Natural Phosphodiesterase-4 Inhibitors with Potential Anti-Inflammatory Activities from Millettia dielsiana.

The results of in silico screening of the 50 isolated compounds from Millettia dielsiana against the target proteins PDE4 (PDE4A, PDE4B, and PDE4D) showed binding affinity ranges from -5.81 to -11.56, -5.27 to -13.01, and -5.80 to -12.12 kcal mol-1, respectively, with median values of -8.83, -8.84, and -8.645 kcal mol-1, respectively. Among these compounds, Millesianin F was identified as the most promising PDE4A inhibitor due to its strongest binding affinity with the target protein PDE4A. (-11.56 kcal mol-1). This was followed by the compound 5,7,4'-trihydroxyisoflavone 7-O-ß-d-apiofuranosyl-(1→6)-ß-d-glucopyranoside (D50) with the binding affinity value of -11.35 kcal mol-1. For the target protein PDE4B, compound D50 exhibited the strongest binding affinity value of -13.01 kcal mol-1, while showing poorer inhibition ability for PDE4D. The 100 ns MD simulation examination (radius of gyration, Solvent Accessible Surface Area (SASA), Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), and hydrogen bonding) was carried out to examine the overall stability and binding efficiency of the protein-ligand complex between compounds (Millesianin F, Millesianin G, Claclrastin-7-O-ß-d-glucopyranoside, 7-hydroxy-4',6 dimethoxyisoflavone-7-O-ß-d-apiofuranosyl-(1→6)-ß-d-glucopyranoside, 7-hydroxy-4',8-dimethoxyisoflavone 7-O-ß-d-apiofuranosyl-(1→6)-ß-d-glucopyranoside, Odoratin-7-O-ß-d-glucopyranoside, and 5,7,4'-trihydroxyisoflavone 7-O-ß-d-apiofuranosyl-(1→6)-ß-d-glucopyranoside) and PDE4 (A, B) subtype proteins. Compound D50 has shown strong anti-inflammatory activity, as evidenced by experimental results. It effectively inhibits PDE4B and PDE4D, with IC50 values of 6.56 ± 0.7 µM and 11.74 ± 1.3 µM, respectively. Additionally, it reduces NO production, with an IC50 value of 5.40 ± 0.9 µM. Based on these findings, it is promising and considered a potential novel anti-inflammatory drug for future development.

Hypereosinophilic vasculitis with Raynaud phenomenon presentation: a case report.

BACKGROUND: Previous case series have reported idiopathic eosinophilic vasculitis as a potential manifestation of hypereosinophilic syndrome (HES). This condition is characterized by digital necrotizing, systemic vasculitis that affects varying-sized blood vessels. This report presents our experience in treating a patient with eosinophilic vasculitis. CASE PRESENTATION: We describe the case of a 23-year-old man who presented with idiopathic HES, which manifested as digital ulcers and peripheral ischemia in both the upper and lower limbs, without the involvement of other organ systems. After ruling out primary and secondary causes of eosinophilia, a diagnosis of HES was established. Our patient has shown a positive response to corticosteroid therapy. CONCLUSIONS: Our case contributes to the existing evidence about diagnosing idiopathic eosinophilic vasculitis in patients with HES. We observed a favorable response to corticosteroid treatment in our patient.

IL11 activates the placental inflammasome to drive preeclampsia.

Introduction: Preeclampsia is a life-threatening disorder of pregnancy unique to humans. Interleukin (IL)11 is elevated in serum from pregnancies that subsequently develop early-onset preeclampsia and pharmacological elevation of IL11 in pregnant mice causes the development of early-onset preeclampsia-like features (hypertension, proteinuria, and fetal growth restriction). However, the mechanism by which IL11 drives preeclampsia is unknown. Method: Pregnant mice were administered PEGylated (PEG)IL11 or control (PEG) from embryonic day (E)10-16 and the effect on inflammasome activation, systolic blood pressure (during gestation and at 50/90 days post-natal), placental development, and fetal/post-natal pup growth measured. RNAseq analysis was performed on E13 placenta. Human 1st trimester placental villi were treated with IL11 and the effect on inflammasome activation and pyroptosis identified by immunohistochemistry and ELISA. Result: PEGIL11 activated the placental inflammasome causing inflammation, fibrosis, and acute and chronic hypertension in wild-type mice. Global and placental-specific loss of the inflammasome adaptor protein Asc and global loss of the Nlrp3 sensor protein prevented PEGIL11-induced fibrosis and hypertension in mice but did not prevent PEGIL11-induced fetal growth restriction or stillbirths. RNA-sequencing and histology identified that PEGIL11 inhibited trophoblast differentiation towards spongiotrophoblast and syncytiotrophoblast lineages in mice and extravillous trophoblast lineages in human placental villi. Discussion: Inhibition of ASC/NLRP3 inflammasome activity could prevent IL11-induced inflammation and fibrosis in various disease states including preeclampsia.

Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort.

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D.Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case's onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood.Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation.KEY MESSAGESThe ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.

Effects of High-Intensity Position-Specific Drills on Physical and Technical Skill Performance in Elite Youth Soccer Players.

ABSTRACT: Cuong Le, C, Ma'ayah, F, Nosaka, K, Hiscock, D, and Latella, C. Effects of high-intensity position-specific drills on physical and technical skill performance in elite youth soccer players. J Strength Cond Res 37(5): e332-e340, 2023-Soccer physical preparation has been extensively researched with previous emphasis on high-intensity interval running and small-sided games. However, neither approach considers positional differences. The purpose of this study was to investigate the feasibility and short-term effects of a novel position-specific conditioning training (PSCT) paradigm on physical and technical abilities of young soccer players. Fifteen male Vietnamese professional youth soccer players (16.1 ± 0.4 years, 171.7 ± 4.8 cm, 63.9 ± 3.8 kg) undertook a 3-week control period followed by a 3-week intervention with PSCT drills performed twice per week. Position-specific conditioning training comprised purposely designed drills for attackers, defenders, and wingers, respectively. The intensity and duration were the same for all drills (4 × 4 minutes at ∼90% heart rate maximum [HRmax], separated by a 4-minute recovery at 70% HRmax) but differed in the technical and tactical actions performed. Outcome measures included Yo-Yo intermittent recovery test level 1, repeated sprint ability, 10-m and 30-m sprint time, and the Loughborough Soccer Passing Test for technical skills in a fatigued and nonfatigued state. Position-specific conditioning training drills induced a desirable intensity for effective conditioning purpose (89.0 ± 2.1% HRmax) with low interplayer variability (coefficient of variation = 2.4%). Yo-Yo intermittent recovery test level 1 performance improved ( p < 0.05) after the control (Δ178.7 ± 203.3 m) and intervention (Δ176.0 ± 225.7 m) periods without a difference between. These results confirmed the feasibility of PSCT as a novel high-intensity training approach for soccer players. Improvements in aerobic capacity were noted, despite no effect on other physical and technical measures. PSCT may be suitable for individual training, return-to-play stages of rehabilitation, during off-season, or in academy settings when time is not a constraint.

PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data.

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.

Multivariate Analysis with the R Package mixOmics.

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Application of deep learning image reconstruction algorithm in low-dose abdominal CT / 西安交通大学学报(医学版)

【Objective】 To investigate the value of deep learning image reconstruction (DLIR) in improving image quality and reducing beam-hardening artifacts of low-dose abdominal CT. 【Methods】 For this study we prospectively enrolled 26 patients (14 males and 12 females, mean age of 60.35±10.89 years old) who underwent CT urography between October 2019 and June 2020. All the patients underwent conventional-dose unenhanced CT and contrast-enhanced CT in the portal venous phase (noise index of 10; volume computed tomographic dose index: 9.61 mGy) and low-dose CT in the excretory phase(noise index of 23; volume computed tomographic dose index: 2.95 mGy). CT images in the excretory phase were reconstructed using four algorithms: ASiR-V 50%, DLIR-L, DLIR-M, and DLIR-H. Repeated measures ANOVA and Kruskal-Wallis H test were used to compare the quantitative (skewness, noise, SNR, CNR) and qualitative (image quality, noise, beam-hardening artifacts) values among the four image groups. Post hoc comparisons were performed using Bonferroni test. 【Results】 In either quantitative or qualitative evaluation, the SNR, CNR, overall image quality score, and noise of DLIR images were similar or better than ASiR-V 50%. In addition, the SNR, CNR, and overall image quality scores increased as the DLIR weight increased, while the noise decreased. There was no statistically significant difference in the distortion artifacts (P=0.776) and contrast-induced beam-hardening artifacts (P=0.881) scores among these groups. 【Conclusion】 Compared with the ASiR-V 50% algorithm, DLIR algorithm, especially DLIR-M and DLIR-H, can significantly improve the image quality of low-dose abdominal CT, but has limitations in reducing contrast-induced beam-hardening artifacts.

Application of one-beat acquisition with wide detector CT in coronary artery CT angiography of patients with atrial fibrillation / 西安交通大学学报(医学版)

【Objective】 To evaluate the effect of one-beat acquisition with wide detector CT on the image quality and diagnostic efficiency of coronary CT angiography (CCTA) in patients with atrial fibrillation. 【Methods】 A total of 52 consecutive patients with atrial fibrillation, including 31 males, (67.32±11.45) years old, who underwent CCTA from July 2022 to February 2023, were analyzed retrospectively. All patients underwent one-beat acquisition CCTA. The subjective and objective image quality of the coronary arteries was evaluated, and using invasive coronary catheter angiography as the gold standard, the diagnostic efficacy of stenosis degrees above moderate and severe degrees was calculated, respectively. 【Results】 Subjective evaluation results: 92.31% (384/416) of the vascular segments were rated as excellent or good, and the diagnosable rate reached 98.08% (408/416, subjective score ≥3 points). Objective evaluation results: The CT value of the right coronary artery, anterior descending branch, and circumflex branch was (433.41±95.17)HU, (422.69±92.81)HU and (420.27±95.43)HU, respectively; the contrast-to-noise ratio was 38.46±7.54, 32.46±13.78 and 37.74±8.89, respectively. The total diagnostic accuracy, sensitivity, and specificity was 94.71%, 87.9% and 96.62%, respectively, for moderate stenosis and 96.15%, 83.64% and 98.06% for severe stenosis. 【Conclusion】 One-beat acquisition with wide detector CT can obtain high-quality coronary artery images and high diagnostic accuracy for patients with atrial fibrillation without radiation dose increase to patients. It has good clinical application value for patients with atrial fibrillation.

Randomized phase I trial of antigen-specific tolerizing immunotherapy with peptide/calcitriol liposomes in ACPA+ rheumatoid arthritis.

BACKGROUNDAntigen-specific regulation of autoimmune disease is a major goal. In seropositive rheumatoid arthritis (RA), T cell help to autoreactive B cells matures the citrullinated (Cit) antigen-specific immune response, generating RA-specific V domain glycosylated anti-Cit protein antibodies (ACPA VDG) before arthritis onset. Low or escalating antigen administration under "sub-immunogenic" conditions favors tolerance. We explored safety, pharmacokinetics, and immunological and clinical effects of s.c. DEN-181, comprising liposomes encapsulating self-peptide collagen II259-273 (CII) and NF-κB inhibitor 1,25-dihydroxycholecalciferol.METHODSA double-blind, placebo-controlled, exploratory, single-ascending-dose, phase I trial assessed the impact of low, medium, and high DEN-181 doses on peripheral blood CII-specific and bystander Cit64vimentin59-71-specific (Cit-Vim-specific) autoreactive T cell responses, cytokines, and ACPA in 17 HLA-DRB1*04:01+ or *01:01+ ACPA+ RA patients on methotrexate.RESULTSDEN-181 was well tolerated. Relative to placebo and normalized to baseline values, Cit-Vim-specific T cells decreased in patients administered medium and high doses of DEN-181. Relative to placebo, percentage of CII-specific programmed cell death 1+ T cells increased within 28 days of DEN-181. Exploratory analysis in DEN-181-treated patients suggested improved RA disease activity was associated with expansion of CII-specific and Cit-Vim-specific T cells; reduction in ACPA VDG, memory B cells, and inflammatory myeloid populations; and enrichment in CCR7+ and naive T cells. Single-cell sequencing identified T cell transcripts associated with tolerogenic TCR signaling and exhaustion after low or medium doses of DEN-181.CONCLUSIONThe safety and immunomodulatory activity of low/medium DEN-181 doses provide rationale to further assess antigen-specific immunomodulatory therapy in ACPA+ RA.TRIAL REGISTRATIONAnzctr.org.au identifier ACTRN12617001482358, updated September 8, 2022.FUNDINGInnovative Medicines Initiative 2 Joint Undertaking (grant agreement 777357), supported by European Union's Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations; Arthritis Queensland; National Health and Medical Research Council (NHMRC) Senior Research Fellowship; and NHMRC grant 2008287.

Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples.

Fecal samples are frequently used to characterize bacterial populations of the gastrointestinal tract. A protocol is provided to profile gut bacterial populations using rodent fecal samples. We describe the optimal procedures for collecting rodent fecal samples, isolating genomic DNA, 16S rRNA gene V4 region sequencing, and bioinformatic analyses. This protocol includes detailed instructions and example outputs to ensure accurate, reproducible results and data visualization. Comprehensive troubleshooting and limitation sections address technical and statistical issues that may arise when profiling microbiota. For complete details on the use and execution of this protocol, please refer to Gubert et al. (2022).

Faecal microbiota transplant ameliorates gut dysbiosis and cognitive deficits in Huntington's disease mice.

Huntington's disease is a neurodegenerative disorder involving psychiatric, cognitive and motor symptoms. Huntington's disease is caused by a tandem-repeat expansion in the huntingtin gene, which is widely expressed throughout the brain and body, including the gastrointestinal system. There are currently no effective disease-modifying treatments available for this fatal disorder. Despite recent evidence of gut microbiome disruption in preclinical and clinical Huntington's disease, its potential as a target for therapeutic interventions has not been explored. The microbiota-gut-brain axis provides a potential pathway through which changes in the gut could modulate brain function, including cognition. We now show that faecal microbiota transplant (FMT) from wild-type into Huntington's disease mice positively modulates cognitive outcomes, particularly in females. In Huntington's disease male mice, we revealed an inefficiency of FMT engraftment, which is potentially due to the more pronounced changes in the structure, composition and instability of the gut microbial community, and the imbalance in acetate and gut immune profiles found in these mice. This study demonstrates a role for gut microbiome modulation in ameliorating cognitive deficits modelling dementia in Huntington's disease. Our findings pave the way for the development of future therapeutic approaches, including FMT and other forms of gut microbiome modulation, as potential clinical interventions for Huntington's disease.

Statistical challenges in longitudinal microbiome data analysis.

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

Risk assessment with low-pass whole-genome sequencing of cell-free DNA before CD19 CAR T-cell therapy for large B-cell lymphoma.

Patients with relapsed or refractory large B-cell lymphomas (rrLBCL) can achieve long-term remission after CD19 chimeric antigen receptor T-cell therapy (CART19). However, more than half of recipients will experience treatment failure. Thus, approaches are needed to identify high-risk patients who may benefit from alternative or consolidative therapy. We evaluated low-pass whole-genome sequencing (lpWGS) of cell-free DNA (cfDNA) before CART19 as a new approach for risk stratification. We performed lpWGS on pretreatment plasma samples from 122 patients at time of leukapheresis who received standard-of-care CART19 for rrLBCL to define DNA copy number alterations (CNAs). In multivariable selection, high focal CNA score (FCS) denoting genomic instability was the most significant pretreatment variable associated with inferior 3-month complete response rates (28% vs 56%, P = .0029), progression-free survival (PFS; P = .0007; hazard ratio, 2.11), and overall survival (OS; P = .0026; hazard ratio, 2.10). We identified 34 unique focal CNAs in 108 (89%) patients; of these, deletion 10q23.3 leading to loss of FAS death receptor was the most highly associated with poor outcomes, leading to inferior PFS (P < .0001; hazard ratio, 3.49) and OS (P = .0027; hazard ratio, 2.68). By combining FCS with traditional markers of increased tumor bulk (elevated lactate dehydrogenase and >1 extranodal site), we built a simple risk model that could reliably risk stratify patients. Thus, lpWGS of cfDNA is a minimally invasive assay that could rapidly identify high-risk patients and may guide patient selection for and targeted therapies to evaluate in future clinical trials.

Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references.

Characterizing the molecular identity of a cell is an essential step in single-cell RNA sequencing (scRNA-seq) data analysis. Numerous tools exist for predicting cell identity using single-cell reference atlases. However, many challenges remain, including correcting for inherent batch effects between reference and query data andinsufficient phenotype data from the reference. One solution is to project single-cell data onto established bulk reference atlases to leverage their rich phenotype information. Sincast is a computational framework to query scRNA-seq data by projection onto bulk reference atlases. Prior to projection, single-cell data are transformed to be directly comparable to bulk data, either with pseudo-bulk aggregation or graph-based imputation to address sparse single-cell expression profiles. Sincast avoids batch effect correction, and cell identity is predicted along a continuum to highlight new cell states not found in the reference atlas. In several case study scenarios, we show that Sincast projects single cells into the correct biological niches in the expression space of the bulk reference atlas. We demonstrate the effectiveness of our imputation approach that was specifically developed for querying scRNA-seq data based on bulk reference atlases. We show that Sincast is an efficient and powerful tool for single-cell profiling that will facilitate downstream analysis of scRNA-seq data.

Alterations in the Gut Fungal Community in a Mouse Model of Huntington's Disease.

Huntington's disease (HD) is a neurodegenerative disorder caused by a trinucleotide expansion in the HTT gene, which is expressed throughout the brain and body, including the gut epithelium and enteric nervous system. Afflicted individuals suffer from progressive impairments in motor, psychiatric, and cognitive faculties, as well as peripheral deficits, including the alteration of the gut microbiome. However, studies characterizing the gut microbiome in HD have focused entirely on the bacterial component, while the fungal community (mycobiome) has been overlooked. The gut mycobiome has gained recognition for its role in host homeostasis and maintenance of the gut epithelial barrier. We aimed to characterize the gut mycobiome profile in HD using fecal samples collected from the R6/1 transgenic mouse model (and wild-type littermate controls) from 4 to 12 weeks of age, corresponding to presymptomatic through to early disease stages. Shotgun sequencing was performed on fecal DNA samples, followed by metagenomic analyses. The HD gut mycobiome beta diversity was significantly different from that of wild-type littermates at 12 weeks of age, while no genotype differences were observed at the earlier time points. Similarly, greater alpha diversity was observed in the HD mice by 12 weeks of age. Key taxa, including Malassezia restricta, Yarrowia lipolytica, and Aspergillus species, were identified as having a negative association with HD. Furthermore, integration of the bacterial and fungal data sets at 12 weeks of age identified negative correlations between the HD-associated fungal species and Lactobacillus reuteri. These findings provide new insights into gut microbiome alterations in HD and may help identify novel therapeutic targets. IMPORTANCE Huntington's disease (HD) is a fatal neurodegenerative disorder affecting both the mind and body. We have recently discovered that gut bacteria are disrupted in HD. The present study provides the first evidence of an altered gut fungal community (mycobiome) in HD. The genomes of many thousands of gut microbes were sequenced and used to assess "metagenomics" in particular the different types of fungal species in the HD versus control gut, in a mouse model. At an early disease stage, before the onset of symptoms, the overall gut mycobiome structure (array of fungi) in HD mice was distinct from that of their wild-type littermates. Alterations of multiple key fungi species were identified as being associated with the onset of disease symptoms, some of which showed strong correlations with the gut bacterial community. This study highlights the potential role of gut fungi in HD and may facilitate the development of novel therapeutic approaches.

Host Traits and Phylogeny Contribute to Shaping Coral-Bacterial Symbioses.

The success of tropical scleractinian corals depends on their ability to establish symbioses with microbial partners. Host phylogeny and traits are known to shape the coral microbiome, but to what extent they affect its composition remains unclear. Here, by using 12 coral species representing the complex and robust clades, we explored the influence of host phylogeny, skeletal architecture, and reproductive mode on the microbiome composition, and further investigated the structure of the tissue and skeleton bacterial communities. Our results show that host phylogeny and traits explained 14% of the tissue and 13% of the skeletal microbiome composition, providing evidence that these predictors contributed to shaping the holobiont in terms of presence and relative abundance of bacterial symbionts. Based on our data, we conclude that host phylogeny affects the presence of specific microbial lineages, reproductive mode predictably influences the microbiome composition, and skeletal architecture works like a filter that affects bacterial relative abundance. We show that the ß-diversity of coral tissue and skeleton microbiomes differed, but we found that a large overlapping fraction of bacterial sequences were recovered from both anatomical compartments, supporting the hypothesis that the skeleton can function as a microbial reservoir. Additionally, our analysis of the microbiome structure shows that 99.6% of tissue and 99.7% of skeletal amplicon sequence variants (ASVs) were not consistently present in at least 30% of the samples, suggesting that the coral tissue and skeleton are dominated by rare bacteria. Together, these results provide novel insights into the processes driving coral-bacterial symbioses, along with an improved understanding of the scleractinian microbiome.

Gene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome.

Gut dysbiosis in Huntington's disease (HD) has recently been reported using microbiome profiling in R6/1 HD mice and replicated in clinical HD. In HD mice, environmental enrichment (EE) and exercise (EX) were shown to have therapeutic impacts on the brain and associated symptoms. We hypothesize that these housing interventions modulate the gut microbiome, configuring one of the mechanisms that mediate their therapeutic effects observed in HD. We exposed R6/1 mice to a protocol of either EE or EX, relative to standard-housed control conditions, before the onset of gut dysbiosis and motor deficits. We characterized gut structure and function, as well as gut microbiome profiling using 16S rRNA sequencing. Multivariate analysis identified specific orders, namely Bacteroidales, Lachnospirales and Oscillospirales, as the main bacterial signatures that discriminate between housing conditions. Our findings suggest a promising role for the gut microbiome in mediating the effects of EE and EX exposures, and possibly other environmental interventions, in HD mice.

Metaphor-A workflow for streamlined assembly and binning of metagenomes.

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyze genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customizable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly-combining the short-input reads into longer, contiguous fragments (contigs)-and binning, clustering these contigs into individual genome bins. The limitations of assembly and binning algorithms also pose different challenges depending on the selected strategy to execute them. Both of these processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data and by combining multiple binning algorithms with a bin refinement step to achieve high-quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets and the impact of available assembly and binning strategies on the final results.