Miracle-dz/UniFAR-FTUs huggingface dataset:
Adrenomedullin promotes interneuron migration in a dual human model for hypoxic interneuronopathy of prematurity

Extremely preterm born individuals at < 28 postconceptional weeks (PCW) are at high risk for encephalopathy of prematurity and life-long neuropsychiatric conditions. Clinical studies and animal models of preterm brain injury suggest that encephalopathy of prematurity is strongly associated with exposure to hypoxia and/or inflammation in the perinatal period. Histologic examination of postmortem brain tissue from children born preterm demonstrates decreased numbers of cortical GABAergic interneurons in the cerebral cortex. However, the cellular and molecular mechanisms underlying the decreased numbers of GABAergic interneurons in the cerebral cortex of extremely preterm individuals remain unclear. Here, we developed a dual, complementary human cellular model to study hypoxia-induced interneuronopathies using human forebrain assembloids (hFA) derived from human induced pluripotent stem cells (hiPSCs) and ex vivo human prenatal cerebral cortex at mid-gestation. The hFA are generated through the integration of region-specific neural organoids containing either dorsal forebrain (excitatory) glutamatergic neurons or ventral forebrain (inhibitory) GABAergic interneurons. We discover a substantial reduction in migration of cortical interneurons during exposure to hypoxic stress in both hFA and ex vivo human prenatal cerebral cortex. Next, we identify that this migration defect is restored by supplementation of hypoxic cell culture media with exogenous adrenomedullin (ADM), a peptide hormone member of the calcitonin gene related peptide (CGRP) family. Lastly, we demonstrate that the rescue is mediated through increased activity of the PKA molecular pathway and increased pCREB-dependent expression of GABA receptors. Overall, these findings provide important insights into the cellular mechanisms contributing to cortical interneuron depletion in preterm infants, and pinpoint novel therapeutic molecular pathways with high translational potential for hypoxic encephalopathy of prematurity.
Identification of a core transcriptional program driving the human renal mesenchymal-to-epithelial transition

During kidney development, nephron epithelia arise de novo from fate-committed mesenchymal progenitors through a mesenchymal-to-epithelial transition (MET). Downstream of fate specification, transcriptional mechanisms that drive establishment of epithelial morphology through MET are poorly understood. We used human renal organoids derived from induced pluripotent stem cells, which recapitulate nephrogenesis, to investigate mechanisms controlling the renal MET programme. Multi-ome profiling of organoids revealed dynamic changes in gene expression and chromatin accessibility driven by transcriptional activators and repressors throughout renal MET. CRISPR-interference-based gene perturbation revealed that PAX8 is essential for initiation of MET in human renal organoids, contrary to mouse, by activating a cell adhesion programme. Furthermore, while Wnt/β-Catenin signalling specifies nephron fate, we find that it must be attenuated to allow HNF1B and TEAD transcription factors to drive completion of MET. These results reveal how the developing kidney balances fate-commitment and morphogenesis with implications for understanding both developmental kidney diseases and aberrant epithelial plasticity following adult renal tubular injury.
Plant neopolyploidy and genetic background differentiates the microbiome of duckweed across a variety of natural freshwater sources

Whole genome duplication has long been appreciated for its role in driving phenotypic novelty in plants, often altering the way organisms interface with the abiotic environment. Only recently, however, have we begun to investigate how polyploidy influences interactions of plants with other species, despite the biotic niche being predicted as one of the main determinants of polyploid establishment. Nevertheless, we lack critical information about how polyploidy affects the diversity and composition of the microbial taxa that colonize plants, and whether this is genotype-dependent and repeatable across natural environments. This information is a critical first step toward understanding whether the microbiome contributes to polyploid establishment. We thus tested the immediate effect of polyploidy on the diversity and composition of the bacterial microbiome of the aquatic plant Spirodela polyrhiza using four pairs of diploids and synthetic autotetraploids. Under controlled conditions, axenic plants were inoculated with pond waters collected from 10 field sites across a broad environmental gradient. Autotetraploids hosted 4-11 % greater bacterial taxonomic and phylogenetic diversity than their diploid progenitors. Polyploidy, along with its interactions with the inoculum source and genetic lineage, collectively explained 7 % of the total variation in microbiome composition. Furthermore, polyploidy broadened the core microbiome, with autotetraploids having 15 unique bacterial taxa in addition to the 55 they shared with diploids. Our results show that whole genome duplication directly leads to novelty in plant microbiome and importantly, that the effect is dependent on the genetic ancestry of the polyploid and generalizable over many environmental contexts.
Genomic survey maps differences in the molecular complement of vesicle formation machinery between Giardia intestinalis assemblages

Giardia intestinalis is a globally important microbial pathogen with considerable public health, agricultural, and economic burden. Genome sequencing and comparative analyses have elucidated G. intestinalis to be a taxonomically diverse species consisting of at least eight different sub-types (assemblages A-H) that can infect a great variety of animal hosts, including humans. The best studied of these are assemblages A and B which have a broad host range and have zoonotic transmissibility towards humans where clinical Giardiasis can range from asymptomatic to diarrheal disease. Epidemiological surveys as well as previous molecular investigations have pointed towards critical genomic level differences within numerous molecular pathways and families of parasite virulence factors within assemblage A and B isolates. In this study, we explored the necessary machinery for the formation of vesicles and cargo transport in 89 Canadian isolates of assemblage A and B G. intestinalis. Considerable variability within the molecular complement of the endolysosomal ESCRT protein machinery, adaptor coat protein complexes, and ARF regulatory system have previously been reported. Here, we confirm inter-assemblage, but find no intra-assemblage variation within the trafficking systems examined. This variation includes losses of subunits belonging to the ESCRTIII as well as novel lineage specific duplications in components of the COPII machinery, ARF1, and ARFGEF families (BIG and CYTH). Since differences in disease manifestation between assemblages A and B have been controversially reported, our findings may well have clinical implications and even taxonomic, as the membrane trafficking system underpin parasite survival, pathogenesis, and propagation.
Atomic Models of All Major Trans-Envelope Complexes Involved in Lipid Trafficking in Escherichia Coli Constructed Using a Combination of AlphaFold2, AF2Complex, and Membrane Morphing Simulations

In Gram-negative bacteria, several trans-envelope complexes (TECs) have been identified that span the periplasmic space in order to facilitate lipid transport between the inner- and outer- membranes. While partial or near-complete structures of some of these TECs have been solved by conventional experimental techniques, most remain incomplete. Here we describe how a combination of computational approaches, constrained by experimental data, can be used to build complete atomic models for four TECs implicated in lipid transport in Escherichia coli. We use DeepMind’s protein structure prediction algorithm, AlphaFold2, and a variant of it designed to predict protein complexes, AF2Complex, to predict the oligomeric states of key components of TECs and their likely interfaces with other components. After obtaining initial models of the complete TECs by superimposing predicted structures of subcomplexes, we use the membrane orientation prediction algorithm OPM to predict the likely orientations of the inner- and outer- membrane components in each TEC. Since, in all cases, the predicted membrane orientations in these initial models are tilted relative to each other, we devise a novel molecular mechanics-based strategy that we call “membrane morphing” that adjusts each TEC model until the two membranes are properly aligned with each other and separated by a distance consistent with estimates of the periplasmic width in E. coli. The study highlights the potential power of combining computational methods, operating within limits set by both experimental data and by cell physiology, for producing useable atomic structures of very large protein complexes.
HDAC-6 inhibition ameliorates the early neuropathology in a mouse model of Krabbe disease

In Krabbe disease (KD), mutations in β-galactosylceramidase (GALC), a lysosomal enzyme responsible for the catabolism of galactolipids, lead to the accumulation of its substrates galactocerebroside and psychosine. This neurologic condition is characterized by a severe and progressive demyelination together with neuron-autonomous defects and degeneration. Twitcher mice mimic the infantile form of KD, which is the most common form of the human disease. The Twitcher CNS and PNS present demyelination, axonal loss and neuronal defects including decreased levels of acetylated tubulin, decreased microtubule stability and impaired axonal transport. Here, we tested whether inhibiting the α-tubulin deacetylase HDAC6 with a specific inhibitor, ACY-738, was able to counteract the early neuropathology and neuronal defects of Twitcher mice. Our data show that delivery of ACY-738 corrects the low levels of acetylated tubulin in the Twitcher nervous system. Furthermore, it reverts the loss myelinated axons in the sciatic nerve and in the optic nerve when administered from birth to postnatal day 9, suggesting that the drug holds neuroprotective properties. The extended delivery of ACY-738 to Twitcher mice delayed axonal degeneration in the CNS and ameliorated the general presentation of the disease. ACY-738 was effective in rescuing neuronal defects of Twitcher neurons, stabilizing microtubule dynamics and increasing the axonal transport of mitochondria. Overall, our results support that ACY-738 has a neuroprotective effect in KD and should be considered as an add-on therapy combined with strategies targeting metabolic correction.
Single‐cell profiling and zebrafish avatars reveal LGALS1 as immunomodulating target in glioblastoma

Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs – features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.
Single Cell Resolution Spatial Mapping of Human Hematopoiesis Reveals Aging-Associated Topographic Remodeling

The spatial anatomy of hematopoiesis in bone marrow has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain thousands of paraffinized bone marrow core biopsy tissue specimens, providing a rich resource for studying the intact human bone marrow topography in a variety of physiologic states. Thus, we developed an end-to-end pipeline involving multiparameter whole tissue staining, in situ imaging at single-cell resolution, and artificial intelligence (AI)-based digital image analysis, and then applied it to a cohort of disease-free samples to survey alterations in the hematopoietic topography associated with aging. Our data indicate heterogeneity in marrow adipose tissue (MAT) content within each age group, and an inverse correlation between MAT content and proportions of early myeloid and erythroid precursors, irrespective of age. We identify consistent endosteal and perivascular positioning of hematopoietic stem and progenitor cells (HSPCs) with medullary localization of more differentiated elements and, importantly, uncover new evidence of aging-associated changes in cellular and vascular morphologies, microarchitectural alterations suggestive of inflammaging, and diminution of a potentially active megakaryocytic niche. Overall, our findings suggest that there is topographic remodeling of human hematopoiesis associated with aging. More generally, we demonstrate the potential to deeply unravel the spatial biology of normal and pathologic human bone marrow states using intact archival tissue specimens.
Adaptive coordinated leader–follower control of autonomous over-actuated electric vehicles

In this paper, the leader–follower formation control problem of autonomous over-actuated electric vehicles on a highway is studied. As the autonomous over-actuated electric vehicles have the characteristics of non-linearities, external disturbances and strong coupling, a novel coordinated three-level control system is constructed to supervise the longitudinal and lateral motions of autonomous electric vehicles. Firstly, an adaptive terminal sliding high-level control algorithm is designed to compute a vector of total forces and torque of vehicles, and the stability of the high-level control system is proven via Lyapunov analysis where uniform ultimate boundedness of the closed-loop signals is guaranteed. Then, a pseudo-inverse control allocation algorithm, which can achieve fault tolerance and reconfiguration of the redundant tyre actuation system, is presented to generate the desired longitudinal and lateral tyre forces. Then, a separate low-level controller consisting of an inverse tyre model and two inner loops for each wheel is designed to achieve its desired forces. Finally, simulation results demonstrate that the proposed control system not only enhance the tracking performance, but also improve the stability and riding comfort of autonomous over-actuated electric vehicles in a platoon.
A biochemical, theoretical and immunohistochemical study comparing the therapeutic efficacy of curcumin and taurine on T-2 toxin induced hepatotoxicity in rats

Introduction: Foodborne trichothecene T-2 Toxin, is a highly toxic metabolite produced by Fusarium species contaminating animal and human food, causing multiple organ failure and health hazards. T-2 toxins induce hepatotoxicity via oxidative stress causing hepatocytes cytotoxicity and genotoxicity. In this study, curcumin and taurine were investigated and compared as antioxidants against T-2-provoked hepatotoxicity. Methods: Wistar rats were administrated T-2 toxin sublethal oral dose (0.1 mg/kg) for 2 months, followed by curcumin (80 mg/kg) and taurine (50 mg/kg) for 3 weeks. Biochemical assessment of liver enzymes, lipid profiles, thiobarbituric acid reactive substances (TBARs), AFU, TNF-α, total glutathione, molecular docking, histological and immunohistochemical markers for anti-transforming growth factor-β1 (TGFβ1), double-strand DNA damage (H2AX), regeneration (KI67) and apoptosis (Active caspase3) were done. Results and Discussion: Compared to T-2 toxin, curcumin and taurine treatment significantly ameliorated hepatoxicity as; hemoglobin, hematocrit and glutathione, hepatic glycogen, and KI-67 immune-reactive hepatocytes were significantly increased. Although, liver enzymes, inflammation, fibrosis, TGFβ1 immunoexpressing and H2AX and active caspase 3 positive hepatocytes were significantly decreased. Noteworthy, curcumin’s therapeutic effect was superior to taurine by histomorphometry parameters. Furthermore, molecular docking of the structural influence of curcumin and taurine on the DNA sequence showed curcumin’s higher binding affinity than taurine. Conclusion: Both curcumin and taurine ameliorated T-2 induced hepatotoxicity as strong antioxidative agents with more effectiveness for curcumin.
Identification of prognostic cancer-associated fibroblast markers in luminal breast cancer using weighted gene co-expression network analysis

Background Cancer-associated fibroblasts (CAFs) play a pivotal role in cancer progression and are known to mediate endocrine and chemotherapy resistance through paracrine signaling. Additionally, they directly influence the expression and growth dependence of ER in Luminal breast cancer (LBC). This study aims to investigate stromal CAF-related factors and develop a CAF-related classifier to predict the prognosis and therapeutic outcomes in LBC. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to obtain mRNA expression and clinical information from 694 and 101 LBC samples, respectively. CAF infiltrations were determined by estimating the proportion of immune and cancer cells (EPIC) method, while stromal scores were calculated using the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify stromal CAF-related genes. A CAF risk signature was developed through univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. The Spearman test was used to evaluate the correlation between CAF risk score, CAF markers, and CAF infiltrations estimated through EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. The TIDE algorithm was further utilized to assess the response to immunotherapy. Additionally, Gene set enrichment analysis (GSEA) was applied to elucidate the molecular mechanisms underlying the findings. Results We constructed a 5-gene prognostic model consisting of RIN2, THBS1, IL1R1, RAB31, and COL11A1 for CAF. Using the median CAF risk score as the cutoff, we classified LBC patients into high- and low-CAF-risk groups and found that those in the high-risk group had a significantly worse prognosis. Spearman correlation analyses demonstrated a strong positive correlation between the CAF risk score and stromal and CAF infiltrations, with the five model genes showing positive correlations with CAF markers. In addition, the TIDE analysis revealed that high-CAF-risk patients were less likely to respond to immunotherapy. Gene set enrichment analysis (GSEA) identified significant enrichment of ECM receptor interaction, regulation of actin cytoskeleton, epithelial-mesenchymal transition (EMT), and TGF-β signaling pathway gene sets in the high-CAF-risk group patients. Conclusion The five-gene prognostic CAF signature presented in this study was not only reliable for predicting prognosis in LBC patients, but it was also effective in estimating clinical immunotherapy response. These findings have significant clinical implications, as the signature may guide tailored anti-CAF therapy in combination with immunotherapy for LBC patients.
Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

Purpose This study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS). Methods The MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models. Results Twenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively. Conclusion The ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model.