The intestinal immune system has the challenging task of tolerating foreign nutrients and the commensal microbiome, while excluding or eliminating ingested pathogens. Failure of this balance leads to conditions such as inflammatory bowel diseases, food allergies and invasive gastrointestinal infections1. Multiple immune mechanisms are therefore in place to maintain tissue integrity, including balanced generation of effector T (TH) cells and FOXP3+ regulatory T (pTreg) cells, which mediate resistance to pathogens and regulate excessive immune activation, respectively1–4. The gut-draining lymph nodes (gLNs) are key sites for orchestrating adaptive immunity to luminal perturbations5–7. However, it is unclear how they simultaneously support tolerogenic and inflammatory reactions. Here we show that gLNs are immunologically specific to the functional gut segment that they drain. Stromal and dendritic cell gene signatures and polarization of T cells against the same luminal antigen differ between gLNs, with the proximal small intestine-draining gLNs preferentially giving rise to tolerogenic responses and the distal gLNs to pro-inflammatory T cell responses. This segregation permitted the targeting of distal gLNs for vaccination and the maintenance of duodenal pTreg cell induction during colonic infection. Conversely, the compartmentalized dichotomy was perturbed by surgical removal of select distal gLNs and duodenal infection, with effects on both lymphoid organ and tissue immune responses. Our findings reveal that the conflict between tolerogenic and inflammatory intestinal responses is in part resolved by discrete gLN drainage, and encourage antigen targeting to specific gut segments for therapeutic immune modulation. Immune responses in the gut and associated draining lymph nodes differ between tolerogenic and inflammatory depending on their anatomical location.
BACKGROUND Monkeypox virus has recently infected more than 88 000 people, raising concerns about our preparedness against this emerging viral pathogen. Licensed and approved for mpox, the JYNNEOS vaccine has fewer side-effects than previous smallpox vaccines and has shown immunogenicity against monkeypox in animal models. This study aims to elucidate human immune responses to JYNNEOS vaccination compared with mpox-induced immunity. METHODS Peripheral blood mononuclear cells and sera were obtained from ten individuals vaccinated with one or two doses of JYNNEOS and six individuals diagnosed with monkeypox virus infection. Samples were obtained from seven individuals before vaccination to serve as a baseline. We examined the polyclonal serum (ELISA) and single B-cell (heavy chain gene and transcriptome data) antibody repertoires and T-cell responses (activation-induced marker and intracellular cytokine staining assays) induced by the JYNNEOS vaccine versus monkeypox virus infection. FINDINGS All participants were men between the ages of 21 and 60 years, except for one woman in the group of mpox-convalescent individuals, and none had previous orthopoxvirus exposure. All mpox cases were mild. Vaccinee samples were collected 6-33 days after the first dose and 5-40 days after the second dose. Mpox-convalescent samples were collected 20-102 days after infection. In vaccine recipients, gene-level plasmablast and antibody responses were negligible and sera displayed moderate binding to recombinant orthopoxviral proteins (A29L, A35R, E8L, A30L, A27L, A33R, B18R, and L1R) and native proteins from the 2022 monkeypox outbreak strain. By contrast, recent monkeypox virus infection (within 20-102 days) induced robust serum antibody responses to monkeypox virus proteins and to native monkeypox virus proteins from a viral isolate obtained during the 2022 outbreak. JYNNEOS vaccine recipients presented robust orthopoxviral CD4+ and CD8+ T-cell responses. INTERPRETATION Infection with monkeypox virus resulted in robust B-cell and T-cell responses, whereas immunisation with JYNNEOS elicited more robust T-cell responses. These data can help to inform vaccine design and policies for preventing mpox in humans. FUNDING National Cancer Institute (National Institutes of Health), National Institute of Allergy and Infectious Diseases (National Institutes of Health), and Icahn School of Medicine.
AbstractDespite advances in medicine and antimicrobial research, viral infections continue to pose a major threat to human health. While major strides have been made in generating vaccines and small molecules to combat emerging pathogens, new modalities of treatment are warranted in diseases where there is a lack of treatment options, or where treatment cannot fully eradicate pathogens, as in HIV infection. Cellular therapies, some of which are FDA approved for treating cancer, take advantage of our developing understanding of the immune system, and harness this knowledge to enhance, or direct, immune responses toward infectious agents. As with cancer, viruses that evade immunity, do so by avoiding immune recognition or by redirecting the cellular responses that would eradicate them. As such, infusing virus specific immune cells has the potential to improve patient outcomes and should be investigated as a potential tool in the arsenal to fight infection. The present manuscript summarizes key findings made using cellular therapies for the treatment of viral infections, focusing on the potential that these strategies might have in controlling disease.
This study investigated the effects of dietary supplementation of mulberry leaf oligosaccharides (MLO) on the growth performance, serum biochemistry, glucose and lipid metabolism, antioxidant activity, liver health, and virus resistance in largemouth bass (Micropterus salmoides). The fish were fed with CK (basal diet), MLOL (basal diet supplemented with 0.5%MLO), and MLOH (basal diet supplemented with 1.0% MLO) for 80 days, and then subjected to a 21-day viral challenge experiment. The results showed that MLO supplementation had no adverse effect on the weight gain rate, specific growth rate, feed intake, and condition factor (P > 0.05), but significantly decreased the feed conversion rate and viscerosomatic index (P< 0.05). Moreover, the MLOL and MLOH group had significantly lower contents of triglyceride, blood glucose, and malondialdehyde and activities of serum alanine aminotransferase and aspartate aminotransferase, while significantly higher levels of serum and liver total superoxide dismutase and lower levels of glutathione than the CK group (P< 0.05). MLO supplementation significantly up-regulated the relative expression of glycolytic genes gk and pfk and lipid catabolism genes ppar-α and cpt-1, while obviously down-regulated that of acc, fas, and dgat related to fatty acid synthesis in the liver tissue (P< 0.05). In terms of liver health, MLO supplementation significantly up-regulated the relative expression of anti-inflammatory cytokines il-10 and tgf-β, while decreased that of pro-inflammatory cytokines nf-κb, il-8, and tnf-α in the liver tissue (P< 0.05). The viral challenge test showed that MLO supplementation significantly improved the survival rate of M. salmoides after largemouth bass ranavirus (LMBV) infection. Dietary MLO supplementation promoted liver glucose and lipid metabolism, and improved the immunity and resistance of M. salmoides to LMBV by regulating the PPAR signaling way and inhibiting the NF-kB signaling pathway. The appropriate addition amount of MLO to the diet was determined to be 1.0%.
As global living standards improve and medical technology advances, many infectious diseases have been effectively controlled. However, certain diseases, such as the recent COVID-19 pandemic, continue to pose significant threats to public health. This paper explores the evolution of infectious disease modeling, from early ordinary differential equation-based models like the SIR framework to more complex reaction-diffusion models that incorporate both temporal and spatial dynamics. The study highlights the importance of numerical methods, such as the Runge-Kutta method, implicit-explicit time-discretization techniques, and finite difference methods, in solving these models. By analyzing the development and application of these methods, this research underscores their critical role in predicting disease spread, informing public health strategies, and mitigating the impact of future pandemics.
Paola F. Antonietti, Mattia Corti, Sergio Gómez
et al.
This work presents a structure-preserving, high-order, unconditionally stable numerical method for approximating the solution to the Fisher-Kolmogorov equation on polytopic meshes, with a particular focus on its application in simulating misfolded protein spreading in neurodegenerative diseases. The model problem is reformulated using an entropy variable to guarantee solution positivity, boundedness, and satisfaction of a discrete entropy-stability inequality at the numerical level. The scheme combines a local discontinuous Galerkin method on polytopal meshes for the space discretization with a $ν$-step backward differentiation formula for the time integration. Implementation details are discussed, including a detailed derivation of the linear systems arising from Newton's iteration. The accuracy and robustness of the proposed method are demonstrated through extensive numerical tests. Finally, the method's practical performance is demonstrated through simulations of $α$-synuclein propagation in a two-dimensional brain geometry segmented from MRI data, providing a relevant computational framework for modeling synucleopathies (such as Parkinson's disease) and, more generally, neurodegenerative diseases.
Abstract Background Parasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system’s functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems. Results Here, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments. Conclusions This investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.
Necroptosis is a type of proinflammatory programmed necrosis essential for innate immunity. The receptor interacting protein kinases 1/3 (RIPK1/3) and the substrate mixed lineage kinase domain-like protein (MLKL) are core components of the necroptotic axis. The activation and immunological function of necroptosis in fish remain elusive. Herein, we studied the function and activation of RIPK1/3 (PoRIPK1/3) and MLKL (PoMLKL) in teleost Paralichthys olivaceus. Bacterial infection increased the expression of RIPK1/3 and MLKL. The N-terminal four-helix bundle (4HB) domain of PoMLKL exhibited necroptosis-inducing activity, and the C-terminal pseudokinase domain exerted auto-inhibitory effect on the 4HB domain. PoRIPK3 was capable of phosphorylating the T360/S361 residues in the PoMLKL C-terminal domain and initiated necroptosis, and this necroptosis-inducing activity was enhanced by PoRIPK1. PoRIPK1/3 interacted with PoMLKL in a manner that depended on the RIP homotypic interaction motif (RHIM), and deletion of RHIM from PoRIPK1/3 led to the dissociation of PoRIPK1/3 with PoMLKL. Inhibition of PoMLKL-mediated necroptosis increased Edwardsiella tarda infection in fish cells and tissues, and led to significantly enhanced lethality of the host. Taken together, these results revealed the activation mechanism of PoRIPK1/3-PoMLKL signaling pathway and the immunological function of necroptosis in the immune defense of teleost.
Abstract Objectives CAR‐T cells are being investigated as a novel immunotherapy for glioblastoma, but clinical success has been limited. We recently described fibroblast activation protein (FAP) as an ideal target antigen for glioblastoma immunotherapy, with expression on both tumor cells and tumor blood vessels. However, CAR‐T cells targeting FAP have never been investigated as a therapy for glioblastoma. Methods We generated a novel FAP targeting CAR with CD3ζ and CD28 signalling domains and tested the resulting CAR‐T cells for their lytic activity and cytokine secretion function in vitro (using real‐time impedance, flow cytometry, imaging and bead‐based cytokine assays), and in vivo (using a xenograft mimicking the natural heterogeneity of human glioblastoma). Results FAP‐CAR‐T cells exhibited target specificity against model cell lines and potent cytotoxicity against patient‐derived glioma neural stem cells, even when only a subpopulation expressed FAP, indicating a bystander killing mechanism. Using co‐culture assays, we confirmed FAP‐CAR‐T cells mediate bystander killing of antigen‐negative tumor cells, but only after activation by FAP‐positive target cells. This bystander killing was at least partially mediated by soluble factors and amplified by IL‐2 which activated the non‐transduced fraction of the CAR‐T product. Finally, a low dose of intravenously administered FAP‐CAR‐T cells controlled, without overt toxicity, the growth of subcutaneous tumors created using a mixture of antigen‐negative and antigen‐positive glioblastoma cells. Conclusions Our findings advance FAP as a leading candidate for clinical CAR‐T therapy of glioblastoma and highlight under‐recognised antigen nonspecific mechanisms that may contribute meaningfully to the antitumor activity of CAR‐T cells.
Min-Jia Hsieh, Ping-Hsing Tsai, Pin-Hsuan Chiang
et al.
Genetic polymorphisms have been linked to the differential waning of vaccine-induced immunity against COVID-19 following vaccination. Despite this, evidence on the mechanisms behind this waning and its implications for vaccination policy remains limited. We hypothesize that specific gene variants may modulate the development of vaccine-initiated immunity, leading to impaired immune function. This study investigates genetic determinants influencing the sustainability of immunity post-mRNA vaccination through a genome-wide association study (GWAS). Utilizing a hospital-based, test negative case-control design, we enrolled 1,119 participants from the Taiwan Precision Medicine Initiative (TPMI) cohort, all of whom completed a full mRNA COVID-19 vaccination regimen and underwent PCR testing during the Omicron outbreak. Participants were classified into breakthrough and protected groups based on PCR results. Genetic samples were analyzed using SNP arrays with rigorous quality control. Cox regression identified significant single nucleotide polymorphisms (SNPs) associated with breakthrough infections, affecting 743 genes involved in processes such as antigenic protein translation, B cell activation, and T cell function. Key genes identified include CD247, TRPV1, MYH9, CCL16, and RPTOR, which are vital for immune responses. Polygenic risk score (PRS) analysis revealed that individuals with higher PRS are at greater risk of breakthrough infections post-vaccination, demonstrating a high predictability (AUC = 0.787) in validating population. This finding confirms the significant influence of genetic variations on the durability of immune responses and vaccine effectiveness. This study highlights the importance of considering genetic polymorphisms in evaluating vaccine-induced immunity and proposes potential personalized vaccination strategies by tailoring regimens to individual genetic profiles.
Thalita Mendonça Antico, Larissa F. Rodrigues Moreira, Rodrigo Moreira
The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.
Juliette Ortholand, Nicolas Gensollen, Stanley Durrleman
et al.
Introduction: Heterogeneity of the progression of neurodegenerative diseases is one of the main challenges faced in developing effective therapies. With the increasing number of large clinical databases, disease progression models have led to a better understanding of this heterogeneity. Nevertheless, these diseases may have no clear onset and biological underlying processes may start before the first symptoms. Such an ill-defined disease reference time is an issue for current joint models, which have proven their effectiveness by combining longitudinal and survival data. Objective In this work, we propose a joint non-linear mixed effect model with a latent disease age, to overcome this need for a precise reference time. Method: To do so, we utilized an existing longitudinal model with a latent disease age as a longitudinal sub-model and associated it with a survival sub-model that estimates a Weibull distribution from the latent disease age. We then validated our model on different simulated scenarios. Finally, we benchmarked our model with a state-of-the-art joint model and reference survival and longitudinal models on simulated and real data in the context of Amyotrophic Lateral Sclerosis (ALS). Results: On real data, our model got significantly better results than the state-of-the-art joint model for absolute bias (4.21(4.41) versus 4.24(4.14)(p-value=1.4e-17)), and mean cumulative AUC for right censored events (0.67(0.07) versus 0.61(0.09)(p-value=1.7e-03)). Conclusion: We showed that our approach is better suited than the state-of-the-art in the context where the reference time is not reliable. This work opens up the perspective to design predictive and personalized therapeutic strategies.
Yeganeh Madadi, Mohammad Delsoz, Priscilla A. Lao
et al.
Objective: To evaluate the efficiency of large language models (LLMs) such as ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on detailed case descriptions. Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.
DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers. However, diagnostics based on DNA methylation face challenges due to large feature sets and small sample sizes, resulting in overfitting and suboptimal performance. To address these issues, we propose MIRACLE, a novel interpretable neural network that leverages autoencoder-based multi-task learning to integrate multiple datasets and jointly identify common patterns in DNA methylation. MIRACLE's architecture reflects the relationships between methylation sites, genes, and pathways, ensuring biological interpretability and meaningfulness. The network comprises an encoder and a decoder, with a bottleneck layer representing pathway information as the basic unit of heredity. Customized defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency matrix information, which provides explainability and expresses the site-gene-pathway hierarchical structure explicitly. And from the embedding, there are different multi-task classifiers to predict diseases. Tested on six datasets, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and type 1 diabetes, MIRACLE demonstrates robust performance in identifying common functions of DNA methylation across different phenotypes, with higher accuracy in prediction dieseases than baseline methods. By incorporating biological prior knowledge, MIRACLE offers a meaningful and interpretable framework for DNA methylation data analysis in the context of autoimmune diseases.
Daniel L. Eisenson, Yu Hisadome, Michelle R. Santillan
et al.
Islet transplantation has emerged as a curative therapy for diabetes in select patients but remains rare due to shortage of suitable donor pancreases. Islet transplantation using porcine islets has long been proposed as a solution to this organ shortage. There have already been several small clinical trials using porcine islets in humans, but results have been mixed and further trials limited by calls for more rigorous pre-clinical data. Recent progress in heart and kidney xenograft transplant, including three studies of pig-to-human xenograft transplant, have recaptured popular imagination and renewed interest in clinical islet xenotransplantation. This review outlines immunologic barriers to islet transplantation, summarizes current strategies to overcome these barriers with a particular focus on approaches to induce tolerance, and describes an innovative strategy for treatment of diabetic nephropathy with composite islet-kidney transplantation.