MicroRNAs as Key Regulators in RA and SLE: Insights into Biological Functions.

MicroRNAs (miRNAs) are non-coding RNA molecules that bind to mRNAs to regulate gene expression. Since changes in miRNA expression levels have been found in a variety of autoimmune illnesses, miRNAs are important in autoimmune diseases. MiRNAs serve not only as pathogenic factors and biomarkers for autoimmune diseases but also as important targets for disease therapeutics. Although miRNA-based treatments are still in the research stage, in-depth investigations into the biological functions of miRNAs have significantly enhanced our understanding of their mechanisms in autoimmune diseases. The purpose of this review is to summarize the biological functions of miRNAs, their roles in rheumatoid arthritis and systemic lupus erythematosus, therapeutic strategies, and challenges.

Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics.

Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.

Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.

Spatially resolved transcriptomics (SRT) has emerged as a powerful tool for investigating gene expression in spatial contexts, providing insights into the molecular mechanisms underlying organ development and disease pathology. However, the expression sparsity poses a computational challenge to integrate other modalities (e.g. histological images and spatial locations) that are simultaneously captured in SRT datasets for spatial clustering and variation analyses. In this study, to meet such a challenge, we propose multi-modal domain adaption for spatial transcriptomics (stMDA), a novel multi-modal unsupervised domain adaptation method, which integrates gene expression and other modalities to reveal the spatial functional landscape. Specifically, stMDA first learns the modality-specific representations from spatial multi-modal data using multiple neural network architectures and then aligns the spatial distributions across modal representations to integrate these multi-modal representations, thus facilitating the integration of global and spatially local information and improving the consistency of clustering assignments. Our results demonstrate that stMDA outperforms existing methods in identifying spatial domains across diverse platforms and species. Furthermore, stMDA excels in identifying spatially variable genes with high prognostic potential in cancer tissues. In conclusion, stMDA as a new tool of multi-modal data integration provides a powerful and flexible framework for analyzing SRT datasets, thereby advancing our understanding of intricate biological systems.

Synergistic suppression of BDNF via epigenetic mechanism deteriorating learning and memory impairment caused by Mn and Pb co-exposure.

Microglia, the resident immune cells of the central nervous system (CNS), play a dual role in neurotoxicity by releasing the NLR Family Pyrin Domain Containing 3 (NLRP3) inflammasome and brain-derived neurotrophic factor (BDNF) in response to environmental stress. Suppression of BDNF is implicated in learning and memory impairment induced by exposure to manganese (Mn) or lead (Pb) individually. Methyl CpG Binding Protein 2 (MeCp2) and its phosphorylation status are related to BDNF suppression. Protein phosphatase2A (PP2A), a member of the serine/threonine phosphatases family, dephosphorylates substrates based on the methylation state of its catalytic C subunit (PP2Ac). However, the specific impairment patterns and molecular mechanisms resulting from co-exposure to Mn and Pb remain unclear. Therefore, the purpose of this study was to explore the effects of Mn and Pb exposure, alone and in combination, on inducing neurotoxicity in the hippocampus of mice and BV2 cells, and to determine whether simultaneous exposure to both metals exacerbate their toxicity. Our findings reveal that co-exposure to Mn and Pb leads to severe learning and memory impairment in mice, which correlates with the accumulation of metals in the hippocampus and synergistic suppression of BDNF. This suppression is accompanied by up-regulation of the epigenetic repressor MeCp2 and its phosphorylation status, as well as demethylation of PP2Ac. Furthermore, inhibition of PP2Ac demethylation using ABL127, an inhibitor for its protein phosphatase methylesterase1 (PME1), or knockdown of MeCp2 via siRNA transfection in vitro effectively increases BDNF expression and mitigates BV2 cell damage induced by Mn and Pb co-exposure. We also observe abnormal activation of microglia characterized by enhanced release of the NLRP3 inflammasome, Casepase-1 and pro-inflammatory cytokines IL-1ß, in the hippocampus of mice and BV2 cells. In summary, our experiments demonstrate that simultaneous exposure to Mn and Pb results in more severe hippocampus-dependent learning and memory impairment, which is attributed to epigenetic suppression of BDNF mediated by PP2A regulation.

YAP1-CPNE3 positive feedback pathway promotes gastric cancer cell progression.

Hippo-Yes-associated protein 1 (YAP1) plays an important role in gastric cancer (GC) progression; however, its regulatory network remains unclear. In this study, we identified Copine III (CPNE3) was identified as a novel direct target gene regulated by the YAP1/TEADs transcription factor complex. The downregulation of CPNE3 inhibited proliferation and invasion, and increased the chemosensitivity of GC cells, whereas the overexpression of CPNE3 had the opposite biological effects. Mechanistically, CPNE3 binds to the YAP1 protein in the cytoplasm, inhibiting YAP1 ubiquitination and degradation mediated by the E3 ubiquitination ligase ß-transducin repeat-containing protein (ß-TRCP). Thereby activating the transcription of YAP1 downstream target genes, which creates a positive feedback cycle to facilitate GC progression. Immunohistochemical analysis demonstrated significant upregulation of CPNE3 in GC tissues. Survival and Cox regression analyses indicated that high CPNE3 expression was an independent prognostic marker for GC. This study elucidated the pivotal involvement of an aberrantly activated CPNE3/YAP1 positive feedback loop in the malignant progression of GC, thereby uncovering novel prognostic factors and therapeutic targets in GC.

Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics.

Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.

Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect.

For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.

Biocontrol Efficacy and Induced Resistance of Paenibacillus polymyxa J2-4 Against Meloidogyne incognita Infection in Cucumber.

Meloidogyne incognita is one of the most destructive agricultural pathogens around the world, resulting in severe damage to yield and quality in agricultural production. Biological control promises to be a great potential alternative to chemical agents against M. incognita. Paenibacillus polymyxa J2-4, isolated from ginger plants injured by M. incognita, has shown excellent biocontrol efficacy against M. incognita in cucumber. In vitro experiments with the strain J2-4 resulted in a correct mortality rate of 88.79% (24 h) and 98.57% (48 h) for second-stage juveniles (J2s) of M. incognita. Strain J2-4 significantly suppressed nematode infection on potted plants, with a 65.94% reduction in galls and a 51.64% reduction in eggs compared with the control. The split-root assay demonstrated that strain J2-4 not only reduced J2s' invasion but also inhibited nematode development through the dependence on salicylic acid and jasmonic acid signaling of strain J2-4 induction of plant resistance in local and systemic roots of cucumbers. Genomic analysis of strain J2-4 indicated biosynthetic gene clusters encoding polymyxin, fusaricidin B, paenilan, and tridecaptin. In addition, genetic analysis showed that none of the genes encoding virulence factors were detected in the genome of J2-4 compared with the pathogenic Bacillus species. Taking all the data together, we conclude that P. polymyxa J2-4 has potential as a biological control agent against M. incognita on cucumbers and can be considered biologically safe when used in agriculture.

Volatile organic compounds produced by Paenibacillus polymyxa J2-4 exhibit toxic activity against Meloidogyne incognita.

BACKGROUND: Root knot nematodes cause great damage to crops worldwide. Due to the negative effects of the application of fumigant and old chemical nematicides, biological nematicides have drawn increasing attention in recent years. Here we tested the fumigant activity of the volatile organic compounds (VOCs) blends emitted from Paenibacillus polymyxa and pure commercial VOCs against M. incognita. RESULTS: In this study, we investigated whether P. polymyxa strain J2-4 could produce VOCs that exhibit nematicidal activity. In vitro assays indicated that J2-4 VOCs were highly toxic to second stage juveniles (J2s) and could inhibit egg hatching. Three-layered pot experiments showed that the number of nematodes that penetrating in cucumber roots was reduced by 69.27% after the application of J2-4 VOCs under greenhouse conditions. We identified 14 volatiles using solid-phase micro-extraction gas chromatography-mass spectrometry. The efficacy of six commercially available VOCs, namely 2-isobutyl-3-methylpyrazine, 2,4-dimethoxybenzaldoxime, 2-dodecanone, 2-tridecanol, 2-tridecanone, and 2-tetradecanol, against M. incognita were examined. Except for 2,4-dimethoxybenzaldoxime, the remaining five VOCs showed strong direct-contact nematicidal activity against J2s of M. incognita, and only 2-isobutyl-3-methylpyrazine showed strong fumigant activity against J2s of M. incognita. In pot experiments, 2-isobutyl-3-methylpyrazine and 2-dodecanone reduced the number of root galls by about 70%, and 2-tridecanone reduced the number of root galls and egg masses by about 63% compared with controls. CONCLUSION: Paenibacillus polymyxa strain J2-4 exhibited high fumigant activity against M. incognita. Our results provide evidence for the use of J2-4 and its VOCs as biocontrol agents in the management of root-knot nematodes. © 2023 Society of Chemical Industry.

Enhancing the solubility and emulsion properties of rice protein by deamidation of citric acid-based natural deep eutectic solvents.

The characteristics of rice protein deamidated (DRP) by choline chloride-citric acid and glucose-citric acid natural deep eutectic solvents (C-C NADES, G-C NADES) at different dilutions were investigated. Compared with the effect of citric acid deamidation on the structural and functional properties of the protein, the DRP from the NADESs led to remarkable differences in the degree of hydrolysis (DH), SDS-PAGE, morphology, surface hydrophobicity, average particle size, intrinsic fluorescence, amino acid compositions, and emulsion activity. The results of SDS-PAGE, DH, and SEM showed the NADESs reduced the occurrence of uncontrolled hydrolysis of protein during acid deamidation. DRP from C-C and G-C NADESs was found to significantly improve solubility. DRP prepared by C-C NADES showed a more than 40 % solubility over a wide pH range associated with its higher emulsifying activity (37.62-44.19 m2/g) and emulsifying stability (73.76-86.9 min), as well as a better deamidation effect while lower DH. Thus, these findings showed that acid-based NADESs had great potential as a deamidation solvent to expand the application of protein.

Genetic analysis of isolated methylmalonic acidemia in Henan, China: c.1663G>A variant of MMUT prevalent in the Henan population.

BACKGROUND: Methylmalonic acidemia (MMA) is the most common organic acidemia in China, and isolated MMA accounts for approximately 30 % of all types of MMA. Common variants of the MMUT gene vary greatly around the world. The present study aims to determine the high-frequency and novel genetic variants of the MMUT gene in the Henan population of China and evaluate the prognosis of patients carrying the c.1663G>A (p.Ala555Thr) variant. METHODS: We performed next-generation sequencing for 41 patients with isolated MMA screened by tandem mass spectrometry (MS/MS) and analysed the genetic results. We also evaluated the prognosis of patients with the c.1663G>A variant. We used Jalview software for multispecies sequence alignment and Missense3D and DynaMut to predict the protein function of the detected novel variants. RESULTS: A total of 43 variants from 41 patients with isolated MMA were detected, of which c.1663G>A (14.63 %), c.729_730insTT (10.98 %), and c.1106G>A (8.53 %) are high-frequency variants of the MMUT gene in the Henan population. The patients carrying the c.1663G>A variant tended to be responsive to vitamin B12, have a low mortality rate. We also identified 5 novel variants (c.479C>T, c.811G>C, c.965T>A, c.1142G>A and c.1667C>T). CONCLUSION: The rare variant c.1663G>A is prevalent in the Henan population, and infants with this variant tend to have good prognosis. Our findings, especially novel variants, will help broaden the spectrum of genetic variants and facilitate clinical diagnosis and genetic counselling for affected families.

Hamilton energy balance and synchronization behaviors of two functional neurons.

The nervous system is composed of various functional neurons, some of which perceive sound or light, and these physical signals can be converted into bioelectrical signals. From the biophysical point of view, piezoelectric ceramic embedded in neuronal circuits can detect the external auditory waves, while phototube can capture light signals, so as to obtain two functional neurons with auditory recognition and light-dependent recognition. Considering the two identical or different functional neurons are connected by an induction coil to stimulate magnetic field coupling, and there will be energy diversity when they are driven by different initial conditions or external stimulation. Thus, synaptic connections can be activated and awakened in an adaptive manner when field energy is exchanged, and the coupling channel remains open until the energy diversity between neurons is controlled at a limited threshold. For this purpose, a criterion of the coupling strength increases exponentially is proposed to discuss the enhancement of neuronal synaptic connections. It is found that two neurons can be coupled adaptively to achieve complete synchronization, quasi-synchronization or intermittent quasi-synchronization. These results could help in designing functional assistive devices for patients with hearing or vision impairment.

Biological Characteristics and Functional Analysis of the Linoleic Acid Synthase Gene ZjFAD2 in Jujube.

Jujube fruit is rich in linoleic acid and other bioactive components and has great potential to be used for the development of functional foods. However, the roles of FAD2 genes in linoleic acid biosynthesis in jujube fruit remain unclear. Here, we identified 15 major components in jujube and found that linoleic acid was the main unsaturated fatty acid; major differences in the content and distribution of linoleic acid in the pulp and seeds were observed, and levels of linoleic acid decreased during fruit maturation. Analysis of the fatty acid metabolome, genome, and gene expression patterns of cultivated and wild-type jujube revealed five ZjFAD2 family members highly related to linoleic acid biosynthesis. The heterologous expression of these five ZjFAD2 family members in tobacco revealed that all five of these genes increased the content of linoleic acid. Additionally, transient expression of these genes in jujube fruit and the virus-induced gene silencing (VIGS) test further confirmed the key roles of ZjFAD2-11 and ZjFAD2-1 in the biosynthesis of linoleic acid. The results of this research provide valuable insights into the molecular mechanism underlying linoleic acid synthesis in jujube and will aid the development of quality-oriented breeding strategies.

Cu2+ -Anchored Carbon Nano-Photocatalysts for Visible Water Splitting to Boost Hydrogen Cuproptosis.

Both hydrogen (H2 ) and copper ions (Cu+ ) can be used as anti-cancer treatments. However, the continuous generation of H2 molecules and Cu+ in specific sites of tumors is challenging. Here we anchored Cu2+ on carbon photocatalyst (Cu@CDCN) to allow the continuous generation of H2 and hydrogen peroxide (H2 O2 ) in tumors using the two-electron process of visible water splitting. The photocatalytic process also generated redox-active Cu-carbon centers. Meanwhile, the Cu2+ residues reacted with H2 O2 (the obstacle to the photocatalytic process) to accelerate the two-electron process of water splitting and cuprous ion (Cu+ ) generation, in which the Cu2+ residue promoted a pro-oxidant effect with glutathione through metal-reducing actions. Both H2 and Cu+ induced mitochondrial dysfunction and intracellular redox homeostasis destruction, which enabled hydrogen therapy and cuproptosis to inhibit cancer cell growth and suppress tumor growth. Our research is the first attempt to integrate hydrogen therapy and cuproptosis using metal-enhanced visible solar water splitting in nanomedicine, which may provide a safe and effective cancer treatment.

Diagnosis and Characterization of Ditylenchus destructor Isolated from Mazus japonicus in China.

The potato rot nematode (Ditylenchus destructor) is one of the most destructive pests in the production of tuber crops, resulting in severely decreased yields and inferior product quality. In 2021, a great number of nematodes were detected in the roots of Mazus japonicus, a weed that is harmful to crop growth, in Qingdao, Shandong Province, China. The present study was undertaken to characterize and identify the nematodes isolated from M. japonicus through morphological identification and molecular approaches. Their morphological characteristics were highly consistent with the descriptions of D. destructor Thorne, 1945. The nematodes collected from M. japonicus were identified as D. destructor haplotype B using D1/D2 and sequence characterized amplified region (SCAR) primers. PCR-ITS-RFLP analysis was conducted to monitor intraspecific variations. In addition, the phylogenetic analysis of the internal transcribed spacer (ITS) demonstrated that this D. destructor population was clustered in haplotype B, supported by a 100% bootstrap value. Another assay, in which M. japonicus was inoculated with a mixture of the life stages of D. destructor, was performed. This assay showed that M. japonicus exhibited a high susceptibility to D. destructor in pots. This is the first record of D. destructor parasitizing M. japonicus in China, and it is of great importance because M. japonicus could be a potential reservoir for D. destructor in the field.

Ca-DEX biomineralization-inducing nuts reverse oxidative stress and bone loss in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a common autoimmune disease, and the inflammatory response during its development can lead to joint cartilage and bone damage up to disability. Dexamethasone (DEX) can effectively alleviate the inflammatory response in RA, but the severe adverse effects that occur after its long-term administration limit its clinical development. Herein, we propose a Ca-DEX biomineralization-inducing nut (CaCO3-DEX) with controlled release properties for mitigating the toxic side effects of DEX in RA treatment, especially the damage to cartilage and bone. CaCO3-DEX releases the drug and Ca2+ preferentially in an inflammatory environment. Both in vitro and in vivo studies demonstrate that CaCO3-DEX significantly reduces the secretion of pro-inflammatory factors and inhibits ROS production in vitro, as well as demonstrates superior pro-biomineralization and osteogenic differentiation potential. In the collagen-induced rheumatoid arthritis model (CIA model), CaCO3-DEX significantly reduces the clinical score of arthritis in mice, and the imaging results show a noticeable relief of edema and bone erosion in CIA model mice treated with CaCO3-DEX, while inflammatory factors at the injury areas are significantly reduced, which provides favorable protection to cartilage and bone.

Corrigendum: A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: a real-world retrospective study.

[This corrects the article DOI: 10.3389/fnins.2023.1130831.].

Virtual screening-based discovery of AI-2 quorum sensing inhibitors that interact with an allosteric hydrophobic site of LsrK and their functional evaluation.

Introduction: Quorum sensing (QS) is a bacterial intracellular and intercellular communication system that regulates virulence factor production, biofilm formation, and antibiotic sensitivity. Quorum-sensing inhibitors (QSIs) are a novel class of antibiotics that can effectively combat antibiotic resistance. Autoinducer-2 (AI-2) is a universal signaling molecule that mediates inter- and intraspecies QS systems among different bacteria. Furthermore, LsrK plays an important role in regulating the activity and stability of the intracellular AI-2 signaling pathway. Thus, LsrK is considered an important target for the development of QSIs. Methods: We designed a workflow integrating molecular dynamic (MD) simulations, virtual screening, LsrK inhibition assays, cell-based AI-2-mediated QS interference assays, and surface plasmon resonance (SPR)-based protein affinity assays to screen for potential LsrK kinase inhibitors. Results: MD simulation results of the LsrK/ATP complex revealed hydrogen bonds and salt bridge formation among four key residues, namely, Lys 431, Tyr 341, Arg 319, and Arg 322, which are critical for the binding of ATP to LsrK. Furthermore, MD simulation results indicated that the ATP-binding site has an allosteric pocket that can become larger and be occupied by small molecule compounds. Based on these MD simulation results, a constraint of forming at least one hydrogen bond with Arg 319, Arg 322, Lys 431, or Tyr 341 residues was introduced when performing virtual screening using Glide's virtual screening workflow (VSW). In the meantime, compounds with hydrophobic group likely to interact with the allosteric hydrophobic pocket are preferred when performing visual inspection. Seventy-four compounds were selected for the wet laboratory assays based on virtual screening and the absorption, distribution, metabolism, and excretion (ADME) properties of these compounds. LsrK inhibition assays revealed 12 compounds inhibiting LsrK by more than 60% at a 200 µM concentration; four of these (Y205-6768, D135-0149, 3284-1358, and N025-0038) had IC50 values below 50 µM and were confirmed as ATP-competitive inhibitors. Six of these 12 LsrK inhibitors exhibited high AI-2 QS inhibition, of which, Y205-6768 had the highest activity with IC50 = 11.28 ± 0.70 µM. The SPR assay verified that compounds Y205-6768 and N025-0038 specifically bound to LsrK. MD simulation analysis of the docking complexes of the four active compounds with LsrK further confirmed the importance of forming hydrogen bonds and salt bridges with key basic amino acid residues including Lys 431, Tyr 341, Arg 319, and Arg 322 and filling the allosteric hydrophobic pocket next to the purine-binding site of LsrK. Discussion: Our study clarified for the first time that there is an allosteric site near the ATP-binding site of Lsrk and that it enriches the structure-activity relationship information of Lsrk inhibitors. The four identified compounds showed novel structures, low molecular weights, high activities, and novel LsrK binding modes, rendering them suitable for further optimization for effective AI-2 QSIs. Our work provides a valuable reference for the discovery of QSIs that do not inhibit bacterial growth, thereby avoiding the emergence of drug resistance.

Metabolome Sequencing Reveals that Protein Arginine-N-Methyltransferase 1 Promotes the Progression of Invasive Micropapillary Carcinoma of the Breast and Predicts a Poor Prognosis.

Invasive micropapillary carcinoma (IMPC) of the breast is a special histopathologic type of cancer with a high recurrence rate and the biological features of invasion and metastasis. Previous spatial transcriptome studies indicated extensive metabolic reprogramming in IMPC, which contributes to tumor cell heterogeneity. However, the impact of metabolome alterations on IMPC biological behavior is unclear. Herein, endogenous metabolite-targeted metabolomic analysis was done on frozen tumor tissue samples from 25 patients with breast IMPC and 34 patients with invasive ductal carcinoma not otherwise specified (IDC-NOS) by liquid chromatography-mass spectrometry. An IMPC-like state, which is an intermediate transitional morphologic phenotype between IMPC and IDC-NOS, was observed. The metabolic type of IMPC and IDC-NOS was related to breast cancer molecular type. Arginine methylation modification and 4-hydroxy-phenylpyruvate metabolic changes play a major role in the metabolic reprogramming of IMPC. High protein arginine-N-methyltransferase (PRMT) 1 expression was an independent factor related to the poor prognosis of patients with IMPC in terms of disease-free survival. PRMT1 promoted H4R3me2a, which induced tumor cell proliferation via cell cycle regulation and facilitated tumor cell metastasis via the tumor necrosis factor signaling pathway. This study identified the metabolic type-related features and intermediate transition morphology of IMPC. The identification of potential targets of PRMT1 has the potential to provide a basis for the precise diagnosis and treatment of breast IMPC.

Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network.

Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial contexts. To solve the issue, we develop a novel ensemble model, AE-GCN (autoencoder-assisted graph convolutional neural network), which combines the autoencoder (AE) and graph convolutional neural network (GCN), to identify accurate and fine-grained spatial domains. AE-GCN transfers the AE-specific representations to the corresponding GCN-specific layers and unifies these two types of deep neural networks for spatial clustering via the clustering-aware contrastive mechanism. In this way, AE-GCN accommodates the strengths of both AE and GCN for learning an effective representation. We validate the effectiveness of AE-GCN on spatial domain identification and data denoising using multiple SRT datasets generated from ST, 10x Visium, and Slide-seqV2 platforms. Particularly, in cancer datasets, AE-GCN identifies disease-related spatial domains, which reveal more heterogeneity than histological annotations, and facilitates the discovery of novel differentially expressed genes of high prognostic relevance. These results demonstrate the capacity of AE-GCN to unveil complex spatial patterns from SRT data.