Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations.

Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 µL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes.

Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry.

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Antibacterial metabolites from the beetle-associated fungus Penicillium chrysogenum.

The antibacterial secondary metabolites of the fungus Penicillium chrysogenum associated with the beetle Aspongopus chinensis were investigated through chromatographic fractionation methods of ethyl acetate extracts of the fungal cultures. Five compounds were isolated, and their structures were determined as emodin, 4-(methoxymethyl)benzoic acid, isoochracinic acid, secalonic acid D, and dicerandrol A using mass spectroscopy and nuclear magnetic resonance spectroscopic analyses. Emodin exhibited strong antimicrobial activity, especially against Staphylococcus aureus even when growing on cooked pork, with a minimal inhibitory concentration (MIC) of 6.3 µg/mL. Dimeric tetrahydroxanthones, such as secalonic acid D and dicerandrol A, also exhibited potent activity, with MIC values ranging from 9.5 to 28.5 µg/mL. In summary, P. chrysogenum was isolated as a symbiotic fungus of the beetle A. chinensis for the first time and this strain could generate antibacterial secondary metabolites, which could potently inhibit gram-positive bacteria growth in vitro.

Janus Micro/Nanorobots in Biomedical Applications.

Janus particles possess two or more distinct domains that are anisotropic in composition or surface features. They integrate different or even incompatible properties within a single particle, making them possible to perform diverse functions and multiple tasks simultaneously. Advances in micro/nanorobots demonstrate that they can effectively convert diverse energy sources into movement and reach target locations with precision. Integration of Janus structure with micro/nanobots is emerging as a promising tool for biomedical applications. In this review, the fabrication and energy sources of Janus micro/nanorobots are briefly introduced. After that, the recent progress of Janus micro/nanorobots for biomedicine, with a special focus on their applications for cargo delivery, bioimaging, biosensing, surgery, and therapy are presented and discussed. The application of Janus micro/nanorobots in biomedicine still faces serious challenges from fabrication, engines, biocompatibility, and biodegradation for their widespread in clinical situations. Nevertheless, a few emerging materials and approaches offer potential solutions to these problems.

Silencing of Long Noncoding RNA HLA Complex P5 (HCP5) Suppresses Glioma Progression through the HCP5-miR-205-Vascular Endothelial Growth Factor A Feedback Loop.

Long noncoding RNA (lncRNA) HLA complex P5 (HCP5) is correlated with multiple diseases, especially cancers. However, it remains to be further studied whether HCP5 is involved in the malignant behaviors of gliomas. This study is aimed at investigating the role and regulation mechanisms of HCP5 in gliomas. HCP5 expression in glioma tumor tissues and its association with glioma patients' survival were analyzed based on RNA-sequencing data. The expression of HCP5 was also examined in glioma cells. Then, HCP5 was downregulated in U251 cells and/or primary glioblastoma cells to explore its effects on cell proliferation and migration. The influence of HCP5 downregulation on tumor growth was confirmed in xenograft mice. About the mechanism, we investigated whether HCP5 functioned via interacting with microRNA- (miR-) 205 and regulating vascular endothelial growth factor A (VEGF-A) expression in gliomas. Results showed that HCP5 upregulation was found in glioma tissues and cell lines. Patients with high HCP5 expression showed lower survival probability and shorter survival time. HCP5 downregulation inhibited cell proliferation and migration and mitigated tumor growth. miR-205 was downregulated in glioma cells. Knockdown of HCP5 led to miR-205 upregulation and VEGF-A downregulation. miR-205 overexpression exhibited the similar effects as HCP5 downregulation on cell viability and proliferation. And VEGF-A overexpression could reverse the effects of HCP5 downregulation on cell viability and proliferation, as well as tumor growth. In conclusion, HCP5 silencing suppressed glioma progression through the HCP5-miR-205-VEGF-A feedback loop.

Dual-modal nanoplatform integrated with smartphone for hierarchical diabetic detection.

On-site screening of diabetes and precise diagnosis of diabetic complications may provide a conduit for early intervention and disease burden reduction. However, stratified metabolic analysis needs designed materials for colorimetric detection of targeted biomarkers and direct metabolic fingerprinting of the native blood. Here, an advanced dual-modal nanoplatform is constructed based on PdPtAu alloys, which serve both as the nanoenzymes in colorimetric sensing for targeted metabolite quantitation and as matrix in laser desorption/ionization mass spectrometry for untargeted metabolic fingerprinting. The platform achieved rapid glucose quantitation toward point-of-care testing of 27 participants and identified diabetic retinopathy from diabetic population with a sensitivity and specificity of 84.6%. We further assessed the generalizability of the nanoplatform for real-case applications, through the captured digital images and computing resources equipped in smartphones. The results advance the design of material-based platforms for stratified metabolic analysis and display promise to fit in the current hierarchical medical system in practice.

KLF11 promotes the progression of glioma via regulating Holliday junction recognition protein.

Understanding the molecular mechanism of glioma is very important for the diagnosis and treatment of glioma. Recently, a new study illustrated that KLF11 could be a potential prognostic and diagnostic biomarker in glioma, but the critical role is not illustrated. In this study, we found that KLF11 was highly expressed in glioma cancer tissues and cells, and KLF11 high expression of glioblastoma (GBM) and lower-grade glioma (LGG) were correlated with poorer overall survival and disease-free survival percentages. KLF11 knockdown inhibited glioma cell proliferation and migration, while KLF11 overexpression enhanced cell proliferation and migration. In vivo, knockdown of KLF11 reduced the tumor size of glioma. With regard to the molecular regulatory mechanism, we clarified that the Holliday junction recognition protein (HJURP) was positively regulated by KLF11. Meanwhile, we demonstrated that HJURP knockdown also inhibited glioma carcinoma progression. Overexpression of HJURP rescued the suppressed proliferation and migration function of glioma cells with depletion of KLF11. Therefore, our study demonstrated the function of KLF11 in glioma and showed KLF11 and HJURP could be prognostic and diagnostic markers in glioma, which provides a new insight of glioma therapy.

Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring.

Chemotherapy is a primary cancer treatment strategy, the monitoring of which is critical to enhancing the survival rate and quality of life of cancer patients. However, current chemotherapy monitoring mainly relies on imaging tools with inefficient sensitivity and radiation invasiveness. Herein, we develop the bowl-shaped submicroreactor chip of Au-loaded 3-aminophenol formaldehyde resin (denoted as APF-bowl&Au) with a specifically designed structure and Au loading content. The obtained APF-bowl&Au, used as the matrix of laser desorption/ionization mass spectrometry (LDI MS), possesses an enhanced localized electromagnetic field for strengthened small metabolite detection. The APF-bowl&Au enables the extraction of serum metabolic fingerprints (SMFs), and machine learning of the SMFs achieves chemotherapy monitoring of ovarian cancer with area-under-the-curve (AUC) of 0.81-0.98. Furthermore, a serum metabolic biomarker panel is preliminarily identified, exhibiting gradual changes as the chemotherapy cycles proceed. This work provides insights into the development of nanochips and contributes to a universal detection platform for chemotherapy monitoring.

SPAG5 Is Involved in Human Gliomagenesis Through the Regulation of Cell Proliferation and Apoptosis.

BACKGROUND: Glioma is the most frequent malignant primary brain tumor in adults. OBJECTIVE: To explore the role of sperm-associated antigen 5 (SPAG5) in glioma. METHODS: The association between SPAG5 expression and clinical features was investigated based on The Cancer Genome Atlas (TCGA) datasets. The function of SPAG5 in glioma was analyzed using U87 and U251 cells. Knockdown glioma cells were constructed by shRNA interference. qRT-PCR and Western blotting were used to measure the expression of SPAG5 and Cadherin 2 (CDH2). Cell proliferation and apoptosis were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, caspase 3/7 assay, and high-content screening (HCS) proliferation analysis and colony formation assay. Transwell assays and wound-healing assays were used to investigate cell migration and invasion. RESULTS: The increased expression of SPAG5 was correlated with poor outcomes in glioma patients. Knocking down SPAG5 could inhibit the proliferation and colony formation and promoted the apoptosis of glioma cells. Knocking down SPAG5 could also inhibit cell migration and invasion and the expression of CDH2. Overexpression of CDH2 with SPAG5 depletion could restore the proliferation and inhibit the apoptosis of glioma cells, which also promoted cell migration and invasion. CONCLUSIONS: SPAG5 is a promising prognostic factor and potential therapeutic target for clinical intervention in glioma.

Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer.

Although mass spectrometry (MS) of metabolites has the potential to provide real-time monitoring of patient status for diagnostic purposes, the diagnostic application of MS is limited due to sample treatment and data quality/reproducibility. Here, the generation of a deep stabilizer for ultra-fast, label-free MS detection and the application of this method for serum metabolic diagnosis of coronary heart disease (CHD) are reported. Nanoparticle-assisted laser desorption/ionization-MS is used to achieve direct metabolic analysis of trace unprocessed serum in seconds. Furthermore, a deep stabilizer is constructed to map native MS results to high-quality results obtained by established methods. Finally, using the newly developed protocol and diagnosis variation characteristic surface to characterize sensitivity/specificity and variation, CHD is diagnosed with advanced accuracy in a high-throughput/speed manner. This work advances design of metabolic analysis tools for disease detection as it provides a direct label-free, ultra-fast, and stabilized platform for future protocol development in clinics.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer.

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

Diagnosis and prognosis of myocardial infarction on a plasmonic chip.

Cardiovascular diseases lead to 31.5% of deaths globally, and particularly myocardial infarction (MI) results in 7.4 million deaths per year. Diagnosis of MI and monitoring for prognostic use are critical for clinical management and biomedical research, which require advanced tools with accuracy and speed. Herein, we developed a plasmonic gold nano-island (pGold) chip assay for diagnosis and monitoring of MI. On-chip microarray analysis of serum biomarkers (e.g., cardiac troponin I) afforded up to 130-fold enhancement of near-infrared fluorescence for ultra-sensitive and quantitative detection within controlled periods, using 10 µL of serum only. The pGold chip assay achieved MI diagnostic sensitivity of 100% and specificity of 95.54%, superior to the standard chemiluminescence immunoassay in cardiovascular clinics. Further, we monitored biomarker concentrations regarding percutaneous coronary intervention for prognostic purpose. Our work demonstrated a designed approach using plasmonic materials for enhanced diagnosis and monitoring for prognostic use towards point-of-care testing.

Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation.

Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real-time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd-Au synthetic alloys are reported for mass-spectrometry-based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core-shell structure is designed using magnetic core particles to support Pd-Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application-driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future.

lncRNA AWPPH promotes the migration and invasion of glioma cells by activating the TGF-ß pathway.

The long noncoding RNA (lncRNA) AWPPH, also termed microRNA-4435-2HG, has been associated with the poor prognosis of patients with hepatocellular carcinoma (HCC), and has been demonstrated to promote the progression of HCC and bladder cancer. The present study aimed to investigate the role of lncRNA AWPPH in glioma. The expression levels of AWPPH in tumor tissues obtained from patients with glioma and in plasma samples obtained from patients with glioma and healthy controls were detected by reverse transcription-quantitative polymerase chain reaction. The plasma levels of transforming growth factor (TGF)-ß1 were measured by an enzyme-linked immunosorbent assay. An AWPPH expression vector was transfected into human glioma cell lines. Subsequently, cancer cell migration and invasion were assessed by Transwell migration and invasion assays, respectively. The expression of TGF-ß1 in the transfected-glioma cells was detected by western blot analysis. It was identified that AWPPH expression levels in tumor tissues were higher in patients with metastatic glioma; however, no significant differences in AWPPH expression were revealed between patients with different tumor sizes. The plasma levels of AWPPH were positively correlated with the plasma levels of TGF-ß1 in patients with glioma but not in healthy controls. In addition, AWPPH overexpression enhanced cancer cell migration and invasion, and upregulated TGF-ß1 expression. Treatment with TGF-ß1 demonstrated no significant effect on AWPPH expression; however, a TGF-ß inhibitor attenuated the effects of AWPPH overexpression on cell migration and invasion. Therefore, the present study proposed that AWPPH may promote the migration and invasion of glioma cells by activating the TGF-ß pathway.

A Translation-Activating Function of MIWI/piRNA during Mouse Spermiogenesis.

Spermiogenesis is a highly orchestrated developmental process during which chromatin condensation decouples transcription from translation. Spermiogenic mRNAs are transcribed earlier and stored in a translationally inert state until needed for translation; however, it remains largely unclear how such repressed mRNAs become activated during spermiogenesis. We previously reported that the MIWI/piRNA machinery is responsible for mRNA elimination during late spermiogenesis in preparation for spermatozoa production. Here we unexpectedly discover that the same machinery is also responsible for activating translation of a subset of spermiogenic mRNAs to coordinate with morphological transformation into spermatozoa. Such action requires specific base-pairing interactions of piRNAs with target mRNAs in their 3' UTRs, which activates translation through coupling with cis-acting AU-rich elements to nucleate the formation of a MIWI/piRNA/eIF3f/HuR super-complex in a developmental stage-specific manner. These findings reveal a critical role of the piRNA system in translation activation, which we show is functionally required for spermatid development.

MiR-205 suppresses epithelial-mesenchymal transition and inhibits tumor growth of human glioma through down-regulation of HOXD9.

Epithelial-mesenchymal transition (EMT) plays a pivotal role in cancer progression. Hsa-miR-205 is considered one of the fundamental regulators of EMT. In the present study, we found that miR-205 was down-regulated in glioma tissues and human glioma cells U87 and U251. Meanwhile, miR-205 overexpression enhanced E-cadherin, reduced mesenchymal markers, and decreased cell proliferation, migration, and invasion in vitro. In vivo, miR-205 suppressed tumor growth. Additionally, HOXD9 was confirmed as a direct target of miR-205. Suppression of HOXD9 by miR-205 was demonstrated by luciferase reporter assay, quantitative real time-PCR analysis, and western blot. Moreover, we observed a negative correlation between miR-205 and HOXD9 in human glioma tissues. In summary, our findings demonstrated that miR-205 suppresses glioma tumor growth, invasion, and reverses EMT through down-regulating its target HOXD9.

Identification of potential biomarkers of lung adenocarcinoma brain metastases via microarray analysis of cDNA expression profiles.

Brain metastases originating from lung adenocarcinoma (LAD) occur frequently. The aim of the current study was to assess potential biomarkers for the prognosis of lung adenocarcinoma brain metastasis (LAD-BM) through the analysis of gene expression microarrays. The current study downloaded two gene expression datasets, GSE14108 and GSE10245, from the Gene Expression Omnibus database. From GSE14108 and GSE10245, 19 LAD-BM samples and 40 primary LAD samples were selected for analysis. To identify the differentially expressed genes (DEGs), the current study compared the two sample groups, using the limma R package. Subsequently, pathway enrichment analysis was conducted using the Cluster Profiler R package, and the construction of the protein-protein interaction (PPI) network was executed utilizing the Search Tool for the Retrieval of Interacting Genes database. The microRNA-target network was built using the TargetScore R package. Then, these networks were established and visualized using Cytoscape software. An array of 463 DEGs was identified in the LAD-BM samples, including 256 upregulated and 207 downregulated genes. Based on functional term enrichment analysis using the Gene Ontology database and signaling pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes database, it was identified that the overlapping DEGs were primarily involved in chemokine-associated signal transduction, which may mediate lung cancer cell metastasis to the brain. Chemokine ligand 2, lysozyme, matrix metalloproteinase-2 (MMP-2), lysyl oxidase (LOX) and granzyme B were identified as potential biomarkers according to a topological analysis of the PPI networks. Two notable nodes, MMP-2 and LOX, appeared in the PPI network and were key points in the microRNA-target network, as they were regulated by hsa-let-7d. Many DEGs and microRNAs were regarded as prognostic biomarkers for lung adenocarcinoma metastasis in the current study. These DEGs were primarily associated with chemokine-mediated signaling pathways. In addition, MMP-2 and LOX were predicted to be targets of hsa-let-7d.

A Plasmonic Mass Spectrometry Approach for Detection of Small Nutrients and Toxins.

Nutriology relies on advanced analytical tools to study the molecular compositions of food and provide key information on sample quality/safety. Small nutrients detection is challenging due to the high diversity and broad dynamic range of molecules in food samples, and a further issue is to track low abundance toxins. Herein, we developed a novel plasmonic matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) approach to detect small nutrients and toxins in complex biological emulsion samples. Silver nanoshells (SiO2@Ag) with optimized structures were used as matrices and achieved direct analysis of ~ 6 nL of human breast milk without any enrichment or separation. We performed identification and quantitation of small nutrients and toxins with limit-of-detection down to 0.4 pmol (for melamine) and reaction time shortened to minutes, which is superior to the conventional biochemical method currently in use. The developed approach contributes to the near-future application of MALDI MS in a broad field and personalized design of plasmonic materials for real-case bio-analysis.