it drives the ecosystem services that provide the core of our life-support system. As we, the human species, are the primary factor leading to the decline in biodiversity, we need detailed information about the biodiversity and species composition of specific locations in order to understand how different species contribute to ecosystem services and how humans can sustainably conserve and manage biodiversity. Taxonomy and ecology, two fundamental sciences that generate the knowledge about biodiversity, are associated with a number of limitations that prevent them from providing the information needed to fully understand the relevance of biodiversity in its entirety for human sustainability: (1) biodiversity conservation strategies that tend to be overly focused on research and policy on a global scale with little impact on local biodiversity; (2) the small knowledge base of extant global biodiversity; (3) a lack of much-needed site-specific data on the species composition of communities in human-dominated landscapes, which hinders ecosystem management and biodiversity conservation; (4) biodiversity studies with a lack of taxonomic precision; (5) a lack of taxonomic expertise and trained taxonomists; (6) a taxonomic bottleneck in biodiversity inventory and assessment; and (7) neglect of taxonomic resources and a lack of taxonomic service infrastructure for biodiversity science. These limitations are directly related to contemporary trends in research, conservation strategies, environmental stewardship, environmental education, sustainable development, and local site-specific conservation. Today’s biological knowledge is built on the known global biodiversity, which represents barely 20% of what is currently extant (commonly accepted estimate of 10 million species) on planet Earth. Much remains unexplored and unknown, particularly in hotspots regions of Africa, South Eastern Asia, and South and Central America, including many developing or underdeveloped countries, where localized biodiversity is scarcely studied or described. ‘‘Backyard biodiversity’’, defined as local biodiversity near human habitation, refers to the natural resources and capital for ecosystem services at the grassroots level, which urgently needs to be explored, documented, and conserved as it is the backbone of sustainable economic development in these countries. Beginning with early identification and documentation of local flora and fauna, taxonomy has documented global biodiversity and natural history based on the collection of ‘‘backyard biodiversity’’ specimens worldwide. However, this branch of science suffered a continuous decline in the latter half of the twentieth century, and has now reached a point of potential demise. At present there are very few professional taxonomists and trained local parataxonomists worldwide, while the need for, and demands on, taxonomic services by conservation and resource management communities are rapidly increasing. Systematic collections, the material basis of biodiversity information, have been neglected and abandoned, particularly at institutions of higher learning. Considering the rapid increase in the human population and urbanization, human sustainability requires new conceptual and practical approaches to refocusing and energizing the study of the biodiversity that is the core of natural resources for sustainable development and biotic capital for sustaining our life-support system. In this paper we aim to document and extrapolate the essence of biodiversity, discuss the state and nature of taxonomic demise, the trends of recent biodiversity studies, and suggest reasonable approaches to a biodiversity science to facilitate the expansion of global biodiversity knowledge and to create useful data on backyard biodiversity worldwide towards human sustainability

Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients

<p>Abstract</p> <p>Background</p> <p>Alterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility.</p> <p>Results</p> <p>Microarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes.</p> <p>RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis.</p> <p>Conclusions</p> <p>We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography.</p> <p>Clinical trial registration information</p> <p>PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, <url>http://www.clinicaltrials.gov</url>, <a href="http://www.clinicaltrials.gov/ct2/show/NCT00500617">NCT00500617</a></p