Congenitally underdeveloped intestine drives autism-related gut microbiota and behavior.

Autism spectrum disorder (ASD) is a neurological and developmental disorder accompanied by gut dysbiosis and gastrointestinal symptoms in most cases. However, the development of the autism-related gut microbiota and its relationship with intestinal dysfunction in ASD remain unclear. Using a valproic acid (VPA)-induced ASD mouse model, we showed a congenitally immature intestine of VPA-exposed mice accompanied by prominent oxidative stress and inflammation. Of note, the gut microbiota composition of VPA-exposed mice resembled that of control mice within 24 h after birth; however, their gut microbiota compositions differed on postnatal days 7 and 21. Oral administration of superoxide dismutase (SOD) to attenuate intestinal oxidative stress either before weaning or during juvenile restored the autism-associated gut microbiota, leading to the amelioration of autism-related behaviors. These findings collectively suggest the congenitally underdeveloped intestine as an early driving force shaping the autism-associated gut microbiota and host neurodevelopment through enhancing oxidative stress.

Probiotics are a good choice for the treatment of bacterial vaginosis: a meta-analysis of randomized controlled trial.

BACKGROUND: Bacterial vaginosis (BV) is one of the most common vaginal infectious diseases in female reproductive period. Although the existing view is that probiotic treatment may be one of the feasible methods for the treatment of BV, different intervention methods lead to different treatment results. Therefore, up-to-date and comprehensive evidence in this regard is essential for the development of intervention strategies. OBJECTIVE: This meta-analysis aims to systematically evaluate the role of probiotics in the treatment of BV in adult women. METHODS: We searched the databases of Embase, Cochrane Library, PubMed, Web of Science and ClinicalTrials.gov for Randomized Controlled Trials published until November 7, 2021. Meta-analysis was performed by Revman5.3 software to systematically evaluate the clinical efficacy of probiotics adjunctive therapy in the treatment of BV. The literatures were screened and evaluated according to the inclusion and exclusion criteria. Chi-square test was used to test the heterogeneity between trials. Random or Fixed effect models were used to analyze the cure rate of BV. RESULTS: Fourteen randomized controlled trials compared the efficacy of probiotics with antibiotic therapy (probiotics + antibiotics group) versus antibiotics alone or plus placebo (antibiotics (+ placebo) group) for BV [Risk Ratios (RR) = 1.23, 95% CI (1.05, 1.43), P = 0.009]. Three compared the efficacy of probiotics regimen (probiotics group) and antibiotics (antibiotics group) in the treatment of BV [RR = 1.12, 95% CI (0.60, 2.07), P = 0.72]. Another Three compared the efficacy of probiotics regimen (probiotics group) with placebo (placebo group) [RR = 15.20, 95% CI (3.87, 59.64), P < 0.0001]. CONCLUSION: Our meta-analysis suggests probiotics may play a positive role in the treatment of BV, but more strong evidence is needed.

Our meta-analysis found that probiotics may play an active role in adjuvant treatment of bacterial vaginosis by conventional antibiotic therapy. It was emphasized that oral administration of L. rhamnose was more effective than vaginal application of L. rhamnose in the treatment of bacterial vaginosis. The therapeutic effect of probiotics varies with the administration route and dosage of probiotics.

Rapid gut dysbiosis induced by stroke exacerbates brain infarction in turn.

OBJECTIVE: Stroke is a leading cause of death and disability worldwide. Neuroprotective approaches have failed in clinical trials, thus warranting therapeutic innovations with alternative targets. The gut microbiota is an important contributor to many risk factors for stroke. However, the bidirectional interactions between stroke and gut microbiota remain largely unknown. DESIGN: We performed two clinical cohort studies to capture the gut dysbiosis dynamics after stroke and their relationship with stroke prognosis. Then, we used a middle cerebral artery occlusion model to explore gut dysbiosis post-stroke in mice and address the causative relationship between acute ischaemic stroke and gut dysbiosis. Finally, we tested whether aminoguanidine, superoxide dismutase and tungstate can alleviate post-stroke brain infarction by restoring gut dysbiosis. RESULTS: Brain ischaemia rapidly induced intestinal ischaemia and produced excessive nitrate through free radical reactions, resulting in gut dysbiosis with Enterobacteriaceae expansion. Enterobacteriaceae enrichment exacerbated brain infarction by enhancing systemic inflammation and is an independent risk factor for the primary poor outcome of patients with stroke. Administering aminoguanidine or superoxide dismutase to diminish nitrate generation or administering tungstate to inhibit nitrate respiration all resulted in suppressed Enterobacteriaceae overgrowth, reduced systemic inflammation and alleviated brain infarction. These effects were gut microbiome dependent and indicated the translational value of the brain-gut axis in stroke treatment. CONCLUSIONS: This study reveals a reciprocal relationship between stroke and gut dysbiosis. Ischaemic stroke rapidly triggers gut microbiome dysbiosis with Enterobacteriaceae overgrowth that in turn exacerbates brain infarction.

Gut dysbiosis induces the development of pre-eclampsia through bacterial translocation.

OBJECTIVE: Pre-eclampsia (PE) is one of the malignant metabolic diseases that complicate pregnancy. Gut dysbiosis has been identified for causing metabolic diseases, but the role of gut microbiome in the pathogenesis of PE remains unknown. DESIGN: We performed a case-control study to compare the faecal microbiome of PE and normotensive pregnant women by 16S ribosomal RNA (rRNA) sequencing. To address the causative relationship between gut dysbiosis and PE, we used faecal microbiota transplantation (FMT) in an antibiotic-treated mouse model. Finally, we determined the microbiome translocation and immune responses in human and mouse placental samples by 16S rRNA sequencing, quantitative PCR and in situ hybridisation. RESULTS: Patients with PE showed reduced bacterial diversity with obvious dysbiosis. Opportunistic pathogens, particularly Fusobacterium and Veillonella, were enriched, whereas beneficial bacteria, including Faecalibacterium and Akkermansia, were markedly depleted in the PE group. The abundances of these discriminative bacteria were correlated with blood pressure (BP), proteinuria, aminotransferase and creatinine levels. On successful colonisation, the gut microbiome from patients with PE triggered a dramatic, increased pregestational BP of recipient mice, which further increased after gestation. In addition, the PE-transplanted group showed increased proteinuria, embryonic resorption and lower fetal and placental weights. Their T regulatory/helper-17 balance in the small intestine and spleen was disturbed with more severe intestinal leakage. In the placenta of both patients with PE and PE-FMT mice, the total bacteria, Fusobacterium, and inflammatory cytokine levels were significantly increased. CONCLUSIONS: This study suggests that the gut microbiome of patients with PE is dysbiotic and contributes to disease pathogenesis.

Species-level resolution for the vaginal microbiota with short amplicons.

Specific bacterial species have been found to play important roles in human vagina. Achieving high species-level resolution is vital for analyzing vaginal microbiota data. However, contradictory conclusions were yielded from different methodological studies. More comprehensive evaluation is needed for determining an optimal pipeline for vaginal microbiota. Based on the sequences of vaginal bacterial species downloaded from NCBI, we conducted simulated amplification with various primer sets targeting different 16S regions as well as taxonomic classification on the amplicons applying different combinations of algorithms (BLAST+, VSEARCH, and Sklearn) and reference databases (Greengenes2, SILVA, and RDP). Vaginal swabs were collected from participants with different vaginal microecology to construct 16S full-length sequenced mock communities. Both computational and experimental amplifications were performed on the mock samples. Classification accuracy of each pipeline was determined. Microbial profiles were compared between the full-length and partial 16S sequencing samples. The optimal pipeline was further validated in a multicenter cohort against the PCR results of common STI pathogens. Pipeline V1-V3_Sklearn_Combined had the highest accuracy for classifying the amplicons generated from both the NCBI downloaded data (84.20% ± 2.39%) and the full-length sequencing data (95.65% ± 3.04%). Vaginal samples amplified and sequenced targeting the V1-V3 region but merely employing the forward reads (223 bp) and classified using the optimal pipeline, resembled the mock communities the most. The pipeline demonstrated high F1-scores for detecting STI pathogens within the validation cohort. We have determined an optimal pipeline to achieve high species-level resolution for vaginal microbiota with short amplicons, which will facilitate future studies.IMPORTANCEFor vaginal microbiota studies, diverse 16S rRNA gene regions were applied for amplification and sequencing, which affect the comparability between different studies as well as the species-level resolution of taxonomic classification. We conducted comprehensive evaluation on the methods which influence the accuracy for the taxonomic classification and established an optimal pipeline to achieve high species-level resolution for vaginal microbiota with short amplicons, which will facilitate future studies.

Regional perception and multi-scale feature fusion network for cardiac segmentation.

Objective.Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach.Approach.In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information.Main results.The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods.Significance.Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.

Limosilactobacillus reuteri ameliorates preeclampsia in mice via improving gut dysbiosis and endothelial dysfunction.

BACKGROUND: As a leading cause of maternal and neonatal morbidity and mortality, preeclampsia (PE) remains enigmatic. We confirmed that PE is associated with gut dysbiosis. However, whether probiotics could improve PE via regulating gut microbiota remains unclear. Exploring specific probiotic that could alleviate PE by modulating gut microbiota and elucidating detailed mechanisms will be essential for treating PE. METHODS: PE model was induced by nitric oxide (NO) inhibition with L-NAME in mice, and treated with Limosilactobacillus reuteri (L. reuteri). 16 S rDNA analysis was conducted on feces from mice. In vitro and in vivo experiments were performed to explore the roles and mechanisms of L. reuteri in PE. RESULTS: NO inhibition by L-NAME induced PE and gut dysbiosis in mice, characterized by differential gut microbiome, reduced alpha diversity, and markedly decreased abundance of Lactobacillales and L. reuteri. Importantly, L. reuteri could improve PE induced by L-NAME in mice, and ameliorate the gut dysbiosis of PE mice, including restorative gut microbiota composition, increased alpha diversity, and upregulated L. reuteri content. Moreover, L. reuteri could improve NO synthesis, angiogenesis, inflammation and oxidative stress of PE mice. Consistently, L. reuteri could ameliorate NO synthesis, endothelial dysfunction and inflammation mediated by L-NAME in vitro. CONCLUSIONS: Our results reveal that L. reuteri could ameliorate PE induced by NO inhibition in mice via improving gut dysbiosis and endothelial dysfunction, supporting gut microbiota serving as therapeutic target for treating PE originated from endothelial dysfunction and L. reuteri protecting patients from PE via modulating gut microbiota and endothelial function.

High-fat diet impairs gut barrier through intestinal microbiota-derived reactive oxygen species.

Gut barrier disruption is a key event in bridging gut microbiota dysbiosis and high-fat diet (HFD)-associated metabolic disorders. However, the underlying mechanism remains elusive. In the present study, by comparing HFD- and normal diet (ND)-treated mice, we found that the HFD instantly altered the composition of the gut microbiota and subsequently damaged the integrity of the gut barrier. Metagenomic sequencing revealed that the HFD upregulates gut microbial functions related to redox reactions, as confirmed by the increased reactive oxygen species (ROS) levels in fecal microbiota incubation in vitro and in the lumen, which were detected using in vivo fluorescence imaging. This microbial ROS-producing capability induced by HFD can be transferred through fecal microbiota transplantation (FMT) into germ-free (GF) mice, downregulating the gut barrier tight junctions. Similarly, mono-colonizing GF mice with an Enterococcus strain excelled in ROS production, damaged the gut barrier, induced mitochondrial malfunction and apoptosis of the intestinal epithelial cells, and exacerbated fatty liver, compared with other low-ROS-producing Enterococcus strains. Oral administration of recombinant high-stability-superoxide dismutase (SOD) significantly reduced intestinal ROS, protected the gut barrier, and improved fatty liver against the HFD. In conclusion, our study suggests that extracellular ROS derived from gut microbiota play a pivotal role in HFD-induced gut barrier disruption and is a potential therapeutic target for HFD-associated metabolic diseases.

DeePVP: Identification and classification of phage virion proteins using deep learning.

BACKGROUND: Many biological properties of phages are determined by phage virion proteins (PVPs), and the poor annotation of PVPs is a bottleneck for many areas of viral research, such as viral phylogenetic analysis, viral host identification, and antibacterial drug design. Because of the high diversity of PVP sequences, the PVP annotation of a phage genome remains a particularly challenging bioinformatic task. FINDINGS: Based on deep learning, we developed DeePVP. The main module of DeePVP aims to discriminate PVPs from non-PVPs within a phage genome, while the extended module of DeePVP can further classify predicted PVPs into the 10 major classes of PVPs. Compared with the present state-of-the-art tools, the main module of DeePVP performs better, with a 9.05% higher F1-score in the PVP identification task. Moreover, the overall accuracy of the extended module of DeePVP in the PVP classification task is approximately 3.72% higher than that of PhANNs. Two application cases show that the predictions of DeePVP are more reliable and can better reveal the compact PVP-enriched region than the current state-of-the-art tools. Particularly, in the Escherichia phage phiEC1 genome, a novel PVP-enriched region that is conserved in many other Escherichia phage genomes was identified, indicating that DeePVP will be a useful tool for the analysis of phage genomic structures. CONCLUSIONS: DeePVP outperforms state-of-the-art tools. The program is optimized in both a virtual machine with graphical user interface and a docker so that the tool can be easily run by noncomputer professionals. DeePVP is freely available at https://github.com/fangzcbio/DeePVP/.

Large-scale characterisation of the pregnancy vaginal microbiome and sialidase activity in a low-risk Chinese population.

Vaginal microbiota-host interactions are linked to preterm birth (PTB), which continues to be the primary cause of global childhood mortality. Due to population size, the majority of PTB occurs in Asia, yet there have been few studies of the pregnancy vaginal microbiota in Asian populations. Here, we characterized the vaginal microbiome of 2689 pregnant Chinese women using metataxonomics and in a subset (n = 819), the relationship between vaginal microbiota composition, sialidase activity and leukocyte presence and pregnancy outcomes. Vaginal microbiota were most frequently dominated by Lactobacillus crispatus or L. iners, with the latter associated with vaginal leukocyte presence. Women with high sialidase activity were enriched for bacterial vaginosis-associated genera including Gardnerella, Atopobium and Prevotella. Vaginal microbiota composition, high sialidase activity and/or leukocyte presence was not associated with PTB risk suggesting underlying differences in the vaginal microbiota and/or host immune responses of Chinese women, possibly accounting for low PTB rates in this population.

Vaginal dysbiosis dominated by miscellaneous bacteria correlates with more severe clinical symptoms.

With the development of next-generation sequencing methods, dysbiosis patterns of the vaginal microbiome have been described worldwide, but the correlation between dysbiosis and clinical symptoms, which is crucial for understanding the vaginal microbiome-host interaction, is largely unknown. The present study identified 4 types of community states in 88 women with vaginal dysbiosis. These community state types (CSTs) were dominated by non-iners Lactobacillus (CST1), L.iners (CST2), and Gardnerella vaginalis (CST3), and a variety of dominant species other than the above three (CST4). We found that urodynia or lumbodynia was more prevalent in women with CST4 than in women with CST3, and cervical hypertrophy was more prevalent in women with CST4 than in women with CST2 and CST3. These results indicate that patients in CST4 may have a greater tendency of vaginal symptoms, and the detailed mechanisms underlying these correlations of specific microbes with host symptoms should be further studied.

[A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after prebiotics consumption].

OBJECTIVE: To investigate the effects of prebiotics supplementation for 9 days on gut microbiota structure and function and establish a machine learning model based on the initial gut microbiota data for predicting the variation of Bifidobacterium after prebiotic intake. METHODS: With a randomized double-blind self-controlled design, 35 healthy volunteers were asked to consume fructo-oligosaccharides (FOS) or galacto-oligosaccharides (GOS) for 9 days (16 g per day). 16S rRNA gene high-throughput sequencing was performed to investigate the changes of gut microbiota after prebiotics intake. PICRUSt was used to infer the differences between the functional modules of the bacterial communities. Random forest model based on the initial gut microbiota data was used to identify the changes in Bifidobacterium after 5 days of prebiotic intake and then to build a continuous index to predict the changes of Bifidobacterium. The data of fecal samples collected after 9 days of GOS intervention were used to validate the model. RESULTS: Fecal samples analysis with QIIME revealed that FOS intervention for 5 days reduced the intestinal flora alpha diversity, which rebounded on day 9; in GOS group, gut microbiota alpha diversity decreased progressively during the intervention. Neither FOS nor GOS supplement caused significant changes in ß diversity of gut microbiota. The area under the curve (AUC) of the prediction model was 89.6%. The continuous index could successfully predict the changes in Bifidobacterium (R=0.45, P=0.01), and the prediction accuracy was verified by the validation model (R=0.62, P=0.01). CONCLUSION: Short-term prebiotics intervention can significantly decrease α-diversity of the intestinal flora. The machine learning model based on initial gut microbiota data can accurately predict the changes in Bifidobacterium, which sheds light on personalized nutrition intervention and precise modulation of the intestinal flora.

Regional variation limits applications of healthy gut microbiome reference ranges and disease models.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression1-3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis4, colorectal cancer prescreening5 and therapeutic choices in melanoma6. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic7 and cardiovascular diseases8. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks.

Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis.

BACKGROUND: The metabolic syndrome (MetS) epidemic is associated with economic development, lifestyle transition and dysbiosis of gut microbiota, but these associations are rarely studied at the population scale. Here, we utilised the Guangdong Gut Microbiome Project (GGMP), the largest Eastern population-based gut microbiome dataset covering individuals with different economic statuses, to investigate the relationships between the gut microbiome and host physiology, diet, geography, physical activity and socioeconomic status. RESULTS: At the population level, 529 OTUs were significantly associated with MetS. OTUs from Proteobacteria and Firmicutes (other than Ruminococcaceae) were mainly positively associated with MetS, whereas those from Bacteroidetes and Ruminococcaceae were negatively associated with MetS. Two hundred fourteen OTUs were significantly associated with host economic status (140 positive and 74 negative associations), and 157 of these OTUs were also MetS associated. A microbial MetS index was formulated to represent the overall gut dysbiosis of MetS. The values of this index were significantly higher in MetS subjects regardless of their economic status or geographical location. The index values did not increase with increasing personal economic status, although the prevalence of MetS was significantly higher in people of higher economic status. With increased economic status, the study population tended to consume more fruits and vegetables and fewer grains, whereas meat consumption was unchanged. Sedentary time was significantly and positively associated with higher economic status. The MetS index showed an additive effect with sedentary lifestyle, as the prevalence of MetS in individuals with high MetS index values and unhealthy lifestyles was significantly higher than that in the rest of the population. CONCLUSIONS: The gut microbiome is associated with MetS and economic status. A prolonged sedentary lifestyle, rather than Westernised dietary patterns, was the most notable lifestyle change in our Eastern population along with economic development. Moreover, gut dysbiosis and a Western lifestyle had an additive effect on increasing MetS prevalence.

Author Correction: Regional variation limits applications of healthy gut microbiome reference ranges and disease models.

In the version of this article originally published, in the sentence "Applying the same approach to obesity (Fig. 2b), MetS (Fig. 2c) and fatty liver (Fig. 2d) yielded similar results," two figure panels were cited incorrectly. The data for obesity are in Fig. 2c, and the data for MetS are in Fig. 2b. The sentence has been updated with the correct citations in the print, PDF and HTML versions of the article.

Impaired renal function and dysbiosis of gut microbiota contribute to increased trimethylamine-N-oxide in chronic kidney disease patients.

Chronic kidney disease (CKD) patients have an increased risk of cardiovascular diseases (CVDs). The present study aimed to investigate the gut microbiota and blood trimethylamine-N-oxide concentration (TMAO) in Chinese CKD patients and explore the underlying explanations through the animal experiment. The median plasma TMAO level was 30.33 µmol/L in the CKD patients, which was significantly higher than the 2.08 µmol/L concentration measured in the healthy controls. Next-generation sequence revealed obvious dysbiosis of the gut microbiome in CKD patients, with reduced bacterial diversity and biased community constitutions. CKD patients had higher percentages of opportunistic pathogens from gamma-Proteobacteria and reduced percentages of beneficial microbes, such as Roseburia, Coprococcus, and Ruminococcaceae. The PICRUSt analysis demonstrated that eight genes involved in choline, betaine, L-carnitine and trimethylamine (TMA) metabolism were changed in the CKD patients. Moreover, we transferred faecal samples from CKD patients and healthy controls into antibiotic-treated C57BL/6 mice and found that the mice that received gut microbes from the CKD patients had significantly higher plasma TMAO levels and different composition of gut microbiota than did the comparative mouse group. Our present study demonstrated that CKD patients had increased plasma TMAO levels due to contributions from both impaired renal functions and dysbiosis of the gut microbiota.

Fructooligosaccharide (FOS) and Galactooligosaccharide (GOS) Increase Bifidobacterium but Reduce Butyrate Producing Bacteria with Adverse Glycemic Metabolism in healthy young population.

The gut microbiota has been implicated in glucose intolerance and its progression towards type-2 diabetes mellitus (T2DM). Relevant randomized clinical trial with prebiotic intervention was inadequate. We sought to evaluate the impact of fructooligosaccharides (FOS) and galactooligosaccharides (GOS) on glycemia during oral glucose tolerance test (OGTT) and intestinal microbiota. A randomized double-blind cross-over study was performed with 35 adults treated with FOS and GOS for 14 days (16 g/day). Faeces sampling, OGTT and anthropometric parameters were performed. Short-term intake of high-dose prebiotics had adverse effect on glucose metabolism, as in FOS intervention demonstrated by OGTT (P < 0.001), and in GOS intervention demonstrated by fasting glucose (P < 0.05). A significant increase in the relative abundance of Bifidobacterium was observed both in FOS and GOS group, while the butyrate-producing bacteria like Phascolarctobacterium in FOS group and Ruminococcus in GOS group were decreased. A random forest model using the initial microbiota was developed to predict OGTT levels after prebiotic intervention with relative success (R = 0.726). Our study alerted even though FOS and GOS increased Bifidobacterium, they might have adverse effect on glucose metabolism by reducing butyrate-producing microbes. Individualized prebiotics intervention based on gut microbiome needs to be evaluated in future.