Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Towards the biogeography of prokaryotic genes.

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats1-3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.

STAB2: an updated spatio-temporal cell atlas of the human and mouse brain.

The brain is constituted of heterogeneous types of neuronal and non-neuronal cells, which are organized into distinct anatomical regions, and show precise regulation of gene expression during development, aging and function. In the current database release, STAB2 provides a systematic cellular map of the human and mouse brain by integrating recently published large-scale single-cell and single-nucleus RNA-sequencing datasets from diverse regions and across lifespan. We applied a hierarchical strategy of unsupervised clustering on the integrated single-cell transcriptomic datasets to precisely annotate the cell types and subtypes in the human and mouse brain. Currently, STAB2 includes 71 and 61 different cell subtypes defined in the human and mouse brain, respectively. It covers 63 subregions and 15 developmental stages of human brain, and 38 subregions and 30 developmental stages of mouse brain, generating a comprehensive atlas for exploring spatiotemporal transcriptomic dynamics in the mammalian brain. We also augmented web interfaces for querying and visualizing the gene expression in specific cell types. STAB2 is freely available at https://mai.fudan.edu.cn/stab2.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset.

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

A survey on computational strategies for genome-resolved gut metagenomics.

Recovering high-quality metagenome-assembled genomes (HQ-MAGs) is critical for exploring microbial compositions and microbe-phenotype associations. However, multiple sequencing platforms and computational tools for this purpose may confuse researchers and thus call for extensive evaluation. Here, we systematically evaluated a total of 40 combinations of popular computational tools and sequencing platforms (i.e. strategies), involving eight assemblers, eight metagenomic binners and four sequencing technologies, including short-, long-read and metaHiC sequencing. We identified the best tools for the individual tasks (e.g. the assembly and binning) and combinations (e.g. generating more HQ-MAGs) depending on the availability of the sequencing data. We found that the combination of the hybrid assemblies and metaHiC-based binning performed best, followed by the hybrid and long-read assemblies. More importantly, both long-read and metaHiC sequencings link more mobile elements and antibiotic resistance genes to bacterial hosts and improve the quality of public human gut reference genomes with 32% (34/105) HQ-MAGs that were either of better quality than those in the Unified Human Gastrointestinal Genome catalog version 2 or novel.

Deciphering the genetic architecture of human brain structure and function: a brief survey on recent advances of neuroimaging genomics.

Brain imaging genomics is an emerging interdisciplinary field, where integrated analysis of multimodal medical image-derived phenotypes (IDPs) and multi-omics data, bridging the gap between macroscopic brain phenotypes and their cellular and molecular characteristics. This approach aims to better interpret the genetic architecture and molecular mechanisms associated with brain structure, function and clinical outcomes. More recently, the availability of large-scale imaging and multi-omics datasets from the human brain has afforded the opportunity to the discovering of common genetic variants contributing to the structural and functional IDPs of the human brain. By integrative analyses with functional multi-omics data from the human brain, a set of critical genes, functional genomic regions and neuronal cell types have been identified as significantly associated with brain IDPs. Here, we review the recent advances in the methods and applications of multi-omics integration in brain imaging analysis. We highlight the importance of functional genomic datasets in understanding the biological functions of the identified genes and cell types that are associated with brain IDPs. Moreover, we summarize well-known neuroimaging genetics datasets and discuss challenges and future directions in this field.

NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep.

Engineering of cytosine base editors with DNA damage minimization and editing scope diversification.

Cytosine base editors (CBEs), which enable precise C-to-T substitutions, have been restricted by potential safety risks, including DNA off-target edits, RNA off-target edits and additional genotoxicity such as DNA damages induced by double-strand breaks (DSBs). Though DNA and RNA off-target edits have been ameliorated via various strategies, evaluation and minimization of DSB-associated DNA damage risks for most CBEs remain to be resolved. Here we demonstrate that YE1, an engineered CBE variant with minimized DNA and RNA off-target edits, could induce prominent DSB-associated DNA damage risks, manifested as γH2AX accumulation in human cells. We then perform deaminase engineering for two deaminases lamprey LjCDA1 and human APOBEC3A, and generate divergent CBE variants with eliminated DSB-associated DNA damage risks, in addition to minimized DNA/RNA off-target edits. Furthermore, the editing scopes and sequence preferences of APOBEC3A-derived CBEs could be further diversified by internal fusion strategy. Taken together, this study provides updated evaluation platform for DSB-associated DNA damage risks of CBEs and further generates a series of safer toolkits with diversified editing signatures to expand their applications.

A Diffusive Artificial Synapse Based on Charged Metal Nanoparticles.

We show that a diffusive memristor with analogue switching characteristics can be achieved in a layer of gold nanoparticles (AuNPs) functionalized with charged self-assembled monolayers (deprotonated 11-mercaptoundecanoic acid). The nanoparticle core and the anchored stationary charges are jammed within the layer while the mobile counterions [N(CH3)4+] can respond to the electric field and spontaneously diffuse back to the initial positions upon removal of the field. This metal nanoparticle device is set-step free, energy consumption efficient, mechanically flexible, and analogous to bio-Ca2+ dynamics and has tunable conductance modulation capabilities at the counterion concentrations. The gradual resistive switching behavior enables us to implement several important synaptic functions such as potentiation/depression, spike voltage-dependent plasticity, spike duration-dependent plasticity, spike frequency-dependent plasticity, and paired-pulse facilitation. Finally, on the basis of the paired-pulse facilitation characteristics, the metal nanoparticle diffusive artificial synapse is used for edge extraction with exhibits excellent performance.

Wearable Hydrogel SERS Chip Utilizing Plasmonic Trimers for Uric Acid Analysis in Sweat.

Uric acid is typically measured through blood tests, which can be inconvenient and uncomfortable for patients. Herein, we propose a wearable surface-enhanced Raman scattering (SERS) chip, incorporating a hydrogel membrane with integrated plasmonic trimers, for noninvasive monitoring of uric acid in sweat. The plasmonic trimers feature sub 5 nm nanogaps, generating strong electromagnetic fields to boost the Raman signal of surrounding molecules. Simultaneously, the hydrogel membrane pumps sweat through these gaps, efficiently capturing sweat biomarkers for SERS detection. The chip can achieve saturation adsorption of sweat within 5 min, eliminating variations in individual sweat production rates. Dynamic SERS tracking of uric acid and lactic acid levels during anaerobic exercise reveals a temporary suppression of uric acid metabolism, likely due to metabolic competition with lactic acid. Furthermore, long-term monitoring correlates well with blood test results, confirming that regular exercise helps reduce serum uric acid levels and supporting its potential in managing hyperuricemia.

Probing Oxidation Mechanisms in Plasmonic Catalysis: Unraveling the Role of Reactive Oxygen Species.

Plasmon-induced oxidation has conventionally been attributed to the transfer of plasmonic hot holes. However, this theoretical framework encounters challenges in elucidating the latest experimental findings, such as enhanced catalytic efficiency under uncoupled irradiation conditions and superior oxidizability of silver nanoparticles. Herein, we employ liquid surface-enhanced Raman spectroscopy (SERS) as a real-time and in situ tool to explore the oxidation mechanisms in plasmonic catalysis, taking the decarboxylation of p-mercaptobenzoic acid (PMBA) as a case study. Our findings suggest that the plasmon-induced oxidation is driven by reactive oxygen species (ROS) rather than hot holes, holding true for both the Au and Ag nanoparticles. Subsequent investigations suggest that plasmon-induced ROS may arise from hot carriers or energy transfer mechanisms, exhibiting selectivity under different experimental conditions. The observations were substantiated by investigating the cleavage of the carbon-boron bonds. Furthermore, the underlying mechanisms were clarified by energy level theories, advancing our understanding of plasmonic catalysis.

Occlusion enhanced pan-cancer classification via deep learning.

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

Boosting the Efficiency and Stability of Li-CO2 Batteries via a Ruthenium-Based Olefin-Metathesis Catalyst.

The practical application of Li-CO2 batteries is significantly hindered by high charge potential and short lifespan, mainly due to sluggish reaction kinetics and inadequate reaction reversibility. Homogeneous catalysts added to the electrolyte provide a promising strategy to address these issues. In this work, the third-generation Grubbs catalyst (G-III), which is efficient for olefin metathesis reactions, has been adopted as a homogeneous catalyst for Li-CO2 batteries. Batteries with G-III exhibited a low overpotential of 0.86 V and a lifespan of 1300 h at a current density of 300 mA g-1. Even at a high current density of 2000 mA g-1, the batteries remained stable for over 300 cycles, with an initial overpotential of 1.11 V. A two-step discharge/charge reaction involving Li2C2O4 as an intermediate was well illustrated, attributed to both low overpotentials and high specific capacity. These findings provide insights into catalyst selection and mechanism analysis for Li-CO2 batteries, offering practical strategies for Li-CO2 battery performance enhancement and practical applications.

The activation of asparagine synthetase by the transcription factor FOXM1 plays a pivotal role in the initiation and progression of ESCC.

This study explores the role of the transcription factor FOXM1 in the initiation and progression of oesophageal squamous cell carcinoma (ESCC). Our findings reveal that FOXM1 is highly expressed in ESCC and correlates with the prognosis of the disease. The relationship between FOXM1 and asparagine synthetase (ASNS) is investigated, and the study demonstrates that FOXM1 activates ASNS, impacting the tumour stemness of ESCC. In this study, we reveal the association between FOXM1 and ESCC development, as well as FOXM1's promotion of migration and proliferation in ESCC cells. The study also highlights FOXM1's regulation of ASNS transcription and the functional role of ASNS in ESCC metastasis and growth. Furthermore, the study explores the impact of FOXM1 and ASNS on ESCC stemness and their potential implications for chemotherapy resistance.

Medication non-adherence and self-inflicted violence behaviors among 185,800 patients with schizophrenia in the community: a 12-year cohort study.

BACKGROUND: Despite the importance of medication adherence in treatment effectiveness, little is known about the association between medication non-adherence and self-inflicted violence behaviors. We aimed to assess whether medication non-adherence increased the risk of self-inflicted violence behaviors among schizophrenics in communities (hypothesis 1) and whether the dose-response relationship existed (hypothesis 2). METHODS: This 12-year cohort study in western China recruited 292,667 community-dwelling schizophrenics. The proportion of regular medication (PRM) was calculated by dividing the time of "regular adherence" by the total time of antipsychotic treatment during follow-up period as an indicator of medication adherence. For hypothesis 1, medication adherence was designated as a binary variable with a threshold of 0.8 (PRM); for hypothesis 2, medication adherence was specified as five-category and continuous variables, respectively. Inverse probability weighting and mixed effects Cox proportional hazards models were conducted for confounders control and survival analyses. RESULTS: One hundred eighty-five thousand eight hundred participants were eligible for the final analyses, with a mean age of 47.49 years (SD 14.55 years), of whom 53.6% were female. For hypothesis 1, the medication non-adherence group (PRM < 0.8) had a lower risk of suicide (HR, 0.527, 95% CI, 0.447-0.620), an increased risk of NSSI (HR, 1.229, 95% CI, 1.088-1.388), and non-significant risk of attempted suicide compared with adherence group (PRM ≥ 0.8). For hypothesis 2, the lowest medication adherence (PRM < 0.2) was associated with increased risks of suicide attempt (HR, 1.614, 95% CI, 1.412-1.845), NSSI (HR, 1.873, 95% CI, 1.649-2.126), and a decreased risk of suicide (HR, 0.593, 95% CI, 0.490-0.719). The other non-adherence groups had lower risks for all three self-inflicted violence behaviors. The associations between medication adherence in continuous-variable and three outcomes were consistent with the categorical medication adherence results. CONCLUSIONS: Almost no medication taken as prescribed was associated with an increased risk of suicide attempt and NSSI. However, medication adherence did not appear to prevent completed suicide. Besides, patients with moderate adherence had a lower incidence of suicide attempt and NSSI. These findings highlight the need for a more detailed portrayal of medication adherence and the need to be vigilant for suicide intent in schizophrenics with good medication adherence who may be overlooked previously.

Air pollution and risk of 32 health conditions: outcome-wide analyses in a population-based prospective cohort in Southwest China.

BACKGROUND: Uncertainty remains about the long-term effects of air pollutants (AP) on multiple diseases, especially subtypes of cardiovascular disease (CVD). We aimed to assess the individual and joint associations of fine particulate matter (PM2.5), along with its chemical components, nitrogen dioxide (NO2) and ozone (O3), with risks of 32 health conditions. METHODS: A total of 17,566 participants in Sichuan Province, China, were included in 2018 and followed until 2022, with an average follow-up period of 4.2 years. The concentrations of AP were measured using a machine-learning approach. The Cox proportional hazards model and quantile g-computation were applied to assess the associations between AP and CVD. RESULTS: Per interquartile range (IQR) increase in PM2.5 mass, NO2, O3, nitrate, ammonium, organic matter (OM), black carbon (BC), chloride, and sulfate were significantly associated with increased risks of various conditions, with hazard ratios (HRs) ranging from 1.06 to 2.48. Exposure to multiple air pollutants was associated with total cardiovascular disease (HR 1.75, 95% confidence intervals (CIs) 1.62-1.89), hypertensive diseases (1.49, 1.38-1.62), cardiac arrests (1.52, 1.30-1.77), arrhythmia (1.76, 1.44-2.15), cerebrovascular diseases (1.86, 1.65-2.10), stroke (1.77, 1.54-2.03), ischemic stroke (1.85, 1.61-2.12), atherosclerosis (1.77, 1.57-1.99), diseases of veins, lymphatic vessels, and lymph nodes (1.32, 1.15-1.51), pneumonia (1.37, 1.16-1.61), inflammatory bowel diseases (1.34, 1.16-1.55), liver diseases (1.59, 1.43-1.77), type 2 diabetes (1.48, 1.26-1.73), lipoprotein metabolism disorders (2.20, 1.96-2.47), purine metabolism disorders (1.61, 1.38-1.88), anemia (1.29, 1.15-1.45), sleep disorders (1.54, 1.33-1.78), renal failure (1.44, 1.21-1.72), kidney stone (1.27, 1.13-1.43), osteoarthritis (2.18, 2.00-2.39), osteoporosis (1.36, 1.14-1.61). OM had max weights for joint effects of AP on many conditions. CONCLUSIONS: Long-term exposure to increased levels of multiple air pollutants was associated with risks of multiple health conditions. OM accounted for substantial weight for these increased risks, suggesting it may play an important role in these associations.

Hybrid Cellular Nanovesicles Block PD-L1 Signal and Repolarize M2 Macrophages for Cancer Immunotherapy.

The PD1/PD-L1 immune checkpoint blocking is a promising therapy, while immunosuppressive tumor microenvironment (TME) and poor tumor penetration of therapeutic antibodies limit its efficacy. Repolarization of tumor-associated macrophages (TAMs) offers a potential method to ameliorate immunosuppression of TME and further boost T cell antitumor immunity. Herein, hybrid cell membrane biomimetic nanovesicles (hNVs) are developed by fusing M1 macrophage-derived nanovesicles (M1-NVs) and PD1-overexpressed tumor cell-derived nanovesicles (PD1-NVs) to improve cancer immunotherapy. The M1-NVs promote the transformation of M2-like TAMs to M1-like phenotype and further increase the release of pro-inflammatory cytokines, resulting in improved immunosuppressive TME. Concurrently, the PD1-NVs block PD1/PD-L1 pathway, which boosts cancer immunotherapy when combined with M1-NVs. In a breast cancer mouse model, the hNVs efficiently accumulate at the tumor site after intravenous injection and significantly inhibit the tumor growth. Mechanically, the M1 macrophages and CD8+ T lymphocytes in TME increase by twofold after the treatment, indicating effective immune activation. These results suggest the hNVs as a promising strategy to integrate TME improvement with PD1/PD-L1 blockade for cancer immunotherapy.

Injectable Oxygen-Carrying Microsphere Hydrogel for Dynamic Regulation of Redox Microenvironment of Wounds.

The delayed healing of infected wounds can be attributed to the increased production of reactive oxygen species (ROS) and consequent damages to vascellum and tissue, resulting in a hypoxic wound environment that further exacerbates inflammation. Current clinical treatments including hyperbaric oxygen therapy and antibiotic treatment fail to provide sustained oxygenation and drug-free resistance to infection. To propose a dynamic oxygen regulation strategy, this study develops a composite hydrogel with ROS-scavenging system and oxygen-releasing microspheres in the wound dressing. The hydrogel itself reduces cellular damage by removing ROS derived from immune cells. Simultaneously, the sustained release of oxygen from microspheres improves cell survival and migration in hypoxic environments, promoting angiogenesis and collagen regeneration. The combination of ROS scavenging and oxygenation enables the wound dressing to achieve drug-free anti-infection through activating immune modulation, inhibiting the secretion of pro-inflammatory cytokines interleukin-6, and promoting tissue regeneration in both acute and infected wounds of rat skins. Thus, the composite hydrogel dressing proposed in this work shows great potential for dynamic redox regulation of infected wounds and accelerates wound healing without drugs.

Effect of physical activity intervention on weight change in rural older Chinese: a cluster randomized controlled trial.

BACKGROUND: Current randomized trial evidence for the effects of physical activity intervention on weight change in adults was mainly from western countries, with little reliable evidence from low- and middle-income countries, such as China, where lifestyle factors and obesity patterns differ substantially from those in western countries. We examined the effects of physical activity intervention on weight change using cluster randomized trial data among Chinese older adults. METHODS: The cluster randomized controlled trial included an 8-week physical activity intervention period and was followed up to 24 months. Eight villages were randomly assigned to the intervention group (4 villages, n = 240) or the control group (4 villages, n = 268). The intervention group received physical activity intervention based on the socio-ecological model, while the control group did not. The intervention involved three levels: individual, interpersonal, and community levels, which aimed to promote leisure-time physical activity of participants. The primary outcome of the present study was the difference in percentage weight change at 24 months from baseline. We used Tanita BC-601 analyzer scales to measure weight and recorded it to the nearest 0.1 kg. RESULTS: Among the 508 participants, the mean age was 70.93 (SD, 5.69) years, and 55.5% were female. There were significant differences in percentage weight change between the intervention group and the control group with a mean change of -1.78% (95% CI, -2.67% to -0.90%; p < 0.001) in the total sample, -1.94% (95% CI, -3.14% to -0.73%; p = 0.002) in participants with overweight/obesity, and -1.45% (95% CI, -2.73% to -0.18%; p = 0.027) among participants with underweight/healthy weight in favor of the intervention group at 24 months. CONCLUSIONS: Physical activity intervention resulted in weight loss in rural older sample at 24 months. This suggested that physical activity interventions are feasible for weight loss among older adults, especially for those with overweight/obesity or aged under 80. TRIAL REGISTRATION: The study has been registered on the Chinese Clinical Trial Registry on April 20, 2021 (ChiCTR2100045653), https://www.chictr.org.cn/showproj.html?proj=123704 .

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.