In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

The significant disparities in metabolism between tumor and normal cells have inspired the development of metabolism-based anti-tumor therapeutics. Arginine is a semi-essential amino acid because normal cells can not only synthesize arginine de novo but also take up extracellular arginine. Several types of tumors have abnormalities in arginine metabolism enzymes and completely rely on extracellular arginine to support necessary biological processes. This property is referred to as arginine auxotrophy. Taking advantage of characteristic arginine auxotrophy in tumors, arginine deprivation, which is generally induced by the use of arginine deiminase (ADI) and arginase I, has been investigated as a novel strategy for cancer therapy. Arginine deprivation demonstrated promising efficacy against arginine-auxotrophic tumors. By integrating perspectives from both clinical oncologists and laboratory scientists, this article reviews the important aspects of arginine deprivation as a promising anticancer therapy.
Targeting arginine metabolism pathway to treat arginine-dependent cancers.

Virulence in Staphylococcus aureus is largely under control of the accessory gene regulator (agr) quorum-sensing system. The AgrC receptor histidine kinase detects its autoinducing peptide (AIP) ligand and generates an intracellular signal resulting in secretion of virulence factors. Although agr is a well-studied quorum-sensing system, little is known about the mechanism of AgrC activation. By co-immunoprecipitation analysis and intermolecular complementation of receptor mutants, we showed that AgrC forms ligand-independent dimers that undergo trans-autophosphorylation upon interaction with AIP. Remarkably, addition of specific AIPs to AgrC mutant dimers with only one functional sensor domain caused symmetric activation of either kinase domain despite the sensor asymmetry. Furthermore, mutant dimers involving one constitutive protomer demonstrated ligand-independent activity, irrespective of which protomer was kinase deficient. These results demonstrate that signalling through either individual AgrC protomer causes symmetric activation of both kinase domains. We suggest that such signalling across the dimer interface may be an important mechanism for dimeric quorum-sensing receptors to rapidly elicit a response upon signal detection.
Symmetric signalling within asymmetric dimers of the Staphylococcus aureus receptor histidine kinase AgrC.

BACKGROUND RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments.
RESULTS We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene.
CONCLUSIONS RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome