The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements.

The mammalian genome harbors up to one million regulatory elements often located at great distances from their target genes. Long-range elements control genes through physical contact with promoters and can be recognized by the presence of specific histone modifications and transcription factor binding. Linking regulatory elements to specific promoters genome-wide is currently impeded by the limited resolution of high-throughput chromatin interaction assays. Here we apply a sequence capture approach to enrich Hi-C libraries for >22,000 annotated mouse promoters to identify statistically significant, long-range interactions at restriction fragment resolution, assigning long-range interacting elements to their target genes genome-wide in embryonic stem cells and fetal liver cells. The distal sites contacting active genes are enriched in active histone modifications and transcription factor occupancy, whereas inactive genes contact distal sites with repressive histone marks, demonstrating the regulatory potential of the distal elements identified. Furthermore, we find that coregulated genes cluster nonrandomly in spatial interaction networks correlated with their biological function and expression level. Interestingly, we find the strongest gene clustering in ES cells between transcription factor genes that control key developmental processes in embryogenesis. The results provide the first genome-wide catalog linking gene promoters to their long-range interacting elements and highlight the complex spatial regulatory circuitry controlling mammalian gene expression.

In silico models of cancer.

Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.

Systems biology of embryogenesis.

The development of a complete organism from a single cell involves extraordinarily complex orchestration of biological processes that vary intricately across space and time. Systems biology seeks to describe how all elements of a biological system interact in order to understand, model and ultimately predict aspects of emergent biological processes. Embryogenesis represents an extraordinary opportunity (and challenge) for the application of systems biology. Systems approaches have already been used successfully to study various aspects of development, from complex intracellular networks to four-dimensional models of organogenesis. Going forward, great advancements and discoveries can be expected from systems approaches applied to embryogenesis and developmental biology.

Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases.

BACKGROUND: Identification of molecular classifiers from genome-wide gene expression analysis is an important practice for the investigation of biological systems in the post-genomic era--and one with great potential for near-term clinical impact. The 'Top-Scoring Pair' (TSP) classification method identifies pairs of genes whose relative expression correlates strongly with phenotype. In this study, we sought to assess the effectiveness of the TSP approach in the identification of diagnostic classifiers for a number of human diseases including bacterial and viral infection, cardiomyopathy, diabetes, Crohn's disease, and transformed ulcerative colitis. We examined transcriptional profiles from both solid tissues and blood-borne leukocytes. RESULTS: The algorithm identified multiple predictive gene pairs for each phenotype, with cross-validation accuracy ranging from 70 to nearly 100 percent, and high sensitivity and specificity observed in most classification tasks. Performance compared favourably with that of pre-existing transcription-based classifiers, and in some cases was comparable to the accuracy of current clinical diagnostic procedures. Several diseases of solid tissues could be reliably diagnosed through classifiers based on the blood-borne leukocyte transcriptome. The TSP classifier thus represents a simple yet robust method to differentiate between diverse phenotypic states based on gene expression profiles. CONCLUSION: Two-transcript classifiers have the potential to reliably classify diverse human diseases, through analysis of both local diseased tissue and the immunological response assayed through blood-borne leukocytes. The experimental simplicity of this method results in measurements that can be easily translated to clinical practice.

Metabolic context affects hemodynamic response to bupivacaine in the isolated rat heart.

Previous studies have demonstrated that the local anesthetic bupivacaine selectively inhibits oxidative metabolism of fatty acids in isolated cardiac mitochondria. In the present investigation, we compare the development of bupivacaine cardiotoxicity during fatty acid and carbohydrate metabolism. Hearts from adult male Sprague-Dawley rats were excised and retrograde perfused with a solution containing fatty acid (oleate or octanoate) or carbohydrate substrates for cardiac metabolism. An infusion of bupivacaine was initiated and sustained until asystole, after which full cardiac recovery was allowed. During fatty acid metabolism, substantially lower bupivacaine doses induced both arrhythmia (60.4+/-11.5 microg oleate and 106.8+/-14.8 octanoate versus 153.4+/-21.4 carbohydrate; P<0.05) and asystole (121.0+/-30.1 microg and 171.5+/-20.2 versus 344.7+/-34.6; P<0.001). Dose-response analysis revealed significantly increased sensitivity to bupivacaine toxicity during fatty acid metabolism, indicated by lower V50 doses for both heart rate (70.6+/-5.6 microg oleate and 122.3+/-6.2 octanoate versus 152.6+/-8.6) and rate-pressure product (63.4+/-5.1 microg and 133.7+/-7.9 versus 165.1+/-12.2). Time to recovery following bupivacaine exposure was elevated in the fatty acid group (24.3+/-2.0 s versus 15.8+/-3.1; P<0.04). Fatty acid metabolism was shown to predispose the isolated heart to bupivacaine toxicity, confirming that the local anesthetic exerts specific effects on lipid processes in cardiomyocytes.

Lipid infusion accelerates removal of bupivacaine and recovery from bupivacaine toxicity in the isolated rat heart.

BACKGROUND AND OBJECTIVES: Infusion of a lipid emulsion has been advocated for treatment of severe bupivacaine cardiac toxicity. The mechanism of lipid rescue is unknown. These studies address the possibility that lipid infusion reduces cardiac bupivacaine content in the context of cardiac toxicity. METHODS: We compared the effects of a 1% lipid emulsion with standard Krebs buffer after inducing asystole in isolated rat heart with 500 micromol/L bupivacaine. We compared times to first heart beat and recovery of 90% of baseline rate pressure product (RPP = heart rate x [left ventricular systolic pressure - left ventricular diastolic pressure]) between controls and hearts receiving 1% lipid immediately after bupivacaine. We also used minibiopsies to compare control bupivacaine tissue content with hearts getting lipid immediately after an infusion of radiolabeled bupivacaine. We then compared bupivacaine efflux from hearts with and without lipid infusion started 75 seconds after radiolabeled bupivacaine was administered. RESULTS: Infusion of lipid resulted in more rapid return of spontaneous contractions and full recovery of cardiac function. Average (+/- SEM) times to first beat and to 90% recovery of rate pressure product were 44.6 +/- 3.5 versus 63.8 +/- 4.3 seconds (P < .01) and 124.7 +/- 12.4 versus 219.8 +/- 25.6 seconds (P < .01) for lipid and controls, respectively. Lipid treatment resulted in more rapid loss of bupivacaine from heart tissue (P < .0016). Late lipid infusion, 75 seconds after bupivacaine infusion ended, increased the release of bupivacaine measured in effluent for the first 15-second interval compared with controls (183 vs. 121 nmol, n = 5 for both groups, P < .008). CONCLUSIONS: Lipid emulsion speeds loss of bupivacaine from cardiac tissue while accelerating recovery from bupivacaine-induced asystole. These findings are consistent with the hypothesis that bupivacaine partitions into the emulsion and supports the concept of a "lipid sink." However, the data do not exclude other possible mechanisms of action.