A Tripartite Microbial-Environment Network Indicates How Crucial Microbes Influence the Microbial Community Ecology.

Current technologies could identify the abundance and functions of specific microbes, and evaluate their individual effects on microbial ecology. However, these microbes interact with each other, as well as environmental factors, in the form of complex network. Determination of their combined ecological influences remains a challenge. In this study, we developed a tripartite microbial-environment network (TMEN) analysis method that integrates microbial abundance, metabolic function, and environmental data as a tripartite network to investigate the combined ecological effects of microbes. Applying TMEN to analyzing the microbial-environment community structure in the sediments of Hangzhou Bay, one of the most seriously polluted coastal areas in China, we found that microbes were well-organized into 4 bacterial communities and 9 archaeal communities. The total organic carbon, sulfate, chemical oxygen demand, salinity, and nitrogen-related indexes were detected as crucial environmental factors in the microbial-environmental network. With close interactions with these environmental factors, Nitrospirales and Methanimicrococcu were identified as hub microbes with connection advantage. Our TMEN method could close the gap between lack of efficient statistical and computational approaches and the booming of large-scale microbial genomic and environmental data. Based on TMEN, we discovered a potential microbial ecological mechanism that crucial species with significant influence on the microbial community ecology would possess one or two of the community advantages for enhancing their ecological status and essentiality, including abundance advantage and connection advantage.

Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence.

Domestic and industrial wastewater treatment plants (WWTPs) are facing formidable challenges in effectively eliminating emerging pollutants and conventional nutrients. In microbiome engineering, two approaches have been developed: a top-down method focusing on domesticating seed microbiomes into engineered ones, and a bottom-up strategy that synthesizes engineered microbiomes from microbial isolates. However, these approaches face substantial hurdles that limit their real-world applicability in wastewater treatment engineering. Addressing this gap, we propose the creation of a Global WWTP Microbiome-based Integrative Information Platform, inspired by the untapped microbiome and engineering data from WWTPs and advancements in artificial intelligence (AI). This open platform integrates microbiome and engineering information globally and utilizes AI-driven tools for identifying seed microbiomes for new plants, providing technical upgrades for existing facilities, and deploying microbiomes for accidental pollution remediation. Beyond its practical applications, this platform has significant scientific and social value, supporting multidisciplinary research, documenting microbial evolution, advancing Wastewater-Based Epidemiology, and enhancing global resource sharing. Overall, the platform is expected to enhance WWTPs' performance in pollution control, safeguarding a harmonious and healthy future for human society and the natural environment.

Screening of priority antibiotics in Chinese seawater based on the persistence, bioaccumulation, toxicity and resistance.

Antibiotics are emerging pollutants that have detrimental effects on both target and non-target organisms in the environment. However, current methods for environmental risk assessment primarily focus on the risk to non-target organisms in ecosystems, overlooking a crucial risk of antibiotics - the induction of resistance in targeted bacteria. To address this oversight, we have incorporated resistance (R) risk with persistence, bioaccumulation and toxicity (PBT) to establish a more comprehensive PBTR (persistence, bioaccumulation, toxicity, and resistance) framework for antibiotic-specific risk assessment. Using the PBTR framework, we evaluated 74 antibiotics detected in Chinese seawater from 2000 to 2021, and identified priority antibiotics. Our analysis revealed that the priority antibiotics with R risk accounted for the largest proportion (50% to 70%), followed by P risk (40% to 58%), T risk (16% to 35%) and B risk (0 to 13%). To further categorize these priority antibiotics, we assigned them a risk level according to their fulfillment of criteria related to P, B, T, and R. Antibiotics meeting all four indicators were classified as Grade I, representing the highest risk level. Grade II and Grade III were assigned to antibiotics meeting three or two indicators, respectively. Antibiotics meeting only one indicator were classified as Grade IV, representing the lowest risk level. The majority of priority antibiotics fell into Grade IV, indicating low risk (55% to 79%), followed by Grade III (16% to 45%). The highest risk antibiotic identified in this study was clindamycin (CLIN), categorized as Grade II, in the East China Sea. Our findings aligned with previous studies for 25 antibiotics, affirming the validity of the PBTR framework. Moreover, we identified 13 new priority antibiotics, highlighting the advancement of this approach. This study provides a feasible screening strategy and monitoring recommendations for priority antibiotics in Chinese seawater.

Manf Enhances the Pyroptosis Inhibition of Bone Marrow-derived Mesenchymal Stem Cells to Relieve Cerebral Infarction Injury.

Cerebral infarction is a common disease characterized by high mortality, a narrow therapeutic window, and limited therapeutic options. Recently, cell therapy based on gene modification has brought a glimmer of hope to the treatment of cerebral infarction although the explicit underlying mechanism is beyond being well dissected. In the present study, we constructed an animal model of middle cerebral artery occlusion (MCAO), compared differentially expressed genes (DEGs) between the sham and MCAO groups by single-cell RNA sequencing (scRNA-seq) to explore the potential cell death-related pathways involved in cerebral infarction, and transfected Manf into BMSCs by lentivirus. Subsequently, we injected BMSCs (bone marrow-derived mesenchymal stem cells), Manf-modified BMSCs, or lentivirus encoding Manf into the brain. Their effects on MANF content, apoptosis, pyroptosis, infarct volume in the brain, and neurological function were evaluated after MCAO. We found that the DEGs upregulated in four major cell clusters after MCAO and were enriched with not only apoptosis, ferroptosis, and necroptosis but also with pyroptosis-related pathways. In addition, transfection of Manf into BMSCs significantly increased the expression and secretion of MANF in BMSCs; BMSCs, Manf-modified BMSCs, and Manf treatment all resulted in an increase in Manf content in the brain, a decrease in the expression of apoptosis- and pyroptosis-related molecules, a reduction in infarct volume, and an improvement in neurological function after MCAO. Moreover, Manf-modified BMSCs have the strongest therapeutic effect. Collectively, Manf-modified BMSCs ameliorate ischemic injury after cerebral infarction by repressing apoptosis- and pyroptosis-related molecules, which represents a new cell therapy strategy for cerebral infarction.

Industrial effluents boosted antibiotic resistome risk in coastal environments.

Wastewater treatment plants (WWTPs) have been regarded as an important source of antibiotic resistance genes (ARGs) in environment, but out of municipal domestic WWTPs, few evidences show how environment is affected by industrial WWTPs. Here we chose Hangzhou Bay (HZB), China as our study area, where land-based municipal and industrial WWTPs discharged their effluent into the bay for decades. We adopted high-throughput metagenomic sequencing to examine the antibiotic resistome of the WWTP effluent and coastal sediment samples. And we proposed a conceptual framework for the assessment of antibiotic resistome risk, and a new bioinformatic pipeline for the evaluation of the potential horizontal gene transfer (HGT) frequency. Our results revealed that the diversity and abundance of ARGs in the WWTP's effluent were significantly higher than those in the sediment. Furthermore, the antibiotic resistome in the effluent-receiving area (ERA) showed significant difference from that in HZB. For the first time, we identified that industrial WWTP effluent boosted antibiotic resistome risk in coastal sediment. The crucial evidences included: 1) the proportion of ARGs derived from WWTP activated sludge (WA) was higher (14.3 %) and two high-risky polymyxin resistance genes (mcr-4 and mcr-5) were enriched in the industrial effluent receiving area; 2) the HGT potential was higher between resistant microbiome of the industrial effluent and its ERA sediment; and 3) the highest resistome risk was determined in the industrial effluent, and some biocide resistance genes located on high-risky contigs were related to long-term stress of industrial chemicals. These findings highlight the important effects of industrial activities on the development of environmental antimicrobial resistance.

Human nasal mucosa ectomesenchymal stem cells derived extracellular vesicles loaded omentum/chitosan composite scaffolds enhance skull defects regeneration.

Engineered bone tissue that can promote osteogenic differentiation is considered an ideal substitute for materials to heal bone defects. Extracellular vesicle (EV)-based cell-free regenerative therapies represent an emerging promising alternative for bone tissue engineering. We hypothesized that EVs derived from human nasal mucosa-derived ectomesenchymal stem cells (hEMSCs) can promote bone tissue regeneration. Herein, hEMSCs were cultured with osteogenic induction medium or normal medium to generate two types of EVs. We first demonstrated that the two EVs exhibited strong potential to promote rat suture mesenchymal stem cell (SMSC) osteogenesis by transferring TG2 to SMSCs and regulating extracellular matrix (ECM) synthesis. Next, we developed a composite hydrogel made of porcine omentum and chitosan into which EVs were adsorbed to enable the effective delivery of EVs with sustained release kinetics. Implantation of the EV-loaded hydrogels in a critical-size rat cranial defect model significantly promoted bone regeneration. Therefore, we suggest that our hEMSC-derived EV-loading system can serve as a new therapeutic paradigm for promoting bone tissue regeneration in the clinic.

Identification of pyroptosis-related immune signature and drugs for ischemic stroke.

Background: Ischemic stroke (IS) is a common and serious neurological disease, and multiple pathways of cell apoptosis are implicated in its pathogenesis. Recently, extensive studies have indicated that pyroptosis is involved in various diseases, especially cerebrovascular diseases. However, the exact mechanism of interaction between pyroptosis and IS is scarcely understood. Thus, we aimed to investigate the impact of pyroptosis on IS-mediated systemic inflammation. Methods: First, the RNA regulation patterns mediated by 33 pyroptosis-related genes identified in 20 IS samples and 20 matched-control samples were systematically evaluated. Second, a series of bioinformatics algorithms were used to investigate the contribution of PRGs to IS pathogenesis. We determined three composition classifiers of PRGs which potentially distinguished healthy samples from IS samples according to the risk score using single-variable logistic regression, LASSO-Cox regression, and multivariable logistic regression analyses. Third, 20 IS patients were classified by unsupervised consistent cluster analysis in relation to pyroptosis. The association between pyroptosis and systemic inflammation characteristics was explored, which was inclusive of immune reaction gene sets, infiltrating immunocytes and human leukocyte antigen genes. Results: We identified that AIM2, SCAF11, and TNF can regulate immuno-inflammatory responses after strokes via the production of inflammatory factors and activation of the immune cells. Meanwhile, we identified distinct expression patterns mediated by pyroptosis and revealed their immune characteristics, differentially expressed genes, signaling pathways, and target drugs. Conclusion: Our findings lay a foundation for further research on pyroptosis and IS systemic inflammation, to improve IS prognosis and its responses to immunotherapy.

Bone marrow mesenchymal stem cells induce M2 microglia polarization through PDGF-AA/MANF signaling.

BACKGROUND: Bone marrow mesenchymal stem cells (BMSCs) are capable of shifting the microglia/macrophages phenotype from M1 to M2, contributing to BMSCs-induced brain repair. However, the regulatory mechanism of BMSCs on microglia/macrophages after ischemic stroke is unclear. Recent evidence suggests that mesencephalic astrocyte-derived neurotrophic factor (MANF) and platelet-derived growth factor-AA (PDGF-AA)/MANF signaling regulate M1/M2 macrophage polarization. AIM: To investigate whether and how MANF or PDGF-AA/MANF signaling influences BMSCs-mediated M2 polarization. METHODS: We identified the secretion of MANF by BMSCs and developed transgenic BMSCs using a targeting small interfering RNA for knockdown of MANF expression. Using a rat middle cerebral artery occlusion (MCAO) model transplanted by BMSCs and BMSCs-microglia Transwell coculture system, the effect of BMSCs-induced downregulation of MANF expression on the phenotype of microglia/macrophages was tested by Western blot, quantitative reverse transcription-polymerase chain reaction, and immunofluorescence. Additionally, microglia were transfected with mimics of miR-30a*, which influenced expression of X-box binding protein (XBP) 1, a key transcription factor that synergized with activating transcription factor 6 (ATF6) to govern MANF expression. We examined the levels of miR-30a*, ATF6, XBP1, and MANF after PDGF-AA treatment in the activated microglia. RESULTS: Inhibition of MANF attenuated BMSCs-induced functional recovery and decreased M2 marker production, but increased M1 marker expression in vivo or in vitro. Furthermore, PDGF-AA treatment decreased miR-30a* expression, had no influence on the levels of ATF6, but enhanced expression of both XBP1 and MANF. CONCLUSION: BMSCs-mediated MANF paracrine signaling, in particular the PDGF-AA/miR-30a*/XBP1/MANF pathway, synergistically mediates BMSCs-induced M2 polarization.

Over-Expression of TRPC6 via CRISPR Based Synergistic Activation Mediator in BMSCs Ameliorates Brain Injury in a Rat Model of Cerebral Ischemia/Reperfusion.

Stroke is a major life-threatening and disabling disease with a restricted therapeutic approach. Bone marrow stromal cells (BMSCs) possess proliferative ability and a multi-directional differentiation potential, and secrete a range of trophic/growth factors that can protect neurons after cerebral ischemia/reperfusion. Transient receptor potential canonical (TRPC) is a family of non-selective channels permeable to Ca2+, with several functions including neuronal survival. Over-expression of TRPC6, a subtype of the TRPC family, was shown to protect neurons against cerebral ischemia/reperfusion injury. However, it remains unclear whether over-expression of TRPC6 in BMSCs can further reduce brain injury after ischemia/reperfusion. In the present study, we report that over-expression of TRPC6 via a CRISPR-based synergistic activation mediator in BMSCs provided a greater reduction of brain injury in a rat model of ischemia/reperfusion. Further, the improved neurofunctional outcomes were associated with increased TRPC6 and brain derived neurotrophic factor expression levels. Overall, these data suggest that TRPC6 over-expressing BMSCs may be a promising therapeutic agent for ischemic stroke.

Machine Learning With K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients With Breast Cancer.

OBJECTIVE: Despite existing prognostic markers, breast cancer prognosis remains a difficult subject due to the complex relationships between many contributing factors and survival. This study seeks to integrate multiple clinicopathological and genomic factors with dimensional reduction across machine learning algorithms to compare survival predictions. METHODS: This is a secondary analysis of the data from a prospective cohort study of female patients with breast cancer enrolled in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We constructed a series of predictive models: ensemble models (Gradient Boosting and Random Forest), support vector machine (SVM), and artificial neural networks (ANN) for 5-year survival based on clinicopathological and gene expression data after K-means clustering with K-nearest-neighbor (KNN) classification. Model performance was evaluated by receiver operating characteristic (ROC) curve, accuracy, and calibration slope (CS). Model stability was assessed over 10 random runs in terms of ROC, accuracy, CS, and variable importance. RESULTS: The analytic cohort is composed of 1874 patients with breast cancer. Overall, the median age was 62 years; the 5-year survival rate was 75%. ROC and accuracy were not significantly different between models (ROC and accuracy around 0.67 and 0.72 across models, respectively). However, ensemble methods resulted in better fit (CS) with stable measures of variable importance across 10 random training/validation splits. K-means clustering of gene expression profiles on training data points along with KNN classification of validation data points was a robust method of dimensional reduction. Furthermore, the gene expression cluster with the highest mortality risk was an influential factor in model prediction. CONCLUSIONS: Using machine learning methods to construct predictive models for 5-year survival in patients with breast cancer, we demonstrated discrimination ability across models with new insight into the stability and utility of dimensional reduction on genomic features in breast cancer survival prediction.

Dynamics of Sediment Microbial Functional Capacity and Community Interaction Networks in an Urbanized Coastal Estuary.

Coastal estuaries and bays are exposed to both natural and anthropogenic environmental changes, inflicting intensive stress on the microbial communities inhabiting these areas. However, it remains unclear how microbial community diversity and their eco-functions are affected by anthropogenic disturbances rather than natural environmental changes. Here, we explored sediment microbial functional genes dynamics and community interaction networks in Hangzhou Bay (HZB), one of the most severely polluted bays on China's eastern coast. The results indicated key microbial functional gene categories, including N, P, S, and aromatic compound metabolism, and stress response, displayed significant spatial dynamics along environmental gradients. Sensitive feedbacks of key functional gene categories to N and P pollutants demonstrated potential impacts of human-induced seawater pollutants to microbial functional capacity. Seawater ammonia and dissolved inorganic nitrogen (DIN) was identified as primary drivers in selecting adaptive populations and varying community composition. Network analysis revealed distinct modules that were stimulated in inner or outer bay. Importantly, the network keystone species, which played a fundamental role in community interactions, were strongly affected by N-pollutants. Our results provide a systematic understanding of the microbial compositional and functional dynamics in an urbanized coastal estuary, and highlighted the impact of human activities on these communities.

Sediment bacterial community structures and their predicted functions implied the impacts from natural processes and anthropogenic activities in coastal area.

Coastal ecosystem structures and functions are changing under natural and anthropogenic influences. In this study, surface sediment samples were collected from disturbed zone (DZ), near estuary zone (NEZ), and far estuary zone (FEZ) of Hangzhou Bay, one of the most seriously polluted bays in China. The bacterial community structures and predicted functions varied significantly in different zones. Firmicutes were found most abundantly in DZ, highlighting the impacts of anthropogenic activities. Sediment total phosphorus was most influential on the bacterial community structures. Predicted by PICRUSt analysis, DZ significantly exceeded FEZ and NEZ in the subcategory of Xenobiotics Biodegradation and Metabolism; and DZ enriched all the nitrate reduction related genes, except nrfA gene. Seawater salinity and inorganic nitrogen, respectively as the representative natural and anthropogenic factor, performed exact-oppositely in nitrogen metabolism functions. The changes of bacterial community compositions and predicted functions provide a new insight into human-induced pollution impacts on coastal ecosystem.

Identifying the key taxonomic categories that characterize microbial community diversity using full-scale classification: a case study of microbial communities in the sediments of Hangzhou Bay.

Coastal areas are land-sea transitional zones with complex natural and anthropogenic disturbances. Microorganisms in coastal sediments adapt to such disturbances both individually and as a community. The microbial community structure changes spatially and temporally under environmental stress. In this study, we investigated the microbial community structure in the sediments of Hangzhou Bay, a seriously polluted bay in China. In order to identify the roles and contribution of all microbial taxa, we set thresholds as 0.1% for rare taxa and 1% for abundant taxa, and classified all operational taxonomic units into six exclusive categories based on their abundance. The results showed that the key taxa in differentiating the communities are abundant taxa (AT), conditionally abundant taxa (CAT), and conditionally rare or abundant taxa (CRAT). A large population in conditionally rare taxa (CRT) made this category collectively significant in differentiating the communities. Both bacteria and archaea demonstrated a distance decay pattern of community similarity in the bay, and this pattern was strengthened by rare taxa, CRT and CRAT, but weakened by AT and CAT. This implied that the low abundance taxa were more deterministically distributed, while the high abundance taxa were more ubiquitously distributed.

Study on peak shape fitting method in radon progeny measurement.

Alpha spectrum measurement is one of the most important methods to measure radon progeny concentration in environment. However, the accuracy of this method is affected by the peak tailing due to the energy losses of alpha particles. This article presents a peak shape fitting method that can overcome the peak tailing problem in most situations. On a typical measured alpha spectrum curve, consecutive peaks overlap even their energies are not close to each other, and it is difficult to calculate the exact count of each peak. The peak shape fitting method uses combination of Gaussian and exponential functions, which can depict features of those peaks, to fit the measured curve. It can provide net counts of each peak explicitly, which was used in the Kerr method of calculation procedure for radon progeny concentration measurement. The results show that the fitting curve fits well with the measured curve, and the influence of the peak tailing is reduced. The method was further validated by the agreement between radon equilibrium equivalent concentration based on this method and the measured values of some commercial radon monitors, such as EQF3220 and WLx. In addition, this method improves the accuracy of individual radon progeny concentration measurement. Especially for the (218)Po peak, after eliminating the peak tailing influence, the calculated result of (218)Po concentration has been reduced by 21 %.