NET1

The cytoplasmic tyrosine kinase Src supports many phenotypes in cancer cells, including proliferation, migration and invasion, survival, and metastasis. We have previously shown that Src promotes cytoplasmic localization of the RhoGEF Net1, where it stimulates RhoA activation, breast cancer cell motility, and extracellular matrix invasion. In the present work, we show that the Net1 expression in human breast tumors correlates with Src phosphorylation on its activating site Y419. We also show in human breast cancer cell lines that endogenous Net1 and Src interact, and that Net1 expression is required for full Src activation. Net1 must localize to the cytosol to promote Src activation, but surprisingly, the catalytic activity of Net1 toward Rho GTPases is not necessary for Src activation. Instead, Net1 requires interaction with the scaffolding protein Dlg1. Dlg1 knockdown prevents Src activation by Net1 and precludes interaction between Net1 and Src. Moreover, Net1 knockdown cooperates with small molecule inhibition of Src to inhibit breast cancer cell motility and extracellular matrix invasion. These data show a previously unrecognized relationship between Net1 and Src in human breast tumors and breast cancer cell lines, and suggest that therapeutic targeting of Net1 may be of benefit in breast cancers with elevated Src activity.

Neuroepithelial transforming gene 1 (NET1) is a RhoA subfamily-specific guanine/nucleotide-exchange factor that exhibits critical roles in diverse biological processes. However, the functions in mouse preimplantation embryonic development have not yet determined. In the present study we demonstrated that NET1 is a key factor in the outcome of early mouse embryonic development. Immunofluorescence detection showed that NET1 is principally localized to the nucleus during mouse pre-implantation embryonic development. Silencing Net1 at the zygote stage using a specific siRNA impaired the developmental competence of early mouse embryos, and Net1-knockdown (Net1-KD) induced mitotic spindle-assembly defects and chromosomal alignment abnormalities at the first embryonic cleavage. In addition, reduced NET1 exacerbated reactive oxygen species production and DNA lesions in two-cell stage embryos, further augmenting cellular apoptosis in the preimplantation blastocyst. In summary, our data display key roles for NET1 in mitotic spindle assembly, oxidative stress, and DNA damage during early mouse embryonic development.

COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients’ proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.

A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.

Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.

The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.