Experimental Biology and Medicine最新文献

In recent years, the relationship between the immunosuppressive niche of the bone marrow and therapy resistance in acute myeloid leukemia (AML) has become a research focus. The abnormal number and function of immunosuppressive cells, including regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs), along with the dysfunction and exhaustion of immunological effector cells, including cytotoxic T lymphocytes (CTLs), dendritic cells (DCs) and natural killer cells (NKs), can induce immune escape of leukemia cells and are closely linked to therapy resistance in leukemia. This article reviews the research progress on the relationship between immune cells in the marrow microenvironment and chemoresistance in AML, aiming to provide new ideas for the immunotherapy of AML.

Cancer progression is orchestrated by the accrual of mutations in driver genes, which endow malignant cells with a selective proliferative advantage. Identifying cancer driver genes is crucial for elucidating the molecular mechanisms of cancer, advancing targeted therapies, and uncovering novel biomarkers. Based on integrated analysis of Multi-Omics data and Network models, we present MONet, a novel cancer driver gene identification algorithm. Our method utilizes two graph neural network algorithms on protein-protein interaction (PPI) networks to extract feature vector representations for each gene. These feature vectors are subsequently concatenated and fed into a multi-layer perceptron model (MLP) to perform semi-supervised identification of cancer driver genes. For each mutated gene, MONet assigns the probability of being potential driver, with genes identified in at least two PPI networks selected as candidate driver genes. When applied to pan-cancer datasets, MONet demonstrated robustness across various PPI networks, outperforming baseline models in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve. Notably, MONet identified 37 novel driver genes that were missed by other methods, including 29 genes such as APOBEC2, GDNF, and PRELP, which are corroborated by existing literature, underscoring their critical roles in cancer development and progression. Through the MONet framework, we successfully identified known and novel candidate cancer driver genes, providing biologically meaningful insights into cancer mechanisms.

The current meta-analysis was performed to analyze the efficacy and safety of platelet-rich plasma (PRP) as an epidural injectate, in comparison with steroids in the management of radiculopathy due to lumbar disc disease (LDD). We conducted independent and duplicate searches of the electronic databases (PubMed, Embase and Cochrane Library) in March 2024 to identify randomized controlled trials (RCTs) analyzing the efficacy of epidural PRP for pain relief in the management of LDD. Animal or in vitro studies, clinical studies without a comparator group, and retrospective or non-randomised clinical studies were excluded. Diverse post-intervention pain scores [visual analog score (VAS)] and functional scores [Oswestry Disability Index (ODI), SF-36], as reported in the reviewed studies, were evaluated. Statistical analysis was performed using STATA 17 software. 5 RCTs including 310 patients (PRP/Steroids = 153/157) were included in the analysis. The included studies compared the efficacy and safety of epidural PRP and steroids at various time-points including 1, 3, 6, 12, 24, and 48 weeks. Epidural PRP injection was found to offer comparable pain relief (VAS; WMD = -0.09, 95% CI [-0.66, 0.47], p = 0.641; I² = 96.72%, p < 0.001), functional improvement (ODI; WMD = 0.72, 95% CI [-6.81, 8.25], p = 0.524; I² = 98.73%, p < 0.001), and overall health improvement (SF-36; WMD = 1.01, 95% CI [-1.14, 3.17], p = 0.224; I² = 0.0%, p = 0.36) as epidural steroid injection (ESI) at all the observed time points in the included studies without any increase in adverse events or complications. Epidural administration of PRP offers comparable benefit as epidural steroid injection (ESI) in the management of radiculopathy due to LDD. The safety profile of the epidural PRP is also similar to ESI.

Bone fractures and bone loss represent significant global health challenges, with their incidence rising due to an aging population. Despite autologous bone grafts remain the gold standard for treatment, challenges such as limited bone availability, immune reactions, and the risk of infectious disease transmission have driven the search for alternative cell-based therapies for bone regeneration. Stem cells derived from oral tissues and umbilical cord mesenchymal stem cells (MSCs) have shown potential in both preclinical and clinical studies for bone tissue regeneration. However, their limited differentiation capacity and wound healing abilities necessitate the exploration of alternative cell sources. In this study, we generated induced pluripotent stem cells (iPSCs) using a safe, nonviral and mRNA-based approach from human periodontal ligament fibroblasts (PDLF), an easily accessible cell source. These iPSCs were subsequently differentiated into MSCs, referred to as induced MSCs (iMSCs). The resulting iMSCs were homogeneous, highly proliferative, and possessed anti-inflammatory properties, suggesting their potential as a superior alternative to traditional MSCs for regenerative therapy. These iMSCs demonstrated trilineage differentiation potential, giving rise to osteocytes, chondrocytes, and adipocytes. The iMSC-derived osteocytes (iOSTs) were homogeneous, patient-specific and showed excellent attachment and growth on commercial collagen-based membranes, highlighting their suitability for bone tissue regeneration applications. Given their promising characteristics compared to traditional MSCs, PDLF-derived iMSCs are strong candidates for future clinical studies in bone regeneration and other regenerative dental therapies.

Investigations on some innate immunity proteins can yield misleading information, as investigators often rely on static measurements and assume a direct correlation to function. As protein function is often not directly proportional to protein abundance, and mechanistic pathways are interconnected and under constant feedback regulatory control, functional analysis is required. In this study, we used functional mass spectrometry to measure anti-protease and complement activity in plasma obtained from coronavirus disease 2019 (COVID-19) patients. Our data suggests that within 48 h of hospital admission, COVID-19 patients undergo a protease storm with significantly elevated neutrophil elastase (p < 0.001) and lymphocyte granzyme B (p < 0.01), while, anti-protease activity is significantly increased, including alpha-1 antitrypsin (AAT; p < 0.001) and alpha-1-antichymotrypsin (ACT; p < 0.001). Concurrently, the ratio of C3a to C3beta activity significantly decreased with increasing COVID-19 severity, suggesting more complement activation (Mild COVID-19 p < 0.05; Severe COVID-19 p < 0.001). Activity levels of AAT, ACT and C3a/C3beta remained unchanged over 10 hospital days. Our data suggests that COVID-19 is associated with both a protease storm and complement activation, with the former somewhat balanced with increased anti-protease activity. Evaluation of the AAT/ACT ratio and C3a/C3beta ratio indicated that COVID-19 severity is associated with both neutrophil elastase neutralization and complement activation.

Periostin and osteopontin are matricellular proteins abundantly expressed in bone fracture callus. Null mutation of either the periostin (Postn) gene or the osteopontin (Spp1) gene can impair bone fracture healing. However, the cell and molecular pathways affected by loss of POSTN or SPP1 which lead to impaired fracture healing are not well understood. To identify potential pathways, a detailed radiological, histological, and immunohistochemical analysis of femur fracture healing in Postn-null (PostnKO), Spp1-null (Spp1KO), and normal (WT) mice was performed. Apparent changes in specific protein levels identified by immunohistochemistry were confirmed by mRNA quantitation. Comparisons between the PostnKO and Spp1KO fracture calluses were confounded by interactions between the two genes; loss of Postn reduced Spp1 expression and loss of Spp1 reduced Postn expression. Consequently, alterations in fracture healing between mice heterozygous for the Postn-null allele (PostnHET) as well as the PostnKO and Spp1KO mice were similar. Calluses from PostnHET, PostnKO, and Spp1KO mice all had dysmorphic chondro-osseous junctions and reduced numbers of osteoclasts. The dysmorphic chondro-osseous junctions in the PostnHET, PostnKO, and Spp1KO calluses were associated with reduced numbers of MMP-13 expressing hypertrophic chondrocytes, consistent with delayed cartilage resolution. Unlike collagen X expressing callus chondrocytes, chondrocytes only expressed MMP-13 when localized to the chondro-osseous junction or after traversing the chondro-osseous junction. Cyclooxygenase-2 (COX-2) expression also appeared to be reduced in osteoclasts from the PostnHET, PostnKO, and Spp1KO calluses, including in those osteoclasts localized at the chondro-osseous junction. The results indicate that POSTN and SPP1 are necessary for normal chondro-osseous junction formation and that signaling from the chondro-osseous junction, possibly from COX-2 expressing osteoclasts, regulates callus vasculogenesis and chondrocyte hypertrophy necessary for endochondral ossification during fracture healing.

FPRL2 has been shown to be associated with a variety of tumours but has not been well studied in breast cancer. In this study, We combine molecular biology techniques with bioinformatics to analyze the role of FPRL2 in breast cancer and adriamycin resistance. By utilizing bioinformatics, we mine TCGA and GEO public databases to assess FPRL2 expression in breast cancer patients and its correlation with patient prognosis. Additionally, we employ the DepMap tool to probe the CCLE database, examining the relationship between FPRL2 gene effects and adriamycin sensitivity. Chemosensitivity of Adriamycin in breast cancer cells was tested by CCK-8 method. The apoptosis of breast cancer cells was determined by flow cytometry assay. Expression of p-ERK5 and p-AKT was determined by Western blot assay. Our results indicate that the expression level of FPRL2 in tumor tissues of breast cancer patients is significantly higher than that in normal tissues, and it correlates with poor prognosis in patients. Furthermore, the expression level of FPRL2 in tumor tissues of adriamycin-resistant breast cancer patients is also significantly higher than that in adriamycin-sensitive patients. The IC₅₀ (Inhibitory Concentration 50). Of Adriamycin was significantly lower in FPRL2 silenced cells than those control cells. The apoptosis was markedly increased in FPRL2-silenced cells. p-ERK5 and p-AKT in breast cancer cells was significantly reduced after FPRL2 knocked down. In Conclusion, FPRL2 mediates Adriamycin resistance in breast cancer cells, and knockdown of FPRL2 increased apoptosis and decreased Adriamycin resistance in breast cancer cells.

[This retracts the article DOI: 10.1258/ebm.2011.010361.].

Blood-based biomarkers for motor neuron disease are needed for better diagnosis, progression prediction, and clinical trial monitoring. We used whole blood-derived total RNA and performed whole transcriptome analysis to compare the gene expression profiles in (motor neurone disease) MND patients to the control subjects. We compared 42 MND patients to 42 aged and sex-matched healthy controls and described the whole transcriptome profile characteristic for MND. In addition to the formal differential analysis, we performed functional annotation of the genomics data and identified the molecular pathways that are differentially regulated in MND patients. We identified 12,972 genes differentially expressed in the blood of MND patients compared to age and sex-matched controls. Functional genomic annotation identified activation of the pathways related to neurodegeneration, RNA transcription, RNA splicing and extracellular matrix reorganisation. Blood-based whole transcriptomic analysis can reliably differentiate MND patients from controls and can provide useful information for the clinical management of the disease and clinical trials.

Screening tests for disease have their performance measured through sensitivity and specificity, which inform how well the test can discriminate between those with and without the condition. Typically, high values for sensitivity and specificity are desired. These two measures of performance are unaffected by the outcome prevalence of the disease in the population. Research projects into the health of the American Indian frequently develop Machine learning algorithms as predictors of conditions in this population. In essence, these models serve as in silico screening tests for disease. A screening test's sensitivity and specificity values, typically determined during the development of the test, inform on the performance at the population level and are not affected by the prevalence of disease. A screening test's positive predictive value (PPV) is susceptible to the prevalence of the outcome. As the number of artificial intelligence and machine learning models flourish to predict disease outcomes, it is crucial to understand if the PPV values for these in silico methods suffer as traditional screening tests in a low prevalence outcome environment. The Strong Heart Study (SHS) is an epidemiological study of the American Indian and has been utilized in predictive models for health outcomes. We used data from the SHS focusing on the samples taken during Phases V and VI. Logistic Regression, Artificial Neural Network, and Random Forest were utilized as in silico screening tests within the SHS group. Their sensitivity, specificity, and PPV performance were assessed with health outcomes of varying prevalence within the SHS subjects. Although sensitivity and specificity remained high in these in silico screening tests, the PPVs' values declined as the outcome's prevalence became rare. Machine learning models used as in silico screening tests are subject to the same drawbacks as traditional screening tests when the outcome to be predicted is of low prevalence.