Holocarboxylase Synthetase-dependent Biotinylation of Histone H4

Holocarboxylase synthetase (HCS) catalyzes the binding of biotin to lysine (K) residues in histones H3 and H4. Histone biotinylation marks play important roles in the repression of genes and retrotransposons. Preliminary studies suggested that K16 in histone H4 is a target for biotinylation by HCS. Here we demonstrated that H4K16bio is overrepresented in repeat regions {pericentromeric alpha satellite repeats; long terminal repeats (LTR)} compared with euchromatin promoters. H4K16bio was also enriched in the repressed interleukin-2 gene promoter. The enrichment at LTR22 and promoter 1 of the sodium-dependent multivitamin transporter (SMVT) depended on biotin supply; and was significantly lower in fibroblasts from an HCS-deficient patient compared with an HCS wild-type control. We conclude that H4K16bio is a real phenomenon and plays a role in the transcriptional repression of repeats and genes. HCS catalyzes the covalent binding of biotin to carboxylases, in addition to its role as a histone biotinyl ligase. HCS null individuals are not viable whereas HCS deficiency is linked to developmental delays and phenotypes such as short life span and low stress resistance. Here, we developed a 96-well plate assay for high-throughput analysis of HCS based on the detection of biotinylated p67 using IRDye-streptavidin and infrared spectroscopy. We demonstrated that the catalytic activity of rHCS depends on temperature and time, and proposed optimal substrate and enzyme concentrations to ensure ideal measurement of rHCS activity and its kinetics. Additionally, we demonstrated that this assay is sensitive enough to detect biotinylation of p67 by endogenous HCS from Jurkat lymphoid cells.

JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability.