eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Development and Validation of a Prognostic Nomogram for Breast Cancer Patients With Multi-Organ Metastases: An Analysis of the Surveillance, Epidemiology, and End Results Program Database.

BACKGROUND: Multi-organ metastases represent a substantial life-threatening risk for breast cancer (BC) patients. Nonetheless, the current dearth of assessment tools for patients with multi-organ metastatic BC adversely impacts their evaluation. METHODS: We conducted a retrospective analysis of BC patients with multi-organ metastases using data from the SEER database from 2010 to 2019. The patients were randomly allocated into a training cohort and a validation cohort in a 7:3 ratio. Univariate COX regression analysis, the LASSO, and multivariate Cox regression analyses were performed to identify independent prognostic factors in the training set. Based on these factors, a nomogram was constructed to estimate overall survival (OS) probability for BC patients with multi-organ metastases. The performance of the nomogram was evaluated using C-indexes, ROC curves, calibration curves, decision curve analysis (DCA) curves, and the risk classification system for validation. RESULTS: A total of 3626 BC patients with multi-organ metastases were included in the study, with 2538 patients in the training cohort and 1088 patients in the validation cohort. Age, grade, metastasis location, surgery, chemotherapy, and subtype were identified as significant independent prognostic factors for OS in BC patients with multi-organ metastases. A nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. The evaluation metrics, including C-indexes, ROC curves, calibration curves, and DCA curves, demonstrated the excellent predictive performance of the nomogram. Additionally, the risk grouping system effectively stratified BC patients with multi-organ metastases into distinct prognostic categories. CONCLUSION: The developed nomogram showed high accuracy in predicting the survival probability of BC patients with multi-organ metastases, providing valuable information for patient counseling and treatment decision making.

Chimeric mutations in grapevine ENHANCED DISEASE RESISTANCE1 improve resistance to powdery mildew without growth penalty.

Grapevine (Vitis vinifera L.) incurs severequality degradation and yield loss from powdery mildew, a major fungal disease caused by Erysiphe necator. ENHANCED DISEASE RESISTANCE1 (EDR1), a Raf-like mitogen-activated protein kinase kinase kinase (MAPKKK), negatively regulates defense responses against powdery mildew in Arabidopsis (Arabidopsis thaliana). However, little is known about the role of the putatively orthologous EDR1 gene in grapevine. In this study, we obtained grapevine VviEDR1-edited lines using CRISPR/Cas9. Plantlets containing homozygous and bi-allelic indels in VviEDR1 developed leaf lesions shortly after transplanting into the soil and died at the seedling stage. Transgenic plants expressing wild-type VviEDR1 and mutant Vviedr1 alleles as chimera (designated as VviEDR1-chi) developed normally and displayed enhanced resistance to powdery mildew. Interestingly, VviEDR1-chi plants maintained a spatiotemporally distinctive pattern of VviEDR1 mutagenesis: while almost no mutations were detected from terminal buds, ensuring normal function of the apical meristem, mutations occurred in young leaves and increased as leaves matured, resulting in resistance to powdery mildew. Further analysis showed that the resistance observed in VviEDR1-chi plants was associated with callose deposition, increased production of salicylic acid (SA) and ethylene (ET), H2O2 production and accumulation, and host cell death. Surprisingly, no growth penalty was observed with VviEDR1-chi plants. Hence, this study demonstrated a role of VviEDR1 in the negative regulation of resistance to powdery mildew in grapevine and provided an avenue for engineering powdery mildew resistance in grapevine.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

An effector of Erysiphe necator translocates to chloroplasts and plasma membrane to suppress host immunity in grapevine.

The powdery mildew (Erysiphe necator) is a prevalent pathogen hampering grapevine growth in the vineyard. An arsenal of candidate secreted effector proteins (CSEPs) was encoded in the E. necator genome, but it is largely unclear what role CSEPs plays during the E. necator infection. In the present study, we identified a secreted effector CSEP080 of E. necator, which was located in plant chloroplasts and plasma membrane. Transient expressing CSEP080 promotes plant photosynthesis and inhibits INF1-induced cell death in tobacco leaves. We found that CSEP080 was a necessary effector for the E. necator pathogenicity, which interacted with grapevine chloroplast protein VviB6f (cytochrome b6-f complex iron-sulfur subunit), affecting plant photosynthesis. Transient silencing VviB6f increased the plant hydrogen peroxide production, and the plant resistance to powdery mildew. In addition, CSEP080 manipulated the VviPE (pectinesterase) to promote pectin degradation. Our results demonstrated the molecular mechanisms that an effector of E. necator translocates to host chloroplasts and plasma membrane, which suppresses with the grapevine immunity system by targeting the chloroplast protein VviB6f to suppress hydrogen peroxide accumulation and manipulating VviPE to promote pectin degradation.

Mechanism of exosomes from adipose-derived mesenchymal stem cells on sepsis-induced acute lung injury by promoting TGF-ß secretion in macrophages.

OBJECTIVE: Acute lung injury (ALI) caused by sepsis is a life-threatening condition characterized by uncontrollable lung inflammation. The current study sought to investigate the mechanism of adipose-derived mesenchymal stem cell-derived exosomes (ADMSC-Exos) in attenuating sepsis-induced ALI through TGF-ß secretion in macrophages. METHODS: Adipose-derived mesenchymal stem cell-derived exosomes (ADMSC-Exos) were extracted from ADMSCs and identified. Septic ALI mouse models were established via cecal ligation and puncture (CLP), followed by administration of ADMSC-Exos or sh-TGF-ß lentiviral vector. Mouse macrophages (cell line RAW 264.7) were treated with lipopolysaccharide (LPS), co-cultured with Exos and splenic T cells, and transfected with TGF-ß siRNA. The lung injury of CLP mice was evaluated, and levels of inflammatory indicators and macrophage markers were measured. The localization of macrophage markers and TGF-ß was determined, and the level of TGF-ß in lung tissues was measured. The effect of TGF-ß knockdown on sepsis-induced ALI in CLP mice was evaluated, and the percentages of CD4+CD25+Foxp3+ Tregs in mononuclear cells/macrophages and Foxp3 levels in lung tissues/co-cultured splenic T cells were examined. RESULTS: ADMSC-Exos were found to alleviate sepsis-induced ALI, inhibit inflammatory responses, and induce macrophages to secrete TGF-ß in CLP mice. TGF-ß silencing reversed the alleviating effect of ADMSC-Exos on sepsis-induced ALI. ADMSC-Exos also increased the number of Tregs in the spleen of CLP mice and promoted M2 polarization and TGF-ß secretion in LPS-induced macrophages. After knockdown of TGF-ß in macrophages in the co-culture system, the number of Tregs decreased, suggesting that ADMSC-Exos increased the Treg number by promoting macrophages to secrete TGF-ß. CONCLUSION: Our findings suggest ADMSC-Exos can effectively alleviate sepsis-induced ALI in CLP mice by promoting TGF-ß secretion in macrophages.

Exploring the most appropriate lymph node staging system for node-positive breast cancer patients and constructing corresponding survival nomograms.

BACKGROUND: The lymph node (LN) status is a crucial prognostic factor for breast cancer (BC) patients. Our study aimed to compare the predictive capabilities of three different LN staging systems in node-positive BC patients and develop nomograms to predict overall survival (OS). METHODS: We enrolled 71,213 eligible patients from the SEER database, and 667 cases from our hospital were used for external validation. All patients were divided into two groups based on the number of removed lymph nodes (RLNs). The prognostic abilities of pN stage, positive LN ratio (LNR), and log odds of positive LN (LODDS) were compared using the C-indexes and AUC values. LASSO regression was performed to identify significant factors associated with prognosis and develop corresponding nomogram models. RESULTS: Our study found that LNR had superior predictive performance compared to pN and LODDS among patients with RLNs < 10, while pN performed better in patients with RLNs ≥ 10. In the training set, the nomogram models exhibited excellent clinical applicability, as evidenced by the C-indexes, ROC curves, calibration plots, and decision curve analysis curves. Moreover, the nomogram classification accurately differentiated risk subgroups and improved discrimination. These results were externally validated in the validation cohort. CONCLUSION: Physicians should select different LN staging systems based on the number of RLNs. Our novel nomograms demonstrated excellent performance in both internal and external validations, which may assist in patient counseling and guide treatment decision-making.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging.

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

Identification of prognostic cancer-associated fibroblast markers in luminal breast cancer using weighted gene co-expression network analysis.

Background: Cancer-associated fibroblasts (CAFs) play a pivotal role in cancer progression and are known to mediate endocrine and chemotherapy resistance through paracrine signaling. Additionally, they directly influence the expression and growth dependence of ER in Luminal breast cancer (LBC). This study aims to investigate stromal CAF-related factors and develop a CAF-related classifier to predict the prognosis and therapeutic outcomes in LBC. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to obtain mRNA expression and clinical information from 694 and 101 LBC samples, respectively. CAF infiltrations were determined by estimating the proportion of immune and cancer cells (EPIC) method, while stromal scores were calculated using the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify stromal CAF-related genes. A CAF risk signature was developed through univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. The Spearman test was used to evaluate the correlation between CAF risk score, CAF markers, and CAF infiltrations estimated through EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. The TIDE algorithm was further utilized to assess the response to immunotherapy. Additionally, Gene set enrichment analysis (GSEA) was applied to elucidate the molecular mechanisms underlying the findings. Results: We constructed a 5-gene prognostic model consisting of RIN2, THBS1, IL1R1, RAB31, and COL11A1 for CAF. Using the median CAF risk score as the cutoff, we classified LBC patients into high- and low-CAF-risk groups and found that those in the high-risk group had a significantly worse prognosis. Spearman correlation analyses demonstrated a strong positive correlation between the CAF risk score and stromal and CAF infiltrations, with the five model genes showing positive correlations with CAF markers. In addition, the TIDE analysis revealed that high-CAF-risk patients were less likely to respond to immunotherapy. Gene set enrichment analysis (GSEA) identified significant enrichment of ECM receptor interaction, regulation of actin cytoskeleton, epithelial-mesenchymal transition (EMT), and TGF-ß signaling pathway gene sets in the high-CAF-risk group patients. Conclusion: The five-gene prognostic CAF signature presented in this study was not only reliable for predicting prognosis in LBC patients, but it was also effective in estimating clinical immunotherapy response. These findings have significant clinical implications, as the signature may guide tailored anti-CAF therapy in combination with immunotherapy for LBC patients.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics.

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions.

BACKGROUND: Unrevealing the interplay between diet, the microbiome, and the health state could enable the design of personalized intervention strategies and improve the health and well-being of individuals. A common approach to this is to divide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. RESULTS: We present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data-driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson's disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson's Disease but also for identifying diet-specific microbial signatures of disease. CONCLUSION: In summary, NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases. Video Abstract.

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data.

Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.

iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data.

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

Cytotoxic indole alkaloids and polyketides produced by a marine-derived fungus Aspergillus flavipes DS720.

Marine-derived microorganisms possess the unique metabolic pathways to produce structurally novel secondary metabolites with potent biological activities. In this study, bioactivity-guided isolation of the marine deep-sea-derived fungus Aspergillus flavipes DS720 led to the characterization of four indole alkaloids (compounds 1-4) and four polyketides (compounds 5-8), such as two new indoles, flavonoids A (1) and B (2) with a C-6 reversed prenylation, and a new azaphilone, flaviazaphilone A (5). Their chemical structures were unambiguously established by an extensive interpretation of spectroscopic data, such as 1D/2D NMR and HRESIMS data. The absolute configurations of the new compound 5 were solved by comparing the experimental and calculated Electronic Circular Dichroism (ECD) spectra. Since sufficient amount of flavonoids A (1) was obtained, 1 was subjected to a large-scale cytotoxic activity screening against 20 different human tumor cell lines. The results revealed that 1 showed broad-spectrum cytotoxicities against HeLa, 5637, CAL-62, PATU8988T, A-375, and A-673 cell lines, with the inhibition rates of more than 90%. This study indicated that the newly discovered indole alkaloid 1 may possess certain potential for the development of lead compounds in the future.

Nutritional Intake and Gut Microbiome Composition Predict Parkinson's Disease.

Background: Models to predict Parkinson's disease (PD) incorporating alterations of gut microbiome (GM) composition have been reported with varying success. Objective: To assess the utility of GM compositional changes combined with macronutrient intake to develop a predictive model of PD. Methods: We performed a cross-sectional analysis of the GM and nutritional intake in 103 PD patients and 81 household controls (HCs). GM composition was determined by 16S amplicon sequencing of the V3-V4 region of bacterial ribosomal DNA isolated from stool. To determine multivariate disease-discriminant associations, we developed two models using Random Forest and support-vector machine (SVM) methodologies. Results: Using updated taxonomic reference, we identified significant compositional differences in the GM profiles of PD patients in association with a variety of clinical PD characteristics. Six genera were overrepresented and eight underrepresented in PD patients relative to HCs, with the largest difference being overrepresentation of Lactobacillaceae at family taxonomic level. Correlation analyses highlighted multiple associations between clinical characteristics and select taxa, whilst constipation severity, physical activity and pharmacological therapies associated with changes in beta diversity. The random forest model of PD, incorporating taxonomic data at the genus level and carbohydrate contribution to total energy demonstrated the best predictive capacity [Area under the ROC Curve (AUC) of 0.74]. Conclusion: The notable differences in GM diversity and composition when combined with clinical measures and nutritional data enabled the development of a predictive model to identify PD. These findings support the combination of GM and nutritional data as a potentially useful biomarker of PD to improve diagnosis and guide clinical management.

The Gut Microbiome in Parkinson's Disease: A Longitudinal Study of the Impacts on Disease Progression and the Use of Device-Assisted Therapies.

Background: Altered gut microbiome (GM) composition has been established in Parkinson's disease (PD). However, few studies have longitudinally investigated the GM in PD, or the impact of device-assisted therapies. Objectives: To investigate the temporal stability of GM profiles from PD patients on standard therapies and those initiating device-assisted therapies (DAT) and define multivariate models of disease and progression. Methods: We evaluated validated clinical questionnaires and stool samples from 74 PD patients and 74 household controls (HCs) at 0, 6, and 12 months. Faster or slower disease progression was defined from levodopa equivalence dose and motor severity measures. 19 PD patients initiating Deep Brain Stimulation or Levodopa-Carbidopa Intestinal Gel were separately evaluated at 0, 6, and 12 months post-therapy initiation. Results: Persistent underrepresentation of short-chain fatty-acid-producing bacteria, Butyricicoccus, Fusicatenibacter, Lachnospiraceae ND3007 group, and Erysipelotrichaceae UCG-003, were apparent in PD patients relative to controls. A sustained effect of DAT initiation on GM associations with PD was not observed. PD progression analysis indicated that the genus Barnesiella was underrepresented in faster progressing PD patients at t = 0 and t = 12 months. Two-stage predictive modeling, integrating microbiota abundances and nutritional profiles, improved predictive capacity (change in Area Under the Curve from 0.58 to 0.64) when assessed at Amplicon Sequence Variant taxonomic resolution. Conclusion: We present longitudinal GM studies in PD patients, showing persistently altered GM profiles suggestive of a reduced butyrogenic production potential. DATs exerted variable GM influences across the short and longer-term. We found that specific GM profiles combined with dietary factors improved prediction of disease progression in PD patients.

Survival Nomogram for Young Breast Cancer Patients Based on the SEER Database and an External Validation Cohort.

BACKGROUND: Young breast cancer (YBC) patients are more prone to lymph node metastasis than other age groups. Our study aimed to investigate the predictive value of lymph node ratio (LNR) in YBC patients and create a nomogram to predict overall survival (OS), thus helping clinical diagnosis and treatment. METHODS: Patients diagnosed with YBC between January 2010 and December 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into a training set and an internal validation set with a ratio of 7:3. An independent cohort from our hospital was used for external validation. Univariate and least absolute shrinkage and selection operator (LASSO) regression were used to identify the significant factors associated with prognosis, which were used to create a nomogram for predicting 3- and 5-year OS. RESULTS: We selected seven survival predictors (tumor grade, T-stage, N-stage, LNR, ER status, PR status, HER2 status) for nomogram construction. The C-indexes in the training set, the internal validation set, and the external validation set were 0.775, 0.778 and 0.817, respectively. The nomogram model was well calibrated, and the time-dependent ROC curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more precisely differentiate risk subgroups and improve the discrimination of YBC prognosis. CONCLUSIONS: LNR is a strong predictor of OS in YBC patients. The novel nomogram based on LNR is a reliable tool to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments.

Hypoxia-reinforced antitumor RNA interference mediated by micelleplexes with programmed disintegration.

The performance of polycation-mediated siRNA delivery is often hurdled by the multiple systemic and cellular barriers that pose conflicting requirements for materials properties. Herein, micelleplexes (MPs) capable of programmed disintegration were developed to mediate efficient delivery of siRNA against XIAP (siXIAP) in a hypoxia-reinforced manner. MPs were assembled from azobenzene-crosslinked oligoethylenimine (AO), acid-transformable diblock copolymer PPDHP with conjugated photosensitizer, and siXIAP. AO efficiently condensed siXIAP via electrostatic interaction, and PPDHP rendered additional hydrophobic interaction with AO to stabilize the MPs against salt. The hydrophilic PEG corona enhanced the serum stability of MPs to prolong blood circulation and promote tumor accumulation. After internalization into cancer cells, the endolysosomal acidity triggered shedding of PPDHP, exposing AO to induce endolysosomal escape. Then, light irradiation generated lethal amount of ROS, and concurrently aggravated intracellular hypoxia level to degrade AO into low-molecular weight segments, release siXIAP, and potentiate the XIAP silencing efficiency. Thus, siXIAP-mediated pro-apoptosis cooperated with generated ROS to provoke pronounced anti-cancer efficacy against Skov-3 tumors in vitro and in vivo. This study provides a hypoxia-instructed strategy to overcome the multiple barriers against anti-cancer siRNA delivery in a programmed manner. STATEMENT OF SIGNIFICANCE: The success of RNA interference (RNAi) heavily depends on delivery systems that can enable spatiotemporal control over siRNA delivery. Herein, we developed micelleplexes (MPs) constructed from hypoxia-degradable, azobenzene-crosslinked oligoethylenimine (AO) and acid-responsive, photosensitizer-conjugated diblock copolymer PPDHP, to mediate efficient anti-tumor siRNA (siXIAP) delivery via programmed disintegration. MPs possessed high salt/serum stability and underwent acid-triggered PPDHP detachment to promote endolysosomal escape. Then, light irradiation aggravated hypoxia to trigger AO degradation and intracellular siXIAP release, which cooperated with photodynamic therapy to eradicate tumor cells. This study presents a new example of hypoxia-degradable polycation to mediate hypoxia-reinforced RNAi, and it also renders an effective strategy to overcome the complicated extracellular/intracellular barriers against systemic siRNA delivery.

The impact of device-assisted therapies on the gut microbiome in Parkinson's disease.

BACKGROUND: Microbiome feedbacks are proposed to influence Parkinson's disease (PD) pathophysiology. A number of studies have evaluated the impact of oral medication on the gut microbiome (GM) in PD. However, the influence of PD device-assisted therapies (DATs) on the GM remains to be investigated. OBJECTIVES: To profile acute gut microbial community alterations in response to PD DAT initiation. METHODS: Clinical data and stool samples were collected from 21 PD patients initiating either deep brain stimulation (DBS) or levodopa-carbidopa intestinal gel (LCIG) and ten spousal healthy control (HC) subjects. 16S amplicon sequencing of stool DNA enabled comparison of temporal GM stability between groups and with clinical measures, including disease alterations relative to therapy initiation. RESULTS: We assessed GM response to therapy in the PD group by comparing pre-therapy (- 2 and 0 weeks) with post-therapy initiation timepoints (+ 2 and + 4 weeks) and HCs at baseline (0 weeks). Altered GM compositions were noted between the PD and HC groups at various taxonomic levels, including specific differences for DBS (overrepresentation of Clostridium_XlVa, Bilophila, Parabacteroides, Pseudoflavonifractor and underrepresentation of Dorea) and LCIG therapy (overrepresentation of Pseudoflavonifractor, Escherichia/Shigella, and underrepresentation of Gemmiger). Beta diversity changes were also found over the 4 week post-treatment initiation period. CONCLUSIONS: We report on initial short-term GM changes in response to the initiation of PD DATs. Prior to the introduction of the DAT, a PD-associated GM was observed. Following initiation of DAT, several DAT-specific changes in GM composition were identified, suggesting DATs can influence the GM in PD.