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Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects. Availability synVep webserver for online query: https://services.bromberglab.org/synvep; For local runs Python script (https://bitbucket.org/bromberglab/synvep_local) and prediction database (https://zenodo.org/record/4763256) are also available.

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

Bioinformatics, 33 (14), ISSN:1367-4803, ISSN:1460-2059

Metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. We developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is nearly 90% accurate in recognizing proteins that bind metal ions and ion containing ligands. Moreover, the identity of ten ubiquitously present metal ions and ion-containing ligands can be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and outperforms other prediction methods, both in speed and accuracy. mebipred can also identify protein metal-binding capabilities from short sequence stretches and, thus, may be useful for the annotation of metagenomic samples metal requirements inferred from translated sequencing reads. We performed an analysis of microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human-hosted microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results are highlight mebipreds utility in analyzing microbiome metal requirements. mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/

Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.

Background After years of concentrated research efforts, the exact cause of Crohn’s disease (CD) remains unknown. Its accurate diagnosis, however, helps in management and preventing the onset of disease. Genome-wide association studies have identified 241 CD loci, but these carry small log odds ratios and are thus diagnostically uninformative. Methods Here, we describe a machine learning method—AVA,Dx (Analysis of Variation for Association with Disease)—that uses exonic variants from whole exome or genome sequencing data to extract CD signal and predict CD status. Using the person-specific coding variation in genes from a panel of only 111 individuals, we built disease-prediction models informative of previously undiscovered disease genes. By additionally accounting for batch effects, we were able to accurately predict CD status for thousands of previously unseen individuals from other panels. Results AVA,Dx highlighted known CD genes including NOD2 and new potential CD genes. AVA,Dx identified 16% (at strict cutoff) of CD patients at 99% precision and 58% of the patients (at default cutoff) with 82% precision in over 3000 individuals from separately sequenced panels. Conclusions Larger training panels and additional features, including other types of genetic variants and environmental factors, e.g., human-associated microbiota, may improve model performance. However, the results presented here already position AVA,Dx as both an effective method for revealing pathogenesis pathways and as a CD risk analysis tool, which can improve clinical diagnostic time and accuracy. Links to the AVA,Dx Docker image and the BitBucket source code are at https://bromberglab.org/project/avadx/.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein’s infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.AvailabilityFreely accessible webserverPredictProtein.org; Source and docker images: github.com/rostlab

The majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a “universal language of life”. LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained “biological language” representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.AvailabilityThe pretrained LookingGlass model and the transfer learning-derived models demonstrated in this paper are available in the LookingGlass release v1.01. The open source fastBio Github repository and python package provides classes and functions for training and fine tuning deep learning models with biological data2. Code for reproducing analyses presented in this paper are available as an open source Github repository3.

Background The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Microbes active in extremely cold environments are not as well explored as those of other extreme environments. Studies have revealed a substantial microbial diversity and identified cold-specific microbiome molecular functions. We analyzed the metagenomes and metatranscriptomes of twenty snow samples collected during early and late spring in Svalbard, Norway using our computational read-based microbiome function annotation tool, mi-faser. Our results revealed a more diverse microbiome functional capacity and activity in the early compared to in the late spring samples. The dissimilarity between the metagenomes and metatranscriptomes of the same samples was also significantly higher in the early spring. These findings suggest that early spring samples may contain a larger fraction of DNA of dormant organisms, while late spring samples reflect a new community that is metabolically active. We additionally showed that the abundance of the sequencing reads mapping to the fatty acid synthesis-related microbial pathways was significantly positively correlated with organic acid levels, in both our late spring metagenomes and metatranscriptomes. Moreover, the geraniol degradation pathway and the styrene degradation pathway read abundances correlated and inversely correlated, respectively, with the organic acid levels. These results suggest a possible nutrient switch. Our study thus highlights the activity of microbial degradation pathways of complex organic compounds previously unreported at low temperatures.

Microbes active in extreme cold are not as well explored as those of other extreme environments. Studies have revealed a substantial microbial diversity and identified cold‐specific microbiome molecular functions. We analyzed the metagenomes and metatranscriptomes of 20 snow samples collected in early and late spring in Svalbard, Norway using mi‐faser, our read‐based computational microbiome function annotation tool. Our results reveal a more diverse microbiome functional capacity and activity in the early‐ vs. late‐spring samples. We also find that functional dissimilarity between the same‐sample metagenomes and metatranscriptomes is significantly higher in early than late spring samples. These findings suggest that early spring samples may contain a larger fraction of DNA of dormant (or dead) organisms, while late spring samples reflect a new, metabolically active community. We further show that the abundance of sequencing reads mapping to the fatty acid synthesis‐related microbial pathways in late spring metagenomes and metatranscriptomes is significantly correlated with the organic acid levels measured in these samples. Similarly, the organic acid levels correlate with the pathway read abundances of geraniol degradation and inversely correlate with those of styrene degradation, suggesting a possible nutrient change. Our study thus highlights the activity of microbial degradation pathways of complex organic compounds previously unreported at low temperatures., Computational analysis of metagenomic and metatranscriptomic data from snow samples collected at different springtimes in Svalbard, Norway suggests that a new microbial community is metabolically active in the late spring, while early spring samples likely overwhelmingly carry genomic residue from prior seasons. Furthermore, the correlation between the sequencing‐based, computationally determined metabolic pathways, and the experimentally measured organic acid concentration highlights active microbial degradation of complex organic compounds previously unreported at low temperatures.

SUMMARY We previously established the contribution of de novo damaging sequence variants to Tourette disorder (TD) through whole-exome sequencing of 511 trios. Here, we sequence an additional 291 TD trios and analyze the combined set of 802 trios. We observe an overrepresentation of de novo damaging variants in simplex, but not multiplex, families; we identify a high-confidence TD risk gene, CELSR3 (cadherin EGF LAG seven-pass G-type receptor 3); we find that the genes mutated in TD patients are enriched for those related to cell polarity, suggesting a common pathway underlying pathobiology; and we confirm a statistically significant excess of de novo copy number variants in TD. Finally, we identify significant overlap of de novo sequence variants between TD and obsessive-compulsive disorder and de novo copy number variants between TD and autism spectrum disorder, consistent with shared genetic risk., In Brief Wang et al. expand their earlier exome-sequencing work in TD, adding 291 trios and conducting combined analyses suggesting de novo variants carry more risk in individuals with unaffected parents, establishing de novo structural variants as risk factors, identifying CELSR3 as a risk gene, and implicating cell polarity in pathogenesis., Graphical Abstract

Motivation The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods. Results Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts. Supplementary information Supplementary data are available at Bioinformatics online.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Venous thromboembolism (VTE) is a common hematological disorder. VTE affects millions of people around the world each year and can be fatal. Earlier studies have revealed the possible VTE genetic risk factors in Europeans. The 2018 Critical Assessment of Genome Interpretation (CAGI) challenge had asked participants to distinguish between 66 VTE and 37 non-VTE African American (AA) individuals based on their exome sequencing data. We used variants from AA VTE association studies and VTE genes from DisGeNET database to evaluate VTE risk via four different approaches; two of these methods were most successful at the task. Our best performing method represented each exome as a vector of predicted functional effect scores of variants within the known genes. These exome vectors were then clustered with k-means. This approach achieved 70.8% precision and 69.7% recall in identifying VTE patients. Our second-best ranked method had collapsed the variant effect scores into gene-level function changes, using the same vector clustering approach for patient/control identification. These results show predictability of VTE risk in AA population and highlight the importance of variant-driven gene functional changes in judging disease status. Of course, more in-depth understanding of AA VTE pathogenicity is still needed for more precise predictions.

The recent years have seen a drastic increase in the amount of available genomic sequences. Alongside this explosion, hundreds of computational tools were developed to assess the impact of observed genetic variation. Critical Assessment of Genome Interpretation (CAGI) provides a platform to evaluate the performance of these tools in experimentally relevant contexts. In the CAGI-5 challenge assessing the 38 missense variants affecting the human Pericentriolar Material 1 protein (PCM1), our SNAP-based submission was the top performer, although it did worse than expected from other evaluations. Here, we compare the CAGI-5 submissions, and 24 additional commonly used variant effect predictors, to analyze the reasons for this observation. We identified per residue conservation, structural, and functional PCM1 characteristics, which may be responsible. As expected, predictors had a hard time distinguishing effect variants in non-conserved positions. They were also better able to call effect variants in a structurally rich region than in a less-structured one; in the latter, they more often correctly identified benign than effect variants. Curiously, most of the protein was predicted to be functionally robust to mutation – a feature that likely makes it a harder problem for generalized variant effect predictors.

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation (CAGI), we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 non-synonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerged as top-performers depending on the metric, it is non-trivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges. ispartof: HUMAN MUTATION vol:40 issue:9 pages:1530-1545 ispartof: location:United States status: published

Background Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now. Results As a part of the 2018 CAMDA challenge, we functionally profiled the available ~ 400 subway metagenomes and built predictor for city origin. In cross-validation, our model reached 81% accuracy when only the top-ranked city assignment was considered and 95% accuracy if the second city was taken into account as well. Notably, this performance was only achievable if the similarity of distribution of cities in the training and testing sets was similar. To assure that our methods are applicable without such biased assumptions we balanced our training data to account for all represented cities equally well. After balancing, the performance of our method was slightly lower (76/94%, respectively, for one or two top ranked cities), but still consistently high. Here we attained an added benefit of independence of training set city representation. In testing, our unbalanced model thus reached (an over-estimated) performance of 90/97%, while our balanced model was at a more reliable 63/90% accuracy. While, by definition of our model, we were not able to predict the microbiome origins previously unseen, our balanced model correctly judged them to be NOT-from-training-cities over 80% of the time. Our function-based outlook on microbiomes also allowed us to note similarities between both regionally close and far-away cities. Curiously, we identified the depletion in mycobacterial functions as a signature of cities in New Zealand, while photosynthesis related functions fingerprinted New York, Porto and Tokyo. Conclusions We demonstrated the power of our high-speed function annotation method, mi-faser, by analysing ~ 400 shotgun metagenomes in 2 days, with the results recapitulating functional signals of different city subway microbiomes. We also showed the importance of balanced data in avoiding over-estimated performance. Our results revealed similarities between both geographically close (Ofa and Ilorin) and distant (Boston and Porto, Lisbon and New York) city subway microbiomes. The photosynthesis related functional signatures of NYC were previously unseen in taxonomy studies, highlighting the strength of functional analysis.

Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. Understanding corresponding sequence changes is also fundamental for synthetic protein design and stability assessments. However, the performance gain of variant effect predictors observed in recent years is not in line with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene/protein features for modeling variant effect, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the in vivo observable variation is arguably weaker in its impact and, thus, requires evaluation at a higher level of resolution. Here we describe function Neutral/Toggle/Rheostat predictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types reflect experimentally determined functional effects and can thus improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

Lecithin:retinol acyltransferase and retinol-binding protein enable vitamin A (VA) storage and transport, respectively, maintaining tissue homeostasis of retinoids (VA derivatives). The precarious VA status of the lecithin:retinol acyltransferase–deficient (Lrat−/−) retinol-binding protein–deficient (Rbp−/−) mice rapidly deteriorates upon dietary VA restriction, leading to signs of severe vitamin A deficiency (VAD). As retinoids impact gut morphology and functions, VAD is often linked to intestinal pathological conditions and microbial dysbiosis. Thus, we investigated the contribution of VA storage and transport to intestinal retinoid homeostasis and functionalities. We showed the occurrence of intestinal VAD in Lrat−/−Rbp−/− mice, demonstrating the critical role of both pathways in preserving gut retinoid homeostasis. Moreover, in the mutant colon, VAD resulted in a compromised intestinal barrier as manifested by reduced mucins and antimicrobial defense, leaky gut, increased inflammation and oxidative stress, and altered mucosal immunocytokine profiles. These perturbations were accompanied by fecal dysbiosis, revealing that the VA status (sufficient vs. deficient), rather than the amount of dietary VA per se, is likely a major initial discriminant of the intestinal microbiome. Our data also pointed to a specific fecal taxonomic profile and distinct microbial functionalities associated with VAD. Overall, our findings revealed the suitability of the Lrat−/−Rbp−/− mice as a model to study intestinal dysfunctions and dysbiosis promoted by changes in tissue retinoid homeostasis induced by the host VA status and/or intake.