The importance of serum LMAN2 level in septic shock and prognosis prediction in sepsis patients

LMAN2 interacts with HEATR3 to expedite HER2-positive breast cancer advancement and inflammation and Akt/ERK/NF-κB signaling

The paper aimed to reveal the impacts and the possible mechanism of action of lectin mannose-binding 2 protein (LMAN2) in HER2-positive breast cancer (BC). The expression, prognostic potential of LMAN2, and the correlation between LMAN2 and HEAT repeat containing 3 (HEATR3) in BC were analyzed in TCGA database. Intact, Mentha, and BioGrid databases predicted LMAN2-HEATR3 interactions. Reverse transcription-quantitative PCR and Western blot examined LMAN2 expression. Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine staining, wound healing, and transwell assays, respectively, detected the aggressive cellular biological behaviors including proliferation, migration, and invasion. Western blot analyzed the expression of matrix metalloproteinases, HEATR3, and protein kinase B (Akt)/extracellular signal-regulated kinase (ERK)/nuclear factor-kappaB (NF-κB) signaling-related proteins. Co-immunoprecipitation assay was used to prove the relationship of LMAN2 with HEATR3. Enzyme-linked immunosorbent assay detected inflammatory cytokine levels. LMAN2 was overexpressed in HER2-positive BC tissues and cells and indicated unfavorable prognosis of BC patients. LMAN2 knockdown suppressed HER2-positive BC cell proliferation, migration, and invasion. LMAN2 interacted with and had a positive correlation with HEATR3. HEATR3 up-regulation reversed the repressive role of LMAN2 interference in the progression of HER2-positive BC, Akt/ERK/NF-κB signaling, and inflammatory response. Altogether, LMAN2 silencing might exert anti-tumor and anti-inflammatory properties and inactivate Akt/ERK/NF-κB signaling in HER2-positive BC via binding to HEATR3.

Development of a Proteomic Workflow for the Identification of Heparan Sulphate Proteoglycan-Binding Substrates of ADAM17

Ectodomain shedding, which is the proteolytic release of transmembrane proteins from the cell surface, is crucial for cell-to-cell communication and other biological processes. The metalloproteinase ADAM17 mediates ectodomain shedding of over 50 transmembrane proteins ranging from cytokines and growth factors, such as TNF and EGFR ligands, to signalling receptors and adhesion molecules. Yet, the ADAM17 sheddome is only partly defined and biological functions of the protease have not been fully characterized. Some ADAM17 substrates (e.g., HB-EGF) are known to bind to heparan sulphate proteoglycans (HSPG), and we hypothesised that such substrates would be under-represented in traditional secretome analyses, due to their binding to cell surface or pericellular HSPGs. Thus, to identify novel HSPG-binding ADAM17 substrates, we developed a proteomic workflow that involves addition of heparin to solubilize HSPG-binding proteins from the cell layer, thereby allowing their mass spectrometry detection by heparin-treated secretome (HEP-SEC) analysis. Applying this methodology to murine embryonic fibroblasts stimulated with an ADAM17 activator enabled us to identify 47 transmembrane proteins that were shed in response to ADAM17 activation. This included known HSPG-binding ADAM17 substrates (i.e., HB-EGF, CX3CL1) and 14 novel HSPG-binding putative ADAM17 substrates. Two of these, MHC-I and IL1RL1, were validated as ADAM17 substrates by immunoblotting.

Exploring susceptibility and therapeutic targets for kidney stones through proteome-wide Mendelian randomization

Given the high recurrence rate of kidney stones, surgical lithotripsy and stone removal are not the ultimate treatments for kidney stones. There's an urgent need to explore the genetic mechanisms behind the susceptibility to kidney stones and to identify potential targets for prevention, to reduce the renal damage caused by recurrent stone formation. In this study, we screened 4548 circulating proteins using proteome-wide Mendelian Randomization (MR) to find proteins with a causal relationship to kidney stone risk. Additionally, proteome-wide association study (PWAS) and colocalization analysis were used to validate and prioritize candidate proteins. Moreover, downstream analyses including single-cell analysis, enrichment analysis, protein-protein interaction (PPI), and druggability analysis were conducted on the proteins causally related to kidney stones, to further explore the genetic mechanisms of susceptibility and the potential of proteins as drug targets. Ultimately, 22 target proteins associated with the risk of kidney stones were identified. Six plasma proteins (COLGALT1, CLMP, LECT1, ITIH1, CDHR3, CPLX2) were negatively correlated with kidney stone risk, while the genetic overexpression of 16 target proteins (GJA1, STOM, IRF9, F9, TMPRSS11D, ADH1B, SPINK13, CRYBB2, TNS2, DOCK9, OXSM, MST1, IL2, LMAN2, ITIH3, KLRF1) increased the risk of kidney stones. Based on the PWAS and colocalization analysis results, the 22 target proteins were classified into 3 tiers: IL2, CPLX2, and LMAN2 as tier 1 proteins with the most compelling evidence, MST1, ITIH1, and ITIH3 as tier 2 proteins, and the rest as tier 3 proteins. Enrichment analysis and PPI showed that target proteins mainly affect the occurrence of kidney stones through leukocyte activation and cell junction assembly. Druggability analysis suggested that IL2, MST1, and ITIH1 have potential as drug targets, and potential drugs were evaluated through molecular docking. In summary, this study employed multiple analytical methods to screen plasma proteins related to susceptibility to kidney stones, providing new insights into the genetic mechanisms of kidney stones and potential targets for treatment and prevention.

LncRNA HOTTIP as a diagnostic biomarker for acute respiratory distress syndrome in patients with sepsis and to predict the short-term clinical outcome: a case-control study

BACKGROUND

The present research aims to investigate the clinical diagnostic value of LncRNA HOXA distal transcript antisense RNA (HOTTIP) in acute respiratory distress syndrome (ARDS) of sepsis and its predictive significance for mortality.

METHODS

One hundred eighteenth patients with sepsis and 96 healthy individuals were enrolled. RT-qPCR to examine HOTTIP levels. The incidence of ARDS and death was recorded. The diagnostic significance of HOTTIP in sepsis ARDS was examined using ROC and logistic regression analysis. The correlation between HOTTIP and disease severity was evaluated using Pearson's coefficients. Kaplan-Meier analysis and COX regression were employed to examine the predictive significance of mortality. Validation of HOTTIP target miRNA by dual-luciferase assay.

RESULTS

HOTTIP was persistently up-regulated in patients with ARDS sepsis than in patients without ARDS patients (P < 0.05). HOTTIP was a risk factor for the development of ARDS, which could be diagnosed in ARDS patients from non-ARDS patients (AUC = 0.847). Both the SOFA score (r = 0.6793) and the APACHE II score (r = 0.6384) were positively correlated with the HOTTIP levels. Furthermore, serum HOTTIP was an independent predictor of short-term mortality (HR = 4.813. 95%CI: 1.471-15.750, P = 0.009) and noticeably predicted the occurrence of short-term death (log rank = 0.020). miR-574-5p, a target miRNA for HOTTIP, was reduced in patients with sepsis ARDS and negatively correlated with HOTTIP.

CONCLUSIONS

The presence of HOTTIP serves as a diagnostic biomarker for the occurrence of ARDS, exhibits correlation with disease severity, and provides predictive value of short-term mortality in sepsis patients. HOTTIP may be involved in ARDS progression by targeting miR-574-5p.

The prognostic value of C-reactive protein to albumin ratio in patients with sepsis: a systematic review and meta-analysis

OBJECTIVE

This study aimed to determine whether the C-reactive protein-to-albumin ratio (CAR) can serve as a prognostic marker in patients with sepsis.

METHODS

Chinese and English databases were searched to retrieve the included literature. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) with their 95% confidence interval (CI) were calculated using the bivariate model. Moreover, the hazard ratio (HR) and 95% CI were calculated using the random effect model.

RESULTS

Nine articles comprising 3224 patients with sepsis were included in the meta-analysis. The pooled SEN was 0.73 (95% CI 0.65-0.80), the pooled SPE was 0.78 (95% CI 0.69-0.84), the pooled PLR was 3.29 (95% CI 2.15-5.03), the pooled NLR was 0.35 (95% CI 0.24-0.49), and the pooled DOR was 9.50 (95% CI 4.38-20.59). The AUC under the SROC was 0.82 (95% CI 0.78-0.85) for the prognostic meta-analysis. The pooled HR was 1.10 (95% CI 1.02-1.18).

CONCLUSIONS

This meta-analysis suggests that a high CAR level is associated with increased mortality and a poor prognosis.