Microbiota-microglia crosstalk between Blautia producta and neuroinflammation of Parkinson's disease: A bench-to-bedside translational approach.

Parkinson's disease (PD) is intricately linked to abnormal gut microbiota, yet the specific microbiota influencing clinical outcomes remain poorly understood. Our study identified a deficiency in the microbiota genus Blautia and a reduction in fecal short-chain fatty acid (SCFA) butyrate level in PD patients compared to healthy controls. The abundance of Blautia correlated with the clinical severity of PD. Supplementation with butyrate-producing bacterium B. producta demonstrated neuroprotective effects, attenuating neuroinflammation and dopaminergic neuronal death in mice, consequently ameliorating motor dysfunction. A pivotal inflammatory signaling pathway, the RAS-related pathway, modulated by butyrate, emerged as a key mechanism inhibiting microglial activation in PD. The change of RAS-NF-κB pathway in PD patients was observed. Furthermore, B. producta-derived butyrate demonstrated the inhibition of microglial activation in PD through regulation of the RAS-NF-κB pathway. These findings elucidate the causal relationship between specific gut microbiota and PD, presenting a novel microbiota-based treatment perspective for PD.

Association Analysis of Gut Microbiota and Prognosis of Patients with Acute Ischemic Stroke in Basal Ganglia Region.

Previous studies have implied the potential impact of gut microbiota on acute ischemic stroke (AIS), but the relationships of gut microbiota with basal ganglia region infarction (BGRI) and the predictive power of gut microbiota in BGRI prognosis is unclear. The aim of this study was to ascertain characteristic taxa of BGRI patients with different functional outcomes and identify their predictive value. Fecal samples of 65 BGRI patients were collected at admission and analyzed with 16s rRNA gene sequencing. Three-month functional outcomes of BGRI were evaluated using modified Rankin Scale (mRS), and patients with mRS score of 0-1 were assigned to good-BGRI group while others were assigned to poor-BGRI group. We further identified characteristic microbiota using linear discriminant analysis effect size, and receiver operating characteristic (ROC) curve was used to determine the predictive value of differential bacteria. According to the mRS score assessed after 3 months of stroke onset, 22 patients were assigned to poor-BGRI group, while 43 patients were assigned to good-BGRI group. Short chain fatty acids-producing bacteria, Romboutsia and Fusicatenibacter, were characteristic microbiota of the good-BGRI group, while pro-inflammatory taxa, Acetanaerobacterium, were characteristic microbiota of the poor-BGRI group. Furthermore, the differential bacteria showed extensive associations with clinical indices. ROC curves, separately plotted based on Romboutsia and Fusicatenibacter, achieved area under the curve values of 0.7193 and 0.6839, respectively. This study identified the efficient discriminative power of characteristic microbiota in BGRI patients with different outcomes and provided novel insights into the associations of gut microbiota with related risk factors.

Association analysis of the gut microbiota in predicting outcomes for patients with acute ischemic stroke and H-type hypertension.

Introduction: H-type hypertension (HHTN) is a subtype of hypertension that tends to worsen the prognosis of acute ischemic stroke (AIS). Recent studies have highlighted the vital role of gut microbiota in both hypertension and AIS, but there is little available data on the relationship between gut microbiota and the progression of AIS patients with HHTN. In this study, we investigated the microbial signature of AIS patients with HHTN and identified characteristic bacteria as biomarkers for predicting prognosis. Methods: AIS patients with HHTN (n = 150) and without HHTN (n = 50) were enrolled. All patients received a modified Rankin Scale (mRS) assessment at 3 months after discharge. Fecal samples were collected from the participants upon admission, including 150 AIS patients with HHTN, 50 AIS patients with non-HHTN, and 90 healthy subjects with HHTN. These samples were analyzed using 16S rRNA sequencing to characterize the bacterial taxa, predict functions, and conduct correlation analysis between specific taxa and clinical features. Results: Our results showed that the composition of the gut microbiota in HHTN patients differed significantly from that in non-HHTN patients. The abundance of the genera Bacteroides, Escherichia-Shigella, Lactobacillus, Bifidobacterium, and Prevotella in AIS patients with HHTN was significantly increased compared to AIS patients without HHTN, while the genus Streptococcus, Faecalibacterium, and Klebsiella were significantly decreased. Moreover, Bacteroides, Lactobacillus, Bifidobacterium, and Klebsiella in AIS patients with HHTN were more abundant than healthy subjects with HHTN, while Escherichia-Shigella, Blautia, and Faecalibacterium were less abundant. Moreover, the genera Butyricicoccus, Rothia, and Family_XIII_UCG-001 were negatively connected with the NIHSS score, and the genera Butyricicoccus and Rothia were observed to be negatively associated with the mRS score. The genera Butyricicoccus, Romboutsia, and Terrisporobacter were associated with a poor prognosis, whereas the increase in Butyricimonas and Odoribacter was correlated with good outcomes. Generated by eight genera and clinical indexes, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve achieved 0.739 to effectively predict the prognosis of AIS patients with HHTN. Conclusion: These findings revealed the microbial signature of AIS patients with HHTN and further provided potential microbial biomarkers for the clinical diagnosis of AIS patients with HHTN.

The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders.

BACKGROUND: Gut microbiota have been associated with many psychiatric disorders. However, the changes in the composition of gut microbiota in patients with post-stroke sleep disorders (PSSDs) remain unclear. Here, we determined the gut microbial signature of PSSD patients. METHODS: Fecal samples of 205 patients with ischemic stroke were collected within 24 h of admission and were further analyzed using 16 s RNA gene sequencing followed by bioinformatic analysis. The diversity, community composition, and differential microbes of gut microbiota were assessed. The outcome of sleep disorders was determined by the Pittsburgh Sleep Quality Index (PSQI) at 3 months after admission. The diagnostic performance of microbial characteristics in predicting PSSDs was assessed by receiver operating characteristic (ROC) curves. RESULTS: Our results showed that the composition and structure of microbiota in patients with PSSDs were different from those without sleep disorders (PSNSDs). Moreover, the linear discriminant analysis effect size (LEfSe) showed significant differences in gut-associated bacteria, such as species of Streptococcus, Granulicatella, Dielma, Blautia, Paeniclostridium, and Sutterella. We further managed to identify the optimal microbiota signature and revealed that the predictive model with eight operational-taxonomic-unit-based biomarkers achieved a high accuracy in PSSD prediction (AUC = 0.768). Blautia and Streptococcus were considered to be the key microbiome signatures for patients with PSSD. CONCLUSIONS: These findings indicated that a specific gut microbial signature was an important predictor of PSSDs, which highlighted the potential of microbiota as a promising biomarker for detecting PSSD patients.

The gut microbial signatures of patients with lacunar cerebral infarction.

BACKGROUND: Emerging evidence revealed that gut microbial dysbiosis is involved in the pathogenesis of multiple neurological diseases, but there is little available data on the relationship between gut microbiota and lacunar cerebral infarction (LCI). METHODS: Fecal samples from acute LCI patients (n = 65) and matched healthy controls (n = 65) were collected. The compositions and potential functions of the gut microbiota were estimated. RESULTS: The results showed that there were significant gut microbial differences between LCI and control groups. Patients with LCI had higher abundances of genus Lactobacillus, Streptococcus, Veillonella, Acidaminococcus, Bacillus, Peptoclostridium, Intestinibacter, Alloscardovia and Cloacibacillus but lower proportions of genus Agathobacter and Lachnospiraceae_UCG-004. Investigating further these microbes such as Lactobacillus and Veillonella were correlated with clinical signs. Moreover, we found that 9 gene functions of gut microbiota were different between LCI patients and controls, which were associated with amino acid metabolism and inflammatory signal transduction. Notably, four optimal microbial markers were determined, and the combination of Streptococcus, Lactobacillus, Agathobacter, Lachnospiraceae_UCG-004 and the three risk factors achieved an area under the curve (AUC) value of 0.854 to distinguish LCI from controls. CONCLUSION: These findings revealed the characterizing of gut microbiota in LCI patients and provided potential microbial biomarkers for clinical diagnosis of LCI.

Synthesis of magnetic sodium lignosulfonate hydrogel(Fe3O4@LS) and its adsorption behavior for Cd2+ in wastewater.

Heavy metal pollution is becoming increasingly serious. Heavy metal pollutants are nonbiodegradable and can be bioenriched through the food chain, and thus, they greatly threaten the environment and human health. Hydrogels, as an ideal adsorbent, have been widely used to treat heavy metal industrial wastewater. Sodium lignosulfonate hydrogel (LS) was prepared by free-radical grafting copolymerization, and nano-Fe3O4 particles were loaded in LS by an in-situ precipitation method (Fe3O4@LS). The magnetic properties and adsorption capacity of Fe3O4@LS are closely related to the load capacity of Fe3O4. XRD, FTIR, XPS, SEM, TEM, BET, and TGA analyses of the materials were performed. Subsequently, the removal effect of the typical pollutant Cd2+ in heavy metal-polluted water was studied with Fe3O4@LS as the adsorbent. The influences of the Fe3O4@LS dosage and initial pH were investigated, and the adsorption kinetics and thermodynamics were further explored and discussed. Finally, the adsorption mechanism of Fe3O4@LS on Cd2+ was obtained. Results show that Fe3O4@LS has a more stable spatial network structure than LS, and the pore size, specific surface area and active sites increase. The maximum adsorption capacity can reach 88.00 mg/g when pH = 6 and the dosage of Fe3O4@LS is 1000 mg/L. The adsorption of Cd2+ by Fe3O4@LS conforms to pseudosecond-order kinetics and the Temkin isothermal adsorption model. Further mechanistic investigations show that the sorption of Cd2+ on Fe3O4@LS is mainly attributed to surface complexation, electrostatic attraction and coprecipitation. The coexistence of cations in water will inhibit the adsorption of Fe3O4@LS. Fe3O4@LS has superparamagnetism and a good response to an external magnetic field. The adsorption rate can still reach >60 % after four elutions with NaCl as the eluent. This material can be reused and has good application potential.

Predictive microbial feature analysis in patients with depression after acute ischemic stroke.

Introduction: Post-stroke depression (PSD) is the most common emotional problem following a stroke, which requires early diagnosis to improve the prognosis. Gut microbiota plays important role in the pathological mechanisms of acute ischemic stroke and influences the outcome of patients. However, the relationship between PSD and gut microbiota remains unknown. Here, we explored whether the microbial signatures of gut microbiota in the patients with stroke could be an appropriate predictor of PSD. Methods: Fecal samples were collected from 232 acute ischemic stroke patients and determined by 16s rRNA sequencing. All patients then received 17-Hamilton Depression Rating Scale (HAMD-17) assessment 3 months after discharge, and were further divided into PSD group and non-PSD group. We analyzed the differences of gut microbiota between these groups. To identify gut microbial biomarkers, we then established microbial biomarker model. Results: Our results showed that the composition of gut microbiota in the PSD patients differed significantly from that in non-PSD patients. The genus Streptococcus, Akkermansia, and Barnesiella were significantly increased in PSD patients compared to non-PSD, while the genus Escherichia-Shigella, Butyricicoccus, and Holdemanella were significantly decreased. Correlation analyses displayed that Akkermansia, Barnesiella, and Pyramidobacter were positively correlated with HAMD score, while Holdemanella was negatively correlated with HAMD score. The optimal microbial markers were determined, and the combination achieved an area under the curve (AUC) value of 0.705 to distinguish PSD from non-PSD. Conclusions: Our findings suggest that PSD patients had distinct gut microbiota compared to non-PSD patients, and explore the potential of microbial markers, which might provide clinical decision-making in PSD.

Gut microbiota alterations are associated with functional outcomes in patients of acute ischemic stroke with non-alcoholic fatty liver disease.

Introduction: Patients with acute ischemic stroke (AIS) with non-alcoholic fatty liver disease (NAFLD) frequently have poor prognosis. Many evidences suggested that the changes in gut microbiota may play an important role in the occurrence and development of AIS patients with NAFLD. The purpose of this study was to explore microbial characteristics in patients of AIS with NAFLD, and the correlation between gut microbiota and functional outcomes. Methods: The patients of AIS were recruited and divided into NAFLD group and non-NAFLD group. The stool samples and clinical information were collected. 16 s rRNA sequencing was used to analyze the characteristics of gut microbiota. The patients of AIS with NAFLD were followed-up to evaluate the functional outcomes of disease. The adverse outcomes were determined by modified Rankin scale (mRS) scores at 3 months after stroke. The diagnostic performance of microbial marker in predicting adverse outcomes was assessed by recipient operating characteristic (ROC) curves. Results: Our results showed that the composition of gut microbiota between non-NAFLD group and NAFLD group were different. The characteristic bacteria in the patients of AIS with NAFLD was that the relative abundance of Dorea, Dialister, Intestinibacter and Flavonifractor were decreased, while the relative abundance of Enorma was increased. Moreover, the characteristic microbiota was correlated with many clinical parameters, such as mRS scores, mean arterial pressure and fasting blood glucose level. In addition, ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish functional dependence patients and functional independence patients in AIS with NAFLD (area under curve is 0.765 and 0.882 respectively). Conclusion: These findings revealed the microbial characteristics in patients of AIS with NAFLD, and further demonstrated the predictive capability of characteristic microbiota for adverse outcomes in patients of AIS with NAFLD.

Gut microbiota signature as predictors of adverse outcomes after acute ischemic stroke in patients with hyperlipidemia.

Introduction: The alterations of gut microbiota have been associated with multiple diseases. However, the relationship between gut microbiota and adverse outcomes of hyperlipidemic stroke patients remains unclear. Here we determined the gut microbial signature to predict the poor outcome of acute ischemic stroke (AIS) with hyperlipidemia (POAH). Methods: Fecal samples from hyperlipidemic stroke patients were collected, which further analyzed by 16s rRNA gene sequencing. The diversity, community composition and differential gut microbiota were evaluated. The adverse outcomes were determined by modified Rankin Scale (mRS) scores at 3 months after admission. The diagnostic performance of microbial characteristics in predicting adverse outcomes was assessed by receiver operating characteristic (ROC) curves. Results: Our results showed that the composition and structure of gut microbiota between POAH patients and good outcome of AIS with hyperlipidemia (GOAH) patients were different. The characteristic gut microbiota of POAH patients was that the relative abundance of Enterococcaceae and Enterococcus were increased, while the relative abundance of Lachnospiraceae, Faecalibacterium, Rothia and Butyricicoccus were decreased. Moreover, the characteristic gut microbiota were correlated with many clinical parameters, such as National Institutes of Health Stroke Scale (NIHSS) score, mean arterial pressure, and history of cerebrovascular disease. Moreover, the ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish POAH patients and GOAH patients (area under curve is 0.694 and 0.971 respectively). Conclusions: These findings revealed the microbial characteristics of POAH, which highlighted the predictive capability of characteristic microbiota in POAH patients.

Nitrogen modulates early growth and physio-biochemical attributes in fragrant rice grown under cadmium and multiwall carbon nanotubes stresses.

Nitrogen (N) modulates plant growth, but its impact on the early growth and physio-biochemical characteristics of rice under cadmium (Cd) and multiwall carbon nanotubes (MWCNTs) toxicity has received little attention. In this study, a hydroponic experiment was conducted on two fragrant rice cultivars, e.g., Xiangyaxiangzhan (XYXZ) and Yuxiangyouzhan (YXYZ), grown under two N levels (N and 1/4 N) and various Cd and MWCNTs treatments (CK: without CdCl2 or MWCNTs; Cd: 100 µmol L-1 CdCl2; MWCNTs: 100 mg L-1 MWCNTs; and Cd-MWCNTs: 100 µmol L-1 CdCl2 + 100 mg L-1 MWCNTs). Results showed that when compared to CK, the total dry weight of the Cd and MWCNTs treatments did not change under 1/4 N for both varieties, whereas Cd and Cd-MWCNTs treatments resulted in a significant reduction in total dry weight by 18.78-37.85% for XYXZ and the Cd-MWCNTs treatment resulted in a significant reduction in the total dry weight by 20.24% for YXYZ. The changes in total dry weight were linked to changes in the dry weight of the different parts of the plant. Besides, the antioxidant parameters and the enzymes involved in the nitrogen metabolism changed in different varieties and different plant parts under two N levels and various Cd and MWCNTs treatments. In addition, differences in total dry weight changes at the N levels and various Cd and MWCNTs treatments were identified between the two varieties, and the relations between total dry weight and other investigated parameters indicated that the modulation processes varied between varieties. Overall, N modulates the early growth and physio-biochemical attributes in fragrant rice seedlings under Cd, MWCNTs, and their combined toxicity.