Architecture of the outbred brown fat proteome defines regulators of metabolic physiology.

Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.

Declining Chinese attitudes toward the United States amid COVID-19.

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers.

Targeting lysosomal quality control as a therapeutic strategy against aging and diseases.

Previously, lysosomes were primarily referred to as the digestive organelles and recycling centers within cells. Recent discoveries have expanded the lysosomal functional scope and revealed their critical roles in nutrient sensing, epigenetic regulation, plasma membrane repair, lipid transport, ion homeostasis, and cellular stress response. Lysosomal dysfunction is also found to be associated with aging and several diseases. Therefore, function of macroautophagy, a lysosome-dependent intracellular degradation system, has been identified as one of the updated twelve hallmarks of aging. In this review, we begin by introducing the concept of lysosomal quality control (LQC), which is a cellular machinery that maintains the number, morphology, and function of lysosomes through different processes such as lysosomal biogenesis, reformation, fission, fusion, turnover, lysophagy, exocytosis, and membrane permeabilization and repair. Next, we summarize the results from studies reporting the association between LQC dysregulation and aging/various disorders. Subsequently, we explore the emerging therapeutic strategies that target distinct aspects of LQC for treating diseases and combatting aging. Lastly, we underscore the existing knowledge gap and propose potential avenues for future research.

Clinical features and prognostic model for viral encephalitis after allogeneic haematopoietic stem cell transplantation.

The objective of this study was to identify independent prognostic factors of viral encephalitis (VE) after allogeneic haematopoietic stem cell transplantation (allo-HSCT) and establish a prognostic model to identify post-transplant VE patients with a greater likelihood of mortality. Among 5380 patients in our centre from 2014 to 2022, 211 patients who developed VE after allo-HSCT were reviewed in this retrospective study. Prognostic factors were selected, and a prognostic model was constructed using Cox regression analysis. The model was subsequently validated and estimated using the area under the receiver operating characteristic curve (AUC), a calibration plot and decision curve analysis (DCA). Glasgow Coma Scale score <9, lesions >3 lobes on magnetic resonance imaging and severe thrombocytopenia were identified as independent prognostic risk factors for VE patients who underwent allo-HSCT. The prognostic model GTM (GTM is an abbreviation for a model composed of three risk factors: GCS score <9, severe thrombocytopenia [platelet count <20 000 per microliter], and lesions >3 lobes on MRI) was established according to the regression coefficients. The validated internal AUC was 0.862 (95% confidence interval [CI], 0.773-0.950), and the external AUC was 0.815 (95% CI, 0.708-0.922), indicating strong discriminatory ability. Furthermore, we constructed calibration plots that demonstrated good consistency between the predicted outcomes and the observed outcomes. DCA exhibited high accuracy in this system, leading to potential benefits for patients.

LipiDex 2 Integrates MSn Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics.

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silico spectral library generation now supports tandem mass spectral (MSn) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Azaphosphinate Dyes: A Low Molecular Weight Near-Infrared Scaffold for Development of Photoacoustic or Fluorescence Imaging Probes.

Near-infrared (NIR) dyes are desirable for biological imaging applications including photoacoustic (PA) and fluorescence imaging. Nonetheless, current NIR dyes are often plagued by relatively large molecular weights, poor water solubility, and limited photostability. Herein, we provide the first examples of azaphosphinate dyes which display desirable properties such as low molecular weight, absorption/emission above 750 nm, and remarkable water solubility. In PA imaging, an azaphosphinate dye exhibited a 4.1-fold enhancement in intensity compared to commonly used standards, the ability to multiplex with existing dyes in whole blood, imaging depths of 2.75 cm in a tissue model, and contrast in mice. An improved derivative for fluorescence imaging displayed a >10-fold reduction in photobleaching in water compared to the FDA-approved indocyanine green dye and could be visualized in mice. This new dye class provides a robust scaffold for the development of photoacoustic or NIR fluorescence imaging agents.

Gamma auditory steady-state response as a promising electrophysiological biomarker for depression: an in vivo study with chronic unpredictable mild stress (CUS)-induced rats.

Gamma oscillations play a functional role in brain cognitions. Recently, auditory steady-state response (ASSR) has been reported abnormally in depression clinically, particularly in the low-gamma band. However, clinical electroencephalography research has challenges obtaining pure signals straight from the source level, making information isolation and precise localization difficult. Besides, the ASSR deficits pattern remains unclear. Herein, we focused on the origin of ASSR-primary auditory cortex (A1), the central node in the auditory pathway. We assessed the evoked-power and phase-synchronization using local field potentials (LFP) in depression (n = 21) and control (n = 22) rats. Subsequent processing of the received auditory information was examined using event-related potentials (AEPs). Results showed that depressed rats exhibited significant gamma ASSR impairments in peak-to-peak amplitude, inter-trial phase coherence, and signal-to-noise ratio. These deficits were more pronounced during 40-Hz auditory stimuli in right-A1, indicating severe gamma network abnormalities in the right auditory pathway. Besides, increased N2 and P3 amplitudes in depression group were found, indicating excessive inhibitory control and contextual processing. Taken together, these ASSR abnormalities have a high specificity of more than 90% and high sensitivity of more than 80% to distinguish depression under 40-Hz auditory stimuli. Our findings provided an abnormal gamma network in the auditory pathway, as a promising diagnostic biomarker in the future.

Standardized Parts for Activation of Small GTPase Signaling in Living Cells.

Small GTPases comprise a superfamily of over 167 proteins in the human genome and are critical regulators of a variety of pathways including cell migration and proliferation. Despite the importance of these proteins in cell signaling, a standardized approach for controlling small GTPase activation within living cells is lacking. Herein, we report a split-protein-based approach to directly activate small GTPase signaling in living cells. Importantly, our fragmentation site can be applied across the small GTPase superfamily. We highlight the utility of these standardized parts by demonstrating the ability to directly modulate the activity of four different small GTPases with user-defined inputs, providing the first plug and play system for direct activation of small GTPases in living cells.

Insights into the Distinct Behaviors between Bifunctional and Binary Organoborane Catalysts through Terpolymerization of Epoxide, CO2, and Anhydride.

Alkyl borane compounds-mediated polymerizations have expanded to Lewis pair polymerization, free radical polymerization, ionic ring-opening polymerization, and polyhomologation. The bifunctional organoborane catalysts that contain the Lewis acid and ammonium or phosphonium salt in one molecule have demonstrated superior catalytic performance for ring-opening polymerization of epoxides and ring-opening copolymerization of epoxides and CO2 than their two-component analogues, i.e., the blend of organoborane and ammonium or phosphonium salt. To explore the origin of the differences of the one-component and two-component organoborane catalysts, here we conducted a systematic investigation on the catalytic performances of these two kinds of organoborane catalysts via terpolymerization of epoxide, carbon dioxide and anhydride. The resultant terpolymers produced independently by bifunctional and binary organoborane catalyst exhibited distinct microstructures, where a series of gradient polyester-polycarbonate terpolymers with varying polyester content were afforded using the bifunctional catalyst, while tapering diblock terpolymers were obtained using the binary system. The bifunctional catalyst enhances the competitiveness of CO2 insertion than anhydride, which leads to the premature incorporation of CO2 into the polymer chains and ultimately results in the formation of gradient terpolymers. DFT calculations revealed the role of electrostatic interaction and charge distribution caused by intramolecular synergistic effect for bifunctional organoborane catalyst.

Rapid Multi-Omics Sample Preparation for Mass Spectrometry.

Multi-omics analysis is a powerful and increasingly utilized approach to gain insight into complex biological systems. One major hindrance with multi-omics, however, is the lengthy and wasteful sample preparation process. Preparing samples for mass spectrometry (MS)-based multi-omics involves extraction of metabolites and lipids with organic solvents, precipitation of proteins, and overnight digestion of proteins. These existing workflows are disparate and laborious. Here, we present a simple, efficient, and unified approach to prepare lipids, metabolites, and proteins for MS analysis. Our approach, termed the Bead-enabled Accelerated Monophasic Multi-omics (BAMM) method, combines an n-butanol-based monophasic extraction with unmodified magnetic beads and accelerated protein digestion. We demonstrate that the BAMM method affords comparable depth, quantitative reproducibility, and recovery of biomolecules as state-of-the-art multi-omics methods (e.g., Matyash extraction and overnight protein digestion). However, the BAMM method only requires about 3 h to perform, which saves 11 steps and 19 h on average compared to published multi-omics methods. Furthermore, we validate the BAMM method for multiple sample types and formats (biofluid, culture plate, and pellet) and show that in all cases, it produces high biomolecular coverage and data quality.

Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching.

In mass spectrometry-based lipidomics, complex lipid mixtures undergo chromatographic separation, are ionized, and are detected using tandem MS (MSn) to simultaneously quantify and structurally characterize eluting species. The reported structural granularity of these identified lipids is strongly reliant on the analytical techniques leveraged in a study. For example, lipid identifications from traditional collisionally activated data-dependent acquisition experiments are often reported at either species level or molecular species level. Structural resolution of reported lipid identifications is routinely enhanced by integrating both positive and negative mode analyses, requiring two separate runs or polarity switching during a single analysis. MS3+ can further elucidate lipid structure, but the lengthened MS duty cycle can negatively impact analysis depth. Recently, functionality has been introduced on several Orbitrap Tribrid mass spectrometry platforms to identify eluting molecular species on-the-fly. These real-time identifications can be leveraged to trigger downstream MSn to improve structural characterization with lessened impacts on analysis depth. Here, we describe a novel lipidomics real-time library search (RTLS) approach, which utilizes the lipid class of real-time identifications to trigger class-targeted MSn and to improve the structural characterization of phosphotidylcholines, phosphotidylethanolamines, phosphotidylinositols, phosphotidylglycerols, phosphotidylserine, and sphingomyelins in the positive ion mode. Our class-based RTLS method demonstrates improved selectivity compared to the current methodology of triggering MSn in the presence of characteristic ions or neutral losses.

Evaluation of the Orbitrap Ascend Tribrid Mass Spectrometer for Shotgun Proteomics.

Mass spectrometry (MS)-based proteomics is a powerful technology to globally profile protein abundances, activities, interactions, and modifications. The extreme complexity of proteomics samples, which often contain hundreds of thousands of analytes, necessitates continuous development of MS techniques and instrumentation to improve speed, sensitivity, precision, and accuracy, among other analytical characteristics. Here, we systematically evaluated the Orbitrap Ascend Tribrid mass spectrometer in the context of shotgun proteomics, and we compared its performance to that of the previous generation of Tribrid instruments─the Orbitrap Eclipse. The updated architecture of the Orbitrap Ascend includes a second ion-routing multipole (IRM) in front of the redesigned C-trap/Orbitrap and a new ion funnel that allows gentler ion introduction, among other changes. These modifications in Ascend hardware configuration enabled an increase in parallelizable ion injection time during higher-energy collisional dissociation (HCD) Orbitrap tandem MS (FTMS2) analysis of ∼5 ms. This enhancement was particularly valuable in the analyses of limited sample amounts, where improvements in sensitivity resulted in up to 140% increase in the number of identified tryptic peptides. Further, analysis of phosphorylated peptides enriched from the K562 human cell line yielded up to ∼50% increase in the number of unique phosphopeptides and localized phosphosites. Strikingly, we also observed a ∼2-fold boost in the number of detected N-glycopeptides, likely owing to the improvements in ion transmission and sensitivity. In addition, we performed the multiplexed quantitative proteomics analyses of TMT11-plex labeled HEK293T tryptic peptides and observed 9-14% increase in the number of quantified peptides. In conclusion, the Orbitrap Ascend consistently outperformed its predecessor the Orbitrap Eclipse in various bottom-up proteomic analyses, and we anticipate that it will generate reproducible and in-depth datasets for numerous proteomic applications.

Human iPSC-Derived Proinflammatory Macrophages cause Insulin Resistance in an Isogenic White Adipose Tissue Microphysiological System.

Chronic white adipose tissue (WAT) inflammation has been recognized as a critical early event in the pathogenesis of obesity-related disorders. This process is characterized by the increased residency of proinflammatory M1 macrophages in WAT. However, the lack of an isogenic human macrophage-adipocyte model has limited biological studies and drug discovery efforts, highlighting the need for human stem cell-based approaches. Here, human induced pluripotent stem cell (iPSC) derived macrophages (iMACs) and adipocytes (iADIPOs) are cocultured in a microphysiological system (MPS). iMACs migrate toward and infiltrate into the 3D iADIPOs cluster to form crown-like structures (CLSs)-like morphology around damaged iADIPOs, recreating classic histological features of WAT inflammation seen in obesity. Significantly more CLS-like morphologies formed in aged and palmitic acid-treated iMAC-iADIPO-MPS, showing the ability to mimic inflammatory severity. Importantly, M1 (proinflammatory) but not M2 (tissue repair) iMACs induced insulin resistance and dysregulated lipolysis in iADIPOs. Both RNAseq and cytokines analyses revealed a reciprocal proinflammatory loop in the interactions of M1 iMACs and iADIPOs. This iMAC-iADIPO-MPS thus successfully recreates pathological conditions of chronically inflamed human WAT, opening a door to study the dynamic inflammatory progression and identify clinically relevant therapies.

High-quality and high-diversity conditionally generative ghost imaging based on denoising diffusion probabilistic model.

Deep-learning (DL) methods have gained significant attention in ghost imaging (GI) as promising approaches to attain high-quality reconstructions with limited sampling rates. However, existing DL-based GI methods primarily emphasize pixel-level loss and one-to-one mapping from bucket signals or low-quality GI images to high-quality images, tending to overlook the diversity in image reconstruction. Interpreting image reconstruction from the perspective of conditional probability, we propose the utilization of the denoising diffusion probabilistic model (DDPM) framework to address this challenge. Our designed method, known as DDPMGI, can not only achieve better quality but also generate reconstruction results with high diversity. At a sampling rate of 10%, our method achieves an average PSNR of 21.19 dB and an SSIM of 0.64, surpassing the performance of other comparison methods. The results of physical experiments further validate the effectiveness of our approach in real-world scenarios. Furthermore, we explore the potential application of our method in color GI reconstruction, where the average PSNR and SSIM reach 20.055 dB and 0.723, respectively. These results highlight the significant advancements and potential of our method in achieving high-quality image reconstructions in GI, including color image reconstruction.

Involvement of YTHDF1 in renal fibrosis progression via up-regulating YAP.

Renal fibrosis is a progressive, fatal renal disease characterized by the aberrant accumulation of myofibroblasts that produce excess extracellular matrix (ECM) in the renal interstitium and glomeruli. Yes-associated protein (YAP) has been regarded as a crucial modulator in myofibroblast transformation, but its upstream regulator remains a mystery. In the present study investigating the participation of m6A methylation during renal fibrosis through bioinformatics analysis, we identified YTHDF1, a modulator of m6A methylation, as a key contributor for renal fibrosis because it was highly expressed in human fibrotic kidneys and had a significant correction with YAP. Their co-localization in human fibrotic kidneys was additionally shown by immunofluorescence. We then found that YTHDF1 was also up-regulated in fibrotic mouse kidneys induced by unilateral ureteral obstruction (UUO), high-dose folic acid administration, or the unilateral ischemia-reperfusion injury, further supporting a causal role of YTHDF1 during renal fibrosis. Consistent with this notion, YTHDF1 knockdown alleviated the progression of renal fibrosis both in cultured cells induced by transforming growth factor-beta administration and in the UUO mouse model. Meanwhile, YAP was accordingly down-regulated when YTHDF1 was inhibited. Furthermore, the specific binding of YTHDF1 to YAP mRNA was detected using RNA Binding Protein Immunoprecipitation, and the up-regulation of fibrotic related molecules in cultured cells induced by YTHDF1 over-expression plasmid was attenuated by YAP siRNA. Taken together, our data highlight the potential utility of YTHDF1 as an indicator for renal fibrosis and suggest that YTHDF1 inhibition might be a promising therapeutic strategy to alleviate renal fibrosis via downregulating YAP.

Enhancing Molecular Representations Via Graph Transformation Layers.

Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test.

Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.

The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

Continuous enrichment and trace analysis of tetracyclines in bovine milk using dual-functionalized aqueous biphasic system combined with high-performance liquid chromatography.

Two PPG1000 based temperature-sensitive magnetic ionic liquid were synthesized and characterized. The temperature-sensitive magnetic ionic liquid aqueous biphasic system combined with HPLC was applied for the continuous enrichment and trace analysis of tetracycline antibiotics (TC) in bovine milk for the first time. High enrichment factors were achieved and the detection was highly sensitive. The trace analysis of TC was rapid, free of organic solvent, recyclable and magnetically assisted for phase separation. Under optimum conditions, wide linear ranges of 0.25-300 ng/mL for all TC, high enrichment factors of 217.7-231.4, good precisions with relative standard deviation in the range of 0.74-3.97%, very low limits of detection of 0.031-0.067 ng/mL, limits of quantification of 0.103-0.223 ng/mL, and good recoveries of 94.28-99.76% were acquired for the proposed analytical method. Real milk analysis was satisfactory. This developed analytical method is showing great potential for trace analysis of targeted analytes in foods and drinks.

Maximizing MS/MS Acquisition for Lipidomics Using Capillary Separation and Orbitrap Tribrid Mass Spectrometer.

Liquid chromatography-mass spectrometry (LC-MS) is a typical strategy for lipidomics analysis. Although capillary LC-MS is a common analytical technique for proteomics analysis, its application to lipidomics has been limited. In this study, we aim at improving lipid identifications achieved in a single LC-MS analysis by a 3-fold approach: capillary LC and nanoelectrospray for enhanced ionization, ion trap for higher sensitivity tandem MS, and parallelization of mass analyzers for increased speed of acquisition on an Orbitrap hybrid system. By applying the methods to a complex lipid mixture of human plasma, we identified and performed relative quantification on over 1500 lipids within a 60 min capillary LC-MS analysis.

Semantic ghost imaging based on recurrent-neural-network.

Ghost imaging (GI) illuminates an object with a sequence of light patterns and obtains the corresponding total echo intensities with a bucket detector. The correlation between the patterns and the bucket signals results in the image. Due to such a mechanism different from the traditional imaging methods, GI has received extensive attention during the past two decades. However, this mechanism also makes GI suffer from slow imaging speed and poor imaging quality. In previous work, each sample, including an illumination pattern and its detected bucket signal, was treated independently with each other. The correlation is therefore a linear superposition of the sequential data. Inspired by human's speech, where sequential words are linked with each other by a certain semantic logic and an incomplete sentence could still convey a correct meaning, we here propose a different perspective that there is potentially a non-linear connection between the sequential samples in GI. We therefore built a system based on a recurrent neural network (RNN), called GI-RNN, which enables recovering high-quality images at low sampling rates. The test with MNIST's handwriting numbers shows that, under a sampling rate of 1.28%, GI-RNN have a 12.58 dB higher than the traditional basic correlation algorithm and a 6.61 dB higher than compressed sensing algorithm in image quality. After trained with natural images, GI-RNN exhibits a strong generalization ability. Not only does GI-RNN work well with the standard images such as "cameraman", but also it can recover the natural scenes in reality at the 3% sampling rate while the SSIMs are greater than 0.7.