The scaffold RhoGAP protein ARHGAP8/BPGAP1 synchronizes Rac and Rho signaling to facilitate cell migration.

Rho GTPases regulate cell morphogenesis and motility under the tight control of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs). However, the underlying mechanism(s) that coordinate their spatiotemporal activities, whether separately or together, remain unclear. We show that a prometastatic RhoGAP, ARHGAP8/BPGAP1, binds to inactive Rac1 and localizes to lamellipodia. BPGAP1 recruits the RacGEF Vav1 under epidermal growth factor (EGF) stimulation and activates Rac1, leading to polarized cell motility, spreading, invadopodium formation, and cell extravasation and promotes cancer cell migration. Importantly, BPGAP1 down-regulates local RhoA activity, which influences Rac1 binding to BPGAP1 and its subsequent activation by Vav1. Our results highlight the importance of BPGAP1 in recruiting Vav1 and Rac1 to promote Rac1 activation for cell motility. BPGAP1 also serves to control the timing of Rac1 activation with RhoA inactivation via its RhoGAP activity. BPGAP1, therefore, acts as a dual-function scaffold that recruits Vav1 to activate Rac1 while inactivating RhoA to synchronize both Rho and Rac signaling in cell motility. As epidermal growth factor receptor (EGFR), Vav1, RhoA, Rac1, and BPGAP1 are all associated with cancer metastasis, BPGAP1 could provide a crucial checkpoint for the EGFR-BPGAP1-Vav1-Rac1-RhoA signaling axis for cancer intervention.

Supervised Machine Learning Enables Geospatial Microbial Provenance.

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

Spatial arrangement of LD motif-interacting residues on focal adhesion targeting domain of Focal Adhesion Kinase determine domain-motif interaction affinity and specificity.

BACKGROUND: Leucine rich Aspartate motifs (LD motifs) are molecular recognition motifs on Paxillin that recognize LD-motif binding domains (LDBD) of a number of focal adhesion proteins in order to carry out downstream signaling and actin cytoskeleton remodeling. In this study, we identified structural features within LDBDs that influence their binding affinity with Paxillin LD motifs. METHODS: Various point mutants of focal adhesion targeting (FAT) domain of Focal Adhesion Kinase (FAK) were created by moving a key Lysine residue two and three helical turns in order to match the unique conformations as observed in LDBDs of two other focal adhesion proteins, Vinculin and CCM3. RESULTS: This led to identify a mutant of FAT domain of FAK, named as FAT(NV) (Asn992 of FAT domain was replaced by Val), with remarkable high affinity for LD1 (Kd = 1.5 µM vs no-binding with wild type) and LD2 peptides (Kd = 7.2 µM vs 63 µM with wild type). Consistently, the focal adhesions of MCF7 cells expressing FAK(NV) were highly stable (turnover rate = 1.25 × 10-5 µm2/s) as compared to wild type FAK transfected cells (turnover rate = 1.5 × 10-3 µm2/s). CONCLUSIONS: We observed that the relative disposition of key LD binding amino-acids at LDBD surface, hydrophobic burial of long Leucine side chains of LD-motifs and complementarity of charged surfaces are the key factors determining the binding affinities of LD motifs with LDBDs. GENERAL SIGNIFICANCE: Our study will help in protein engineering of FAT domain of FAK by modulating FAK-LD motif interactions which have implications in cellular focal adhesions and cell migration.