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Xi, C.

Publications and source records attributed to Xi, C..

12 recordsLinked to original sources

Deep learning-enabled speckle reduction for cleared-sample coherent scattering tomography

Clearing Assisted Scattering Tomography (CAST) extends coherent scattering tomography to whole-brain imaging, enabling visualization of fine-scale brain-wide connectivity. As a coherent optical tomography modality, CAST is inherently affected by speckle noise, which degrades image quality and limits quantitative analysis. However, existing speckle reduction methods developed for optical coherence tomography (OCT) are not directly transferable to CAST images due to differences in sample and noise statistics. Here, we present a learning-based cleared-sample speckle reduction network, termed CLEAR Net, specifically designed for CAST imaging, which effectively suppresses speckle noise in whole-brain white matter images while preserving fine structural details. We quantitatively benchmarked CLEAR Net against representative speckle reduction algorithms on CAST datasets and further evaluated its generalizability using publicly available ophthalmic datasets.

bioengineering↗

Modeling Post-Gastrula Development via Bidirectional Pluripotent Stem Cells

The absence of stem cells capable of efficiently generating both trophoblast and epiblast lineages has hindered precise recapitulation of embryonic development. Through high-content chemical screening, we established an AL medium to generate mouse and human bidirectional pluripotent stem cells (BPSCs) characterized by concurrent OCT4/CDX2 expression. Mouse BPSCs demonstrated high plastic differentiation into trophoblast, epiblast and primitive endoderm lineages in vitro within 48 hours without exogenous induction factors and efficiently contributed to the embryo and extraembryonic tissues in vivo. Mechanistically, hyperactivation of the Wnt signaling pathway breaks the early lineage differentiation barrier by initiating a Lef1-dependent bypass. Remarkably, BPSCs can efficiently generated E8.5 embryoids that completed gastrulation and displayed advanced features such as brain development, a closed neural tube, a beating heart, somite formation, and primordial germ cells. These findings highlight BPSCs as a powerful tool for investigating early lineage specification and post-gastrulation embryonic development, with potential applications across multiple species.

developmental biology↗

Identification of Glucosidase II that regulates STIM1 activation dynamics

The ubiquitous presence of stromal interaction molecule (STIM), endoplasmic reticulum (ER) Ca2+ sensors, plays a crucial role for maintaining Ca2+ homeostasis and signaling in mammalian cells by linking ER Ca2+ depletion with extracellular Ca2+ influx. Although recent advancements have shed light on glycosylation in STIM proteins, further exploration of deglycosylation enzyme modification and its role remains limited by methodological constraints. In this study, leveraging the miniTurbo-driven proximal biotinylation labeling method, we were able to screen for weak and transiently binding protein molecules within specific environments like ER lumina. Herein, we unveil glucosidase II, comprising GANAB and PRKCSH, as a novel partner of the STIM1 complex. Through investigations into Glucosidase IIs (Glu II) regulation of STIM1s Ca2+ affinities both in cellulo and in situ, alongside its impact on store-operated Ca2+ entry (SOCE), we propose a novel mechanism wherein Glu II governs STIM1 activation by modulating Ca2+ binding affinity, thereby adjusting the level of activated STIM1 in response to physiological stimulation.

cell biology↗

Highly Dynamic and Sensitive NEMOer Calcium Indicators for Imaging ER Calcium Signals in Excitable Cells

Endoplasmic/sarcoplasmic reticulum (ER/SR) sits at the heart of the calcium (Ca2+) signaling machinery, yet current genetically encoded Ca2+ indicators (GECIs) lack the ability to detect elementary Ca2+ release events from ER/SR, particularly in muscle cells. Here we report a set of organellar GECIs, termed NEMOer, to efficiently capture ER Ca2+ dynamics with increased sensitivity and responsiveness. Compared to G-CEPIA1er, NEMOer indicators exhibit dynamic ranges that are an order of magnitude larger, which enables up to 5-fold more sensitive detection of Ca2+ oscillation in both non-excitable and excitable cells. The ratiometric version further allows super-resolution monitoring of local ER Ca2+ homeostasis and dynamics. Notably, the NEMOer-f variant enabled the inaugural detection of Ca2+ blinks, elementary Ca2+ releasing signals from the SR of cardiomyocytes, as well as in vivo spontaneous SR Ca2+ releases in zebrafish. In summary, the highly dynamic NEMOer sensors expand the repertoire of organellar Ca2+ sensors that allow real-time monitoring of intricate Ca2+ dynamics and homeostasis in live cells with high spatiotemporal resolution.

synthetic biology↗

Genetic polymorphisms of Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3) gene in African American kidney transplant recipients are associated with post-transplant graft failure

BackgroundAfrican American (AA) kidney transplant recipients exhibit a higher rate of graft loss compared to other racial and ethnic populations, highlighting the need to identify causative factors underlying this disparity. MethodWe analyzed RNA sequences of pretransplant whole blood from subjects followed in three kidney transplant cohorts to identify single nucleotide polymorphisms (SNPs) associated with death censored graft loss (DCGL). We employed a meta-analysis to uncover key transcriptional signatures and pathways associated with the identified SNPs and used single cell RNA to define cellular specificity. We characterized SNP functions using in vitro immunological and survival assays and tested for associations between the identified SNPs and other immune-related diseases using a [~]30,100 subject, electronic health record (EHR)-linked database. ResultsWe uncovered a cluster of four consecutive missense SNPs in the Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3, a negative immune response regulator) gene that strongly associated with DCGL. This LILRB3-4SNPs cluster encodes missense mutations at amino acids 617-618 proximal to a SHP-1/2 phosphatase-binding ITIM motif. LILRB3-4SNPs is specifically enriched within subjects of AA ancestry (8.6% prevalence vs 2.3% in Hispanic and 0.1% in European populations), is not linked to APOL1 G1/G2 alleles, and exhibited a strong association with DCGL. Analysis of PBMC and transplant biopsies from recipients with LILRB3-4SNPs showed evidence of enhanced adaptive immune responsiveness and ferroptosis-associated death in monocytes. Overexpression of the variant allele in THP-1 cells (macrophage line) induced augmented inflammation and ferroptosis, which were attenuated by a ferroptosis inhibitor, verifying a causal link. The LILRB3-4SNPs also associated with multiple systemic and organ-specific immune-related diseases in AAs, consistent with conferring a broadly relevant immune function. Conclusionthe LILRB3-4SNPs represent a functionally important, distinct genetic risk factor for kidney transplant outcome and development/severity of other immune-related diseases in patients of AA ancestry. Pharmacological targeting of ferroptosis should be tested to prevent or treat these disease processes in AA recipients carrying LILRB3-4SNPs.

genetics↗

The MuSK-BMP pathway regulates synaptic Nav1.4 localization and muscle excitability

The neuromuscular junction (NMJ) is the linchpin of nerve-evoked muscle contraction. Broadly considered, the function of the NMJ is to transduce a nerve action potential into a muscle fiber action potential (MFAP). Efficient information transfer requires both cholinergic signaling, responsible for the generation of endplate potentials (EPPs), and excitation, the activation of postsynaptic voltage-gated sodium channels (Nav1.4) to trigger MFAPs. In contrast to the cholinergic apparatus, the signaling pathways that organize Nav1.4 and muscle fiber excitability are poorly characterized. Muscle-specific kinase (MuSK), in addition to its Ig1 domain-dependent role as an agrin-LRP4 receptor, is also a BMP co-receptor that binds BMPs via its Ig3 domain and shapes BMP-induced signaling and transcriptional output. Here we probed the function of the MuSK-BMP pathway at the NMJ using mice lacking the MuSK Ig3 domain ( {Delta}Ig3-MuSK). Synapses formed normally in {Delta}Ig3-MuSK animals, but the postsynaptic apparatus was fragmented from the first weeks of life. Anatomical denervation was not observed at any age examined. Moreover, spontaneous and nerve-evoked acetylcholine release, AChR density, and endplate currents were comparable to WT. However, trains of nerve-evoked MFAPs in {Delta}Ig3-MuSK muscle were abnormal as revealed by increased jitter and blocking in single fiber electromyography. Further, nerve-evoked compound muscle action potentials (CMAPs), as well as twitch and tetanic muscle torque force production, were also diminished. Finally, Nav1.4 levels were reduced at {Delta}Ig3-MuSK synapses but not at the extrajunctional sarcolemma, indicating that the observed excitability defects are the result of impaired localization of this voltage-gated ion channel at the NMJ. We propose that MuSK plays two distinct roles at the NMJ: as an agrin-LRP4 receptor necessary for establishing and maintaining cholinergic signaling, and as a BMP co-receptor required for maintaining proper Nav1.4 density, nerve-evoked muscle excitability and force production. The MuSK-BMP pathway thus emerges as a target for modulating excitability and functional innervation, which are defective in conditions such as congenital myasthenic syndromes and aging. Significance StatementThe neuromuscular junction (NMJ) is required for nerve-evoked muscle contraction and movement, and its function is compromised during aging and disease. Although the mechanisms underlying neurotransmitter release and cholinergic response at this synapse have been studied extensively, the machinery necessary for nerve-evoked muscle excitation are incompletely characterized. We show that MuSK (Muscle-specific kinase), in its role as a BMP co-receptor, regulates NMJ structure as well as the localization of the voltage-gated sodium channels necessary for full nerve-evoked muscle fiber excitation and force production. This novel function of MuSK is structurally and mechanistically distinct from its role in organizing cholinergic machinery. The MuSK-BMP pathway thus presents a new opportunity to understand mechanisms that may preserve or enhance neuromuscular excitability in the face of aging and disease.

neuroscience↗

Deep-learning Based Pathological Assessment of Frozen Procurement Kidney Biopsies Predicts Graft Loss and Guides Organ Utilization: A Large-scale Retrospective Study

BackgroundLesion scores on procurement donor biopsies are commonly used to guide organ utilization. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropriate discard. MethodsWe constructed deep-learning based models to recognize kidney tissue compartments in H&E stained sections from procurement biopsies performed at 583 hospitals nationwide in year 2011-2020. The models were trained and tested respectively on 11473 and 3986 images sliced from 100 slides. We then extracted whole-slide abnormality features from 2431 kidneys, and correlated with pathologists scores and transplant outcomes. Finally, a Kidney Donor Quality Score (KDQS) incorporating digital features and the Kidney Donor Profile Index (KDPI) was derived and used in combination with recipient demographic and peri-transplant characteristics to predict graft loss or assist organ utilization. ResultsOur model accurately identified 96% and 91% of normal/sclerotic glomeruli respectively; 94% of arteries/arterial intimal fibrosis regions; 90% of tubules. Three whole-slide features (Sclerotic Glomeruli%, Arterial Intimal Fibrosis%, and Interstitial Fibrosis%) demonstrated strong correlations with corresponding pathologists scores (n=2431), but had superior associations with post-transplant eGFR (n=2033) and graft loss (n=1560). The combination of KDQS and other factors predicted 1- and 4-year graft loss (discovery: n=520, validation: n=1040). Finally, by matching 398 discarded kidneys due to "biopsy findings" to transplanted population, the matched transplants from discarded KDQS<4 group (110/398, 27.6%) showed similar graft survival rate to unmatched transplanted kidneys (2-, 5-year survival rate: 97%, 86%). KDQS [&ge;] 7 (37/398, 9.3%) and 1-year survival model score [&ge;] 0.55 were determined to identify possible discards (PPV=0.92). ConclusionThis deep-learning based approach provides automatic and reliable pathological assessment of procurement kidney biopsies, which could facilitate graft loss risk stratification and organ utilization. Translational StatementThis deep-learning based approach provides rapid but more objective, sensitive and reliable assessment of deceased-donor kidneys before transplantation, and improves the prognostic value of procurement biopsies, thus could potentially reduce inappropriate discard and stratify patients needing monitoring or preventative measures after transplantation. The pipeline can be integrated into various types of scanners and conveniently generates report after slide scanning. Such report can be used in conjunction with pathologists report or independently for centers lacking renal pathologists.

bioinformatics↗

Discovery and characterization of highly potent and selective covalent inhibitors of SARS-CoV-2 PLpro

Coronavirus infections, such as the global COVID-19 pandemic, have had a profound impact on many aspects of our daily life including working style, economy, and the healthcare system. To prevent the rapid viral transmission and speed up recovery from the infection, many academic organizations and industry research labs have conducted extensive research on discovering new therapeutic options for SARS-CoV-2. Among those efforts, RNA-dependent RNA polymerase (RdRp) inhibitors such as Remdesivir, Molnupiravir and 3CLpro inhibitor such as Nirmatrelvir (Paxlovid) have been widely used as the therapeutic options. Given the recent emergence of several new variants that caused a resurgence of the virus, it would be beneficial to discover more diverse therapeutic options with novel anti-viral mechanisms. In this regard, PLpro has been highlighted since it, along with 3CLpro, is one of the two most important proteases that are required for SARS-CoV-2 viral processing. While 3CLpro inhibitors were extensively investigated in the light of Emergency Use Authorizations of Nirmatrelvir, PLpro inhibitors have not been thoroughly investigated even preclinically. Thus, discovery efforts on antivirals acting against PLpro will be valuable. PLpro inhibitors may exert their activity by inhibiting viral replication and enhancing the host defense system through blocking virus-induced cell signaling events for evading host immune response. In this study, we report the discovery and development of two covalent irreversible PLpro inhibitors, HUP0109 and its deuterated analog DX-027, out of our quest for novel anti-COVID 19 therapeutic agents for the past two and half years. HUP0109 selectively targets the viral catalytic cleft of PLpro and covalently modifies its active site cysteine residue (C111). Promising results from preclinical evaluation suggest that DX-027 can be developed as a potential COVID-19 treatment.

pharmacology and toxicology↗

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

BackgroundInterstitial fibrosis, tubular atrophy, and inflammation are major contributors to renal allograft failure. Here we seek an objective, quantitative pathological assessment of these lesions to improve predictive utility. MethodsWe constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte (MNL) infiltrates from Periodic acid-Schiff (PAS) stained slides of transplant biopsies (training: n=60, testing: n=33) that quantified pathological lesions specific for interstitium, tubules and MNL infiltration. The pipeline was applied to 789 whole slide images (WSI) from baseline (n=478, pre-implantation) and 12-month post-transplant (n=311) protocol biopsies in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. ResultsOur model accurately recognized kidney tissue compartments and MNLs. The digital features significantly correlated with Banff scores, but were more sensitive to subtle pathological changes below the thresholds in Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of 1-year graft loss (p=2.8e-05), while a Composite Damage Score (CDS) in 12-month post-transplant protocol biopsies predicted later graft loss (p=7.3e-05). ITAS and CDS outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITAS or CDS also demonstrated significantly higher incidence of eGFR decline and subsequent graft damage. ConclusionsThis deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies, and demonstrated superior ability for prediction of posttransplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

pathology↗

Variations of gut microbiome profile under different storage conditions and preservation periods: A multi-dimensional evaluation

Gut dysbiosis contributes to the development of various human diseases. There are thousands of publications per year for investigating the role of gut microbiota in development of various diseases. However, emerging evidence has indicated data inconsistency between different studies frequently, but gained very little attention by scientists. There are many factors that can cause data variation and inconsistency during the process of microbiota study, in particular, sample storage conditions and subsequent sequencing process. Here, we systemically evaluated the impacts of six fecal sample storage conditions (including -80 {degrees}C, -80 {degrees}C with 70% ethanol (ET_-80 {degrees}C), 4{degrees}C with 70% ethanol (ET_4{degrees}C), and three commercial storage reagents including OMNIgene*GUT OMR-200 (GT), MGIEasy (MGIE), and Longsee (LS)), storage periods (1, 2 weeks or 6 months), and sequencing platform on gut microbiome profile using 16S rRNA gene sequencing. Our results suggested that -80{degrees}C is acceptable for fecal sample storage, and the addition of 70% ethanol offers some benefits. Meanwhile, we found that samples in ET_4 {degrees}Cand GT reagents are comparable, both introduced multi-dimensional variations. The use of MGIE resulted in the least alteration, while the greatest changes were observed in samples stored in LS reagents during the whole experiment. Finally, we also confirmed that variations caused by storage condition were larger than that of storage time and sequencing platform.\n\nIMPORTANCEIn the current study, we performed a multi-dimensional evaluation on the variations introduced by types of storage conditions, preservation period and sequencing platform on the basis of data acquired from 16S rRNA gene sequencing. The efficacy of preservation methods was comprehensively evaluated by DNA yield and quality, and {beta} diversity, relative abundance of the dominant bacteria and functional bacteria associated with SCFAs producing and BAs metabolism. Our results confirmed that variations introduced by storage condition were larger than that of storage periods and sequencing platform. Collectively, our study provided a comprehensive view to the impacts of storage conditions, storage times, and sequencing platform on gut microbial profile.

microbiology↗

Cholecystokinin release triggered by presynaptic NMDA receptors produces LTP and sound-sound associative memory formation

Memory is stored in neural networks via changes in synaptic strength mediated in part by NMDA-dependent long-term potentiation (LTP). There is evidence that entorhinal cortex enables neocortical neuroplasticity through cholecystokinin (CCK)-containing neocortical projections. Here we show that a CCKB antagonist blocks high-frequency stimulation (HFS)-induced LTP in the auditory cortex, whereas local infusion of CCK induces LTP. CCK-/- mice lacked neocortical LTP and showed deficits in a cue-cue associative learning paradigm; administration of CCK rescued associative learning. HFS of CCK-containing entorhino-neocortical projection neurons in anesthetized mice enabled cue-cue associative learning. Furthermore, when one cue was pre-conditioned to footshock, the mouse showed a freezing response to the other cue, indicating that the mice had formed an association. HFS-induced neocortical LTP was completely blocked by either NMDA antagonist or CCK-BR antagonist, while application of either NMDA or CCK induced LTP after low-frequency stimulation (LFS). Moreover, in the presence of CCK LTP was still induced, even after blockade of NMDA receptors. Local application of NMDA induced CCK release in the neocortex. To identify how NMDA receptor switches LTP, a stimulation protocol of 25 pulse-pairs was adopted to replace HFS; NMDA-dependent LTP was induced with the inter-pulse intervals between 10 and 100 ms, but not with those of 5 and 200 ms. LTP-mediated plasticity was linked to localization of the NMDA receptor subunit NR2a on cortical CCK terminals originating in the entorhinal cortex. These novel findings suggest that presynaptic NMDA receptors on CCK terminals control the release of CCK, which enables neocortical LTP and formation of cue-cue associative memory.\n\nOne Sentence SummaryPresynaptic NMDA receptors switches the release of CCK from entorhinal neurons, which enables neocortical LTP and formation of sound-sound associative memory.

neuroscience↗

Modeling the emergence of antibiotic resistance in the environment: an analytical solution for the minimum selection concentration

Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured minimum inhibitory concentration (MIC) and the selection coefficient (sc) that expresses fitness differences between strains. We calibrated the model by optimizing the shape of the bacterial growth dose-response curve to antibiotic or metal exposure (the Hill coefficient, {kappa}) to fit previously published experimental growth rate difference data. The model fit varied among nine compound-taxa combinations examined, but predicted the experimentally observed MSC/MIC ratio well (R2 [&ge;] 0.95). The shape of the antibiotic response curve varied among compounds (0.7 [&le;] {kappa} [&le;] 10.5), with the steepest curve for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the sc, indicating the importance of fitness differences between strains for determining the MSC. The MSC can be more than one order of magnitude lower than the MIC, typically by a factor sc{kappa}. This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacteria communities at low environmental antibiotic concentrations.

microbiology↗