Biology subjects

Zhang, G.

Publications and source records attributed to Zhang, G..

At least 253 records · Page 14Linked to original sources

bioRxiv2023

High throughput detection of variation in single-cell whole transcriptome through streamlined scFAST-seq

High-throughput single-cell full-length RNA sequencing is a powerful tool to explore the entire transcriptome, including non-polyadenylated transcripts. We developed a single cell Full-length RNA Sequence Transcriptome sequencing method (scFAST-seq), which combines semi-random primers with high reverse transcription efficiency, template switching and convenient rRNA removal methods, allowing the construction of full-length RNA libraries of up to 12,000 cells within 8 hours. Compared to regular 3 scRNA-seq, scFAST-seq has similar sensitivity to mRNA detection, sequencing cost and experimental workflow. Moreover, scFAST-seq has clear advantages in detecting non-polyadenylated transcripts, covering longer transcript length, and identifying more splice junctions. In addition, scFAST-seq can more accurately predict the direction of cell differentiation by calculating RNA velocity. Furthermore, we demonstrated that scFAST-seq combined with target region enrichment can simultaneously identify somatic mutations and cellular status of individual tumor cells, which is valuable information for precision medicine.

genomics↗

bioRxiv2023

The maize PLASTID TERMINAL OXIDASE (PTOX) gene controls the carotenoid content of kernels.

O_LICarotenoids perform a broad range of important functions in humans; therefore, carotenoid biofortification of maize (Zea mays L.), one of the most highly produced cereal crops worldwide, would have a global impact on human health. C_LIO_LIPLASTID TERMINAL OXIDASE (PTOX) genes play an important role in carotenoid metabolism; however, the possible function of PTOX in carotenoid biosynthesis in maize has not yet been explored. In this study, we identified the maize PTOX gene ZmPTOX1 by forward genetic screening. C_LIO_LIWhile most higher plant species possess a single copy of the PTOX gene, maize carries two tandemly duplicated copies of ZmPTOX. Characterization of Zmptox1 mutants revealed that disrupting one copy of ZmPTOX1 was enough to impair carotenoid biosynthesis, indicating that ZmPTOX1 is essential for carotenoid biosynthesis in maize kernels. Remarkably, overexpression of ZmPTOX1 significantly improved the content of carotenoids, especially {beta}-carotene (provitamin A), which was increased by ~3-fold, in maize kernels. C_LIO_LIOverall, our study shows that ZmPTOX1 plays a crucial role in carotenoid biosynthesis in maize kernels and suggests that fine-tuning the expression of this gene could improve the nutritional value of cereal grains. C_LI

plant biology↗

bioRxiv2023

Angiopoietin-like 4 promotes the proliferation and migration of epidermal stem cells and contributes to the re-epithelialization of cutaneous wounds

Proliferation and migration of epidermal stem cells (EpSCs) are essential for epithelialization during skin wound healing. Angiopoietin-like 4 (ANGPTL4) has been reported to play an important role in wound healing, but the mechanisms involved are not fully understood. Here we investigate the contribution of ANGPTL4 to full-thickness wound re-epithelialization and the underlying mechanisms using Angptl4 knockout mice. Immunohistochemical staining reveals that ANGPTL4 is significantly upregulated in the basal layer cells of the epidermis around the wound during cutaneous wound healing. ANGPTL4 deficiency impairs wound healing. H & E staining shows that ANGPTL4 deficiency significantly reduces the thickness, length and area of regenerated epidermis postwounding. Immunohistochemical staining for markers of EpSCs (6 integrin and {beta}1 integrin) and cell proliferation (PCNA) shows that the number and proliferation of EpSCs in the basal layer of the epidermis are reduced in ANGPTL4-deficient mice. In vitro studies show that ANGPTL4 deficiency impedes EpSC proliferation, causes cell cycle arrest at the G1 phase and reduced the expression of cyclins D1 and A2, which can be reversed by ANGPTL4 overexpression. ANGPTL4 deletion suppresses EpSC migration, which is also rescued by ANGPTL4 overexpression. Overexpression of ANGPTL4 in EpSCs accelerates cell proliferation and migration. Collectively, our results indicate that ANGPTL4 promotes EpSCs proliferation by upregulating cyclins D1 and A2 expression and accelerating cell cycle transition from G1 to S phase, and ANGPTL4 promotes skin wound re-epithelialization by stimulating EpSC proliferation and migration. Our study reveals a novel mechanism underlying EpSC activation and re-epithelialization during cutaneous wound healing.

cell biology↗

bioRxiv2023

Impacts of Age on the Gut Microbiota in Captive Giant Pandas

The gut microbiota is the most complex and most abundant symbiotic microbial ecosystem in animals. Aging is one of the main factors that cause gut microbiota structure changes, and the relationship between age and the gut microbiota in the giant panda has been a key focus of attention. The giant panda has a specialized diet of bamboo, and it relies on the microbiota that colonizes its gut to complete digestion. However, there is no in-depth understanding of the changes in the gut microbiota across the lifespan of giant pandas. Here, we identified the differences in the gut microbiota between four age groups (cubs, juveniles, adults, and geriatrics) using 16S rRNA gene high-throughput sequencing on an Illumina MiSeq platform. The results revealed that Firmicutes (mean{+/-}SD: 65.45{+/-}30.21%; range: 0.91-99.62%) and Proteobacteria (mean{+/-}SD: 31.49{+/-}27.99%; range: 0.26-85.35%) were the dominant phyla. The relative abundance of Escherichia-Shigella was high in both the cubs and juveniles. It is interesting to note that the adults had the highest richness and lowest diversity, while the cubs had the opposite. In summary, our study indicates that the gut microbial community composition, abundance, and functional pathways differ across four age groups of giant pandas. Exploring the influence of age, an endogenous influencing factor, on gut microbes provides basic scientific data for monitoring gut microbial dynamics and formulating gut microbial health management approaches, thereby improving the protection of giant pandas.

microbiology↗

bioRxiv2023

Changes in physicochemical properties and microbial community succession during leaf stacking fermentation

Leaf stacking fermentation involves enzymatic actions of many microorganisms and is an efficient and environmentally benign process for degrading macromolecular organic compounds. We investigated the dynamics of metabolite profiles, bacterial and fungal communities and their interactions during fermentation using cigar leaves from three geographic regions. The results showed that the contents of total sugar, reducing sugar, starch, cellulose, lignin, pectin, polyphenol and protein in cigar tobacco leaves was significantly decreased during fermentation. Notably, the furfural, neophytadiene, pyridine, benzyl alcohol, geranyl acetone, 2-butanone, 3-hydroxy-, N-hexanal, isoamylalcohol and 2,3-pentanedione were important features volatile aroma compounds during fermentation. The -diversity of fungi and bacteria initially increased and then decreased during fermentation. The microbial diversity was influenced by fermentation stages and growing locations, in which the fermentation stages had greater impacts on the microbial diversity than locations. Microbiome profiling had identified several core bacteria including Sphingomonas, Bacillus, Staphylococcus, Pseudomonas, Ralstonia, Massilia and Fibrobacter. Fungal biomarkers included Aspergillus, Penicillium, Fusarium, Cladosporium and Trichomonascus. Interestingly, the molecular ecological networks showed that the core taxa had significant correlations with metabolic enzymes and physicochemical properties; bacteria and fungi jointly participated in the carbohydrate and nitrogen compound degrading and volatile aroma compound chemosynthesis processes during fermentation. These studies provide insights into the coupling of material conversion and microbial community succession during leaf fermentation. IMPORTANCECigar tobacco leaves are still crude after air curing. Fermentation can eliminate the pungent and bitter taste of the leaves and promote the accumulations of aromatic compounds. Hitherto, there has been no systematic study on the material conversion, metabolic enzymes and microbial community changes and their interactions in fermented leaves. The significance of our research is in identifying the potential metabolic cooperation related to microbial community, which will greatly enhance our understanding of microorganism to convert macromolecular organic to small molecules during the leaf stacking fermentation process.

microbiology↗

bioRxiv2023

E2EDA: Protein domain assembly based on end-to-end deep learning

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modelling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In addition, the available GPU memory limits the size of a full-chain protein which can be predicted. Based on the divide-and-conquer strategy, the single-domain structure is predicted by the state-of-the-art prediction method, such as AlphaFold2, and then assembled into a full-chain model through the domain assembly method, which can effectively reduce the demand for hardware resources. In this work, we propose an End-To-End Domain Assembly method based on deep learning, named E2EDA. We first develop an EffificientNetV2-based deep learning model (RMNet), which is specialised for predicting inter-domain orientations. The RMNet uses an attention mechanism to predict inter-domain rigid motion by fusing sequence features, multiple template features and single-domain features. Then, the predicted rigid motions are converted into inter-domain spatial transformations to assemble full-chain models of multi-domain proteins directly without time-consuming simulation processes. Finally, a scoring strategy, RMscore, is designed to select the best model from multiple assembled models to improve assembly accuracy. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (356) is 0.84, which is better than other domain assembly methods SADA (0.80), DEMO (0.74) and AIDA (0.63). Meanwhile, on our constructed human protein dataset from AlphaFold DB, the model reassembled by E2EDA is 6.8% higher than the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, the average running time of E2EDA on the benchmark is reduced by 74.6% compared with the domain assembly simulation method SADA, which indicates that E2EDA can effectively improve assembly efficiency through an end-to-end manner.The online server is at http://zhanglab-bioinf.com/E2EDA/.

bioinformatics↗

bioRxiv2023

GraphGPSM: a global scoring model for protein structure using graph neural networks

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles, and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14, and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D, and DeepAccNet etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2% and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation.

bioinformatics↗

bioRxiv2023

Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural analogues and sequential homologues

With the breakthrough of AlphaFold2, nearly all single-domain protein structures can be built at experimental resolution. However, accurate modelling of full-chain structures of multidomain proteins, particularly all relevant conformations for those with multiple states remain challenging. In this study, we develop a multidomain protein assembly method, M-SADA, for assembling multiple conformational states. In M-SADA, a multiple population-based evolutionary algorithm is proposed to sample multiple conformational states under the guidance of multiple energy functions constructed by combining homologous and analogous templates with inter-domain distances predicted by deep learning. On a developed benchmark dataset containing 72 multidomain proteins with multiple conformational states, the performance of M-SADA is significantly better than that of AlphaFold2 on multiple conformational states modelling, where 29/72 (40.3%) of proteins can be assembled with a TM-score >0.90 for highly distinct conformational states with M-SADA while AlphaFold2 does so in only 2/72 (2.8%) of proteins. Furthermore, M-SADA is tested on a developed benchmark dataset containing 296 multidomain proteins with single conformational state, and results show that the average TM-score of M-SADA on the best models is 0.913, which is 5.2% higher than that of AlphaFold2 models (0.868).

bioinformatics↗

bioRxiv2023

The "replacing surgery" of cpDNA: de novo chemical synthesis and in vivo functional testing of Chlamydomonas chloroplast genome

We have successfully designed and synthesized the 221,372-bp cpDNA SynCpV1.0 with the native cpDNA of Chlamydomonas reinhardtii as the template. Homoplasmic SynCpv1.0-harboring algal strains were obtained by biolistic transformation and selected with an ascending gradient of antibiotic pressure. Meanwhile, we were pleasantly surprised to find that SynCpV1.0 was able to re-introduce and replicate normally after the total DNA of transplastomic algal strains were transformed to Escherichia coli, it indicated that SynCpV1.0 was able to shuttle between C. reinhardtii and E. coli. Finally, we analyzed the photosynthetic properties of SynCpV1.0-harboring transplastomic strains, the results showed that they exhibited the same photosynthetic efficiency as the wild strain of C. reinhardtii CC125, and could rescue the photosynthetic defect in mutant strain of C. reinhardtii CC5168. Herein, we have performed the "replacing surgery" of cpDNA and established an ideal platform to complete multiple cycles of "Design-Build-Test" for optimizing the cpDNA of photosynthetic organisms. HighlightO_LIAn artificial cpDNA SynCpV1.0 is constructed by de novo chemical synthesis. C_LIO_LIThe "replacing surgery" of cpDNA was performed in the chloroplast of C. reinhardtii C_LIO_LIIt is found that artificial cpDNA was able to shuttle between Chlamydomonas chloroplast and E. coli. C_LIO_LIEstablish an ideal platform to complete multiple cycles of "Design-Build-Test" for optimizing the cpDNA. C_LI One-Sentence SummaryThe chloroplast genome can be replaced by a complete synthesized genome and performs the designed biological function in C. reinhardtii.

molecular biology↗

bioRxiv2022

FunGeneTyper: An Expandable Deep Learning-based Framework for Ultra-accurate Classification and Discovery of Functional Protein-Coding Genes

High-throughput DNA sequencing technologies open the gate to tremendous (meta)genomic data from yet-to-be-explored microbial dark matter. However, accurately assigning protein functions to new gene sequences remains challenging. To this end, we developed FunGeneTyper, an expandable deep learning-based framework with models, structured databases and tools for ultra-accurate (>0.99) and fine-grained classification and discovery of antibiotic resistance genes (ARGs) and virulence factor or toxin genes. Specifically, this new framework achieves superior performance in discovering new ARGs from human gut (accuracy: 0.8512; and F1-score: 0.6948), wastewater (0.7273; 0.6072), and soil (0.8269; 0.5445) samples, beating the state-of-the-art bioinformatics tools and protein sequence-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) alignment approaches. We empowered the generalized application of the framework by implementing a lightweight, privacy-preserving and plug-and-play neural network module shareable among global developers and users. The FunGeneTyper* is released to promote the monitoring of key functional genes and discovery of precious enzymatic resources from diverse microbiomes.

bioinformatics↗

bioRxiv2022

Effects of canagliflozin and irbesartan on renal fibrosis in Dahl salt-sensitive rats

Hypertension is one of the major contributors to cardiovascular and chronic kidney disease (CKD). Sodium-glucose cotransporter 2 (SGLT2) inhibitors and angiotensin receptor blockers (ARBs) have become the preferred treatment for patients with CKD. However, the renoprotective effects of the combined therapy of the two drugs on hypertensive renal fibrosis are still largely understood. The aim of this study was to compare the antifibrotic effects of canagliflozin, with or without irbesartan, in the kidneys of Dahl salt-sensitive (Dahl SS) rats on a high salt (HS) diet. After the preconditioning stage, Dahl SS rats (n = 47) were divided into 5 experimental groups as follows: low salt control (n=7), HS control (n=10), high salt with canagliflozin (n=10), high salt with irbesartan (n=10), and high salt with canagliflozin plus irbesartan (n=10). Mean food and water intake, body weight (BW), and systolic blood pressure (SBP) were measured during the whole experimental period. After 12 weeks, the rats were euthanized, and the kidneys were excised for histomorphometric evaluation and immunohistochemical evaluation. An HS diet increased SBP, renal fibrosis, expression of fibrotic protein factors, and TGF-{beta}/Smad2/3 pathway compared to the LS group. We found that irbesartan reduced SBP and slowed the loss of renal function. Canagliflozin significantly reduced BW and renal fibrosis and downregulated the TGF-{beta}/Smad2/3 pathway. The combined therapy showed better renoprotection in all outcome parameters. In conclusion, these results indicate that canagliflozin and irbesartan exert different benefits on nephroprotection in salt-sensitive hypertensive rats.

molecular biology↗

bioRxiv2022

Inter-domain distance prediction based on deep learning for domain assembly

AlphaFold2 achieved a breakthrough in protein structure prediction through the end-to-end deep learning method, which can predict nearly all single-domain proteins at experimental resolution. However, the prediction accuracy of full-chain proteins is generally lower than that of single-domain proteins because of the incorrect interactions between domains. In this work, we develop an inter-domain distance prediction method, named DeepIDDP. In DeepIDDP, we design a neural network with attention mechanisms, where two new inter-domain features are used to enhance the ability to capture the interactions between domains. Furthermore, we propose a data enhancement strategy termed DPMSA, which is employed to deal with the absence of co-evolutionary information on targets. We integrate DeepIDDP into our previously developed domain assembly method SADA, termed SADA-DeepIDDP. Tested on a given multi-domain benchmark dataset, the accuracy of SADA-DeepIDDP inter-domain distance prediction is 11.3% and 21.6% higher than trRosettaX and trRosetta, respectively. The accuracy of the domain assembly model is 2.5% higher than that of SADA. Meanwhile, we reassemble 68 human multi-domain protein models with TM-score [≤]0.80 from the AlphaFold protein structure database, where the average TM-score is improved by 11.8% after the reassembly by our method. The online server is at http://zhanglab-bioinf.com/DeepIDDP/.

bioinformatics↗

bioRxiv2022

Transposable element and host silencing activity in gigantic genomes

Transposable elements (TEs) and the silencing machinery of their hosts are engaged in a germline arms-race dynamic that shapes TE accumulation and, therefore, genome size. In animal species with extremely large genomes (>10 Gb), TE accumulation has been pushed to the extreme, prompting the question of whether TE silencing also deviates from typical conditions. To address this question, we characterize TE silencing via two pathways -- the piRNA pathway and KRAB-ZFP transcriptional repression -- in the male and female gonads of Ranodon sibiricus, a salamander species with a [~]21 Gb genome. We quantify 1) genomic TE diversity, 2) TE expression, and 3) small RNA expression and find a significant relationship between the expression of piRNAs and TEs they target for silencing in both sexes. We also quantified TE silencing pathway gene expression in R. sibiricus and 14 other vertebrates with genome sizes ranging from 1 - 130 Gb and find no association between pathway expression and genome size. Taken together, our results reveal that the gigantic R. sibiricus genome includes at least 19 putatively active TE superfamilies, all of which are targeted by the piRNA pathway in proportion to their expression levels, suggesting comprehensive piRNA-mediated silencing. Males have higher TE expression than females, suggesting that they may contribute more to the species high genomic TE load. We posit that apparently conflicting interpretations of TE silencing and genomic gigantism in the literature, as well as the absence of a correlation between TE silencing pathway gene expression and genome size, can be reconciled by considering whether the TE community or the host is currently "on the attack" in the arms race dynamic.

genomics↗

bioRxiv2022

Genetic load and adaptive potential of a recovered avian species that narrowly avoided extinction

High genetic diversity is often a good predictor of long-term population viability, yet some species persevere despite having low genetic diversity. Here we study the genomic erosion of the Seychelles paradise flycatcher (Terpsiphone corvina), a species that narrowly avoided extinction after having declined to 28 individuals in the 1960s. The species recovered unassisted to over 250 individuals in the 1990s and was downlisted from Critically Endangered to Vulnerable in the IUCN Red List in 2020. By comparing historical, pre-bottleneck (130+ years old) and modern genomes, we uncovered a 10-fold loss of genetic diversity. The genome shows signs of historical inbreeding during the bottleneck in the 1960s, but low levels of recent inbreeding after the demographic recovery. We show that the proportion of severely deleterious mutations has reduced in modern individuals, but mildly deleterious mutations have remained unchanged. Computer simulations suggest that the Seychelles paradise flycatcher avoided extinction and recovered due to its long-term small Ne. This reduced the masked load and made the species more resilient to inbreeding. However, we also show that the chronically small Ne and the severe bottleneck resulted in very low genetic diversity in the modern population. Our simulations show this is likely to reduce the species adaptive potential when faced with environmental change, thereby compromising its long-term population viability. In light of rapid global rates of population decline, our work highlights the importance of considering genomic erosion and computer modelling in conservation assessments

evolutionary biology↗

bioRxiv2022

Human mutations in SLITRK3 implicated in GABAergic synapse development in mice

We report on biallelic homozygous and monoallelic de-novo variants in SLITRK3 in 3 unrelated families presenting with epileptic encephalopathy associated with a broad neurological involvement characterized by microcephaly, intellectual disability, seizures, and global developmental delay. SLITRK3 encodes for a transmembrane protein that is involved in controlling neurite outgrowth and inhibitory synapse development and that has an important role in brain function and neurological diseases. Using primary cultures of hippocampal neurons carrying patients SLITRK3 variants and in combination with electrophysiology, we demonstrate that recessive variants are loss-of-function alleles. By analyzing the development and phenotype of SLITRK3 KO (SLITRK3-/-) mice, we bring additional evidence of enhanced susceptibility to pentylenetetrazole-induced seizure with the appearance of spontaneous epileptiform EEG, as well as developmental deficits such as higher motor activities and reduced parvalbumin interneurons. Taken together, our results exhibit impaired development of peripheral and central nervous system and support a conserved role of this transmembrane protein in neurological function. Our study delineates an emerging spectrum of human core synaptopathies caused by variants in genes that encode SLITRK proteins and essential regulatory components of the synaptic machinery. The hallmark of these disorders is impaired postsynaptic neurotransmission at nerve terminals; an impaired neurotransmission resulting in a wide array of (often overlapping) clinical features, including neurodevelopmental impairment, weakness, seizures, and abnormal movements. The genetic synaptopathy caused by SLITRK3 mutations highlights the key roles of this gene in human brain development and function.

genetics↗

bioRxiv2022

ExposomeX: Integrative Exposomic Platform Expediates Discovery of "Exposure-Biology-Disease" Nexus

Exposome has become the hotspot of next-generation health studies. To date, there is no available effective platform to standardize the analysis of exposomic data. In this study, we aim to propose one new framework of exposomic analysis and build up one novel integrated platform "ExposomeX" to expediate the discovery of the "Exposure-Biology-Disease" nexus. We have developed 13 standardized modules to accomplish six major functions including statistical learning (E-STAT), exposome database search (E-DB), mass spectrometry data processing (E-MS), meta-analysis (E-META), biological link via pathway integration and protein-protein interaction (E-BIO) and data visualization (E-VIZ). Using ExposomeX, we can effectively analyze the multiple-dimensional exposomics data and investigate the "Exposure-Biology-Disease" nexus by exploring mediation and interaction effects, understanding statistical and biological mechanisms, strengthening prediction performance, and automatically conducting meta-analysis based on well-established literature databases. The performance of ExposomeX has been well validated by re-analyzing two previous multi-omics studies. Additionally, ExposomeX can efficiently help discover new associations, as well as relevant in-depth biological pathways via protein-protein interaction and gene ontology network analysis. In sum, we have proposed a novel framework for standardized exposomic analysis, which can be accessed using both R and online interactive platform (http://www.exposomex.cn/).

bioinformatics↗

bioRxiv2022

A stable reference human transcriptome and proteome as a standard for reproducible omics experiments

In recent years, the development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits its application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. However, the transcriptome and proteome of most cell lines shift during culturing, which limits their applicability to serve as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (R2=0.966-0.995) and proteome (R2=0.934-0.976 for DDA, R2=0.942-0.986 for DIA) after 9 subculturing generations, which allows this stable standard sample to be stably produced on an industrial scale for several decades. Moreover, this stability was maintained across labs and platforms. In sum, our results justified a omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

cell biology↗

bioRxiv2022

Thyroid Hormone T4 Mitigates Traumatic Brain Injury via Dynamically Remodeling Cell Type Specific Genes and Pathways

BackgroundThe complex pathology of mild traumatic brain injury (mTBI) is a main contributor to the difficulties in achieving a successful therapeutic regimen. Thyroxine (T4) administration has been shown to prevent the cognitive impairments induced by mTBI in mice. MethodTo understand the underlying mechanism, we carried out a single cell transcriptomic study to investigate the spatiotemporal effects of T4 on individual cell types in the hippocampus and frontal cortex at three post-injury stages. FindingsOur multi-tissue multi-stage results showed that T4 treatment altered the proportions and transcriptomes of numerous cell types across tissues and timepoints, particularly oligodendrocytes, astrocytes, and microglia, which are crucial for injury repair. T4 also reversed the expression mTBI-affected genes such as Ttr, mt-Rnr2, Ggn12, Malat1, Gnaq, and Myo3a, as well as numerous pathways such as cell/energy/iron metabolism, immune response, nervous system, and cytoskeleton-related pathways. Cell-type specific network modeling revealed that T4 mitigated select mTBI-perturbed dynamic shifts in subnetworks related to cell cycle, stress response, and RNA processing in oligodendrocytes. Cross cell-type ligand-receptor networks recapitulated the roles of App, Hmgb1, Fn1, and Tnf in mTBI, the latter two ligands having been previously identified as TBI network hubs. mTBI and/or T4 signature genes were enriched for human genome-wide association study (GWAS) candidate genes for cognitive, psychiatric and neurodegenerative disorders related to mTBI, supporting T4 as a potential mTBI treatment. InterpretationOur systems-level approach elucidated the temporal and spatial dynamic reprogramming of cell-type specific genes, pathways, and networks, as well as cell-cell communications through which T4 mitigates cognitive dysfunction induced by mTBI. FundingThis work was funded by NIHR01NS117148 to X.Y. and F.G.P. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSDysfunction in the brain resulting from traumatic brain injury can display immediately as well as several years post-injury. It also impacts various brain regions, including the hippocampus and frontal cortex, which are linked to distinct disease pathologies. The complexity of spatiotemporal and molecular dynamics of perturbation caused by TBI hinder our ability to establish an effective therapeutic approach. Recently, thyroid hormone poses promise as a potential therapeutic target based on our previous scRNA-seq studies. Yet, the mechanisms by which T4 alleviates mTBI, specifically those related to spatial, temporal, and cell-type specificity, remain unexplored. Added value of this studyWe examined the impact of T4 intervention in mitigating mTBI by investigating the transcriptome and functional pathways across two affected brain regions, the frontal cortex and hippocampus, in different stages of injury. Utilizing a systems biology approach, we conducted within- and between-cell-type network modeling, cell-cell communication and integrating human genome-wide association studies (GWAS) analysis. This comprehensive strategy aimed to elucidate the cellular and molecular mechanisms through which T4 averts cognitive impairments induced by mTBI. Implications of all the available evidenceOur findings offer molecular evidence that the administration of T4 impacts a wide range of genes, biological processes, and networks, thereby preventing the advancement of mTBI-induced brain dysfunction and associated diseases. This comprehensive impact of T4 suggests potential advantages in efficacy compared to other therapeutic options that concentrate on specific pathways and targets.

neuroscience↗