Small-Volume Flow Cytometry-Based Multiplex Analysis of the Activity of Small GTPases

A model for predicting bacteremia species based on host immune response

Introduction

Clinicians encounter significant challenges in quickly and accurately identifying the bacterial species responsible for patient bacteremia and in selecting appropriate antibiotics for timely treatment. This study introduces a novel approach that combines immune response data from routine blood counts with assessments of immune cell activation, specifically through quantitative measurements of Rho family GTPase activity. The combined data were used to develop a machine-learning model capable of distinguishing specific classes of bacteria and their associations.

Methods

We aimed to determine whether different classes of bacteria elicit distinct patterns of host immune responses, as indicated by quantitative differences in leukocyte populations from routine complete blood counts with differential. Concurrently, we conducted quantitative measurements of activated Rac1 (Rac1•GTP) levels using a novel 'G-Trap assay' we developed. With the G-Trap, we measured Rac1•GTP in peripheral blood monocytes (PBMC) and polymorphonuclear (PMN) cells from blood samples collected from 28 culture-positive patients and over 80 non-infected patients used as controls.

Results

Our findings indicated that 18 of the 28 patients with bacteremia showed an increase of ≥ 3-fold in Rac1•GTP levels compared to the controls. The remaining ten patients with bacteremia exhibited either neutrophilia or pancytopenia and displayed normal to below-normal Rac1 GTPase activity, which is consistent with bacteria-induced immunosuppression. To analyze the data, we employed partial least squares discriminant analysis (PLS-DA), a supervised method that optimizes group separation and aids in building a novel machine-learning model for pathogen identification.

Discussion

The results demonstrated that PLS-DA effectively differentiates between specific pathogen groups, and external validation confirmed the predictive model's utility. Given that bacterial culture confirmation may take several days, our study underscores the potential of combining routine assays with a machine-learning model as a valuable clinical decision-support tool. This approach could enable prompt and accurate treatment on the same day that patients present to the clinic.

FHL1 Overexpression as A Inhibitor of Lung Cancer Cell Invasion via Increasing RhoGDIß mRNA Expression

Objective

Four and a half Lin-11, Isl-1, Mac-3 (LIM) protein 1 (FHL1) is one of the FHL protein family, which is regarded as a tumor suppressor in the multiple malignant tumors. In this study, we aimed to explore the regulatory effects and mechanisms of FHL1 on lung cancer cell invasion.

Materials and Methods

In this experimental study, bioinformatics analysis of FHL1 transcripts in human lung adenocarcinomas of TCGA database was performed. Quantitative real-time polymerase chain reaction (PCR) was performed to detect FHL1 mRNA expression in 15 paired human lung cancer tissues and their adjacent normal lung tissues, or lung cancer cell lines (A549 and H1299) in comparison with human bronchial epithelial cell line (Beas- 2B). Moreover, western blot was used to analyze FHL1 and rho GDP-dissociation inhibitor beta (RhoGDIβ) protein expression in the indicated cell lines. Also, transwell assays were employed to measure the migrated, and invaded of indicated cell lines.

Results

FHL1 transcripts were downregulated in the human lung adenocarcinoma. The impaired FHL1 transcripts were positively correlated with advanced tumor node metastasis (TNM) stage. Moreover, as compared to the adjacent normal lung tissues, FHL1 mRNA was low expressed in 15 paired human lung cancer tissues than their adjacent normal lung tissues. Besides, FHL1 mRNA and protein expression were also reduced in H1299 and A549 cell lines in comparison with Beas-2B cell line. Overexpressed FHL1 protein inhibited the invasive ability of H1299 and A549 cell lines. Mechanically, FHL1 protein overexpression increased the RhoGDIβ protein and mRNA abundance, while knockdown of RhoGDIβ protein, completely restored the invasion ability of A549 (Flag-FHL1) cell line.

Conclusion

Our findings indicated that as a key FHL1 downstream regulator, RhoGDIβ is in charge of FHL1 inhibiting lung cancer cell invasion abilities, providing a critical insight into understanding the role of FHL1 for lung cancer development.

Influence of integrins on thrombus formation: a road leading to the unravelling of DVT

Integrins are a group of transmembrane glycoprotein receptors that are responsible for platelet activation through bidirectional signalling. These receptors have left their footprints in various cellular events and have intrigued many groups of scientists that have led to some significant discoveries. A lot of the recent understanding of haemostasis has been possible due to the integrins filling the gaps in between several cellular mechanism. Apart from this, other important functions carried out by integrins are growth and maturation of cardiomyocytes, mechano-transduction, and interaction with actin cytoskeleton. The signalling cascade for integrin activation involves certain intracellular interacting proteins, which initiates the step-by-step activation procedure through 'inside-out' signalling. The signalling cascade gets activated through 'outside-in' signalling with the involvement of agonists such as ADP, Fibronectin, Vitronectin, and so on. This is a crucial step for the downstream processes of platelet spreading, followed by aggregation, clot progression and finally thrombus formation. Researchers throughout the world have shown direct relation of integrins with CVD and cardiac remodelling. The present review aims to summarize the information available so far on the involvement of integrins in thrombosis and its relationship to DVT. This information could be a bedrock of hidden answers to several questions on pathogenesis of deep vein thrombosis.