Biology subjects

Shang, L.

Publications and source records attributed to Shang, L..

26 records · Page 2Linked to original sources

bioRxiv2022

Odorant molecular feature mining by diverse deep neural networks for prediction of odor perception categories

The use of an artificial intelligence (AI)-based prediction model of the structure-odor relationship (SOR) has shown great potential in the replacement of human panelists in gas chromatography-olfactometry (GCO). However, the Al-based GCO encounters issues such as poor accuracy, generalization, and practicality, owning to the insufficient feature extraction of odorant molecular structure. The purpose of this study is to the prediction of odor perception categories based on the odorant structure feature extraction by diverse deep neural networks, including molecular graphic convolution neural network (MG-CNN), molecular graph transformer neural network, and atom interaction neural networks. The results of the performance comparison of different feature extractors demonstrate that the MG-CNN model produces the highest accuracy and thus may be most suitable for the SOR prediction. It is hoped that the proposed method can be applied in practice as an auxiliary tool of GCO for the sensory evaluation of key compounds in food ingredients. HighlightsO_LIDifferent deep neural networks were used to predict categorized odor descriptors. C_LIO_LIEnd-to-end-based representation learning was performed for molecular feature extraction. C_LIO_LIMolecular graphs with pre-training of convolution neural networks was most accurate. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/488977v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@10495a2org.highwire.dtl.DTLVardef@1fbf27eorg.highwire.dtl.DTLVardef@1ed3664org.highwire.dtl.DTLVardef@8e1383_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Spatially Aware Dimension Reduction for Spatial Transcriptomics

Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with enriched biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored and novel analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a newly detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.

genetics↗

bioRxiv2020

Regulation of intracellular signaling and neuron function by Bardet-Biedl Syndrome proteins in patient-specific iPSC-derived neurons

Bardet-Biedl Syndrome (BBS) is a rare autosomal recessive disorder caused by mutations in genes encoding components of the primary cilium and characterized by hyperphagic obesity. We developed a cellular model of BBS using induced pluripotent stem cell (iPSCs)-derived hypothalamic arcuate-like neurons. BBS mutations BBS1M390R and BBS10C91fsX95 did not affect neuron differentiation efficiency but caused morphological defects including impaired neurite outgrowth and longer primary cilia. Expression of intact BBS10 normalized cilia length. Single-cell RNA sequencing (scRNA-seq) of BBS1M390R hypothalamic neurons identified several down regulated pathways including insulin and cAMP signaling, and axon guidance. In agreement with scRNA-seq data, insulin-induced AKT phosphorylation at Thr308 was reduced in BBS1M390R and BBS10c91fsX95 human fibroblasts and iPSC-derived neurons, as well as in BBS10 knockdown iPSC-derived neurons. Overexpression of intact BBS10 fully restored insulin receptor tyrosine phosphorylation in BBS10c91fsX95 neurons. Mutations in BBS1 and BBS10 impaired leptin-mediated p-STAT3 activation in both human primary fibroblasts and iPSC-derived hypothalamic neurons. Correction of the BBS mutation by CRISPR rescued leptin signaling. POMC expression in BBS1M390R and BBS10 C91fsX95 iPSC-derived hypothalamic neurons was downregulated, as was hypothalamic Pomc in BBS1M390R knockin (KI) mice. In the aggregate, these data provide insights into the anatomic and functional mechanisms by which components of the BBsome in CNS primary cilia mediate effects on energy homeostasis.

cell biology↗

bioRxiv2020

A yeast BiFC-seq method for genome-wide interactome mapping

Genome-wide physical protein-protein interaction (PPI) mapping remains a major challenge for current technologies. Here, we report a high-efficiency yeast bimolecular fluorescence complementation method coupled with next-generation DNA sequencing (BiFC-seq) for interactome mapping. We applied this technology to systematically investigate an intraviral network of Ebola virus (EBOV). Two-thirds (9/13) of known interactions of EBOV were recaptured and five novel interactions were discovered. Next, we used BiFC-seq method to map the interactome of the tumor protein p53. We identified 97 interactors of p53 with more than three quarters are novel. Furthermore, in more complex background, we screened potential interactors by pooling two BiFC-libraries together, and revealing a network of 229 interactions among 205 proteins. These results show that BiFC-seq is a highly sensitive, rapid and economical method in genome-wide interactome mapping.

genomics↗

bioRxiv2020

Transcriptomic expression patterns of two contrasting lowland rice varieties reveal high iron stress tolerance

Iron (Fe) toxicity is a major challenge for plant cultivation in acidic water-logged soil environments, where lowland rice is a major staple food crop. Only few studies addressed the molecular characterization of excess Fe tolerance in rice, and these highlight different mechanisms for Fe tolerance in the studied varieties. Here, we screened 16 lowland rice varieties for excess Fe stress growth responses to identify contrasting lines, Fe-tolerant Lachit and -susceptible Hacha. Hacha and Lachit differed in their physiological and morphological responses to excess Fe, including leaf growth, leaf rolling, reactive oxygen species generation, Fe and metal contents. These responses were mirrored by differential gene expression patterns, obtained through RNA-sequencing, and corresponding GO term enrichment in tolerant versus susceptible lines. From the comparative transcriptomic profiles between Lachit and Hacha in response to excess Fe stress, individual genes of the category metal homeostasis, mainly root-expressed, may contribute to the tolerance of Lachit. 22 out of these 35 metal homeostasis genes are present in selection sweep genomic regions, in breeding signatures and/or differentiated during rice domestication. These findings will serve to design targeted Fe tolerance breeding of rice crops. Summary statementLowland rice varieties Hacha and Lachit were selected for contrasting abilities to cope with iron excess stress. Morphological and physiological phenotypes were mirrored by molecular transcriptome changes, indicating altered metal homeostasis in the root as an adaptive tolerance mechanism in Lachit.

plant biology↗

bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗

bioRxiv2019

An integrated Asian human SNV and indel benchmark combining multiple sequencing methods

Precision medicine of human requires an accurate and complete reference variant benchmark for different populations. A human standard cell line of NA12878 provides a good reference for part of the human populations, but it is still lack of a fine reference standard sample and variant benchmark for the Asians. Here, we constructed a stabilized cell line of a Chinese Han volunteer. We received about 4.16T clean data of the sample using eight sequencing strategies in different laboratories, including two BGI regular NGS platforms, three Illumina regular NGS platforms, two linked-read libraries, and PacBio CCS model. The sequencing depth and reference coverage of eight sequencing strategies have reached the saturation. We detected small variants of SNPs and Indels using the eight data sets and obtained eight variant sets by performing a series of strictly quality control. Finally, we got 3.35M SNPs and 349K indels supported by all of sequencing data, which could be considered as a high confidence standard small variant sets for the studies. Besides, we also detected 5,913 high quality SNPs located in the high homologous regions supported by both linked-reads and CCS data benefited by their long-range information, while these regions are recalcitrant to regular NGS data due to the limited mappability and read length. We compared the later SNPs against the public databases and 969 sites of them were novel SNPs, indicating these SNPs provide a vital complement for the variant database. Moreover, we also phased more than 99% heterozygous SNPs also supported by linked-reads and CCS data. This work provided an integrated Asians SNV and indel benchmark for the further basic studies and precision medicine.

genomics↗

bioRxiv2019

Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect sizes for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases. Our results also provide empirical evidence supporting one hypothesis of the omnigenic model: that trait-relevant gene co-expression networks underlie disease etiology.

genetics↗