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A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases., Importance: We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases., Competing Interests: The authors declare no conflict of interest.

Abundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. We created Jasper, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a " Microbiome Map ", where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order taxa in multiple ways, and the resulting microbiome maps can highlight "hot spots" of microbes that are dominant in taxonomic clades or biological conditions. We use Jasper to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Valdes, Stebliankin, Ruiz-Perez, Park, Lee and Narasimhan.)

Vaginal dysbiosis-induced by an overgrowth of anaerobic bacteria is referred to as bacterial vaginosis (BV). The dysbiosis is associated with an increased risk for acquisition of sexually transmitted infections. Women with symptomatic BV are treated with oral metronidazole (MET), but its effectiveness remains to be elucidated. This study used whole-genome sequencing (WGS) to determine the changes in the microbiota among women treated with MET. WGS was conducted on DNA obtained from 20 vaginal swabs collected at four time points over 12 months from five randomly selected African American (AA) women. The baseline visit included all women who were diagnosed with asymptomatic BV and were untreated. All subjects were tested subsequently once every 2 months and received a course of MET for each BV episode during the 12 months. The BV status was classified according to Nugent scores (NSs) of vaginal smears. The microbial and resistome profiles were analysed along with the sociodemographic metadata. Despite treatment, none of the five participants reverted to normal vaginal flora - two were consistently positive for BV, and the rest experienced episodic cases of BV. WGS analyses showed Gardnerella spp. as the most abundant organism. After treatment with MET, there was an observed decline of Lactobacillus and Prevotella species. One participant had a healthy vaginal microbiota based on NS at one follow-up time point. Resistance genes including tetM and lscA were detected. Though limited in subjects, this study shows specific microbiota changes with treatment, presence of many resistant genes in their microbiota, and recurrence and persistence of BV despite MET treatment. Thus, MET may not be an effective treatment option for asymptomatic BV, and whole metagenome sequence would better inform the choice of antibiotics., Competing Interests: The authors declare that there are no conflicts of interest., (© 2021 The Authors.)

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact., (Copyright © 2021 Ruiz-Perez et al.)

Background: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations., Results: In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders., Conclusions: BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.

Background: Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA)., Results: We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at http://biorg.cs.fiu.edu/plsda CONCLUSIONS: Our results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models.

Long-term survival after lung transplantation remains profoundly limited by graft rejection. Recent work has shown that bronchus-associated lymphoid tissue (BALT), characterized by the development of peripheral nodal addressin (PNAd)-expressing high endothelial venules and enriched in B and Foxp3 + T cells, is important for the maintenance of allograft tolerance. Mechanisms underlying BALT induction in tolerant pulmonary allografts, however, remain poorly understood. Here, we show that the development of PNAd-expressing high endothelial venules within intragraft lymphoid follicles and the recruitment of B cells, but not Foxp3 + cells depends on IL-22. We identify graft-infiltrating gamma-delta (γδ) T cells and Type 3 innate lymphoid cells (ILC3s) as important producers of IL-22. Reconstitution of IL-22 at late time points through retransplantation into wildtype hosts mediates B cell recruitment into lymphoid follicles within the allograft, resulting in a significant increase in their size, but does not induce PNAd expression. Our work has identified cellular and molecular requirements for the induction of BALT in pulmonary allografts during tolerance induction and may provide a platform for the development of new therapies for lung transplant patients., (© 2019 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background: Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data., Results: Here, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions. The extended CGBayesNets package is freely available under the MIT Open Source license agreement. The source code and documentation can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public ., Conclusions: We propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.

Non-apoptotic forms of cell death can trigger sterile inflammation through the release of danger-associated molecular patterns, which are recognized by innate immune receptors. However, despite years of investigation the mechanisms which initiate inflammatory responses after heart transplantation remain elusive. Here, we demonstrate that ferrostatin-1 (Fer-1), a specific inhibitor of ferroptosis, decreases the level of pro-ferroptotic hydroperoxy-arachidonoyl-phosphatidylethanolamine, reduces cardiomyocyte cell death and blocks neutrophil recruitment following heart transplantation. Inhibition of necroptosis had no effect on neutrophil trafficking in cardiac grafts. We extend these observations to a model of coronary artery ligation-induced myocardial ischemia reperfusion injury where inhibition of ferroptosis resulted in reduced infarct size, improved left ventricular systolic function, and reduced left ventricular remodeling. Using intravital imaging of cardiac transplants, we uncover that ferroptosis orchestrates neutrophil recruitment to injured myocardium by promoting adhesion of neutrophils to coronary vascular endothelial cells through a TLR4/TRIF/type I IFN signaling pathway. Thus, we have discovered that inflammatory responses after cardiac transplantation are initiated through ferroptotic cell death and TLR4/Trif-dependent signaling in graft endothelial cells. These findings provide a platform for the development of therapeutic strategies for heart transplant recipients and patients, who are vulnerable to ischemia reperfusion injury following restoration of coronary blood flow.

Ischemia-reperfusion injury, a form of sterile inflammation, is the leading risk factor for both short-term mortality following pulmonary transplantation and chronic lung allograft dysfunction. While it is well recognized that neutrophils are critical mediators of acute lung injury, processes that guide their entry into pulmonary tissue are not well understood. Here, we found that CCR2+ classical monocytes are necessary and sufficient for mediating extravasation of neutrophils into pulmonary tissue during ischemia-reperfusion injury following hilar clamping or lung transplantation. The classical monocytes were mobilized from the host spleen, and splenectomy attenuated the recruitment of classical monocytes as well as the entry of neutrophils into injured lung tissue, which was associated with improved graft function. Neutrophil extravasation was mediated by MyD88-dependent IL-1β production by graft-infiltrating classical monocytes, which downregulated the expression of the tight junction-associated protein ZO-2 in pulmonary vascular endothelial cells. Thus, we have uncovered a crucial role for classical monocytes, mobilized from the spleen, in mediating neutrophil extravasation, with potential implications for targeting of recipient classical monocytes to ameliorate pulmonary ischemia-reperfusion injury in the clinic.