Author Correction: Molecular architecture of lineage allocation and tissue organization in early mouse embryo.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author Correction: Molecular architecture of lineage allocation and tissue organization in early mouse embryo.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Molecular architecture of lineage allocation and tissue organization in early mouse embryo.

During post-implantation development of the mouse embryo, descendants of the inner cell mass in the early epiblast transit from the naive to primed pluripotent state1. Concurrently, germ layers are formed and cell lineages are specified, leading to the establishment of the blueprint for embryogenesis. Fate-mapping and lineage-analysis studies have revealed that cells in different regions of the germ layers acquire location-specific cell fates during gastrulation2-5. The regionalization of cell fates preceding the formation of the basic body plan-the mechanisms of which are instrumental for understanding embryonic programming and stem-cell-based translational study-is conserved in vertebrate embryos6-8. However, a genome-wide molecular annotation of lineage segregation and tissue architecture of the post-implantation embryo has yet to be undertaken. Here we report a spatially resolved transcriptome of cell populations at defined positions in the germ layers during development from pre- to late-gastrulation stages. This spatiotemporal transcriptome provides high-resolution digitized in situ gene-expression profiles, reveals the molecular genealogy of tissue lineages and defines the continuum of pluripotency states in time and space. The transcriptome further identifies the networks of molecular determinants that drive lineage specification and tissue patterning, supports a role of Hippo-Yap signalling in germ-layer development and reveals the contribution of visceral endoderm to the endoderm in the early mouse embryo.

Deep sequencing of 1320 genes reveals the landscape of protein-truncating variants and their contribution to psoriasis in 19,973 Chinese individuals.

Protein-truncating variants (PTVs) have important impacts on phenotype diversity and disease. However, their population genetics characteristics in more globally diverse populations are not well defined. Here, we describe patterns of PTVs in 1320 genes sequenced in 10,539 healthy controls and 9434 patients with psoriasis, all of Han Chinese ancestry. We identify 8720 PTVs, of which 77% are novel, and estimate 88% of all PTVs are deleterious and subject to purifying selection. Furthermore, we show that individuals with psoriasis have a significantly higher burden of PTVs compared to controls (P = 0.02). Finally, we identified 18 PTVs in 14 genes with unusually high levels of population differentiation, consistent with the action of local adaptation. Our study provides insights into patterns and consequences of PTVs.

Functional specialization of monocot DCL3 and DCL5 proteins through the evolution of the PAZ domain.

Monocot DICER-LIKE3 (DCL3) and DCL5 produce distinct 24-nt small interfering RNAs (siRNAs), heterochromatic siRNAs (hc-siRNAs) and phased secondary siRNAs (phasiRNAs), respectively. The former small RNAs are linked to silencing of transposable elements and heterochromatic repeats, and the latter to reproductive processes. It is assumed that these DCLs evolved from an ancient 'eudicot-type' DCL3 ancestor, which may have produced both types of siRNAs. However, how functional differentiation was achieved after gene duplication remains elusive. Here, we find that monocot DCL3 and DCL5 exhibit biochemically distinct preferences for 5' phosphates and 3' overhangs, consistent with the structural properties of their in vivo double-stranded RNA substrates. Importantly, these distinct substrate specificities are determined by the PAZ domains of DCL3 and DCL5, which have accumulated mutations during the course of evolution. These data explain the mechanism by which these DCLs cleave their cognate substrates from a fixed end, ensuring the production of functional siRNAs. Our study also indicates how plants have diversified and optimized RNA silencing mechanisms during evolution.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China.

BACKGROUND: Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. AIMS: We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. METHODS: We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. RESULTS: With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. CONCLUSION: This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 µg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 µg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Potential impact of ambient temperature on maternal blood pressure and hypertensive disorders of pregnancy: A nationwide multicenter study based on the China birth cohort.

Limited evidence exists regarding the association between ambient temperature and blood pressure (BP) level of pregnant women. To investigate the associations of ambient temperature with maternal BP and hypertensive disorders of pregnancy (HDP), we studied 105,063 participants in 38 centers of 17 provinces from November 2017 to December 2021. BP was measured with standardized automated digital sphygmomanometers. Ambient temperature was classified into five classes as very hot, moderate hot, mild, moderate cold, and very cold. Generalized linear mixed models were used to investigate the ambient temperature-BP/HDP associations, controlling for multiple covariates. No significant associations of first-trimester ambient temperature with maternal BP and HDP prevalence were observed. Compared with mild temperature, second-trimester very cold and second-trimester moderate cold were statistically associated with the increase of 1.239 mmHg (95% CI: 0.908, 1.569) and 0.428 mmHg (95% CI: 0.099, 0.757) for second-trimester systolic blood pressure (SBP), respectively. Similar trends were also observed in the association between second-trimester cold exposure and second-trimester diastolic blood pressure (DBP), in the association between second-trimester cold exposure and third-trimester SBP/DBP as well as in the association between third-trimester cold exposure and third-trimester SBP/DBP although some estimates were not statistically significant. Furthermore, in the second and third trimester, very cold [second trimester: adjusted odds ratio (aOR) = 1.298; third trimester: aOR = 1.236) and moderate cold (second trimester: aOR = 1.208; third trimester: aOR = 1.146) exposures also increased the odds of HDP, and these associations were stronger among participants aged ≥35 years or from North China. The second and third trimesters are the critical exposure windows for ambient temperature exposure-BP/HDP associations. During this period, exposure to cold ambient temperature was associated with elevated BP as well as increased HDP prevalence among most Chinese pregnant women, those aged ≥35 years or from North China being more vulnerable.

Addition of a liver to the normothermic perfusion circuit reduces renal pro-inflammatory factors.

BACKGROUND: Normothermic machine perfusion (NMP) provides a novel platform to preserve isolated organs in an artificial condition. Our study aimed to explore the interaction between the liver and kidney at an ex vivo organ level by adding a liver to the kidney NMP circuit. METHODS: Porcine kidney and liver obtained from abattoir were subjected to 9 h NMP after suffering 30-min warm ischemia time and 90-min cold ischemia time. The liver-kidney NMP group (n = 5) and the single-kidney NMP group (n = 5) were designed. During the NMP, perfusion parameters, blood gas analysis, and tissue samples were compared. RESULTS: The perfusate of both groups remained stable, and continuous urine production was observed during NMP. In the liver-kidney NMP group, the lactate level was low, while blood urea nitrogen increased and glucose levels decreased. After the NMP, the renal tissue in the liver-kidney group exhibited fewer histological changes such as tubular epithelium vacuolization, along with reduced expression of IL-6, IL-8, IL-1ß, NLRP3, and GSDMD. CONCLUSIONS: Our results indicated that the expression of renal pro-inflammatory factors was reduced in the liver-kidney NMP system.

Potential causal links between long-term ambient particulate matter exposure and cardiovascular mortality: New evidence from a large community-based cohort in South China.

BACKGROUND: Cardiovascular disease (CVD) mortality is associated with long-term particulate matter (PM) exposure. However, evidence from large, highly-exposed population cohort and observational-data-based causal inference approaches remains limited. AIMS: We examined the potential causal links between PM exposure and the CVD mortality in South China. METHODS: 580,757 participants were recruited during 2009-2015 and followed up through 2020. Satellite-based annual concentrations of PM2.5, PM10, and PMcoarse (i.e., PM10 - PM2.5) at 1 km2 spatial resolution were estimated and assigned to each participant. Marginal structural Cox models with time-varying covariates, adjusted using inverse probability weighting, were developed to evaluate the association between prolonged PM exposure and CVD mortality. RESULTS: For overall CVD mortality, the hazard ratios and 95% confidence interval for each 1 µg/m3 increase in the annual average concentration of PM2.5, PM10, and PMcoarse were 1.033 (1.028-1.037), 1.028 (1.024-1.032), and 1.022 (1.012-1.033), respectively. All three PMs were linked to a higher mortality risk for myocardial infarction and ischemic heart disease (IHD). The mortality risk of chronic IHD and hypertension was linked to PM2.5 and PM10. Significant association between PMcoarse and other heart disease mortality was also observed. The older, women, less-educated participants, or inactive participants exhibited particularly higher susceptibility. Participants who were generally exposed to PM10 concentrations below 70 µg/m3 were more vulnerable to PM2.5-, PM10- and PMcoarse-CVD mortality risks. CONCLUSION: This large cohort study provides evidence for the potential causal links between increased CVD mortality and ambient PM exposure, as well as socio-demographics linked to the highest vulnerability.

Health-related quality of life of COVID-19 patients after discharge: A multicenter follow-up study.

AIMS AND OBJECTIVES: To determine the health-related quality of life (HRQoL) of COVID-19 patients after discharge and its predicting factors. BACKGROUND: COVID-19 has caused a worldwide pandemic and led a huge impact on the health of human and daily life. It has been demonstrated that physical and psychological conditions of hospitalised COVID-19 patients are impaired, but the studies focus on physical and psychological conditions of COVID-19 patients after discharge from hospital are rare. DESIGN: A multicentre follow-up study. METHODS: This was a multicentre follow-up study of COVID-19 patients who had discharged from six designated hospitals. Physical symptoms and HRQoL were surveyed at first follow-up (the third month after discharge). The latest multiple laboratory findings were collected through medical examination records. This study was performed and reported in accordance with STROBE checklist. RESULTS: Three hundred eleven patients (57.6%) were reported with one or more physical symptoms. The scores of HRQoL of COVID-19 patients at third month after discharge, except for the dimension of general health, were significantly lower than Chinese population norm (p < .001). Results of logistic regression showed that female (odds ratio (OR): 1.79, 95% confidence interval (CI): 1.04-3.06), older age (≥60 years) (OR: 2.44, 95% CI: 1.33-4.47) and the physical symptom after discharge (OR: 40.15, 95% CI: 9.68-166.49) were risk factors for poor physical component summary; the physical symptom after discharge (OR: 6.68, 95% CI: 4.21-10.59) was a risk factor for poor mental component summary. CONCLUSIONS: Health-related quality of life of discharged COVID-19 patients did not come back to normal at third month after discharge and affected by age, sex and the physical symptom after discharge. RELEVANCE TO CLINICAL PRACTICE: Healthcare workers should pay more attention to the physical and psychological rehabilitation of discharged COVID-19 patients. Long-term follow-up on COVID-19 patients after discharge is needed to determine the long-term impact of COVID-19.

m6A RNA Methylation and Implications for Hepatic Lipid Metabolism.

This review presents a summary of recent progress in research on the N6-methyladenosine (m6A) modification and regulatory roles in hepatic lipid metabolism. As the most abundant internal modification of eukaryotic RNA, the m6A modification is a dynamic and reversible process of the m6A enzyme system, which includes writers, erasers, and readers. m6A methylation depressed lipid synthesis and facilitated lipolysis in liver. The depletion of m6A methyltransferase Mettl14/Mettl3 raised fatty acid synthase (FAS), stearoyl-CoA desaturase-1 (SCD1), acetyl-CoA carboxylase (ACC), and elongase of very long chain fatty acids 6 (ELOVL6) in rodent liver, causing increases in liver weight, triglyceride (TG) production, and content in hepatocytes. FTO catalyzed m6A demethylation and the suppression m6A reader YTHDC2 promoted hepatocellular TG generation and hepatic steatosis in C57BL/6 mice through sterol regulatory element-binding protein 1c (SREBP-1c) signaling pathway, which upregulated the lipogenic genes FAS, SCD1, ACC, recombinant acetyl coenzyme a carboxylase alpha, and cell death-inducing DNA fragmentation factor-like effector C (CIDEC). Furthermore, FTO overexpression did not only enhance mitochondrial fusion to impair mitochondrial function and lipid oxidation but also promoted lipid peroxidation, accompanied by excessive TG in hepatocytes and rodent liver. Elevated m6A modification potently suppressed hepatic lipid accumulation, while the shrinkage of m6A modification arose hepatic lipid deposition. These findings have highlighted the beneficial role of m6A RNA methylation in hepatic lipid metabolism, potentially protecting liver from lipid metabolic disorders.

L-GrAFT7 has High Accuracy in Predicting Early Allograft Failure after Liver Transplantation: A Multicenter Cohort Study in China.

Background and Aims: Increasing utilization of extended criteria donor leads to an increasing rate of early allograft failure after liver transplantation. However, consensus of definition of early allograft failure is lacking. Methods: A retrospective, multicenter study was performed to validate the Liver Graft Assessment Following Transplantation (L-GrAFT) risk model in a Chinese cohort of 942 adult patients undergoing primary liver transplantation at three Chinese centers. L-GrAFT (L-GrAFT7 and L-GrAFT10) was compared with existing models: the Early Allograft Failure Simplified Estimation (EASE) score, the model of early allograft function (MEAF), and the Early Allograft Dysfunction (EAD) model. Univariate and multivariate logistic regression were used to find risk factors of L-GrAFT high-risk group. Results: L-GrAFT7 had an area under the curve of 0.85 in predicting 90-day graft survival, significantly superior to MEAF [area under the curve (AUC=0.78, p=0.044)] and EAD (AUC=0.78, p=0.006), while there was no statistical significance between the predicting abilities of L-GrAFT7 and EASE (AUC=0.84, p>0.05). Furthermore, L-GrAFT7 maintains good predicting ability in the subgroup of high-donor risk index (DRI) cases (AUC=0.83 vs. MEAF, p=0.007 vs. EAD, p=0.014) and recipients of donors after cardiac death (AUC=0.92 vs. EAD, p<0.001). Through multivariate analysis, pretransplant bilirubin level, units of packed red blood cells, and the DRI score were selected as independent risk factors of a L-GrAFT7 high-risk group. Conclusions: The accuracy of L-GrAFT7 in predicting early allograft failure was validated in a Chinese multicenter cohort, indicating that it has the potential to become an accurate endpoint of clinical practice and transitional study of machine perfusion.

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China.

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

The impact of long-term PM1 exposure on all-cause mortality and its interaction with BMI: A nationwide prospective cohort study in China.

BACKGROUND: China has a serious air pollution problem and a high prevalence of obesity. The interaction between the two and its impact on all-cause mortality is a public health issue of great concern. OBJECTIVES: This study aimed to investigate the association between long-term exposure to particulate matter with aerodynamic diameter ≤ 1 µm (PM1) and all-cause mortality, as well as the interaction effect of body mass index (BMI) in the association. METHODS: A total of 33,087 participants from 162 counties in 25 provinces in China were included, with annual average PM1 exposure being estimated based on the county address. The PM1-mortality relation was evaluated using the time-varying Cox proportional hazards models, with the dose-response relationship being fitted using the penalized splines. Besides, the potential interaction effect of BMI in the PM1-mortality relation was evaluated. RESULTS: The incidence of all-cause deaths was 76.99 per 10,000 person-years over a median of 8.2 years of follow-up. After controlling for potential confounders, the PM1-mortality relation was approximately J-shaped. The full-adjustment analysis observed the hazard ratio (HR) of all-cause mortality was 1.114 [95 % confidence interval (CI): 1.017-1.220] corresponding to a 10 µg/m3 rise in PM1 concentration. Further stratified analyses suggested the adverse effects of PM1 might be more pronounced among the underweight. DISCUSSION: Higher PM1 concentrations were associated with an increase in all-cause mortality. The BMI might further alter the relation, and the underweight population was the sensitive subgroup of the population that needed to be protected.

Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020).

Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM1, PM2.5-1, and PM10-2.5), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 % CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.

Long-term PM1 exposure and hypertension hospitalization: A causal inference study on a large community-based cohort in South China.

Limited evidence exists on the effect of submicronic particulate matter (PM1) on hypertension hospitalization. Evidence based on causal inference and large cohorts is even more scarce. In 2015, 36,271 participants were enrolled in South China and followed up through 2020. Each participant was assigned single-year, lag0-1, and lag0-2 moving average concentration of PM1 and fine inhalable particulate matter (PM2.5) simulated based on satellite data at a 1-km resolution. We used an inverse probability weighting approach to balance confounders and utilized a marginal structural Cox model to evaluate the underlying causal links between PM1 exposure and hypertension hospitalization, with PM2.5-hypertension association for comparison. Several sensitivity studies and the analyses of effect modification were also conducted. We found that a higher hospitalization risk from both overall (HR: 1.13, 95% CI: 1.05-1.22) and essential hypertension (HR: 1.15, 95% CI: 1.06-1.25) was linked to each 1 µg/m3 increase in the yearly average PM1 concentration. At lag0-1 and lag0-2, we observed a 17%-21% higher risk of hypertension associated with PM1. The effect of PM1 was 6%-11% higher compared with PM2.5. Linear concentration-exposure associations between PM1 exposure and hypertension were identified, without safety thresholds. Women and participants that engaged in physical exercise exhibited higher susceptibility, with 4%-22% greater risk than their counterparts. This large cohort study identified a detrimental relationship between chronic PM1 exposure and hypertension hospitalization, which was more pronounced compared with PM2.5 and among certain groups.

Efficient inference of synaptic plasticity rule with Gaussian process regression.

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Sortilin-induced lipid accumulation and atherogenesis are suppressed by HNF1b SUMOylation promoted by flavone of Polygonatum odoratum. / çŽ‰ç«¹é»„é ®é€šè¿‡ä¿ƒè¿›HNF1b蛋白的SUMO化修饰抑制sortilinä»‹å¯¼çš„è„‚è´¨ç§¯ç´¯åŠåŠ¨è„‰ç²¥æ ·ç¡¬åŒ–.

This study aims to investigate the impact of hepatocyte nuclear factor 1ß (HNF1b) on macrophage sortilin-mediated lipid metabolism and aortic atherosclerosis and explore the role of the flavone of Polygonatum odoratum (PAOA-flavone)-promoted small ubiquitin-related modifier (SUMO) modification in the atheroprotective efficacy of HNF1b. HNF1b was predicted to be a transcriptional regulator of sortilin expression via bioinformatics, dual-luciferase reporter gene assay, and chromatin immunoprecipitation. HNF1b overexpression decreased sortilin expression and cellular lipid contents in THP-1 macrophages, leading to a depression in atherosclerotic plaque formation in low-density lipoprotein (LDL) receptor-deficient (LDLR-/-) mice. Multiple SUMO1-modified sites were identified on the HNF1b protein and co-immunoprecipitation confirmed its SUMO1 modification. The SUMOylation of HNF1b protein enhanced the HNF1b-inhibited effect on sortilin expression and reduced lipid contents in macrophages. PAOA-flavone treatment promoted SUMO-activating enzyme subunit 1 (SAE1) expression and SAE1-catalyzed SUMOylation of the HNF1b protein, which prevented sortilin-mediated lipid accumulation in macrophages and the formation of atherosclerotic plaques in apolipoprotein E-deficient (ApoE-/-) mice. Interference with SAE1 abrogated the improvement in lipid metabolism in macrophage cells and atheroprotective efficacy in vivo upon PAOA-flavone administration. In summary, HNF1b transcriptionally suppressed sortilin expression and macrophage lipid accumulation to inhibit aortic lipid deposition and the development of atherosclerosis. This anti-atherosclerotic effect was enhanced by PAOA-flavone-facilitated, SAE1-catalyzed SUMOylation of the HNF1b protein.

Expressive probabilistic sampling in recurrent neural networks.

In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neural dynamics can sample from arbitrary distributions is still lacking. We use tools from functional analysis and stochastic differential equations to explore the minimum architectural requirements for $\textit{recurrent}$ neural circuits to sample from complex distributions. We first consider the traditional sampling model consisting of a network of neurons whose outputs directly represent the samples (sampler-only network). We argue that synaptic current and firing-rate dynamics in the traditional model have limited capacity to sample from a complex probability distribution. We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution. We call such circuits reservoir-sampler networks (RSNs). We propose an efficient training procedure based on denoising score matching that finds recurrent and output weights such that the RSN implements Langevin sampling. We empirically demonstrate our model's ability to sample from several complex data distributions using the proposed neural dynamics and discuss its applicability to developing the next generation of sampling-based brain models.