The HIF-1α/PLOD2 axis integrates extracellular matrix organization and cell metabolism leading to aberrant musculoskeletal repair

While hypoxic signaling has been shown to play a role in many cellular processes,its role in metabolism-linked extracellular matrix(ECM)organization and downstream processes of cell fate after musculoskeletal injury remains to be determined.Heterotopicossification(HO)is a debilitating condition where abnormal bone formation occurs within extra-skeletal tissues.Hypoxia andhypoxia-inducible factor 1α(HIF-1α)activation have been shown to promote HO.However,the underlying molecular mechanisms bywhich the HIF-1αpathway in mesenchymal progenitor cells(MPCs)contributes to pathologic bone formation remain to beelucidated.Here,we used a proven mouse injury-induced HO model to investigate the role of HIF-1αon aberrant cell fate.Usingsingle-cell RNA sequencing(scRNA-seq)and spatial transcriptomics analyses of the HO site,we found that collagen ECM organizationis the most highly up-regulated biological process in MPCs.Zeugopod mesenchymal cell-specific deletion of Hif1α(Hoxa11-CreER^(T2);Hif1a^(fl/fl))significantly mitigated HO in vivo.ScRNA-seq analysis of these Hoxa11-CreER^(T2);Hif1a^(fl/fl)mice identified the PLOD2/LOXpathway for collagen cross-linking as downstream of the HIF-1αregulation of HO.Importantly,our scRNA-seq data and mechanisticstudies further uncovered that glucose metabolism in MPCs is most highly impacted by HIF-1αdeletion.From a translational aspect,a pan-LOX inhibitor significantly decreased HO.A newly screened compound revealed that the inhibition of PLOD2 activity in MPCssignificantly decreased osteogenic differentiation and glycolytic metabolism.This suggests that the HIF-1α/PLOD2/LOX axis linked tometabolism regulates HO-forming MPC fate.These results suggest that the HIF-1α/PLOD2/LOX pathway represents a promisingstrategy to mitigate HO formation.

Spatial transcriptomic interrogation of the murine bone marrow signaling landscape

Self-renewal and differentiation of skeletal stem and progenitor cells(SSPCs)are tightly regulated processes,with SSPC dysregulation leading to progressive bone disease.While the application of single-cell RNA sequencing(scRNAseq)to the bone field has led to major advancements in our understanding of SSPC heterogeneity,stem cells are tightly regulated by their neighboring cells which comprise the bone marrow niche.However,unbiased interrogation of these cells at the transcriptional level within their native niche environment has been challenging.Here,we combined spatial transcriptomics and scRNAseq using a predictive modeling pipeline derived from multiple deconvolution packages in adult mouse femurs to provide an endogenous,in vivo context of SSPCs within the niche.This combined approach localized SSPC subtypes to specific regions of the bone and identified cellular components and signaling networks utilized within the niche.Furthermore,the use of spatial transcriptomics allowed us to identify spatially restricted activation of metabolic and major morphogenetic signaling gradients derived from the vasculature and bone surfaces that establish microdomains within the marrow cavity.Overall,we demonstrate,for the first time,the feasibility of applying spatial transcriptomics to fully mineralized tissue and present a combined spatial and single-cell transcriptomic approach to define the cellular components of the stem cell niche,identify cell-cell communication,and ultimately gain a comprehensive understanding of local and global SSPC regulatory networks within calcified tissue.

Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method

Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1 and a specificity of 0.93.

以髋关节镜检查为标准验证髋关节炎MRI评分(SHOMRI)的准确性

目的基于髋关节镜对髋关节异常的评估来验证术前髋关节MRI检查进行髋关节炎MRI评分(SHOMRI)分级的准确性。方法2名放射科医师回顾性分析了39例股骨髋臼撞击综合征病人[平均年龄(34.7±9.0)岁。

Fluctuation theorem for the mutation process in in vitro evolution

A proposition based on the fluctuation theorem in thermodynamics is formulated to quantitatively describe molecular evolution processes in biology. Although we cannot give full proof of its generality, we demonstrate via computer simulation its applicability in an example of DNA in vitro evolution. According to this theorem, the evolution process is a series of exponentially rare fluctuations fixed by the force of natural selection

The Computational Theory of Intelligence: Feedback

In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in order that the entropy minimization principal of computational intelligence is preserved in the presence of feedback.

A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data

Background： The recently emerged technology of methylated RNA immunoprecipitation sequencing （MeRIP-seq） sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data. Methods： We propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model （HMM） to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter estimation, which greatly improves the model stability when dealing with MeRIP-seq data with a small number of replicates. We use Markov chain Monte Carlo （MCMC） algorithms to simultaneously infer the model parameters in a de novo fashion. The R Shiny demo is available at https：//qiwei. shinyapps.io/BaySeqPeak and the R/C ＋＋ code is available at https：//github.com/liqiwei2000/BaySeqPeak. Results： In simulation studies, the proposed method outperformed the competing methods exomePeak and MeTPeak, especially when an excess of zeros were present in the data. In real MeRIP-seq data analysis, the proposed method identified methylation sites that were more consistent with biological knowledge, and had better spatial resolution compared to the other methods. Conclusions： In this study, we develop a Bayesian hierarchical model to identify methylation peaks in MeRIP-seq data. The proposed method has a competitive edge over existing methods in terms of accuracy, robustness and spatial resolution.

Activating Connexin43 gap junctions primes adipose tissue for therapeutic intervention

Adipose tissue is a promising target for treating obesity and metabolic diseases.However,pharmacological agents usually fail to effectively engage adipocytes due to their extraordinarily large size and insufficient vascularization,especially in obese subjects.We have previously shown that during cold exposure,connexin43(Cx43)gap junctions are induced and activated to connect neighboring adipocytes to share limited sympathetic neuronal input amongst multiple cells.We reason the same mechanism may be leveraged to improve the efficacy of various pharmacological agents that target adipose tissue.Using an adipose tissue-specific Cx43 overexpression mouse model,we demonstrate effectiveness in connecting adipocytes to augment metabolic efficacy of theβ_(3)-adrenergic receptor agonist Mirabegron and FGF21.Additionally,combing those molecules with the Cx43 gap junction channel activator danegaptide shows a similar enhanced efficacy.In light of these findings,we propose a model in which connecting adipocytes via Cx43 gap junction channels primes adipose tissue to pharmacological agents designed to engage it.Thus,Cx43 gap junction activators hold great potential for combination with additional agents targeting adipose tissue.