An Analysis Of The Interaction Between Sin3 And Methionine Metabolism In Drosophila

Chromatin modification and cellular metabolism are tightly connected. The mechanism for this cross-talk, however, remains incompletely understood. SIN3 controls histone acetylation through association with the histone deacetylase RPD3. In this study, my major goal is to explore the mechanism of how SIN3 regulates cellular metabolism. Methionine metabolism generates the major methyl donor S-adenosylmethionine (SAM) for histone methylation. In collaboration with others, I report that reduced levels of some enzymes involved in methionine metabolism and histone demethylases lead to lethality, as well as wing development and cell proliferation defects in Drosophila melanogaster. Additionally, disruption of methionine metabolism can directly affect histone methylation levels. Reduction of little imaginal discs (LID) histone demethylase, but not lysine-specific demethylase 2 (KDM2) demethylase, is able to counter the effects on histone methylation due to reduction of SAM synthetase (SAM-S). Taken together, these results reveal an essential role of key enzymes that control methionine metabolism and histone methylation. Next, we demonstrate the genetic interaction between Sin3A and methionine metabolic genes. We find that SIN3 binds to methionine metabolic genes, affects histone modifications at the promoter regions of these genes and regulates their expression. We provide evidence that alteration of SIN3 level influences the amount of SAM and global H3K4me3. Furthermore, reduction of SIN3 can restore decreased global H3K4me3 caused by knockdown of either SAM-S or the histone methyltransfase SET1 to near control levels. Collectively, these results indicate that SIN3 directly regulates expression of methionine metabolic genes to control SAM levels, which in turn affect global H3K4me3. To further identify specific genes and cellular metabolic pathways requiring the activity of SIN3, we performed RNA-seq and metabolomics analysis when SIN3 and/or SAM-S is reduced. Moreover, we did correlation analysis between global H3K4me3 levels and the metabolic profiles to generate a list of metabolites whose concentration change significantly with the alteration in H3K4me3. We find glycolysis is a major pathway correlated with global H3K4me3 upon reduction of SIN3 and/or SAM-S. We demonstrate that SIN3 binds to glycolytic genes, affects H3K9ac, not H3K4me3, at the promoter regions of these genes and regulates their expression. Altogether, these results suggest that SIN3 directly regulates transcription of glycolytic genes to affect glycolysis, which is associated with H3K4me3 due to unknown mechanism. Overall, our study reveals that SIN3 is an important epigenetic regulator connecting cellular metabolism and histone modification. Supplementary files are included: • Supplementary Data 1_ML – Excel spreadsheet containing detailed RNAseq differential expression analysis • Supplementary Data 2_ML – Excel spreadsheet containing detailed gene ontology and KEGG pathway analyses • Supplementary Data 3_ML – Excel spreadsheet containing detailed metabolomic analysi

ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

Genome-wide studies reveal novel and distinct biological pathways regulated by SIN3 isoforms

Detailed annotation of ChIP-seq peaks for SIN3 187HA (SIN3 187HA ceas) or SIN3 220HA (SIN3 220HA ceas) as determined by the cis-regulatory enrichment annotation (CEAS) system. This table is related to Fig. 2 (XLSX 4014 kb