Institute for Systems Biology

About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.

The complete sequence of a human genome

Abstract: In 2001, Celera Genomics and the International Human Genome Sequencing Consortium published their initial drafts of the human genome, which revolutionized the field of genomics. While these drafts and the updates that followed effectively covered the euchromatic fraction of the genome, the heterochromatin and many other complex regions were left unfinished or erroneous. Addressing this remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium has finished the first truly complete 3.055 billion base pair (bp) sequence of a human genome, representing the largest improvement to the human reference genome since its initial release. The new T2T-CHM13 reference includes gapless assemblies for all 22 autosomes plus Chromosome X, corrects numerous errors, and introduces nearly 200 million bp of novel sequence containing 2,226 paralogous gene copies, 115 of which are predicted to be protein coding. The newly completed regions include all centromeric satellite arrays and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies for the first time.

Combining dependent P-values with an empirical adaptation of Brown’s method

Abstract: Motivation: Combining P -values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P -values. Here, we discuss an empirical adaptation of Brown’s method (an extension of Fisher’s method) for combining dependent P -values which is appropriate for the large and correlated datasets found in high-throughput biology. Results : We show that the Empirical Brown’s method (EBM) outperforms Fisher’s method as well as alternative approaches for combining dependent P -values using both noisy simulated data and gene expression data from The Cancer Genome Atlas. Availability and Implementation : The Empirical Brown’s method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvalues UsingEBM . The R code is also available as a Bioconductor package from https://www.bioconductor.org/packages/devel/bioc/html/EmpiricalBrownsMethod.html . Contact: Theo.Knijnenburg@systemsbiology.org Supplementary information: Supplementary data are available at Bioinformatics online.

Small-molecule Bax agonists for cancer therapy

Abstract: Bax, a central death regulator, is required at the decisional stage of apoptosis. We recently identified serine 184 (S184) of Bax as a critical functional switch controlling its proapoptotic activity. Here we used the structural pocket around S184 as a docking site to screen the NCI library of small molecules using the UCSF-DOCK programme suite. Three compounds, small-molecule Bax agonists SMBA1, SMBA2 and SMBA3, induce conformational changes in Bax by blocking S184 phosphorylation, facilitating Bax insertion into mitochondrial membranes and forming Bax oligomers. The latter leads to cytochrome c release and apoptosis in human lung cancer cells, which occurs in a Bax- but not Bak-dependent fashion. SMBA1 potently suppresses lung tumour growth via apoptosis by selectively activating Bax in vivo without significant normal tissue toxicity. Development of Bax agonists as a new class of anticancer drugs offers a strategy for the treatment of lung cancer and other Bax-expressing malignancies.

Metabonomic study of aristolochic acid-induced nephrotoxicity in rats.

Abstract: This paper describes a metabonomic study characterizing the nephrotoxicity induced by aristolochic acid (AA), a suspected kidney toxicant. For these studies, we examined the biochemical compositions of AA-treated rat urine using LC-MS and pattern recognition methods. The biochemical and histological patterns of rat groups treated with different AA sources showed distinct differences from those of the control group. Certain metabolic pathways, such as homocysteine formation and the folate cycle were significantly accelerated, while others, including arachidonic acid biosynthesis, were decreased. A subset-validation procedure using linear discriminant analysis (LDA) and selected predictive variables indicated that approximately 95% of the treated and nontreated rat urine samples were classified correctly into their respective treatment groups. The results suggested that this metabonomic approach is a promising methodology for the rapid in vivo screening of nephrotoxicity associated with ingesting multi-ingredient medicinal herb supplements, and provides a valid method for comprehending the chemical-induced perturbations in the metabolic network and the networked lesions.

Redox balance is key to explaining full vs. partial switching to low-yield metabolism

Abstract: Low-yield metabolism is a puzzling phenomenon in many unicellular and multicellular organisms. In abundance of glucose, many cells use a highly wasteful fermentation pathway despite the availability of a high-yield pathway, producing many ATP molecules per glucose, e.g., oxidative phosphorylation. Some of these organisms, including the lactic acid bacterium Lactococcus lactis, downregulate their high-yield pathway in favor of the low-yield pathway. Other organisms, including Escherichia coli do not reduce the flux through the high-yield pathway, employing the low-yield pathway in parallel with a fully active high-yield pathway. For what reasons do some species use the high-yield and low-yield pathways concurrently and what makes others downregulate the high-yield pathway? A classic rationale for metabolic fermentation is overflow metabolism. Because the throughput of metabolic pathways is limited, influx of glucose exceeding the pathway's throughput capacity is thought to be redirected into an alternative, low-yield pathway. This overflow metabolism rationale suggests that cells would only use fermentation once the high-yield pathway runs at maximum rate, but it cannot explain why cells would decrease the flux through the high-yield pathway. Using flux balance analysis with molecular crowding (FBAwMC), a recent extension to flux balance analysis (FBA) that assumes that the total flux through the metabolic network is limited, we investigate the differences between Saccharomyces cerevisiae and L. lactis that downregulate the high-yield pathway at increasing glucose concentrations, and E. coli, which keeps the high-yield pathway functioning at maximal rate. FBAwMC correctly predicts the metabolic switching mode in these three organisms, suggesting that metabolic network architecture is responsible for differences in metabolic switching mode. Based on our analysis, we expect gradual, "overflow-like" switching behavior in organisms that have an additional energy-yielding pathway that does not consume NADH (e.g., acetate production in E. coli). Flux decrease through the high-yield pathway is expected in organisms in which the high-yield and low-yield pathways compete for NADH. In support of this analysis, a simplified model of metabolic switching suggests that the extra energy generated during acetate production produces an additional optimal growth mode that smoothens the metabolic switch in E. coli. Maintaining redox balance is key to explaining why some microbes decrease the flux through the high-yield pathway, while other microbes use "overflow-like" low-yield metabolism.