Disruption of TIGAR-TAK1 alleviates immunopathology in a murine model of sepsis.

Macrophage-orchestrated inflammation contributes to multiple diseases including sepsis. However, the underlying mechanisms remain to be defined clearly. Here, we show that macrophage TP53-induced glycolysis and apoptosis regulator (TIGAR) is up-regulated in murine sepsis models. When myeloid Tigar is ablated, sepsis induced by either lipopolysaccharide treatment or cecal ligation puncture in male mice is attenuated via inflammation inhibition. Mechanistic characterizations indicate that TIGAR directly binds to transforming growth factor ß-activated kinase (TAK1) and promotes tumor necrosis factor receptor-associated factor 6-mediated ubiquitination and auto-phosphorylation of TAK1, in which residues 152-161 of TIGAR constitute crucial motif independent of its phosphatase activity. Interference with the binding of TIGAR to TAK1 by 5Z-7-oxozeaenol exhibits therapeutic effects in male murine model of sepsis. These findings demonstrate a non-canonical function of macrophage TIGAR in promoting inflammation, and confer a potential therapeutic target for sepsis by disruption of TIGAR-TAK1 interaction.

Hydroxychloroquine Induces Remission for IgA Nephropathy With Mild to Moderate Proteinuria: A Single-Centered Retrospective Analysis.

BACKGROUND: Hydroxychloroquine (HCQ) influences both toll-like receptor (TLR) signaling and leukocyte activation, which are speculated to play a role in the pathogenesis of IgA nephropathy (IgAN). METHODS: This is a single-centered retrospective study involving 426 IgAN patients diagnosed from May 2016 to August 2020. All patients were matched according to a propensity score matching (PSM) to produce three groups: renin-angiotensin-aldosterone system inhibitors (RAASi) group (RAASi only), corticosteroids group (corticosteroids only or combined with RAASi), and HCQ group (HCQ only or combined with RAASi), consisting of 63 patients for each group. RESULTS: After PSM, the median urine protein/creatinine ratio (UPCR) of overall patients was 0.91 g/g, while their median serum creatinine was 87.00 µmol/L. After the median follow-up period of 11.03 months, the total remission rates of the RAASi group, corticosteroids group, and HCQ groups were 49.21% (n = 31), 74.60% (n = 47), and 52.38% (n = 33), respectively (p = 0.017). Thirteen (6.88%) patients experienced a decline in estimated glomerular filtration rate (eGFR) of more than 25% from baseline, including six (9.52%) patients in the RAASi group, three (4.76%) patients in the corticosteroids group, and four (6.35%) patients in HCQ group (p = 0.677). One (1.59%) patient in the HCQ group had blurred vision and continued to use HCQ after ruling out retinal lesions by ophthalmic examination. CONCLUSION: HCQ is effective in inducing remission and well-tolerated in IgAN patients with mild to moderate proteinuria.

Analysis of copy number variations and possible candidate genes in spontaneous abortion by copy number variation sequencing.

Introduction: Embryonic chromosomal abnormalities represent a major causative factor in early pregnancy loss, highlighting the importance of understanding their role in spontaneous abortion. This study investigates the potential correlation between chromosomal abnormalities and spontaneous abortion using copy number variation sequencing (CNV-seq), a Next-Generation Sequencing (NGS) technology. Methods: We analyzed Copy Number Variations (CNVs) in 395 aborted fetal specimens from spontaneous abortion patients by CNV-seq. And collected correlated data, including maternal age, gestational week, and Body Mass Index (BMI), and analyzed their relationship with the CNVs. Results: Out of the 395 cases, 67.09% of the fetuses had chromosomal abnormalities, including numerical abnormalities, structural abnormalities, and mosaicisms. Maternal age was found to be an important risk factor for fetal chromosomal abnormalities, with the proportion of autosomal trisomy in abnormal karyotypes increasing with maternal age, while polyploidy decreased. The proportion of abnormal karyotypes with mosaic decreased as gestational age increased, while the frequency of polyploidy and sex chromosome monosomy increased. Gene enrichment analysis identified potential miscarriage candidate genes and functions, as well as pathogenic genes and pathways associated with unexplained miscarriage among women aged below or over 35 years old. Based on our study, it can be inferred that there is an association between BMI values and the risk of recurrent miscarriage caused by chromosomal abnormalities. Discussion: Overall, these findings provide important insights into the understanding of spontaneous abortion and have implications for the development of personalized interventions for patients with abnormal karyotypes.

Lipid-anchored proteasomes control membrane protein homeostasis.

Protein degradation in eukaryotic cells is mainly carried out by the 26S proteasome, a macromolecular complex not only present in the cytosol and nucleus but also associated with various membranes. How proteasomes are anchored to the membrane and the biological meaning thereof have been largely unknown in higher organisms. Here, we show that N-myristoylation of the Rpt2 subunit is a general mechanism for proteasome-membrane interaction. Loss of this modification in the Rpt2-G2A mutant cells leads to profound changes in the membrane-associated proteome, perturbs the endomembrane system, and undermines critical cellular processes such as cell adhesion, endoplasmic reticulum-associated degradation and membrane protein trafficking. Rpt2G2A/G2A homozygous mutation is embryonic lethal in mice and is sufficient to abolish tumor growth in a nude mice xenograft model. These findings have defined an evolutionarily conserved mechanism for maintaining membrane protein homeostasis and underscored the significance of compartmentalized protein degradation by myristoyl-anchored proteasomes in health and disease.

Lipid-anchored Proteasomes Control Membrane Protein Homeostasis.

Protein degradation in eukaryotic cells is mainly carried out by the 26S proteasome, a macromolecular complex not only present in the cytosol and nucleus but also associated with various membranes. How proteasomes are anchored to the membrane and the biological meaning thereof have been largely unknown in higher organisms. Here we show that N-myristoylation of the Rpt2 subunit is a general mechanism for proteasome-membrane interaction. Loss of this modification in the Rpt2-G2A mutant cells leads to profound changes in the membrane-associated proteome, perturbs the endomembrane system and undermines critical cellular processes such as cell adhesion, endoplasmic reticulum-associated degradation (ERAD) and membrane protein trafficking. Rpt2 G2A/G2A homozygous mutation is embryonic lethal in mice and is sufficient to abolish tumor growth in a nude mice xenograft model. These findings have defined an evolutionarily conserved mechanism for maintaining membrane protein homeostasis and underscored the significance of compartmentalized protein degradation by m yristoyl- a nchored p roteasomes (MAPs) in health and disease.

Effects of Patchwise Sampling Strategy to Three-Dimensional Convolutional Neural Network-Based Alzheimer's Disease Classification.

In recent years, the rapid development of artificial intelligence has promoted the widespread application of convolutional neural networks (CNNs) in neuroimaging analysis. Although three-dimensional (3D) CNNs can utilize the spatial information in 3D volumes, there are still some challenges related to high-dimensional features and potential overfitting issues. To overcome these problems, patch-based CNNs have been used, which are beneficial for model generalization. However, it is unclear how the choice of a patchwise sampling strategy affects the performance of the Alzheimer's Disease (AD) classification. To this end, the present work investigates the impact of a patchwise sampling strategy for 3D CNN based AD classification. A 3D framework cascaded by two-stage subnetworks was used for AD classification. The patch-level subnetworks learned feature representations from local image patches, and the subject-level subnetwork combined discriminative feature representations from all patch-level subnetworks to generate a classification score at the subject level. Experiments were conducted to determine the effect of patch partitioning methods, the effect of patch size, and interactions between patch size and training set size for AD classification. With the same data size and identical network structure, the 3D CNN model trained with 48 × 48 × 48 cubic image patches showed the best performance in AD classification (ACC = 89.6%). The model trained with hippocampus-centered, region of interest (ROI)-based image patches showed suboptimal performance. If the pathological features are concentrated only in some regions affected by the disease, the empirically predefined ROI patches might be the right choice. The better performance of cubic image patches compared with cuboidal image patches is likely related to the pathological distribution of AD. The image patch size and training sample size together have a complex influence on the performance of the classification. The size of the image patches should be determined based on the size of the training sample to compensate for noisy labels and the problem of the curse of dimensionality. The conclusions of the present study can serve as a reference for the researchers who wish to develop a superior 3D patch-based CNN model with an appropriate patch sampling strategy.

Validation of the Antineutrophil Cytoplasmic Antibody Renal Risk Score and Modification of the Score in a Chinese Cohort With a Majority of Myeloperoxidase-Positive Patients.

OBJECTIVE: We aimed to validate and modify the renal risk score for antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (AAGN) in a Chinese cohort with a majority of myeloperoxidase (MPO)-positive patients. METHODS: A total of 285 patients with biopsy-proven AAGN in our center were retrospectively included. Patients were randomly assigned to the development set (n = 201) and the validation set (n = 84). We calculated the renal risk score and analyzed the clinicopathological characteristics and follow-up data. The nomogram was constructed based on the independent prognostic factors identified by the multivariable Cox regression and then compared with the renal risk score. RESULTS: Over a median follow-up period of 41.3 (range 20.0-63.8) months, 84 (29.5%) patients reached end-stage kidney disease (ESKD). In the development set, hypertension (hazard ratio [HR] 2.16, 95% CI 1.08-4.32, P = 0.03), high serum creatinine (HR 1.002, 95% CI 1.001-1.003, P < 0.001), high daily urine protein (HR 1.34, 95% CI 1.15-1.57, P < 0.001), high glomerular sclerosis (HR 13.98, 95% CI 3.50-55.92, P < 0.001), and interstitial fibrosis > 50% (HR 4.18, 95% CI 1.90-9.19, P < 0.001) were independent risk factors for ESKD, and these indicators were included in the nomogram. The C-indices of the nomogram model in the development set, validation set, and all-data set were 0.838 (range 0.785-0.891), 0.794 (range 0.774-0.814), and 0.822 (range 0.775-0.869), respectively, which were higher than those of the renal risk score model, 0.801 (range 0.748-0.854), 0.746 (range 0.654-0.838) and 0.783 (range 0.736-0.830), respectively. The net reclassification improvement and the integrated discrimination improvement further illustrated the higher predictive ability of the nomogram. CONCLUSION: We present a nomogram as a practical tool to predict renal outcomes in Chinese patients with MPO-ANCA glomerulonephritis.

Concordance of Alzheimer's Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study.

BACKGROUND: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer's disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. METHODS: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. RESULTS: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. CONCLUSIONS: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.

Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a chronic neurodegenerative disease without effective medications or supplemental treatments. Thus, predicting AD progression is crucial for clinical practice and medical research. Due to limited neuroimaging data, two-dimensional convolutional neural networks (2D CNNs) have been commonly adopted to differentiate among cognitively normal subjects (CN), people with mild cognitive impairment (MCI), and AD patients. Therefore, this paper proposes an ensemble learning (EL) architecture based on 2D CNNs, using a multi-model and multi-slice ensemble. First, the top 11 coronal slices of grey matter density maps for AD versus CN classifications were selected. Second, the discriminator of a generative adversarial network, VGG16, and ResNet50 were trained with the selected slices, and the majority voting scheme was used to merge the multi-slice decisions of each model. Afterwards, those three classifiers were used to construct an ensemble model. Multi-slice ensemble learning was designed to obtain spatial features, while multi-model integration reduced the prediction error rate. Finally, transfer learning was used in domain adaptation to refine those CNNs, moving them from working solely with AD versus CN classifications to being applicable to other tasks. This ensemble approach achieved accuracy values of 90.36%, 77.19%, and 72.36% when classifying AD versus CN, AD versus MCI, and MCI versus CN, respectively. Compared with other state-of-the-art 2D studies, the proposed approach provides an effective, accurate, automatic diagnosis along the AD continuum. This technique may enhance AD diagnostics when the sample size is limited.

Urine ß2-Microglobulin and Retinol-Binding Protein and Renal Disease Progression in IgA Nephropathy.

Background: Tubulointerstitial involvement has been reported to have a decisive influence on the progression of IgA nephropathy (IgAN). High levels of urine ß2-microglobulin (ß2-MG) and retinol-binding protein (RBP) were observed in patients with IgAN with tubulointerstitial lesions. However, their roles in disease progression remain unclear. This study aimed to evaluate the associations of urine ß2-MG and RBP with the progression of IgAN. Methods: We retrospectively investigated a cohort of 2,153 patients with IgAN. Clinical and pathological features, outcomes, and urine ß2-MG, and RBP at the time of biopsy were collected. The associations, of urine ß2-MG and RBP with the composite renal outcome, defined as a decline in estimated glomerular filtration rate (eGFR) of ≥50% from baseline or end-stage renal disease (ESRD), were examined using restricted cubic splines and the Cox proportional hazards models. Results: During a median follow-up of 20.40 months, 140 (6.50%) patients reached the composite renal outcomes. Restricted cubic splines showed that patients with higher urinary ß2-MG and RBP levels had worse renal outcomes. The Cox regression analysis revealed that urine ß2-MG and RBP were associated with a risk of the composite renal outcome in the multivariate adjusted model [+1 SD for log ß2-MG, hazard ratio (HR) = 1.462, 95% CI: 1.136-1.882, p = 0.003; +1 SD for log RBP, HR = 1.972, 95% CI: 1.486-2.617, p = 0.001]. The associations were detectable within patients with baseline eGFR <90 ml/min/1.73 m2 (+1 SD for log ß2-MG, HR = 1.657, 95% CI: 1.260-2.180, p < 0.001; +1 SD for log RBP, HR = 1.618, 95% CI: 1.199-2.183, p = 0.002), but not among patients with eGFR ≥90 ml/min/1.73 m2. Conclusion: Higher levels of urine ß2-MG and RBP were independent risk factors for renal disease progression in IgAN.

The Zscan4-Tet2 Transcription Nexus Regulates Metabolic Rewiring and Enhances Proteostasis to Promote Reprogramming.

Evolutionarily conserved SCAN (named after SRE-ZBP, CTfin51, AW-1, and Number 18 cDNA)-domain-containing zinc finger transcription factors (ZSCAN) have been found in both mouse and human genomes. Zscan4 is transiently expressed during zygotic genome activation (ZGA) in preimplantation embryos and induced pluripotent stem cell (iPSC) reprogramming. However, little is known about the mechanism of Zscan4 underlying these processes of cell fate control. Here, we show that Zscan4f, a representative of ZSCAN proteins, is able to recruit Tet2 through its SCAN domain. The Zscan4f-Tet2 interaction promotes DNA demethylation and regulates the expression of target genes, particularly those encoding glycolytic enzymes and proteasome subunits. Zscan4f regulates metabolic rewiring, enhances proteasome function, and ultimately promotes iPSC generation. These results identify Zscan4f as an important partner of Tet2 in regulating target genes and promoting iPSC generation and suggest a possible and common mechanism shared by SCAN family transcription factors to recruit ten-eleven translocation (TET) DNA dioxygenases to regulate diverse cellular processes, including reprogramming.