Integrating diverse genomic data using gene sets

We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually

De novo fatty acid synthesis at the mitotic exit is required to complete cellular division

Although the regulation of the cell cycle has been extensively studied, much less is known about its coordination with the cellular metabolism. Using mass spectrometry we found that lysophospholipid levels decreased drastically from G2/M to G1 phase, while de novo phosphatidylcholine synthesis, the main phospholipid in mammalian cells, increased, suggesting that enhanced membrane production was concomitant to a decrease in its turnover. In addition, fatty acid synthesis and incorporation into membranes was increased upon cell division. The rate-limiting reaction for de novo fatty acid synthesis is catalyzed by acetyl-CoA carboxylase. As expected, its inhibiting phosphorylation decreased prior to cytokinesis initiation. Importantly, the inhibition of fatty acid synthesis arrested the cells at G2/M despite the presence of abundant fatty acids in the media. Our results suggest that de novo lipogenesis is essential for cell cycle completion. This "lipogenic checkpoint" at G2/M may be therapeutically exploited for hyperproliferative diseases such as cancer.Instituto de Investigaciones Bioquímicas de La Plat

Human-macaque comparisons illuminate variation in neutral substitution rates

The evolutionary distance between human and macaque is particularly attractive for investigating neutral substitution rates, which were calculated as a function of a number of genomic parameters

Genomic Environment Predicts Expression Patterns on the Human Inactive X Chromosome

What genomic landmarks render most genes silent while leaving others expressed on the inactive X chromosome in mammalian females? To date, signals determining expression status of genes on the inactive X remain enigmatic despite the availability of complete genomic sequences. Long interspersed repeats (L1s), particularly abundant on the X, are hypothesized to spread the inactivation signal and are enriched in the vicinity of inactive genes. However, both L1s and inactive genes are also more prevalent in ancient evolutionary strata. Did L1s accumulate there because of their role in inactivation or simply because they spent more time on the rarely recombining X? Here we utilize an experimentally derived inactivation profile of the entire human X chromosome to uncover sequences important for its inactivation, and to predict expression status of individual genes. Focusing on Xp22, where both inactive and active genes reside within evolutionarily young strata, we compare neighborhoods of genes with different inactivation states to identify enriched oligomers. Occurrences of such oligomers are then used as features to train a linear discriminant analysis classifier. Remarkably, expression status is correctly predicted for 84% and 91% of active and inactive genes, respectively, on the entire X, suggesting that oligomers enriched in Xp22 capture most of the genomic signal determining inactivation. To our surprise, the majority of oligomers associated with inactivated genes fall within L1 elements, even though L1 frequency in Xp22 is low. Moreover, these oligomers are enriched in parts of L1 sequences that are usually underrepresented in the genome. Thus, our results strongly support the role of L1s in X inactivation, yet indicate that a chromatin microenvironment composed of multiple genomic sequence elements determines expression status of X chromosome genes

Modeling genetic heterogeneity of drug response and resistance in cancer

Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. This study describes recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.</p

De novo fatty acid synthesis at the mitotic exit is required to complete cellular division

Although the regulation of the cell cycle has been extensively studied, much less is known about its coordination with the cellular metabolism. Using mass spectrometry we found that lysophospholipid levels decreased drastically from G2/M to G1 phase, while de novo phosphatidylcholine synthesis, the main phospholipid in mammalian cells, increased, suggesting that enhanced membrane production was concomitant to a decrease in its turnover. In addition, fatty acid synthesis and incorporation into membranes was increased upon cell division. The rate-limiting reaction for de novo fatty acid synthesis is catalyzed by acetyl-CoA carboxylase. As expected, its inhibiting phosphorylation decreased prior to cytokinesis initiation. Importantly, the inhibition of fatty acid synthesis arrested the cells at G2/M despite the presence of abundant fatty acids in the media. Our results suggest that de novo lipogenesis is essential for cell cycle completion. This "lipogenic checkpoint" at G2/M may be therapeutically exploited for hyperproliferative diseases such as cancer.Instituto de Investigaciones Bioquímicas de La Plat

The role of mutation rate variation and genetic diversity in the architecture of human disease

Background We have investigated the role that the mutation rate and the structure of genetic variation at a locus play in determining whether a gene is involved in disease. We predict that the mutation rate and its genetic diversity should be higher in genes associated with disease, unless all genes that could cause disease have already been identified. Results Consistent with our predictions we find that genes associated with Mendelian and complex disease are substantially longer than non-disease genes. However, we find that both Mendelian and complex disease genes are found in regions of the genome with relatively low mutation rates, as inferred from intron divergence between humans and chimpanzees, and they are predicted to have similar rates of non-synonymous mutation as other genes. Finally, we find that disease genes are in regions of significantly elevated genetic diversity, even when variation in the rate of mutation is controlled for. The effect is small nevertheless. Conclusions Our results suggest that gene length contributes to whether a gene is associated with disease. However, the mutation rate and the genetic architecture of the locus appear to play only a minor role in determining whether a gene is associated with disease

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer. Database URL: http://bcb.dfci.harvard.edu/ovariancance

A novel direct activator of AMPK inhibits prostate cancer growth by blocking lipogenesis

5′AMP-activated kinase (AMPK) constitutes a hub for cellular metabolic and growth control, thus representing an ideal therapeutic target for prostate cancers (PCas) characterized by increased lipogenesis and activation of mTORC1 pathway. However, whether AMPK activation itself is sufficient to block cancer cell growth remains to be determined. A small molecule screening was performed and identified MT 63–78, a specific and potent direct AMPK activator. Here, we show that direct activation of AMPK inhibits PCa cell growth in androgen sensitive and castration resistant PCa (CRPC) models, induces mitotic arrest, and apoptosis. In vivo, AMPK activation is sufficient to reduce PCa growth, whereas the allelic loss of its catalytic subunits fosters PCa development. Importantly, despite mTORC1 blockade, the suppression of de novo lipogenesis is the underpinning mechanism responsible for AMPK-mediated PCa growth inhibition, suggesting AMPK as a therapeutic target especially for lipogenesis-driven PCas. Finally, we demonstrate that MT 63–78 enhances the growth inhibitory effect of AR signaling inhibitors MDV3100 and abiraterone. This study thus provides a rationale for their combined use in CRPC treatment