High-Resolution Description of Antibody Heavy-Chain Repertoires in Humans

Antibodies' protective, pathological, and therapeutic properties result from their considerable diversity. This diversity is almost limitless in potential, but actual diversity is still poorly understood. Here we use deep sequencing to characterize the diversity of the heavy-chain CDR3 region, the most important contributor to antibody binding specificity, and the constituent V, D, and J segments that comprise it. We find that, during the stepwise D-J and then V-DJ recombination events, the choice of D and J segments exert some bias on each other; however, we find the choice of the V segment is essentially independent of both. V, D, and J segments are utilized with different frequencies, resulting in a highly skewed representation of VDJ combinations in the repertoire. Nevertheless, the pattern of segment usage was almost identical between two different individuals. The pattern of V, D, and J segment usage and recombination was insufficient to explain overlap that was observed between the two individuals' CDR3 repertoires. Finally, we find that while there are a near-infinite number of heavy-chain CDR3s in principle, there are about 3–9 million in the blood of an adult human being

Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources

An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple data sources. Test data set, a web tool, source codes and supplementary data are available at: http://www.probtf.org

REDUCTION OF INTRATUMORAL PH BY THE MITOCHONDRIAL INHIBITOR M-IODOBENZYLGUANIDINE AND MODERATE HYPERGLYCEMIA

The interstitial pH of RIF-1 tumors was selectively lowered by i.p. administration of the mitochondrial inhibitor meta-iodobenzylguanidine (MIBG; 40-100 mg/kg), supported by sustained moderate hyperglycemia (plasma glucose concentration, 14 mM) in rats or by a single i.p. bolus injection of glucose (1.5 g/kg) in mice. Responses were evaluated is a multicenter study by pH measurements with semimicroelectrodes and P-31 magnetic resonance spectroscopy, by biochemical analysis of tissue and plasma levels of glucose and lactate, and by positron emission tomography analysis of 2-[F-18]fluoro-2-deoxy-D-glucose uptake. In both schedules, treatment with MIBG and glucose reduced the mean intratumoral pH as recorded with semimicroelectrodes to 6.2. In the mouse model, treatment with MIBG plus glucose was accompanied by a 2-3-fold stimulation of 2-[F-18]fluoro-2-deoxy-D-glucose uptake and a corresponding increase in tumor glucose content. Responses were maximal in male mice with tumors of 0.2-0.8 g. P-31 magnetic resonance spectroscopy analysis revealed no changes in intracellular pH or metabolic status, indicating that only extracellular pH was affected. MIBG was synergistic with bolus or continuous glucose administrations by a dual mechanism. The drug reduced by up to 5-fold the amount of glucose required for effective reduction of intratumoral pH and promoted the availability of (extra) glucose to tumor tissue in a stress-related, sympathomimetic response. Moreover, by converting oxic tumor cells into functionally hypoxic cells, combined treatment resulted in a more homogeneous decrease in intratumoral pH which included better perfused peripheral tumor areas. The effects of combined treatment on tumor glucose metabolism could be monitored noninvasively by 2-[F-18]fluoro 2-deoxy-D-glucose positron emission tomography analysis