Showing papers by "Michael Snyder published in 2023"

Lipid droplets and peroxisomes are co-regulated to drive lifespan extension in response to mono-unsaturated fatty acids

Abstract: Dietary mono-unsaturated fatty acids (MUFAs) are linked to longevity in several species. But the mechanisms by which MUFAs extend lifespan remain unclear. Here we show that an organelle network involving lipid droplets and peroxisomes is critical for MUFA-induced longevity in Caenorhabditis elegans. MUFAs upregulate the number of lipid droplets in fat storage tissues. Increased lipid droplet number is necessary for MUFA-induced longevity and predicts remaining lifespan. Lipidomics datasets reveal that MUFAs also modify the ratio of membrane lipids and ether lipids-a signature associated with decreased lipid oxidation. In agreement with this, MUFAs decrease lipid oxidation in middle-aged individuals. Intriguingly, MUFAs upregulate not only lipid droplet number but also peroxisome number. A targeted screen identifies genes involved in the co-regulation of lipid droplets and peroxisomes, and reveals that induction of both organelles is optimal for longevity. Our study uncovers an organelle network involved in lipid homeostasis and lifespan regulation, opening new avenues for interventions to delay aging.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health.

Abstract: Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Integration of spatial and single-cell data across modalities with weak linkage

Abstract: single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori “linked” features. When such linked features are few or uninformative, a scenario that we call “weak linkage”, existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Biological research and self-driving labs in deep space supported by artificial intelligence

Abstract: Space biology research aims to understand fundamental spaceflight effects on organisms, develop foundational knowledge to support deep space exploration and, ultimately, bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data and model organisms from both spaceborne and ground-analogue studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally automated, light, agile and intelligent to accelerate knowledge discovery. Here we present a summary of decadal recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning and modelling applications that offer solutions to these space biology challenges. The integration of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modelling and analytics, support maximally automated and reproducible experiments, and efficiently manage spaceborne data and metadata, ultimately to enable life to thrive in deep space. Deep space exploration missions will require new technologies that can support astronaut health systems, as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this second of two Review articles based on the findings from the workshop, the intersection between artificial intelligence and space biology is discussed.

Biomonitoring and precision health in deep space supported by artificial intelligence

Abstract: Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses. Deep-space exploration missions require new technologies that can support astronaut health systems as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this first of two Review articles based on the findings from the workshop, a vision for autonomous biomonitoring and precision space health is discussed.

Leveraging Physiology and Artificial Intelligence to Deliver Advancements in Healthcare.

Abstract: Artificial Intelligence (AI) in healthcare has generated remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of AI to transform physiology data to advance healthcare. In this review, we will explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of AI, with special attention to the most relevant AI models. We then detail how physiology data has been harnessed by AI to advance the main areas of healthcare such as automating existing healthcare tasks, increasing access to care, and augmenting healthcare capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying AI models to achieve meaningful clinical impact.

Identification of end‐stage renal disease metabolic signatures from human perspiration

Abstract: End stage renal disease (ESRD), characterized by cessation in kidney function, has been linked to severe metabolic disturbances, caused by buildup of toxic solutes in blood. To remove these solutes, ESRD patients undergo dialysis. As a proof of concept, we tested whether ESRD-related metabolic signatures can be detected in perspiration samples using a combined methodology. Our rapid methodology involves swabbing a glass slide across the patient’s forehead, detecting the metabolites in the imprint using desorption electrospray ionization mass spectrometry, and identifying the key differences using machine learning methods. Based on collecting 42 healthy and 27 ESRD samples, we find saturated fatty acids are consistently suppressed in ESRD patients, with little change after dialysis. Also, our method enables the detection of uremic solutes, where we find elevated levels of uric acid (6.7 fold higher on average) that sharply decrease after dialysis. Beyond the study of individual metabolites, we find that a lasso model, which selects for 8 m/z fragments from 24,602 detected analytes, achieves area under the curve performance of 0.85 and 0.87 on training (n=52) and validation sets (n=17) respectively. Together, these results suggest that this methodology is promising for detecting signatures relevant for Precision Health. Hosted file VShankar_Sweat_Analysis_Paper_Final.docx available at https://authorea.com/users/509712/ articles/587032-identification-of-end-stage-renal-disease-metabolic-signatures-fromhuman-perspiration

Proinflammatory polarization of monocytes by particulate air pollutants is mediated by induction of trained immunity in pediatric asthma

Abstract: The impact of exposure to air pollutants, such as fine particulate matter (PM), on the immune system and its consequences on pediatric asthma, are not well understood. We investigated whether ambient levels of fine PM with aerodynamic diameter ≤2.5 microns (PM2.5) are associated with alterations in circulating monocytes in children with or without asthma.

The Roles and Mechanisms of Gut Microbiota in Food Allergy

Abstract: Recent research reveals that the increasing prevalence of food allergies (FA) is due in part to changes in the commensal microbiome. Studies in humans have shown that compared with healthy controls, individuals have distinct gut microbiomes during the onset and progression of FA. Mechanistic studies have established that the gut microbiota can affect the growth of immune tolerance to food antigens by modifying regulatory T cell differentiation, regulating basophil populations, and enhancing intestinal barrier function. New therapeutic and preventive approaches to altering the gut microbiota using diet adjustments, probiotics, prebiotics, synbiotics, postbiotics, fecal microbiota transplantation, and Chinese medicine have been developed towards FA. Herein, we summarized the latest evidence on the gut microbiota profiles and functions associated with FA, oral tolerance mechanisms, and gut microbiota-targeted therapeutic strategies for FA.

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

Abstract: The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3’ end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3’ processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

Development and Validation of a Prognostic Risk Model for Patients with Advanced Melanoma Treated with Immune Checkpoint Inhibitors.

Abstract: BACKGROUND Risk stratification tools for patients with advanced melanoma (AM) treated with immune checkpoint inhibitors (ICI) are lacking. We identified a new prognostic model associated with overall survival (OS). PATIENTS AND METHODS A total of 318 treatment naïve patients with AM receiving ICI were collected from a multi-centre retrospective cohort study. LASSO Cox regression identified independent prognostic factors associated with OS. Model validation was carried out on 500 iterations of bootstrapped samples. Harrel's C-index was calculated and internally validated to outline the model's discriminatory performance. External validation was carried out in 142 advanced melanoma patients receiving ICI in later lines. RESULTS High white blood cell count (WBC), high lactate dehydrogenase (LDH), low albumin, Eastern Cooperative Oncology Group (ECOG) performance status ≥1, and the presence of liver metastases were included in the model. Patients were parsed into 3 risk groups: favorable (0-1 factors) OS of 52.9 months, intermediate (2-3 factors) OS 13.0 months, and poor (≥4 factors) OS 2.7 months. The C-index of the model from the discovery cohort was 0.69. External validation in later-lines (N = 142) of therapy demonstrated a c-index of 0.65. CONCLUSIONS Liver metastases, low albumin, high LDH, high WBC, and ECOG≥1 can be combined into a prognostic model for AM patients treated with ICI.

Abstract 4742: Familial adenomatous polyposis epigenetic landscape as a precancer model of colorectal cancer

Abstract: Aberrant shifts in DNA methylation have long been regarded as an early biomarker for cancer onset and progression. However, it is unclear when methylation aberrance starts and how it interacts with other epigenomic modifications. To address how epigenomic changes occur and interact during the transformation from normal healthy colon tissue to malignant colorectal cancer (CRC), we collected 51 samples from 15 familial adenomatous polyposis (FAP) and non-FAP colorectal cancer patients. We generated 30-70x of whole-genome enzymatic methylation sequencing (WGEM-seq) data via the novel Ultima Genomics ultra high-throughput sequencing platform. We observed hypermethylation and hypomethylation emerge early in the malignant transformation process in gene promoters and distal regulatory elements. We performed multifaceted analysis on methylation alterations with whole-genome sequencing (WGS), transposase accessibility (ATAC-seq), high-resolution chromatin accessibility (Tri-C), and gene expression (RNA-seq) data. Our multidimensional analysis demonstrates how collectively epigenomic alterations have affected gene expression throughout normal colon mucosa, benign and dysplasia polyps to adenocarcinoma. Epigenomic changes start as early as benign polyps, followed by other epigenomic shifts, including bivalent domains. Various epigenomic aberrances are associated with concomitant gene expression level changes. Our integrative analysis of multi-epigenomics data implicates collective and cumulative epigenomic instability in the early onset of colon carcinogenesis. Citation Format: Hayan Lee, Gat Krieger, Tyson Clark, Yizhou Zhu, Aziz Khan, Casey R. Hanson, Aaron Horning, Edward D. Esplin, Mohan Badu, Kristina Paul, Roxanne Chiu, Bahareh Bahmani, Stephanie Nevins, Annika K. Weimer, Ariel Jaimovich, Christina Curtis, William Greenleaf, James M. Ford, Doron Lipson, Zohar Shipony, Michael P. Snyder. Familial adenomatous polyposis epigenetic landscape as a precancer model of colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4742.

601-P: Heterogeneity in Postprandial Glucose Response to Specific Carbohydrates and Mitigation with Premeal Fat, Fiber, or Protein

Abstract: Glycemic responses to foods are determined by total carbohydrates, glycemic index, and addition of fat, fiber, and protein. Interindividual variability in these responses are not well characterized. We propose that postprandial glucose response (PPGR) exhibits variability between individuals with regard to 1) “worst” or most glucose elevating food; 2) degree of mitigation by fat, fiber, or protein. Forty participants, healthy or with prediabetes, were given a repeated series of 7 standardized food challenges with the same amount of carbohydrates but varying in source (rice, bread, potatoes, pasta, berries, grapes, and beans) while wearing a CGM. We summarized PPGR and constructed a typical response to each food challenge. Rice exhibited the highest peak value and potatoes had the longest return to baseline time. However, cluster analysis revealed heterogeneity in PPGR indicating that the “worst” food differed between individuals. Participants were then pretreated with potential mitigators (fat, protein, or fiber) before a standardized food challenge. Cluster analysis revealed heterogeneity of mitigator effect between individuals and foods, and within individuals. Results indicate that diets can be individualized with regard to both carbohydrate choice and addition of fat, protein, or fiber to optimize metabolic health. A.A.Metwally: Employee; Google LLC, Stock/Shareholder; Google LLC. D.Perelman: Advisory Panel; January, Inc. B.W.Ehlert: None. Y.Wu: None. A.Celli: None. C.Bejikian: None. H.Park: None. T.Mclaughlin: Board Member; January, Inc., Research Support; Merck & Co., Inc., Stock/Shareholder; Eiger Bio. M.Snyder: Advisory Panel; Genapsys, Jupiter, Neuvivo, Swaza, Mitrix, Other Relationship; Personalis, QBio, January, Inc., Fodsel, Filtricine, Protos, RTHM, Iollo, Marble Therapeutics, Mirvie, SensOmics. Stanford Precision Health and Integrated Diagnostics Center (5R01DK110186)

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Abstract: Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Multiomic identification of key transcriptional regulatory programs during endurance exercise training

Abstract: Transcription factors (TFs) play a key role in regulating gene expression and responses to stimuli. We conducted an integrated analysis of chromatin accessibility and RNA expression across various rat tissues following endurance exercise training (EET) to map epigenomic changes to transcriptional changes and determine key TFs involved. We uncovered tissue-specific changes across both omic layers, including highly correlated differentially accessible regions (DARs) and differentially expressed genes (DEGs). We identified open chromatin regions associated with DEGs (DEGaPs) and found tissue-specific and genomic feature-specific TF motif enrichment patterns among both DARs and DEGaPs. Accessible promoters of up-vs. down-regulated DEGs per tissue showed distinct TF enrichment patterns. Further, some EET-induced TFs in skeletal muscle were either validated at the proteomic level (MEF2C and NUR77) or correlated with exercise-related phenotypic changes. We provide an in-depth analysis of the epigenetic and trans-factor-dependent processes governing gene expression during EET.

Abstract 3497: The polyclonal path to malignant transformation in familial adenomatous polyposis

Abstract: Familial adenomatous polyposis (FAP) patients develop hundreds of premalignant polyps that progress to colorectal cancer due to a germline mutation in the APC tumor suppressor. Polyps from FAP patients uniquely facilitate interrogation of the continuum of malignant transformation from histologically normal mucosa to benign and dysplastic polyps and eventual adenocarcinomas. As part of the Human Tumor Atlas Network (HTAN), we performed multi-omic profiling, including whole genome sequencing, on 135 samples from six FAP patients across the pre-malignant continuum spanning all physical regions of the large intestine, and serving as the most comprehensive FAP dataset available. Through a comparative analysis with a published FAP cohort (58 multi-region samples across 5 patients) and sporadic colorectal cancer cohort (n=57), each including benign and malignant samples, we evaluate the timing of driver acquisitions at each stage of malignant progression. Despite being separated by vast regions of histologically normal mucosa, independently evolving polyps show extensive mutation sharing, suggesting FAP polyps are polyclonal in origin. Finally, we leverage a simplified mechanistic model of embryonic colonic development demonstrating that the path to malignant transformation in FAP is consistent with polyclonal origins attributable to early mixing, perhaps as early as in-utero. Taken together, the HTAN FAP Atlas provides a novel window into the earliest stages of cancer formation and may illuminate barriers to malignant transformation and opportunities for earlier intervention. Citation Format: Ryan O. Schenck, Aziz Khan, Aaron Horning, Edward D. Esplin, Emma Monte, Si Wu, Casey Hanson, Nasim Bararpour, Stephanie Neves, Lihua Jiang, Kévin Contrepois, Hayan Lee, Tuhin K. Guha, Zheng Hu, Rozelle Laquindanum, Meredith A. Mills, Hassan Chaib, Roxanne Chiu, Ruiqi Jian, Jeannie Chan, Mathew Ellenberger, Winston R. Becker, Bahareh Bahmani, Basil Michael, Jeanne Shen, Samuel Lancaster, Uri Ladabaum, Anshul Kundaje, Teri A. Longacre, William J. Greenleaf, James M. Ford, Michael P. Snyder, Christina Curtis. The polyclonal path to malignant transformation in familial adenomatous polyposis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3497.

Reverse-ChIP Techniques for Identifying Locus-Specific Proteomes: A Key Tool in Unlocking the Cancer Regulome

Abstract: A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a ‘reverse-ChIP’ approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a ‘reverse-ChIP’ technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens.

Acetyl-Click Screening Platform Identifies Small-Molecule Inhibitors of Histone Acetyltransferase 1 (HAT1).

Abstract: HAT1 is a central regulator of chromatin synthesis that acetylates nascent histone H4. To ascertain whether targeting HAT1 is a viable anticancer treatment strategy, we sought to identify small-molecule inhibitors of HAT1 by developing a high-throughput HAT1 acetyl-click assay. Screening of small-molecule libraries led to the discovery of multiple riboflavin analogs that inhibited HAT1 enzymatic activity. Compounds were refined by synthesis and testing of over 70 analogs, which yielded structure-activity relationships. The isoalloxazine core was required for enzymatic inhibition, whereas modifications of the ribityl side chain improved enzymatic potency and cellular growth suppression. One compound (JG-2016 [24a]) showed relative specificity toward HAT1 compared to other acetyltransferases, suppressed the growth of human cancer cell lines, impaired enzymatic activity in cellulo, and interfered with tumor growth. This is the first report of a small-molecule inhibitor of the HAT1 enzyme complex and represents a step toward targeting this pathway for cancer therapy.

Integrative Multi-omic Profiling of Two Human Decedents Receiving Pig Heart Xenografts Reveals Strong Perturbations in Early Immune-Cell and Cellular Metabolism Responses

Abstract: Background Recent advances in xenotransplantation in living and decedent humans using pig xenografts have laid promising groundwork towards future emergency use and first in human trials. Major obstacles remain though, including a lack of knowledge of the genetic incompatibilities between pig donors and human recipients which may led to harmful immune responses against the xenograft or dysregulation of normal physiology. In 2022 two pig heart xenografts were transplanted into two brain-dead human decedents with a minimized immunosuppression regime, primarily to evaluate onset of hyper-acute antibody mediated rejection and sustained xenograft function over 3 days. Methods We performed multi-omic profiling to assess the dynamic interactions between the pig and human genomes in the first two pig heart-xenografts transplants into human decedents. To assess global and specific biological changes that may correlate with immune-related outcomes and xenograft function, we generated transcriptomic, lipidomic, proteomic and metabolomics datasets, across blood and tissue samples collected every 6 hours over the 3-day procedures. Results Single-cell datasets in the 3-day pig xenograft-decedent models show dynamic immune activation processes. We observe specific scRNA-seq, snRNA-seq and geospatial transcriptomic changes of early immune-activation leading to pronounced downstream T-cell activity and hallmarks of early antibody mediated rejection (AbMR) and/or ischemia reperfusion injury (IRI) in the first xenograft recipient. Using longitudinal multiomic integrative analyses from blood in addition to antigen presentation pathway enrichment, we also observe in the first xeno-heart recipient significant cellular metabolism and liver damage pathway changes that correlate with profound physiological dysfunction whereas, these signals are not present in the other xenograft recipient. Conclusions Single-cell and multiomics approaches reveal fundamental insights into early molecular immune responses indicative of IRI and/or early AbMR in the first human decedent, which was not evident in the conventional histological evaluations.