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.

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.

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.

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.

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.

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.

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.

timeOmics: an R package for longitudinal multi-omics data integration.

MOTIVATION: Multi-omics data integration enables the global analysis of biological systems and discovery of new biological insights. Multi-omics experimental designs have been further extended with a longitudinal dimension to study dynamic relationships between molecules. However, methods that integrate longitudinal multi-omics data are still in their infancy. RESULTS: We introduce the R package timeOmics, a generic analytical framework for the integration of longitudinal multi-omics data. The framework includes pre-processing, modeling and clustering to identify molecular features strongly associated with time. We illustrate this framework in a case study to detect seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in patients with diabetes mellitus from the integrative Human Microbiome Project. AVAILABILITYAND IMPLEMENTATION: timeOmics is available on Bioconductor and github.com/abodein/timeOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interpretation of network-based integration from multi-omics longitudinal data.

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Time-course analysis of metabolomic and microbial responses in anaerobic digesters exposed to ammonia.

Omics longitudinal studies are effective experimental designs to inform on the stability and dynamics of microbial communities in response to perturbations, but time-course analytical frameworks are required to fully exploit the temporal information acquired in this context. In this study we investigate the influence of ammonia on the stability of anaerobic digestion (AD) microbiome with a new statistical framework. Ammonia can severely reduce AD performance. Understanding how it affects microbial communities development and the degradation progress is a key operational issue to propose more stable processes. Thirty batch digesters were set-up with different levels of ammonia. Microbial community structure and metabolomic profiles were monitored with 16 S-metabarcoding and GCMS (gas-chromatography-mass-spectrometry). Digesters were first grouped according to similar degradation performances. Within each group, time profiles of OTUs and metabolites were modelled, then clustered into similar time trajectories, evidencing for example a syntrophic interaction between Syntrophomonas and Methanoculleus that was maintained up to 387 mg FAN/L. Metabolites resulting from organic matter fermentation, such as dehydroabietic or phytanic acid, decreased with increasing ammonia levels. Our analytical framework enabled to fully account for time variability and integrate this parameter in data analysis.

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.