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Refining biome labeling for large-scale microbial community samples:Leveraging neural networks and transfer learning

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Environmental Science and Ecotechnology 2024年第1期17卷 128-137页

作者： Nan Wang Teng Wang Kang Ning Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingCenter of AI BiologyDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan430074HubeiChina

Microbiome research has generated an extensive amount of data,resulting in a wealth of publicly accessible *** annotation of these samples is crucial for effectively utilizing microbiome data across scientific ***,a notable challenge arises from the lack of essential annotations,particularly regarding collection location and sample biome information,which significantly hinders environmental microbiome *** this study,we introduce Meta-Sorter,a novel approach utilizing neural networks and transfer learning,to enhance biome labeling for thousands of microbiome samples in the MGnify database that have incomplete *** findings demonstrate that Meta-Sorter achieved a remarkable accuracy rate of 96.7%in classifying samples among the 16,507 lacking detailed biome ***,Meta-Sorter provides precise classifications for representative environmental samples that were previously ambiguously labeled as“Marine”in MGnify,thereby elucidating their specific origins in benthic and water column ***,Meta-Sorter effectively distinguishes samples derived from human-environment interactions,enabling clear differentiation between environmental and human-related *** improving the completeness of biome label information for numerous microbial community samples,our research facilitates more accurate knowledge discovery across diverse disciplines,with particular implications for environmental research.

关键词： Microbial community Transfer learning Sample classification Environmental scientific research Novel knowledge discovery

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3D genome structural variations play important roles in regulating seed oil content of Brassica napus

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Plant Communications 2024年第1期5卷 101-115页

作者： Libin Zhang Lin Liu Huaixin Li Jianjie He Hongbo Chao Shuxiang Yan Yontai Yin Weiguo Zhao Maoteng Li College of Life Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Wuhan Frasergen Bioinformatics Co. Ltd.Wuhan 430075China Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Bioinformatics and Molecular Imaging Key LaboratoryWuhan 430074China

Dissecting the complex regulatory mechanism of seed oil content(SOC)is one of the main research goals in Brassica *** evidence suggests that genome architecture is linked to multiple biological ***,the effect of genome architecture on SOC regulation remains ***,we used high-throughput chromatin conformation capture to characterize differences in the three-dimen-sional(3D)landscape of genome architecture of seeds from two *** lines,N53-2(with high SOC)and Ken-C8(with low SOC).bioinformatics analysis demonstrated that differentially accessible regions and differentially expressed genes between N53-2 and Ken-C8 were preferentially enriched in regions with quantitative trait loci(QTLs)/associated genomic regions(AGRs)for SOC.A multi-omics analysis demonstrated that expression of SOC-related genes was tightly correlated with genome structural varia-tions in QTLs/AGRs of *** candidate gene BnaA09g48250D,which showed structural variation in a QTL/AGR on chrA09,was identified byfine-mapping of a KN double-haploid population derived from hybridization of N53-2 and *** and knockout of BnaA09g48250D led to significant in-creases and decreases in SOC,respectively,in the transgenic *** together,our results reveal the 3D genome architecture of *** seeds and the roles of genome structural variations in SOC regulation,enriching our understanding of the molecular mechanisms of SOC regulation from the perspective of spatial chromatin structure.

关键词： Brassica napus seed oil content Hi-C QTLfine-mapping genome structural variation

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GAN-GMHI: a generative adversarial network with high discriminative power for microbiome-based disease prediction

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Journal of Genetics and Genomics 2023年第12期50卷 1026-1028页

作者： Yuxue Li Gang Xie Yuguo Zha Kang Ning MOE Key Laboratory of Molecular Biophysics Hubei Key Laboratory of Bioinformatics and Molecular-imagingCenter of Artificial Intelligence BiologyDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanHubei 430074China

With the rapid development of research on the human gut microbiome,associations between the microbiome and various complex chronic diseases have been revealed(Bergot et al.,2019).These advancements provide great oppor... 详细信息

关键词： prediction diseases

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Microbial Dark Matter: from Discovery to Applications

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Genomics, Proteomics & bioinformatics 2022年第5期20卷 867-881页

作者： Yuguo Zha Hui Chong Pengshuo Yang Kang Ning MOE Key Laboratory of Molecular Biophysics Hubei Key Laboratory of Bioinformatics and Molecular-imagingCenter of Artificial Intelligence BiologyDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the rapid increase of the microbiome samples and sequencing data,more and more knowledge about microbial communities has been ***,there is still much more to learn about microbial communities,including billions of novel species and genes,as well as countless spatiotemporal dynamic patterns within the microbial communities,which together form the microbial dark *** this work,we summarized the dark matter in microbiome research and reviewed current data mining methods,especially artificial intelligence(AI)methods,for different types of knowledge discovery from microbial dark *** also provided case studies on using AI methods for microbiome data mining and knowledge *** summary,we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore,with the goal of advancing our understanding of microbial communities,as well as developing better solutions to global concerns about human health and the environment.

关键词： Microbiome Dark matter Artificial intelligence Knowledge discovery Application

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Engineering aortic valves via transdifferentiating fibroblasts into valvular endothelial cells without using viruses or iPS cells

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Bioactive Materials 2025年第3期45卷 181-200页

作者： Peng Tang Fuxiang Wei Weihua Qiao Xing Chen Chenyang Ji Wanzhi Yang Xinyu Zhang Sihan Chen Yanyan Wu Mingxing Jiang Chenyu Ma Weiqiang Shen Qi Dong Hong Cao Minghui Xie Ziwen Cai Li Xu Jiawei Shi Nianguo Dong Junwei Chen Ning Wang Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Bioinformatics and Molecular Imaging Key LaboratoryLaboratory for Cellular Biomechanics and Regenerative MedicineDepartment of Biomedical EngineeringCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanHubei430074China Department of Cardiovascular Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China Institute for Mechanobiology Department of BioengineeringCollege of EngineeringNortheastern UniversityBostonMA02115USA

The technology of induced pluripotent stem cells(iPSCs)has enabled the conversion of somatic cells into primitive undifferentiated cells via *** approach provides possibilities for cell replacement therapies and drug screening,but the potential risk of tumorigenesis hampers its further development and in vivo *** to generate differentiated cells such as valvular endothelial cells(VECs)has remained a major *** a combinatorial strategy of selective soluble chemicals,cytokines and substrate stiffness modulation,mouse embryonic fibroblasts are directly and efficiently transdifferentiated into induced aortic endothelial cell-like cells(iAECs),or human primary adult fibroblasts are transdifferentiated into induced valvular endothelial cell-like cells(hiVECs),without expressing pluripotency stem cell *** iAECs and hiVECs express VEC-associated genes and proteins and VEC-specific marker NFATC1 and are functional in culture and on decellu-larized porcine aortic valves,like mouse aortic endothelial cells or human primary aortic valvular endothelial *** iAECs and hiVECs seeded on decellularized porcine aortic valves stay intact and express VEC-associated proteins for 60 days after grafting into abdominal aorta of immune-compromised *** contrast,induced pluripotent stem cells(iPSCs)are less efficient in differentiating into VEC-like cells and pluripotency marker Nanog is expressed in a small subpopulation of iPSC-derived VEC-like cells that generate teratomas in SCID mice whereas hiVECs derived from transdifferentiation do not generate teratomas in *** findings highlight an approach to efficiently convert fibroblasts into iAECs and hiVECs and seed them onto decellularized aortic valves for safely generating autologous tissue-engineered aortic valves without using viruses or first reprogramming the cells into pluripotent stem cells.

关键词： Transdifferentiation Valvular endothelial cells Tissue engineered aortic valves Soluble factors,Substrate stiffness

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Consistent Alterations of Human Fecal Microbes After Transplantation into Germ-free Mice

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Genomics, Proteomics & bioinformatics 2022年第2期20卷 382-393页

作者： Yanze Li Wenming Cao Na L Gao Xing-Ming Zhao Wei-Hua Chen MOE Key Laboratory of Molecular Biophysics Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute of Science and Technology for Brain-Inspired Intelligence(ISTBI) Fudan UniversityShanghai 200433China College of Life Science Henan Normal UniversityXinxiang 453007China

Fecal microbiota transplantation(FMT)of human fecal samples into germ-free(GF)mice is useful for establishing causal relationships between the gut microbiota and human ***,due to the intrinsic differences between human and mouse intestines and the different diets of the two organisms,it may not be possible to replicate human phenotypes in mice through FMT;similarly,treatments that are effective in mouse models may not be effective in *** this study,we aimed to identify human gut microbes that undergo significant and consistent changes(i.e.,in relative abundances)after transplantation into GF mice in multiple experimental *** collected 16S rDNA-seq data from four published studies and analyzed the gut microbiota profiles from 1713 human–mouse ***,on average,we found that only 47%of the human gut microbes could be re-established in mice at the species level,among which more than 1/3 underwent significant changes(referred to as“variable taxa”).Most of the human gut microbes that underwent significant changes were consistent across multiple human–mouse pairs and experimental ***,about 1/3 of human samples changed their enterotypes,i.e.,significant changes in their leading species after *** fed with a controlled diet showed a lower enterotype change rate(23.5%)than those fed with a noncontrolled diet(49.0%),suggesting a possible solution for *** of the variable taxa have been reported to be implicated in human diseases,with some recognized as the causative *** results highlight the challenges of using a mouse model to replicate human gut microbiota-associated phenotypes,provide useful information for researchers using mice in gut microbiota studies,and call for additional validations after *** online database named FMT-DB is publicly available at http://***/#/.

关键词： Germ-free mice Fecal microbiota transplantation Gut microbe Enterotype 16S rDNA

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Hidden Links Between Skin Microbiome and Skin imaging Phenome

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Genomics, Proteomics & bioinformatics 2024年第4期22卷 47-58页

作者： Mingyue Cheng Hong Zhou Haobo Zhang Xinchao Zhang Shuting Zhang Hong Bai Yugo Zha Dan Luo Dan Chen Siyuan Chen Kang Ning Wei Liu College of Life Science and Technology Huazhong University of Science and TechnologyWuhan 430074China National Engineering Research Center for Nanomedicine Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular ImagingCenter of Artificial Intelligence BiologyDepartment of Bioinformatics and Systems BiologyHuazhong University of Science and TechnologyWuhan 430074China Research Institute for Biomaterials Tech Institute for Advanced MaterialsCollege of Materials Science and EngineeringSuqian Advanced Materials Industry Technology Innovation CenterNJTech-BARTY Joint Research Center for Innovative Medical TechnologyNanjing Tech UniversityNanjing 211816China

Despite the skin microbiome has been linked to skin health and diseases,its role in modulating human skin appearance remains *** a total of 1244 face imaging phenomes and 246 cheek metagenomes,we first established three skin age indices by machine learning,including skin phenotype age(SPA),skin microbiota age(SMA),and skin integration age(SIA)as surrogates of phenotypic aging,microbial aging,and their combination,***,we found that besides aging and gender as intrinsic factors,skin microbiome might also play a role in shaping skin imaging phenotypes(SIPs).Skin taxonomic and functionalαdiversity was positively linked to melanin,pore,pigment,and ultraviolet spot levels,but negatively linked to sebum,lightening,and porphyrin ***,certain species were correlated with specific SIPs,such as sebum and lightening levels negatively correlated with Corynebacterium matruchotii,Staphylococcus capitis,and Streptococcus ***,we demonstrated skin microbial potential in predicting SIPs,among which the lightening level presented the least error of 1.8%.Lastly,we provided a reservoir of potential mechanisms through which skin microbiome adjusted the SIPs,including the modulation of pore,wrinkle,and sebum levels by cobalamin and heme synthesis pathways,predominantly driven by Cutibacterium *** pioneering study unveils the paradigm for the hidden links between skin microbiome and skin imaging phenome,providing novel insights into how skin microbiome shapes skin appearance and its healthy aging.

关键词： Skin phenome Skin microbiome Metagenome Machine learning imaging.

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Prediction of TrkB Complex and Antidepressant Targets Leveraging Big Data 12th

Prediction of TrkB Complex and Antidepressant Targets Levera...

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12th International Congress on Big Data, BigData 2023

作者： Xiang, Xufu Qian, Chungen Liu, Xin Xia, Fuzhen The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory Systems Biology Theme Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031447242

Since Major Depressive Disorder (MDD) represents a neurological pathology caused by inter-synaptic messaging errors, membrane receptors, the source of signal cascades, constitute appealing drugs targets. G protein coupled receptors (GPCRs) and ion channel receptors chelated antidepressants (ADs) high-resolution architectures were reported to realize receptors physical mechanism and design prototype compounds with minimal side effects. Tyrosine kinase receptor 2 (TrkB), a receptor that directly modulates synaptic plasticity, has a finite three-dimensional chart due to its high molecular mass and intrinsically disordered regions (IDRs). Leveraging breakthroughs in deep learning, the meticulous architecture of TrkB was projected employing Alphafold 2 (AF2). Furthermore, the Alphafold Multimer algorithm (AF-M) models the coupling of intra- and extra-membrane topologies to chaperones: mBDNF, SHP2, Etc. Conjugating firmly dimeric transmembrane helix with novel compounds like 2R,6R-hydroxynorketamine (2R,6R-HNK) expands scopes of drug screening to encompass all coding sequences throughout genomes. The operational implementation of TrkB kinase-SHP2, PLCγ1, and SHC1 ensembles has paved the path for machine learning in which it can forecast structural transitions in the self-assembly and self-dissociation of molecules during trillions of cellular mechanisms. In silicon, the cornerstone of the alteration will be big data and artificial intelligence (AI), empowering signal networks to operate at the atomic level and picosecond timescales. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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Genomics, Proteomics & bioinformatics 2024年第4期22卷 181-190页

作者： Tao Yue Si-Yi Chen Wen-Kang Shen Zhan-Ye Zhang Liming Cheng An-Yuan Guo Center for Artificial Intelligence Biology Hubei Bioinformatics&Molecular Imaging Key LaboratoryCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Laboratory Medicine Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune *** of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and *** TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced ***,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and *** combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as *** TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 *** is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://***/TCRosetta/.

关键词： T-cell receptor TCR repertoire analysis Batch search Annotation Web server.

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A time-coded multi-concentration microfluidic chemical waveform generator for high-throughput probing suspension single-cell signaling

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Chinese Chemical Letters 2022年第6期33卷 3091-3096页

作者： Yiran Guo Peng Chen Zhaolong Gao Yiwei Li Shunji Li Xiaojun Feng Bi-Feng Liu The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory Systems Biology ThemeDepartment of Biomedical EngineeringCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular *** reconstructing this process and analyzing the changes in receptor conformation on the cell membrane surface and intracellular or intercellular signaling has been a major challenge in analytical chemistry and biophysical *** this paper,a time-coded multiconcentration microfluidic chemical waveform generator was developed for the dynamic signaling probing with single-cell array of high temporal resolution,high throughput,and multi-concentration combination *** on innovative microchannel structure,sophisticated external control methods and multiplexing technology,the system not only allowed for temporally sequential permutations of the four concentrations of stimuli(time code),but also generated pulsed and continuous waveforms at different frequencies in a highly controllable ***,the single-cell trap array was set up to efficiently capture cells in suspension,dramatically increasing throughput and reducing experiment preparation *** maximum frequency of the platform was 1 Hz,and one cell could be stimulated at multiple *** show the ability of the system to investigate rapid biochemical events in high throughput,pulse stimulation and continuous stimulation of different frequencies and different time codes,combined with four concentrations of histamine(HA),were generated for probing G protein-coupled receptor(GPCR)signaling in He La ***,statistical analysis was performed for the mean peak height and mean peak area of the cellular *** believe that the time-coded multi-concentration microfluidic chemical waveform generator will provide a novel strategy for analytical chemistry,biophysics,cell signaling,and individualized medicine applications.

关键词： Microfluidics Dynamic stimulation Time code High-throughput Single-cell array

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