Microbial influences on severity and sex bias of systemic autoimmunity.

Commensal microbes have the capacity to affect development and severity of autoimmune diseases. Germ-free (GF) animals have proven to be a fine tool to obtain definitive answers to the queries about the microbial role in these diseases. Moreover, GF and gnotobiotic animals can be used to dissect the complex symptoms and determine which are regulated (enhanced or attenuated) by microbes. These include disease manifestations that are sex biased. Here, we review comparative analyses conducted between GF and Specific-Pathogen Free (SPF) mouse models of autoimmunity. We present data from the B6;NZM-Sle1NZM2410/AegSle2NZM2410/AegSle3NZM2410/Aeg-/LmoJ (B6.NZM) mouse model of systemic lupus erythematosus (SLE) characterized by multiple measurable features. We compared the severity and sex bias of SPF, GF, and ex-GF mice and found variability in the severity and sex bias of some manifestations. Colonization of GF mice with the microbiotas taken from B6.NZM mice housed in two independent institutions variably affected severity and sexual dimorphism of different parameters. Thus, microbes regulate both the severity and sexual dimorphism of select SLE traits. The sensitivity of particular trait to microbial influence can be used to further dissect the mechanisms driving the disease. Our results demonstrate the complexity of the problem and open avenues for further investigations.

TRIBAL: Tree Inference of B cell Clonal Lineages.

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies.

Microbiota-dependent proteolysis of gluten subverts diet-mediated protection against type 1 diabetes.

Diet and commensals can affect the development of autoimmune diseases like type 1 diabetes (T1D). However, whether dietary interventions are microbe-mediated was unclear. We found that a diet based on hydrolyzed casein (HC) as a protein source protects non-obese diabetic (NOD) mice in conventional and germ-free (GF) conditions via improvement in the physiology of insulin-producing cells to reduce autoimmune activation. The addition of gluten (a cereal protein complex associated with celiac disease) facilitates autoimmunity dependent on microbial proteolysis of gluten: T1D develops in GF animals monocolonized with Enterococcus faecalis harboring secreted gluten-digesting proteases but not in mice colonized with protease deficient bacteria. Gluten digestion by E. faecalis generates T cell-activating peptides and promotes innate immunity by enhancing macrophage reactivity to lipopolysaccharide (LPS). Gnotobiotic NOD Toll4-negative mice monocolonized with E. faecalis on an HC + gluten diet are resistant to T1D. These findings provide insights into strategies to develop dietary interventions to help protect humans against autoimmunity.

In Vivo Genome-Wide PGR Binding in Pregnant Human Myometrium Identifies Potential Regulators of Labor.

The alterations in myometrial biology during labor are not well understood. The myometrium is the contractile portion of the uterus and contributes to labor, a process that may be regulated by the steroid hormone progesterone. Thus, human myometrial tissues from term pregnant in-active-labor (TIL) and term pregnant not-in-labor (TNIL) subjects were used for genome-wide analyses to elucidate potential future preventive or therapeutic targets involved in the regulation of labor. Using myometrial tissues directly subjected to RNA sequencing (RNA-seq), progesterone receptor (PGR) chromatin immunoprecipitation sequencing (ChIP-seq), and histone modification ChIP-seq, we profiled genome-wide changes associated with gene expression in myometrial smooth muscle tissue in vivo. In TIL myometrium, PGR predominantly occupied promoter regions, including the classical progesterone response element, whereas it bound mainly to intergenic regions in TNIL myometrial tissue. Differential binding analysis uncovered over 1700 differential PGR-bound sites between TIL and TNIL, with 1361 sites gained and 428 lost in labor. Functional analysis identified multiple pathways involved in cAMP-mediated signaling enriched in labor. A three-way integration of the data for ChIP-seq, RNA-seq, and active histone marks uncovered the following genes associated with PGR binding, transcriptional activation, and altered mRNA levels: ATP11A, CBX7, and TNS1. In vitro studies showed that ATP11A, CBX7, and TNS1 are progesterone responsive. We speculate that these genes may contribute to the contractile phenotype of the myometrium during various stages of labor. In conclusion, we provide novel labor-associated genome-wide events and PGR-target genes that can serve as targets for future mechanistic studies.

Gestational Insulin Resistance Is Mediated by the Gut Microbiome-Indoleamine 2,3-Dioxygenase Axis.

BACKGROUND & AIMS: Normal gestation involves a reprogramming of the maternal gut microbiome (GM) that contributes to maternal metabolic changes by unclear mechanisms. This study aimed to understand the mechanistic underpinnings of the GM-maternal metabolism interaction. METHODS: The GM and plasma metabolome of CD1, NIH-Swiss, and C57 mice were analyzed with the use of 16S rRNA sequencing and untargeted liquid chromatography-mass spectrometry throughout gestation. Pharmacologic and genetic knockout mouse models were used to identify the role of indoleamine 2,3-dioxygenase (IDO1) in pregnancy-associated insulin resistance (IR). Involvement of gestational GM was studied with the use of fecal microbial transplants (FMTs). RESULTS: Significant variation in GM alpha diversity occurred throughout pregnancy. Enrichment in gut bacterial taxa was mouse strain and pregnancy time point specific, with the species enriched at gestation day 15/19 (G15/19), a point of heightened IR, being distinct from those enriched before or after pregnancy. Metabolomics revealed elevated plasma kynurenine at G15/19 in all 3 mouse strains. IDO1, the rate-limiting enzyme for kynurenine production, had increased intestinal expression at G15, which was associated with mild systemic and gut inflammation. Pharmacologic and genetic inhibition of IDO1 inhibited kynurenine levels and reversed pregnancy-associated IR. FMT revealed that IDO1 induction and local kynurenine level effects on IR derive from the GM in both mouse and human pregnancy. CONCLUSIONS: GM changes accompanying pregnancy shift IDO1-dependent tryptophan metabolism toward kynurenine production, intestinal inflammation, and gestational IR, a phenotype reversed by genetic deletion or inhibition of IDO1. (Gestational Gut Microbiome-IDO1 Axis Mediates Pregnancy Insulin Resistance; EMBL-ENA ID: PRJEB45047. MetaboLights ID: MTBLS3598).

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination.

Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNAseq data into a low-dimensional manifold that maps the transcriptional states a cell can occupy. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results revealed an ordering of key transcription factors regulating CSR to the IgG1 isotype, including the concomitant expression of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors suggest a poised IL-4 signaling state that preceeds CSR to the IgG1 isotype.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks.

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through "Guilt by Association". Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

Predicting Host Phenotype Based on Gut Microbiome Using a Convolutional Neural Network Approach.

Accurate prediction of the host phenotypes from a microbial sample and identification of the associated microbial markers are important in understanding the impact of the microbiome on the pathogenesis and progression of various diseases within the host. A deep learning tool, PopPhy-CNN, has been developed for the task of predicting host phenotypes using a convolutional neural network (CNN). By representing samples as annotated taxonomic trees and further representing these trees as matrices, PopPhy-CNN utilizes the CNN's innate ability to explore locally similar microbes on the taxonomic tree. Furthermore, PopPhy-CNN can be used to evaluate the importance of each taxon in the prediction of host status. Here, we describe the underlying methodology, architecture, and core utility of PopPhy-CNN. We also demonstrate the use of PopPhy-CNN on a microbial dataset.

PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data.

Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multi-class classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa.

Integrating RNA expression and visual features for immune infiltrate prediction.

Patient responses to cancer immunotherapy are shaped by their unique genomic landscape and tumor microenvironment. Clinical advances in immunotherapy are changing the treatment landscape by enhancing a patient's immune response to eliminate cancer cells. While this provides potentially beneficial treatment options for many patients, only a minority of these patients respond to immunotherapy. In this work, we examined RNA-seq data and digital pathology images from individual patient tumors to more accurately characterize the tumor-immune microenvironment. Several studies implicate an inflamed microenvironment and increased percentage of tumor infiltrating immune cells with better response to specific immunotherapies in certain cancer types. We developed NEXT (Neural-based models for integrating gene EXpression and visual Texture features) to more accurately model immune infiltration in solid tumors. To demonstrate the utility of the NEXT framework, we predicted immune infiltrates across four different cancer types and evaluated our predictions against expert pathology review. Our analyses demonstrate that integration of imaging features improves prediction of the immune infiltrate. Of note, this effect was preferentially observed for B cells and CD8 T cells. In sum, our work effectively integrates both RNA-seq and imaging data in a clinical setting and provides a more reliable and accurate prediction of the immune composition in individual patient tumors.

Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks.

Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an early age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. In this work, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life (0-3 years) from subjects' longitudinal gut microbiome profiles. Using the DIABIMMUNE dataset, we show an increase in predictive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Network, Support Vector Machine, Random Forest, and LASSO regression. We further evaluated whether the training of LSTM networks benefits from reduced representations of microbial features. We considered sparse autoencoder for extraction of potential latent representations in addition to standard feature selection procedures based on Minimum Redundancy Maximum Relevance (mRMR) and variance prior to the training of LSTM networks. The comprehensive evaluation reveals that LSTM networks with the mRMR selected features achieve significantly better performance compared to the other tested machine learning models.

Using convolutional neural networks to explore the microbiome.

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.