Identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA-Seq approach

Background:Aspergillus fumigatus(Af)is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic back-ground.The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus(Af)using an RNAseq approach in CC lines and hepatic gene expression.Methods:We studied 31 male mice from 25 CC lines at 8 weeks old;the mice were infected with Af.Liver tissues were extracted from these mice 5 days post-infection,and next-generation RNA-sequencing(RNAseq)was performed.The GENE-E analysis platform was used to generate a clustered heat map matrix.Results:Significant variation in body weight changes between CC lines was ob-served.Hepatic gene expression revealed 12 top prioritized candidate genes differ-entially expressed in resistant versus susceptible mice based on body weight changes.Interestingly,three candidate genes are located within genomic intervals of the previ-ously mapped quantitative trait loci(QTL),including Gm16270 and Stox1 on chromo-some 10 and Gm11033 on chromosome 8.Conclusions:Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af.As a next step,eQTL analysis will be performed for our RNA-Seq data.Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective.To develop an artificial intelligence method predicting lymph node metastasis(LNM)for patients with colorectal cancer(CRC).Impact Statement.A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers.Introduction.Preoperative diagnosis of LNM is essential in treatment planning for CRC patients.Existing radiology imaging and genomic tests approaches are either unreliable or too costly.Methods.A total of 1338 patients were recruited,where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort.We developed a Multimodal Multiple Instance Learning(MMIL)model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status.The heatmaps of the obtained MMIL model were generated for model interpretation.Results.The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve(AUCs)of 0.926,0.878,0.809,and 0.857 for patients with stage T1,T2,T3,and T4 CRC,on the discovery cohort.On the external cohort,it obtained AUCs of 0.855,0.832,0.691,and 0.792,respectively(T1-T4),which indicates its prediction accuracy and potential adaptability among multiple centres.Conclusion.The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers,which is easily accessed in different institutes.We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features.

Interaction of δ-opioid Receptor with Membrane Transporters: Possible Mechanisms in Pain Suppression by Acupuncture

目的:研究阿片受体与神经递质转运蛋白和钠钾泵在针刺镇痛中的可能机制。方法:利用爪蟾卵母细胞所建立的异源性蛋白表达模型,通过基因显微注射技术,应用双电极电压钳方法检测所表达靶蛋白的跨膜稳态电流。结果:δ-阿片受体(DOR)与γ-氨基丁酸转运蛋白(GAT1)、谷氨酸转运蛋白(EAAC1)或钠钾泵共表达均可降低神经递质转运蛋白的活性,阿片受体的激活以不同方式影响转运蛋白的活性:1)GAT1活性被进一步抑制;2)EAAC1活性增强;3)钠钾泵被抑制会导致 DOR 对激动剂(DPDPE)的敏感性增加。结论:DOR 的激活可使突触间隙的 GABA 水平增加,而谷氨酸浓度减少,钠钾泵的抑制导致阿片受体激动剂的敏感性增加,我们认为内源性哇巴因可能放大了这些效应。这些协同性效应可能是痛觉抑制和/或针刺镇痛的分子机制。

Not just a sponge: new functions of circular RNAs discovered

One of the most exciting findings in RNA biology is the discovery of numerous circular RNAs(circ RNA)in mammalian genome.Once being considered as low abundance splicing byproducts,circ RNAs are surprisingly

Functional impacts of non-coding RNA processing on enhancer activity and target gene expression

Tight regulation of gene expression is orchestrated by enhancers.Through recent research advancements,it is becoming clear that enhancers are not solely distal regulatory elements harboring transcription factor binding sites and decorated with specific histone marks,but they rather display sign atures of active transcription,showingdistinct degrees oftranscription unit organization.Thereby,a substantial fraction of enhancers give rise to different species of non-coding RNA transcripts with an unprecedented range of potential functions.In this review,we bring together data from recent studies indicating that non-coding RNA transcription from active enhancers,as well as enhancer-produced long non-coding RNA transcripts,may modulate or define the functional regulatory potential ofthe cognate enhancer.In addition,we summarize supporting evidence that RNA processing ofthe enhancer-associated long non-coding RNA transcripts may constitute an additional layer of regulation of enhancer activity,which contributes to the control and final outcome of enhancer-targeted gene expression.

Subtyping of type A influenza by sequencing the variable regions of HA gene specifically amplified with RT-PCR

The outbreak of a novel influenza A(H1N1) virus across the globe poses a threat to human health.It is of paramount importance to develop a rapid,reliable and inexpensive diagnostic procedure.Based on the bioinformatic information from public database,primers specific for influenza A virus surface protein haemagglutinin(HA) of several subtypes(including H1,H2,H3,H5,H7 and H9) were designed.Primer-specific PCR products were subjected to sequencing for accurately distinguishing H1 and H3 subtypes from others.This sequencing-based detection method will not only be applied to rapid detection and simultaneous subtype identification of new influenza A virus H1N1,but also provide the strategies to monitor other new types ofinfluenza virus with explosive potential.

Structure modeling and spatial epitope analysis for HA protein of the novel H1N1 influenza virus

In recent months,a novel influenza virus H1N1 broke out around the world.With bioinformatics technology,the 3D structure of HA protein was obtained,and the epitope residues were predicted with the method developed in our group for this novel flu virus.58 amino acids were identified as potential epitope residues,the majority of which clustered at the surface of the globular head of HA protein.Although it is located at the similar position,the epitope of HA protein for the novel H1N1 flu virus has obvious differences in the electrostatic potential compared to that of HA proteins from previous flu viruses.

One Yin-Yang Wu-Xing Model of TCM

尽管5000多年来阴阳五行说(即阴阳和五种元素-人体的子系统)一直是传统中医的理论基础,但本质上阴阳五行说主要来自于人们的日常分析和经验,缺乏正规的科学依据。我们基于两极学说提出了一种阴阳五行的均衡/非均衡计算模型。文中我们阐述了该阴阳五行动态系统,以便于构建和验证均衡和非均衡条件。计算机模拟均衡和非均衡序列的实验表明,这种新方法可以给传统中医提供诊断决策支持。因此,这种基于均衡的方法给传统中医、气(生命力)、气功、经络(针刺通道)、草药医疗等领域未来的研究工作提供了唯一的科学依据。同时,该方法还为现代基因组学的科研工作提供了一个基本的阴阳细胞网络架构,这样的话人们就可以解释细胞序列中普遍存在 YY1蛋白质的常规机理了。

How to Diagnose the State of a Dynamic System

When analyzing a system,it is not only the easily quantified parameters (like where an item,e.g.the location of a planet in the planetary system or the weight,the temperature。

Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events

Prognostic models based on survival data frequently make use of the Cox proportional hazards model. Developing reliable Cox models with few events relative to the number of predictors can be challenging, even in low-dimensional datasets, with a much larger number of observations than variables. In such a setting we examined the performance of methods used to estimate a Cox model, including （i） full model using all available predictors and estimated by standard techniques, （ii） backward elimination （BE）, （iii） ridge regression, （iv） least absolute shrinkage and selection operator （lasso）, and （v） elastic net. Based on a prospective cohort of patients with manifest coronary artery disease （CAD）, we performed a simulation study to compare the predictive accuracy, calibration, and discrimination of these approaches, Candidate predictors for incident cardiovascular events we used included clinical variables, biomarkers, and a selection of genetic variants associated with CAD. The penalized methods, i.e., ridge, lasso, and elastic net, showed a comparable performance, in terms of predictive accuracy, calibration, and discrimination, and outperformed BE and the full model. Excessive shrinkage was observed in some cases for the penalized methods, mostly on the simulation scenarios having the lowest ratio of a number of events to the number of variables. We conclude that in similar settings, these three penalized methods can be used interchangeably. The full model and backward elimination are not recommended in rare event scenarios.

Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations

Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases.Limited by the availability of experimental data on the consequences of protein interaction,most existing methods focus on building models to predict changes in protein binding affinity.Here,we introduced MIPPI,an end-to-end,interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx.MIPPI was specifically trained to determine the types of variant impact(increasing,decreasing,disrupting,and no effect)on protein-protein interactions.We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights,which exhibit correlations with the amino acids interacting with the variant.Moreover,we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations.Finally,we experimentally validated the functional impact of several variants identified in patients with such disorders.Overall,MIPPI emerges as a versatile,robust,and interpretable model,capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.

SMAD7和SERPINE1作为新的动态网络生物标记物可检测并调控由TGF-β诱导的上皮间质转化过程临界点

上皮间质转化(EMT)是一个复杂的非线性生物过程,在胚胎发生、伤口创伤愈合、组织再生和肿瘤转移等基本生物过程中发挥着重要作用.上皮间质转化以细胞状态转换时类似开关的行为为标志,该行为是相变的特征.因此,检测上皮细胞即将转为间质细胞前的临界点是理解上皮间质转化过程的分子机制的关键.通过动态网络标志物(DNB)模型,本文确定了一个拥有37个基因的DNB基因群;它可以提供上皮间质转化过程的早期预警信号.值得一提的是,本文研究发现SMAD7和SERPINE1这两个DNB基因通过切换它们的调控网络来促进EMT的发生;该发现被进一步的实验所证实.生存分析显示SMAD7和SERPINE1作为DNB基因.可用作肺腺癌的预后标志物.

PsyMuKB: An Integrative De Novo Variant Knowledge Base for Developmental Disorders

De novo variants(DNVs)are one of the most significant contributors to severe earlyonset genetic disorders such as autism spectrum disorder,intellectual disability,and other developmental and neuropsychiatric(DNP)disorders.Presently,a plethora of DNVs have been identified using next-generation sequencing,and many efforts have been made to understand their impact at the gene level.However,there has been little exploration of the effects at the isoform level.The brain contains a high level of alternative splicing and regulation,and exhibits a more divergent splicing program than other tissues.Therefore,it is crucial to explore variants at the transcriptional regulation level to better interpret the mechanisms underlying DNP disorders.To facilitate a better usage and improve the isoform-level interpretation of variants,we developed NeuroPsychiatric Mutation Knowledge Base(PsyMuKB).It contains a comprehensive,carefully curated list of DNVs with transcriptional and translational annotations to enable identification of isoformspecific mutations.PsyMuKB allows a flexible search of genes or variants and provides both table-based descriptions and associated visualizations,such as expression,transcript genomic structures,protein interactions,and the mutation sites mapped on the protein structures.It also provides an easy-to-use web interface,allowing users to rapidly visualize the locations and characteristics of mutations and the expression patterns of the impacted genes and isoforms.PsyMuKB thus constitutes a valuable resource for identifying tissue-specific DNVs for further functional studies of related disorders.PsyMuKB is freely accessible at http://psymukb.net.

The mutation network for the hemagglutinin gene from the novel influenza A (H1N1) virus

A mutation network for the hemagglutinin gene(HA) of the novel type A(H1N1) influenza virus was constructed.Sequence homology analysis indicated that one HA sequence type from the viruses mainly isolated from Mexico was likely the original type in this epidemic.Based on the 658A and 1408T mutations in HA,the viruses evolving into this epidemic were divided into three categories,the Mexico,the transitional and the New York type.The three groups of viruses presented distinctive clustering features in their geographic distributions.