Tracing unknown tumor origins with a biological-pathway-based transformer model.

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq data. We proposed scRankXMBD, a computational framework to prioritize prognostic-associated cell subpopulations based on within-cell relative expression orderings of gene pairs from single-cell transcriptomes. scRankXMBD achieves higher precision and concordance compared with five existing methods. Moreover, we developed single-cell gene pair signatures to predict recurrence risk for patients individually. Our work facilitates the application of the rank-based method in scRNA-seq data for prognostic biomarker discovery and precision oncology. scRankXMBD is available at https://github.com/xmuyulab/scRank-XMBD. (XMBD:Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.).

Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma.

Background: Single-cell technologies have enabled extensive analysis of complex immune composition, phenotype and interactions within tumor, which is crucial in understanding the mechanisms behind cancer progression and treatment resistance. Unfortunately, knowledge on cell phenotypes and their spatial interactions has only had limited impact on the pathological stratification of patients in the clinic so far. We explore the relationship between different tumor environments (TMEs) and response to immunotherapy by deciphering the composition and spatial relationships of different cell types. Methods: Here we used imaging mass cytometry to simultaneously quantify 35 proteins in a spatially resolved manner on tumor tissues from 26 melanoma patients receiving anti-programmed cell death-1 (anti-PD-1) therapy. Using unsupervised clustering, we profiled 662,266 single cells to identify lymphocytes, myeloid derived monocytes, stromal and tumor cells, and characterized TME of different melanomas. Results: Combined single-cell and spatial analysis reveals highly dynamic TMEs that are characterized with variable tumor and immune cell phenotypes and their spatial organizations in melanomas, and many of these multicellular features are associated with response to anti-PD-1 therapy. We further identify six distinct TME archetypes based on their multicellular compositions, and find that patients with different TME archetypes responded differently to anti-PD-1 therapy. Finally, we find that classifying patients based on the gene expression signature derived from TME archetypes predicts anti-PD-1 therapy response across multiple validation cohorts. Conclusions: Our results demonstrate the utility of multiplex proteomic imaging technologies in studying complex molecular events in a spatially resolved manner for the development of new strategies for patient stratification and treatment outcome prediction.

Cyclosporine A Regulates Influenza A Virus-induced Macrophages Polarization and Inflammatory Responses by Targeting Cyclophilin A.

Cyclosporine A (CsA) is an immunosuppressive drug that suppresses T cell responses and is broadly used in transplantation. Its immunosuppressive action is closely linked to its binding of cyclophilin A (CypA), which widely distributed in different cell types. CsA also regulates the functions of innate immune cells, but the mechanism remains elusive. Here, we investigate the role of CsA in regulating macrophages polarization in influenza A virus-infected mice and mouse bone marrow-derived macrophages. CsA downregulates pro-inflammatory cytokines expression and upregulates anti-inflammatory cytokines expression. Mechanically, CsA decreases the polarization of macrophages into pro-inflammatory M1 phenotype and increases the polarization of macrophages into anti-inflammatory M2 phenotype. Further studies show that CsA regulates macrophages polarization-associated IFN-γ/STAT1 and IL-4/STAT6 signaling pathways. Meanwhile, all these roles of CsA are eliminated when CypA is absent, suggesting that CsA regulates macrophages polarization and inflammatory responses depend on its binding to CypA. Collectively, these results reveal a crucial mechanism of CsA in attenuating IAV-induced inflammatory responses by a switch in macrophages polarization.

DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks.

Understanding the immune cell abundance of cancer and other disease-related tissues has an important role in guiding disease treatments. Computational cell type proportion estimation methods have been previously developed to derive such information from bulk RNA sequencing data. Unfortunately, our results show that the performance of these methods can be seriously plagued by the mismatch between training data and real-world data. To tackle this issue, we propose the DAISM-DNNXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (denoted as DAISM-DNN) pipeline that trains a deep neural network (DNN) with dataset-specific training data populated from a certain amount of calibrated samples using DAISM, a novel data augmentation method with an in silico mixing strategy. The evaluation results demonstrate that the DAISM-DNN pipeline outperforms other existing methods consistently and substantially for all the cell types under evaluation in real-world datasets.

Application of individualized differential expression analysis in human cancer proteome.

Liquid chromatography-mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group.

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.

Analysis of clinical effect of the four-step pouch approach in ocular plastic surgery.

PURPOSE: To investigate the clinical effect of the four-step pouch plastic surgery in ocular plastic surgery. METHODS: Patients with ocular plastic surgery admitted to our hospital from January 2018 to October 2019 were selected as the study subjects. The patients were divided into the observation group (n=41) and the control group (n=43) according to the different surgical modalities received. The traditional skin approach of lower eyelid, and the four-step pouch plastic surgery were used as the control group and observation group, respectively. The operation time, postoperative recovery time of the eye skin, clinical effect, postoperative complications, and satisfaction rate were compared between the two groups. RESULTS: The operation time was compared between the two groups, indicating no significant difference (P > 0.05). The postoperative recovery time of the eye skin was significantly shorter in the observation group than the control group (P < 0.05). The total clinical effective rate (95.12%) was significantly higher in the observation group than that in the control group (79.07%) (P < 0.05). The total incidence of complications (7.32%) was significantly lower in the observation group than that in the control group (25.58%) (P < 0.05). The overall satisfaction rate was significantly higher in the observation group than the control group (P < 0.05). CONCLUSION: The clinical effect of four-step pouch plastic surgery in ocular plastic surgery is significant.

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry.

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

Cyclophilin A is a key positive and negative feedback regulator within interleukin-6 trans-signaling pathway.

Cyclophilin A (CypA), a member of the cyclophilin family, plays a vital role in microorganismal infections, inflammatory diseases, and cancers. Interleukin-6 (IL-6) is a pleiotropic cytokine, exerting variety of effects on inflammation, immune response, hematopoiesis, and tumor proliferation. Binding of IL-6 to soluble IL-6 receptor (sIL-6R) induces pro-inflammatory trans-signaling, which has been described to be stronger than anti-inflammatory classic signaling triggered by the binding of IL-6 to membrane-bound IL-6 receptor. Here we found that upon the treatment of IL-6 and sIL-6R, CypA inhibited the ubiquitination-mediated degradation of IL-6 membrane receptor gp130 and enhanced its dimerization, thereby positively regulated the IL-6 trans-signaling and increased the expression of downstream iNOS, IL-6, and CypA. Furthermore, CypA expression could be negatively regulated by suppressor of cytokine signaling 1 (SOCS1). The SH2 and Box domains of SOCS1 interacted with CypA and promoted its K48-linked ubiquitination-mediated degradation, which inhibited the IL-6 trans-signaling pathway. Collectively, our findings reveal an important role of CypA in the positive and negative feedback regulation of the IL-6 trans-signaling pathway.

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics.

We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

MiR-125b enhances autophagic flux to improve septic cardiomyopathy via targeting STAT3/HMGB1.

We explore the role of miR-125b in septic cardiomyopathy, focusing on miR-125b/STAT3/HMGB1 axis. CLP mouse model and LPS-stimulated primary rat cardiomyocytes (CMs) and H9C2 cell were used as in vivo and in vitro models of septic cardiomyopathy, respectively. qRT-PCR and western blot were performed to measure expression levels of miR-125b, STAT3, HMGB1, and autophagy-related proteins. MTT assay was employed to examine LPS toxicity. Dual luciferase activity assay and CHIP were performed to validate interactions of miR-125b/STAT3 and STAT3/HMGB1 promoter. Immunostaining was used to assess the level of autophagic flux. ROS level was measured by fluorescence assay. Heart functions were examined via intracoronary Doppler ultrasound. miR-125b was diminished while STAT3 and HMGB1 were elevated in the heart tissue following CLP surgery and in LPS-treated H9C2 cells. LPS treatment up-regulated ROS generation and suppressed autophagic flux. Overexpression of miR-125b mimics or knockdown of STAT3 or HMGB1 alleviated LPS-induced hindrance of autophagic flux and ROS production. miR-125b directly targeted STAT3 mRNA and STAT3 bound with HMGB1 promoter. Overexpression of miR-125b mitigated myocardial dysfunction induced by CLP in vivo. Hyperactivation of STAT3/HMGB1 caused by reduced miR-125b contributes to ROS generation and the hindrance of autophagic flux during septic cardiomyopathy, leading to myocardial dysfunction.

Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma.

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumor microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analyzing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 54) and anti-CTLA-4 (n = 42), as well as in patients with gastric cancer treated with anti-PD-1 (n = 45), demonstrating the potential utility of IMS as a predictive biomarker that complements existing GEP signatures for immunotherapy.

Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images.

BACKGROUND: Recent studies showed that immune-checkpoint blockade (ICB) has significantly improved clinical outcomes of melanoma and lung cancer patients. However, only a small subset of patients can benefit from ICB. Deep learning has been successfully implemented in complementary clinical diagnosis. The aim of this study is to demonstrate the potential of deep learning to facilitate the prediction of anti-PD-1 response from H&E images directly. METHODS: In this study, 190 H&E slides of melanoma were segmented into 256 × 256 tiles which were used as the training set for the convolutional neural network (CNN). Additional 54 melanoma and 55 lung cancer H&E slides were collected as independent testing sets. FINDINGS: An AUC of 0.778(95% CI: 63.8%-90.5%) was achieved for 54 melanoma testing samples with 15(65.2%) responders and 23(74.2%) non-responders correctly classified. We also obtained an AUC of 0.645(95% CI: 49.4%-78.4%) for 55 lung cancer samples. INTERPRETATION: To our knowledge, this is the first study of using deep learning to determine patients' anti-PD-1 response from H&E slides directly. Our CNN model achieved the state-of-the-art performance and has the potential to screen ICB beneficial patients in routine clinical practice.

Research on quality changes of grass carp (Ctenopharyngodon idellus) during short-term starvation.

This study was aimed at to investigate the quality changes of grass carp during short-term starvation. The pH, lactic acid, free amino acid, and adenosine triphosphate-related compounds of dorsal meat, belly meat and red meat in grass carp were measured during starvation for 6 days, and the quality of grass carp was evaluated by K value, equivalent umami concentration (EUC), taste activity value (TAV), and electronic tongue. The pH of three parts meat reached the maximum value on the fourth day, which was closely related to the lactic acid content. Concurrently, the contents of fresh sweet amino acids were higher on the fourth day in all parts. The K values in dorsal meat and belly meat were below 10% during starvation. Considering the overall results of electronic tongue, EUC, and TAV analysis, it is suggested that grass carp should be marketed and eaten with a starvation period of 2-4 days for best taste and quality.

Cryo-EM structures of lipopolysaccharide transporter LptB2FGC in lipopolysaccharide or AMP-PNP-bound states reveal its transport mechanism.

Lipopolysaccharides (LPS) of Gram-negative bacteria are critical for the defence against cytotoxic substances and must be transported from the inner membrane (IM) to the outer membrane (OM) through a bridge formed by seven membrane proteins (LptBFGCADE). The IM component LptB2FG powers the process through a yet unclarified mechanism. Here we report three high-resolution cryo-EM structures of LptB2FG alone and complexed with LptC (LptB2FGC), trapped in either the LPS- or AMP-PNP-bound state. The structures reveal conformational changes between these states and substrate binding with or without LptC. We identify two functional transmembrane arginine-containing loops interacting with the bound AMP-PNP and elucidate allosteric communications between the domains. AMP-PNP binding induces an inward rotation and shift of the transmembrane helices of LptFG and LptC to tighten the cavity, with the closure of two lateral gates, to eventually expel LPS into the bridge. Functional assays reveal the functionality of the LptF and LptG periplasmic domains. Our findings shed light on the LPS transport mechanism.

Research on the changes of water-soluble flavor substances in grass carp during steaming.

In this study, adenosine triphosphate (ATP)-related compounds and free amino acids were determined to study the changes of water-soluble flavor substances in grass carp meat during steaming for 18 min. Sensory assessment, electronic tongue, equivalent umami concentration (EUC) value, and taste active value (TAV) were also performed to obtain the best quality of steamed grass carp meat. The results showed that the flavor presented by nucleotides in three parts meat was better within 9 min. The bitter amino acids in the dorsal meat and red meat significantly decreased at 6 min (101.33 mg/100 g and 44.64 mg/100 g, respectively). From the sensory analysis, EUC value, and TAV, it can be found that the quality of grass carp meat was the best when steamed for 6-9 min. The electronic tongue indicated that the taste differences were significantly between 6 and 9 min. Therefore, this study suggested that grass carp should be eaten during 6-9 min of steaming. PRACTICAL APPLICATIONS: As the main way of meat processing and eating, heating not only endows the product with good color, but also kill microorganisms and improve the product quality. The reasonable heating will make the protein denaturation of the food, so that the protein digestion and absorption rate can be improved, which is conducive to people to obtain more abundant nutrition. The quality changes of grass carp meat during 18 min of steaming were studied by measuring ATP-related compounds and free amino acids, combining with sensory assessment, electronic tongue analysis, EUC value, and TAV. These results not only provide useful information for the quality control of grass carp in the heating process, but also provide theoretical reference for the improvement of the nutritional value of grass carp. In addition, the mechanism of flavor change in the heating process of grass carp will be further improved, and effective suggestions will be provided for consumers to reasonably eat grass carp.

Production of Pigs by Hand-Made Cloning Using Mesenchymal Stem Cells and Fibroblasts.

Mesenchymal stem cells (MSCs) exhibited self-renewal and less differentiation, making the MSCs promising candidates for adult somatic cell nuclear transfer (SCNT). In this article, we tried to produce genome identical pigs through hand-made cloning (HMC), with MSCs and adult skin fibroblasts as donor cells. MSCs were derived from either adipose tissue or peripheral blood (aMSCs and bMSCs, respectively). MSCs usually showed the expression pattern of CD29, CD73, CD90, and CD105 together with lack of expression of the hematopoietic markers CD34and CD45. Flow cytometry results demonstrated high expression of CD29 and CD90 in both MSC lines, while CD73, CD34, and CD45 expression were not detected. In contrary, in reverse transcription-polymerase chain reaction (RT-PCR) analysis, CD73 and CD34 were detected indicating that human antibodies CD73 and CD34 were not suitable to identify porcine cell surface markers and porcine MSC cellular surface markers of CD34 might be different from other species. MSCs also had potential to differentiate successfully into chondrocytes, osteoblasts, and adipocytes. After HMC, embryos reconstructed with aMSCs had higher blastocyst rate on day 5 and 6 than those reconstructed with bMSCs and fibroblasts (29.6% ± 1.3% and 41.1% ± 1.4% for aMSCs vs. 23.9% ± 1.2% and 35.5% ± 1.6% for bMSCs and 22.1% ± 0.9% and 33.3% ± 1.1% for fibroblasts, respectively). Live birth rate per transferred blastocyst achieved with bMSCs (1.59%) was the highest among the three groups. This article was the first report to compare the efficiency among bMSCs, aMSCs, and fibroblasts for boar cloning, which offered a realistic perspective to use the HMC technology for commercial breeding.

Transgenic Wuzhishan minipigs designed to express a dominant-negative porcine growth hormone receptor display small stature and a perturbed insulin/IGF-1 pathway.

Growth hormone (GH) is an anabolic mitogen with widespread influence on cellular growth and differentiation as well as on glucose and lipid metabolism. GH binding to the growth hormone receptor (GHR) on hepatocytes prompts expression of insulin growth factor I (IGF-1) involved in nutritionally induced compensatory hyperplasia of pancreatic ß-cell islets and insulin release. A prolonged hyperactivity of the IGF-1/insulin axis in the face of insulinotropic nutrition, on the other hand, can lead to collapse of the pancreatic islets and glucose intolerance. Individuals with Laron syndrome carry mutations in the GHR gene resulting in severe congenital IGF-1 deficiency and elevated GH serum levels leading to short stature as well as perturbed lipid and glucose metabolism. However, these individuals enjoy a reduced prevalence of acne, cancer and possibly diabetes. Minipigs have become important biomedical models for human conditions due to similarities in organ anatomy, physiology, and metabolism relative to humans. The purpose of this study was to generate transgenic Wuzhishan minipigs by handmade cloning with impaired systemic GHR activity and assess their growth profile and glucose metabolism. Transgenic minipigs featuring overexpression of a dominant-negative porcine GHR (GHR(dm)) presented postnatal growth retardation and proportionate dwarfism. Molecular changes included elevated GH serum levels and mild hyperglycemia. We believe that this model may prove valuable in the study of GH functions in relation to cancer, diabetes and longevity.