The secretion of α-hemolysin by uropathogenic Escherichia coli (UPEC) is commonly associated with the severity of urinary tract infections, which makes it a predictor of poor prognosis among patients. Accordingly, this toxin has become a target for diagnostic tests and therapeutic interventions. However, there are several obstacles associated with the process of α-hemolysin purification, therefore limiting its utilization in scientific investigations. In order to overcome the problems associated with α-hemolysin expression, after in silico prediction, a 20.48 kDa soluble α-hemolysin recombinant denoted rHlyA was constructed. This recombinant is composed by a 182 amino acid sequence localized in the aa542–723 region of the toxin molecule. The antigenic determinants of the rHlyA were estimated by bioinformatics analysis taking into consideration the tertiary form of the toxin, epitope analysis tools, and solubility inference. The results indicated that rHlyA has three antigenic domains localized in the aa555–565, aa600–610, and aa674–717 regions. Functional investigation of rHlyA demonstrated that it has hemolytic activity against sheep red cells, but no cytotoxic effect against epithelial bladder cells. In summary, the results obtained in this study indicate that rHlyA is a soluble recombinant protein that can be used as a tool in studies that aim to understand the mechanisms involved in the hemolytic and cytotoxic activities of α-hemolysin produced by UPEC. In addition, rHlyA can be applied to generate monoclonal and/or polyclonal antibodies that can be utilized in the development of diagnostic tests and therapeutic interventions.

The Y1 mouse adrenocortical carcinoma cell line presents amplification of the KRas oncogene and high-basal levels of KRAS-GTP mediated by the GEF SOS. In this research, we developed a dynamic model based on ordinary differential equations of the KRAS-GTP activation mediated by SOS in Y1 cells, which showed that SOS only is not sufficient to reach the high-basal levels of KRAS-GTP experimentally observed for this cell line. Interestingly, a modification in this system, which added another GEF in the model, made the model reach the expected levels of KRAS activation, leading to the hypothesis that there was a missing element in this system. To find this missing element, a PCR panel of RasGEFs was performed and the GEF Rasgrp4 was found highly expressed in parental Y1 cell lines, indicating that this was the missing element in the system. Finally, tumor growth assays in Balb/c-NUDE mice with the Y1 cell versus RASGRP4 CRISPR depleted Y1 cells, showed reduced tumor growth and frequency for the RASGRP4 depleted cells.

The Y1 mouse adrenocortical tumor cell line is a classic model for cancer studies and ACTH-mediated steroidogenesis. The KRAS gene amplification and SOS-mediated activation sustain elevated ERK levels, which drive the tumor phenotype of this cell line. In addition, the mitogen FGF2 induces atypical cytotoxic stress and consequent cell-death rather than proliferation in this system, imposing a strong perturbation on KRAS-ERK signaling dynamics. Recent research from our group revealed that SOS is not solely responsible for KRAS activation in the Y1 cells, indicating a more complex regulatory landscape of GEFs and GAPs. This motivated the generation of a comprehensive time-resolved proteome (DIA) and TMT-phosphoproteome (DDA) in a 0 to 30-minute time course under FGF2 stimulation or combined with SOS inhibitor, with the presence or absence of FBS stimulation, in technical triplicate using Q-Exactive™ and Thermo Lumos™ mass spectrometers, respectively. This dataset can enable latent mechanisms between the life and death switch caused by FGF2 signaling and the alternate stress response caused by impaired SOS-dependent activation. This analysis yielded 14,512 phosphosites and 8,919 protein groups, where 10,471 phosphosites and 111 protein groups showed differential abundance with statistical significance (limma, FDR < 0.05%). This dataset provides a versatile resource for downstream applications, which includes dynamic modeling such as ordinary differential equation–based frameworks, for which temporal datasets remain scarce. The data contained in this preprint will be available on JPOST Repo after the publication.