Biology subjects

Lian, Z.

Publications and source records attributed to Lian, Z..

18 recordsLinked to original sources

bioRxiv2026

Clinical Metabolomics studies in diagnosing Prediabetes and Type 2 Diabetes: Unveiling Trends through Bibliometric Analysis

BackgroundPrediabetes is the transitional stage preceding the onset of type 2 diabetes. Due to its asymptomatic nature and slow progression, conventional screening methods frequently fail to identify individuals in this prediabetic stage. Therefore, this study employs bibliometric methods to reveal the abnormal changes in clinically relevant metabolites in diabetes, providing evidence for early diagnosis, pathogenesis, and precision interventions. MethodsSearch the Web of Science Core Collection database for all papers on metabolomics related to prediabetes and type 2 diabetes clinical research from the databases inception until May 2025. Use VOSviewer and CiteSpace software to perform bibliometric analysis on the included literature. ResultsA total of 1,742 studies were ultimately included in the review of the literature. Researchers from China, the United States, and Germany, as well as institutions, have made significant contributions to metabolomics research in prediabetes and type 2 diabetes. The highest number of publications and citations was from the journals Metabolites and Diabetes. The current research hotspots in this field, as determined by keyword co-occurrence and clustering analysis, include metabolomics, insulin resistance, obesity, risk factors, biomarkers, gut microbiota, amino acids, lipidomics, and mass spectrometry. ConclusionThe outcomes of the study are instrumental in enabling scholars to comprehend the developmental trajectory of metabolites associated with prediabetes and type 2 diabetes and facilitate the rapid identification of emerging research pathways.

immunology↗

bioRxiv2026

In Vivo Surface Reconstruction of Three Signal-defined Intracortical Layers Using 5T 3D FLAIR MR Imaging

BackgroundIn vivo whole-cortex quantification of intracortical signal-defined layering on the routinely acquired structural MRI remains limited. PurposeTo develop and validate an automated framework to reconstruct three intracortical signal-defined layers from 5T three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (FLAIR) and to characterize whole-cortex morphometrics and regional organization across a prespecified cortical organizational framework. Materials and MethodsIn this retrospective study, 5T 3D FLAIR images were acquired between February and July 2024. Brain Multi-Layer Surface Reconstruction (BrainMLSR) reconstructed three intracortical signal-defined layers, and derived intracortical layer thickness and surface area measures and ratios. Performance was evaluated against manual annotations and assessed for test-retest repeatability (n=13) and cross-site feasibility (n=2). Paired two-tailed t-tests and linear mixed-effects models were used. A proof-of-concept analysis compared Heschls gyrus ratios between 19 patients with temporal lobe epilepsy (TLE) and 19 age-matched healthy controls (HC). ResultsA total of 270 healthy participants (mean age, 54.4{+/-}14.5 years; 146 men) were included. Agreement with manual hypointense-layer annotations was high (Dice, 0.960{+/-}0.003), and was similar in the cross-site dataset (Dice, 0.954{+/-}0.009). In the test-retest dataset, average symmetric surface distance was less than 0.1 mm. Across prespecified systems, thickness and surface area ratios varied by region; within an auditory-perisylvian hierarchy, banksSTS showed a localized turning point with an increased hyperintense layer thickness ratio and decreased hypointense layer thickness ratio, accompanied by inflections in surface area ratios (P < .001). In bilateral Heschls gyrus, hypointense (left: 0.619{+/-}0.262 vs 0.881{+/-}0.102; right: 0.607{+/-}0.310 vs 0.907{+/-}0.141 mm) and isointense (left: 0.406{+/-}0.225 vs 0.678{+/-}0.128; right: 0.478{+/-}0.232 vs 0.808{+/-}0.176 mm) layer thicknesses were lower in TLE than in HC (all P<.001). ConclusionBrainMLSR enabled accurate and repeatable in vivo reconstruction of three intracortical signal-defined layers from a single 5T 3D T2-weighted FLAIR acquisition and provided whole-cortex boundary-based morphometry with interpretable regional organization. Key ResultsO_LIIn this retrospective study of 270 participants, Brain Multi-Layer Surface Reconstruction (BrainMLSR) showed high agreement with hypointense-layer annotations (Dice, 0.960{+/-}0.003). C_LIO_LIWithin an auditory-perisylvian hierarchy, the hyperintense-layer thickness ratio peaked (0.620{+/-}0.003) and the hypointense-layer ratio was the lowest (0.156{+/-}0.002) in the bank of the superior temporal sulcus (P < .001). C_LIO_LIIn a proof-of-concept analysis, patients with temporal lobe epilepsy (vs healthy controls) had higher hyperintense-layer thicknesses ratio in the left hemisphere (0.523{+/-}0.085 vs 0.417{+/-}0.036, P<.001). C_LI SummaryAn automated multiple-layer surface reconstruction framework (BrainMLSR), applied to 5T 3D T2-weighted FLAIR images, produced reproducible whole-cortex, signal-defined laminar morphometry and demonstrated coherent patterns across prespecified cortical organizational framework.

neuroscience↗

bioRxiv2026

Plasma proteomics reveal heterogeneous subtypes of depression linked to inflammation and aging

The clinical heterogeneity of depression has defied biological classification, limiting personalized treatment. Previous neuroimaging- or symptom-based subtyping of depression failed to clarify the underlying pathoetiology, while plasma proteins which integrates signals from multiple organ systems, offers a promising way to define biologically grounded subtypes. Using plasma proteomics from 2,127 incident depression cases in a cohort of 53000 individuals, we identified three biologically distinct subtypes differing in inflammation, aging, and metabolic profiles. The most prevalent subtype ( inflammation/ageing) was characterized by aging-related inflammation, poorest prognosis with hippocampal atrophy and highest suicide risk, mediated by age-related amygdala atrophy; this subtype had highest anhedonia burden. A distinct inflammation/energy dysregulation group had metabolic pathway enrichment with high inflammation and lifestyle risk factors (smoking) but no ageing trend, predominantly physical/psychomotor symptoms and decreased thalamic volume. In contrast, the inflammation-resilient group had the lowest inflammatory proteomic loading, lowest depression severity, more resilient lifestyle and increased hippocampal volume. These proteomic signatures, detectable years before symptom onset, enable risk stratification and suggest subtype-specific targeted physical and lifestyle interventions.

neuroscience↗

bioRxiv2026

Disrupted dynamics of brain structure function coupling link genetic risk of Alzheimer's Disease and aging to cognitive decline in 34,067 adults

AbstractUnderstanding how a stable structural connectome supports flexible cognition, especially in aging, is a fundamental question in neuroscience. While static structure-function coupling (SFC) is well-studied, the dynamic decoupling of functional activity from structural constraints--dynamic SFC (DSFC)--remains poorly understood. Leveraging MRI data from 34,067 UK Biobank participants (ages 45-82), we characterized the distinct roles of SFC and DSFC in aging, cognition, and health. We found that SFC and DSFC followed spatially divergent aging trajectories: SFC declined primarily in sensorimotor systems, whereas DSFC decreased most prominently in higher-order networks. Both SFC and DSFC in higher-order networks were positively correlated with cognitive performance (e.g., fluid intelligence). However, the associations with mental and physical health diverged between the two measures: reduced DSFC was predominantly linked to health burdens in high-order default/limbic networks, whereas weakened SFC was primarily associated with health burdens in low-order sensory-motor networks. Genetic analyses revealed that Alzheimers risk, specifically APOE {varepsilon}4 dosage, significantly reduced DSFC in higher-order cognitive networks and SFC in visual cortex. Mediation analyses further demonstrated that aging and APOE {varepsilon}4-linked cognitive decline were mediated via visual SFC and ventral attention DSFC. These findings position static and dynamic coupling as complementary mechanisms: static SFC preserves network robustness, while dynamic SFC enables transient reconfiguration for complex integration. Together, our results highlight these dual mechanisms as crucial for maintaining cognitive flexibility, providing potential biomarkers for age-related neurodegeneration.

neuroscience↗

bioRxiv2025

Deep learning-driven morphology analysis enables label-free classification of therapeutic agent- naive versus resistant cancer cells

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.

cancer biology↗

bioRxiv2024

Exploration of the Metabolomic Mechanisms of Postmenopausal Hypertension Induced by Low Estrogen State

BackgroundEstrogen significantly impacts womens health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce. MethodsAn ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX+E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques. ResultsThe study successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension. ConclusionThis research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.

systems biology↗

bioRxiv2024

Label-Free Melanoma Phenotype Classification Using Artificial Intelligence-Based Morphological Profiling

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Intratumoral heterogeneity drives varied mutable phenotypes, specifically "melanocytic" and "mesenchymal" cell states, which result in differential functional properties and drug responses. Definitive and rigorous classification of these phenotypic states has been challenging with conventional biomarker-based methods, and high-parameter molecular methods are cell-destructive, labor-intensive, and time-consuming. To overcome these technical and practical limitations, we utilized label-free artificial intelligence-based morphological profiling to classify live melanoma cells into melanocytic and mesenchymal phenotypes based on high resolution imaging of single cells. To predict the phenotypes of single melanoma cells based on morphology alone, we developed the AI-based Melanoma Phenotype Classifier trained with 19 patient-derived cell lines with known melanocytic or mesenchymal transcriptional profiles. To link phenotypic state with high-dimensional morphological profiles, cells were subjected to genetic and chemical perturbations known to shift phenotypic states. The AI classifier successfully predicted phenotypic shifts which were confirmed by single-cell RNA-Seq (scRNA-Seq). These results demonstrate that correlations between melanoma cell phenotypes and morphological changes are detectable by AI. Additionally, the Melanoma Phenotype Classifier was applied to dissociated tumor biopsy samples and characterization of phenotypic heterogeneity was supported by scRNA-Seq transcriptional profiles. This work establishes a link between cell morphology and melanoma phenotypes, laying the groundwork for the use of a label-free morphology-based method for phenotyping live melanoma cells combined with additional analyses.

cancer biology↗

bioRxiv2023

Shortcut barcoding and early pooling for scalable multiplex single-cell reduced-representation CpG methylation sequencing at single nucleotide resolution

DNA methylation is essential for a wide variety of biological processes, yet the development of a highly efficient and robust technology remains a challenge for routine single-cell analysis. We developed a multiplex scalable single-cell reduced representation bisulfite sequencing (msRRBS) technology with off-the-shelf reagents and equipment. It allows cell-specific barcoded DNA fragments of individual cells to be pooled before bisulfite conversion, free of enzymatic modification or physical capture of the DNA ends, and achieves unparalleled read mapping rates of 62.51%, covering 59.95% of CpG islands and 71.62% of promoters in K562 cells on average. Its reproducibility is shown in duplicates of bulk cells with near perfect correlation (R=0.97-99). At a low 1 Mb of clean reads, msRRBS provides consistent coverage of CpG islands and promoters, outperforming the conventional methods with orders of magnitude reduction in cost. Here, we use this method to characterize the distinct methylation patterns and cellular heterogeneity of 6 cell lines, and leukemia and hepatocellular carcinoma models. Taking 4 hours of hands-on time, msRRBS offers a unique, highly efficient approach for dissecting methylation heterogeneity in a variety of multicellular systems.

genomics↗

bioRxiv2023

A MSTNDel273C mutation with FGF5 knockout sheep by CRISPR/Cas9 promotes skeletal muscle myofiber hyperplasia via MEK-ERK-FOSL1 axis

Mutations in the well-known Myostatin (MSTN) produce a "double-muscle" phenotype, which makes it commercially invaluable for improving livestock meat production and providing high-quality protein for humans. However, mutations at different loci of the MSTN often produce a variety of different phenotypes. In the current study, we increased the delivery ratio of Cas9 mRNA to sgRNA from the traditional 1:2 to 1:10, which improves the efficiency of the homozygous mutation of biallelic gene. Here, a MSTNDel273C mutation with FGF5 knockout sheep, in which the MSTN and FGF5 dual-gene biallelic homozygous mutations were produced via the deletion of 3-base pairs of AGC in the third exon of MSTN, resulting in cysteine-depleted at amino acid position 273, and the FGF5 double allele mutation led to inactivation of FGF5 gene. The MSTNDel273C mutation with FGF5 knockout sheep highlights a dominant "double-muscle" phenotype, which can be stably inherited. Both F0 and F1 generation mutants highlight the excellent trait of high-yield meat with a smaller cross-sectional area and higher number of muscle fibers per unit area. Mechanistically, the MSTNDel273C mutation with FGF5 knockout mediated the activation of FOSL1 via the MEK-ERK-FOSL1 axis. The activated FOSL1 promotes skeletal muscle satellite cell proliferation and inhibits myogenic differentiation by inhibiting the expression of MyoD1, and resulting in smaller myotubes. In addition, activated ERK1/2 may inhibit the secondary fusion of myotubes by Ca2+-dependent CaMKII activation pathway, leading to myoblasts fusion to form smaller myotubes.

cell biology↗

bioRxiv2023

Artificial intelligence-driven morphology-based enrichment of malignant cells from body fluid

Cell morphology is a fundamental feature used to evaluate patient specimens in pathological analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with high background of non-malignant cells, restricting the ability for downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multi-dimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low-levels or undetectable in pre-sort patient samples. Combined, our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multi-dimensional morphology analysis, and microfluidic sorting.

pathology↗

bioRxiv2022

Deep learning-based predictive identification of functional subpopulations of hematopoietic stem cells and multipotent progenitors

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are crucial for maintaining lifelong hematopoiesis. Developing methods to distinguish stem cells from other progenitors and evaluate stem cell functions has been a central task in stem cell research. Deep learning has been demonstrated as a powerful tool in cell image analysis and classification. In this study, we explored the possibility of using deep learning to differentiate HSCs and MPPs based on their light microscopy (DIC) images. After extensive training and validation with large image data sets, we successfully develop a three-class classifier (we named it the LSM model) that reliably differentiate long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. Importantly, we demonstrated that our LSM model achieved its differentiating capability by learning the intrinsic morphological features from cell images. Furthermore, we showed that the performance of our LSM model was not affected by how these cells were identified and isolated, i.e., sorted by surface markers or intracellular GFP markers. Prospective identification of HSCs and MPPs in Evi1GFP transgenic mice by LSM model suggested that the cells with the highest GFP expression were LT-HSCs, and this prediction was substantiated later by a long-term competitive reconstitution assay. Moreover, based on DIC image data sets, we also successfully built another two-class classifier that can effectively distinguish aged HSCs from young HSCs, which both express the same surface markers but are functionally different. This finding is of particular interest since it may provide a novel quick and efficient approach, obviating the need for a time-consuming transplantation experiment, to evaluate the functional states of HSCs. Together, our study provides evidence for the first time that HSCs and MPPs can be differentiated by deep learning based on cell morphology. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into molecular mechanisms underlying the self-renewal feature of stem cells.

bioengineering↗

bioRxiv2022

Loss of ZNF251 stimulates NHEJ resulting in PARP inhibitor resistance in BRCA1-mutated cancer cells

Poly (ADP-ribose) polymerase inhibitors (PARPis) represent a promising new class of agents that have demonstrated efficacy in treating various cancers, particularly those with BRCA1/2 mutations. Cancer-associated BRCA1/2 mutations disrupt DNA double-strand break (DSB) repair by homologous recombination (HR). PARP inhibitors (PARPis) have been used to trigger synthetic lethality in BRCA1/2-mutated cancer cells by promoting the accumulation of toxic DSBs. Unfortunately, resistance to PARPis is common and can occur through multiple mechanisms, including the restoration of HR and/or stabilization of replication forks. To gain a better understanding of the mechanisms underlying PARPis resistance, we conducted an unbiased CRISPR-pooled genome-wide library screen to identify new genes whose deficiency confers resistance to the PARPi olaparib. Our research revealed that haploinsufficiency of the ZNF251 gene, which encodes zinc finger protein 251, is associated with resistance to PARPis in various breast and ovarian cancer cell lines carrying BRCA1 mutations. Mechanistically, we discovered that ZNF251 haploinsufficiency leads to stimulation of RAD51-mediated HR repair of DSBs in olaparib-treated BRCA1-mutated cancer cells. Moreover, we demonstrated that a RAD51 inhibitor reversed PARPi resistance in ZNF251 haploinsufficient cancer cells harboring BRCA1 mutations. Our findings provide important insights into the mechanisms underlying PARPis resistance by highlighting the role of RAD51 in this phenomenon.

cancer biology↗

bioRxiv2022

Domain fusion TLR2-4 enhances the autophagy-dependent clearance of Staphylococcus aureus in the genetic engineering goat generated by CRISPR/Cas9

Staphylococcus aureus infections pose a potential threat to livestock production and public health. A novel strategy is needed to control S. aureus infection due to its adaptive evolution to antibiotics. Autophagy plays a key role in degrading bacteria for innate immune cells. In order to promote S. aureus clearance via TLR induced autophagy pathway, the domain fusion TLR2-4 with the extracellular domain of TLR2, specific recognizing S. aureus, and transmembrane and intracellular domains of TLR4 is assembled, then the goats expressing TLR2-4 is generated. TLR2-4 substantially augments the removal of S. aureus within macrophages by elevating autophagy level. Phosphorylated JNK/ERK1/2 promote LC3- puncta in TLR2-4 macrophages during S. aureus-induced autophagy via MyD88-mediated the TAK1 signaling cascade. Meantime, the TRIF-dependent TBK1-TFEB-OPTN signaling is involved in TLR2-4-triggered autophagy after S. aureus challenge. Moreover, the transcript of ATG5 and ATG12 is significantly increased via cAMP-PKA-NF-{kappa}B signaling, which facilitates S. aureus-induced autophagy in TLR2-4 macrophages. Overall, the novel receptor TLR2-4 enhances the autophagy-dependent clearance of S. aureus in macrophages via TAK1/TBK1-JNK/ERK, TBK1-TFEB-OPTN and cAMP-PKA-NF-{kappa}B-ATGs signaling pathways, which provide an alternative approach to resistant against S. aureus infection.

immunology↗

bioRxiv2021

Accumulation of auranofin in white adipose tissues lowers leptin levels and exerts anti-diabetic effects

Low-grade, sustained inflammation in white adipose tissue (WAT) characterizes obesity and frequently coincides with insulin resistance and type 2 diabetes (T2D). However, pharmacological targeting of WAT inflammation lacks durable therapeutic effects. Through a computational screen, we identified the FDA-approved rheumatoid arthritis drug auranofin is a putative small molecule for obesity treatment. We discovered that allometrically scaled safe auranofin doses homed to WAT and improved insulin sensitivity in obese wild-type mice. Auranofin treatment also normalized other obesity-associated abnormalities, including hepatic steatosis and hyperinsulinemia. Surprisingly, the anti-diabetic effects of auranofin required leptin lowering and beta-adrenergic receptors in WAT. These metabolic benefits of leptin reduction were superior to any immune impacts of auranofin in WAT. Our studies reveal important metabolic properties of anti-inflammatory treatments and contribute to the notion that leptin reduction in the periphery can be accomplished to treat obesity and T2D.

physiology↗

bioRxiv2020

Phage Peptides Mediate Precision Base Editing with Focused Targeting Window

Cytidine base editors (CBE) are novel genome engineering tools that can generate C-to-T nucleotide substitutions without introducing double-stranded breaks (DSBs). Instead of generating single-point mutations, CBEs induce nucleotide substitutions at wobble positions within the 20-nucleotide target site. A variety of strategies have been developed to improve the targeting scope and window of CBEs. Among these strategies, molecular switches that can temporally control CBE activities represent compelling options. In this study, we investigated the feasibility of using a bacteriophage-derived peptide, referred to as G8PPD, as the off-switch of CBEs. We showed that G8PPD could be employed to control the activities of and improve the targeting window of A3A and BE3 CBEs and adenine base editor 7.10 (ABE7.10). Notably, in a cell-based disease model of Marfan syndrome, G8PPD facilitated A3A CBE-based gene correction with a more focused targeting window and improved the percentage of perfectly edited gene alleles from less than 4% to more than 38% of the whole population. Our study presents the first peptide off-switch that can improve the targeting scope of CBEs, thus highlighting the importance of the temporal control of BE activity for precision base editing.

genomics↗

bioRxiv2020

Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior

Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering, and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing, and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms.

systems biology↗

bioRxiv2020

STAT1 dissociates adipose tissue inflammation from insulin sensitivity in obesity

Obesity fosters low-grade inflammation in white adipose tissue (WAT) that may contribute to the insulin resistance that characterizes type 2 diabetes mellitus (T2DM). However, the causal relationship of these events remains unclear. The established dominance of signal transducer and activator of transcription 1 (STAT1) function in the immune response suggests an obligate link between inflammation and the co-morbidities of obesity. To this end, we sought to determine how STAT1 activity in white adipocytes affects insulin sensitivity. STAT1 expression in WAT inversely correlated with fasting plasma glucose in both obese mice and humans. Metabolomic and gene expression profiling established STAT1 deletion in adipocytes (STAT1 fKO) enhanced mitochondrial function and accelerated TCA cycle flux coupled with subcutaneous WAT hyperplasia. STAT1 fKO reduced WAT inflammation, but insulin resistance persisted in obese mice. Rather, elimination of type I cytokine interferon gamma (IFN{gamma}) activity enhanced insulin sensitivity in diet-induced obesity. Our findings reveal a permissive mechanism that bridges WAT inflammation to whole-body insulin sensitivity.

immunology↗

bioRxiv2019

Experimental study of lumbar ligamentum flavum hypertrophy induced in bipedal mice

Lumbar spinal stenosis (LSS) is a common degenerative disease among the elderly. The role that mechanical stress-induced hypertrophic ligamentum flavum (HLF) plays in patients with LSS remains unclear. Here, we used a finite element analysis to investigate the stress characteristics on the ligamentum flavum (LF) and evaluate the feasibility of a mouse model of HLF. First, we induced a bipedal posture in mice by taking advantage of their hydrophobia. A micro-CT scan was performed to examine their spinal change during bipedal posture. A finite element analysis showed that the stress and strain on the upright posture were significantly increased compared with those on the sprawling posture. Tissue staining showed that the degeneration degree of the LF in bipedal standing group gradually increased over the modeling period. The amount of elastic fibers decreased under HLF, whereas the amount of collagen fibers, the number of the LF cells, and the expression of fibrosis-related factors increased. Compared with aged group, LF degeneration was more severe in the bipedal standing group. Our findings demonstrate that the increased stress caused by a posture change causes HLF and that a bipedal mouse model can be used to study HLF in vivo.

biophysics↗