Biology subjects

Li, H.-X.

Publications and source records attributed to Li, H.-X..

6 recordsLinked to original sources

bioRxiv2025

Mechanosensitive channel PIEZO1 enhances endometrial decidualization via BECN1-dependent autophagy

The mechanosensitive ion channel PIEZO1 plays critical roles in physiological and pathological processes in response to various types of mechanical forces, including shear stress, stretch, and extracellular matrix (ECM) stiffness. Decidualization is crucial for a successful pregnancy, characterized by the differentiation of fibroblastic endometrial stromal cells into round, secretory decidual cells, along with the rapid remodeling of the ECM. Herein, we report that PIEZO1 plays a crucial role in enhancing decidualization in response to extracellular matrix (ECM) stiffness and cell contraction. Uterine-specific knockout of Piezo1 using Pgr-Cre in mice results in subfertility due to decidualization impairment in mid-late pregnancy. Silencing of PIEZO1 in human endometrial stromal cells also results in impaired decidualization. Treatment with the PIEZO1 agonist Yoda1 enhances decidualization in both in vivo and in vitro models. Stromal cells growing on ECM with 25 kPa stiffness display a better decidualization response than cells seeded on softer 2 kPa surface or harder surface of the regulator petri dish, and this difference is abolished by null of Piezo1. Consistent with PIEZO1 as a Ca2+ modulator, blocking of intracellular Ca2+ or pCaMKII significantly inhibits Yoda1-enhanced decidualization. Further investigation reveals that BECN1-dependent autophagy acts as the downstream of PIEZO1. Silencing of Beclin1 abolishes Yoda1-induced decidualization, while Tat-BECN1 fully rescues impaired decidualization caused by the lack of PIEZO1. Finally, the lower expression of PIEZO1 is associated with impaired decidualization in the endometrium of endometriotic baboons. In conclusion, we have uncovered a novel mechanism of decidualization that is regulated by PIEZO1-mediated mechanotransduction, providing further insight into decidualization studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/668790v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@2d25faorg.highwire.dtl.DTLVardef@186e56org.highwire.dtl.DTLVardef@1f3f61borg.highwire.dtl.DTLVardef@1a26bb7_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Immunomodulatory effect of HLA-G Overexpressed Mesenchymal Stromal Cell in Cell-based Therapy for Myocardial Infarction

BackgroundOur previous study demonstrated that intravenous administration of mesenchymal stromal cells (MSCs) significantly increased local cell engraftment and improved heart function. We sought to investigate whether HLA-G1 overexpressed MSCs could further increase local transplanted cells engraftment and improve heart function. Methods and ResultsMice were randomized to receive intravenous administration of saline, human umbilical cord blood derived MSCs (hUCB-MSCs) 7 days prior to acute myocardial infarction (AMI), induced by ligation of the left anterior descending coronary artery. Then, intramyocardial transplantation of human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) was performed 30 minutes following AMI. Echocardiographic assessment was performed to assess heart function. In-vivo fluorescent imaging analysis were used to analyze cell engraftment. Flow cytometry of splenic regulatory T cells (Tregs) and natural killer (NK) cells was conducted to evaluate the immunomodulatory effect. Our result showed that systemic intravenous administration of hUCB-MSCs significantly increased systemic Tregs, decreased systemic NK cells, increased cell engraftment of intramyocardial transplanted hiPSC-CMs, culminating in improvement of heart function. Our in-vitro study showed that HLA-G1 overexpressed hUCB-MSCs modulated immune response by decreasing pro-inflammatory cytokines. ConclusionsSystemic intravenous administration of HLA-G1 overexpressed hUCB-MSCs modulated immune response and enhanced the survival of local transplanted hiPSC-CMs to improve heart function following AMI.

cell biology↗

bioRxiv2023

The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

BackgroundThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. MethodsLeveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, we explored the potential impact of such group-level abnormality profiles on the TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to localize TMS targets individually. ResultsWe found an enhanced sgACC-DLPFC FC in MDD patients compared to healthy controls (HC). Such group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. In two independent clinical samples, we showed that the magnitude of TMS targets case-control differences in sgACC FC was related to clinical improvement. The MDD big data-guided individualized TMS targeting algorithm may generate individualized TMS targets that are clinically superior to group-level targets. InterpretationWe reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. FundingMinistry of Science and Technology of the Peoples Republic of China, National Natural Science Foundation of China, and Chinese Academy of Sciences

neuroscience↗

bioRxiv2022

Neural Representations of Self-Generated Thought during Think-aloud fMRI

Is the brain at rest during the so-called resting-state? Ongoing experiences in the resting-state vary in unobserved and uncontrolled ways across time, individuals, and populations. However, the role of self-generated thoughts in resting-state fMRI remains largely unexplored. In this study, we collected real-time self-generated thoughts during "resting-state" fMRI scans via the think-aloud method (i.e., think-aloud fMRI), which required participants to report whatever they were currently thinking. We first investigated brain activation patterns during a think-aloud condition and found that significantly activated brain areas included all brain regions required for speech. We then calculated the relationship between divergence in thought content and brain activation during think-aloud and found that divergence in thought content was associated with many brain regions. Finally, we explored the neural representation of self-generated thoughts by performing representational similarity analysis (RSA) at three neural scales: a voxel-wise whole-brain searchlight level; a region-level whole-brain analysis using the Schaefer 400-parcels; at the systems level using the Yeo seven-networks. We found that "resting-state" self-generated thoughts were distributed across a wide range of brain regions, involving all seven Yeo networks. This study highlights the value of considering ongoing experiences during resting-state fMRI, as well as providing preliminary methodological support for think-aloud fMRI.

neuroscience↗

bioRxiv2020

An Industrial-Grade Brain Imaging-Based Deep Learning Classifier

Beyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimers disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on a dataset of unprecedented size and diversity. A retrospective MRI dataset pooled from more than 217 sites/scanners constituted one of the largest brain MRI samples to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94.9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. After transfer learning, the model fine-tuned for AD classification achieved 90.9% accuracy in leave-sites-out cross-validation on the Alzheimers Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94.5%/93.6%/91.1% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65.2% vs 20.6%). Predicted scores from the AD classifier showed significant correlations with illness severity. In sum, the proposed AD classifier offers a medical-grade marker that has potential to be integrated into AD diagnostic practice.

neuroscience↗

bioRxiv2019

Reproducible observations revealing patterns of the default mode network underlie rumination

Rumination is a repetitive self-referential thinking style and posited to be an expression of abnormalities in the default mode network (DMN) in major depressive disorder (MDD). Recent evidences indicate DMN is not a unitary network but can be further divided into 3 functionally heterogenous subsystems. However, the subsystem mechanism through which DMN underlie rumination remain unclear. Here, with a modified continuous state-like paradigm, we induced healthy participants to ruminate or imagine objective scenarios (as a distraction control condition) on 3 different MRI scanners. We compared functional connectivities (FC) and inter-subject correlations (ISC) of the DMN and its 3 subsystems between rumination and distraction states. Results yielded a highly reproducible and dissociated pattern. During rumination, within-DMN FC was generally decreased compared to the distraction state. At the subsystem level, we found increased FC between the core and medial temporal lobe (MTL) subsystem and decreased FC between the core and dorsal medial prefrontal cortex (DMPFC) subsystem and within the MTL subsystem. Furthermore, we found decreased ISC within the MTL subsystem. These results suggest a specific and less synchronous activity pattern of DMN during rumination and shed new light on the association between rumination and DMN substrates regarding MDD.

neuroscience↗