Many features of cell behavior are regulated by Rho family GTPases, but the most profound effects of these proteins are on the actin cytoskeleton and it was these that first drew attention to this family of signaling proteins. Focusing on Rho and Rac, we will discuss how their effectors regulate the actin cytoskeleton. We will describe how the activity of Rho proteins is regulated downstream from growth factor receptors and cell adhesion molecules by guanine nucleotide exchange factors and GTPase activating proteins. Additionally, we will discuss how there is signaling crosstalk between family members and how various bacterial pathogens have developed strategies to manipulate Rho protein activity so as to enhance their own survival.
BACKGROUND T-cell large granular lymphocytic leukemia is a rare lymphoproliferative disorder characterized by the expansion of clonal CD3+CD8+ cytotoxic T lymphocytes (CTLs) and often associated with autoimmune disorders and immune-mediated cytopenias. METHODS We used next-generation exome sequencing to identify somatic mutations in CTLs from an index patient with large granular lymphocytic leukemia. Targeted resequencing was performed in a well-characterized cohort of 76 patients with this disorder, characterized by clonal T-cell–receptor rearrangements and increased numbers of large granular lymphocytes. RESULTS Mutations in the signal transducer and activator of transcription 3 gene (STAT3) were found in 31 of 77 patients (40%) with large granular lymphocytic leukemia. Among these 31 patients, recurrent mutational hot spots included Y640F in 13 (17%), D661V in 7 (9%), D661Y in 7 (9%), and N647I in 3 (4%). All mutations were located in exon 21, encoding the Src homology 2 (SH2) domain, which mediates the dimerization and activation of STAT protein. The amino acid changes resulted in a more hydrophobic protein surface and were associated with phosphorylation of STAT3 and its localization in the nucleus. In vitro functional studies showed that the Y640F and D661V mutations increased the transcriptional activity of STAT3. In the affected patients, downstream target genes of the STAT3 pathway (IFNGR2, BCL2L1, and JAK2) were up-regulated. Patients with STAT3 mutations presented more often with neutropenia and rheumatoid arthritis than did patients without these mutations. CONCLUSIONS The SH2 dimerization and activation domain of STAT3 is frequently mutated in patients with large granular lymphocytic leukemia; these findings suggest that aberrant STAT3 signaling underlies the pathogenesis of this disease. (Funded by the Academy of Finland and others.)
Rational design of multi-targeted drug combinations is a promising strategy to tackle the drug resistance problem for many complex disorders. A drug combination is usually classified as synergistic or antagonistic, depending on the deviation of the observed combination response from the expected effect calculated based on a reference model of non-interaction. The existing reference models were proposed originally for low-throughput drug combination experiments, which make the model assumptions often incompatible with the complex drug interaction patterns across various dose pairs that are typically observed in large-scale dose–response matrix experiments. To address these limitations, we proposed a novel reference model, named zero interaction potency (ZIP), which captures the drug interaction relationships by comparing the change in the potency of the dose–response curves between individual drugs and their combinations. We utilized a delta score to quantify the deviation from the expectation of zero interaction, and proved that a delta score value of zero implies both probabilistic independence and dose additivity. Using data from a large-scale anticancer drug combination experiment, we demonstrated empirically how the ZIP scoring approach captures the experimentally confirmed drug synergy while keeping the false positive rate at a low level. Further, rather than relying on a single parameter to assess drug interaction, we proposed the use of an interaction landscape over the full dose–response matrix to identify and quantify synergistic and antagonistic dose regions. The interaction landscape offers an increased power to differentiate between various classes of drug combinations, and may therefore provide an improved means for understanding their mechanisms of action toward clinical translation.
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