Search bioRxiv⌕ Search

Biology subjects

Xing, J.

Publications and source records attributed to Xing, J..

At least 55 records · Page 3Linked to original sources
bioRxiv2023

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

Clinical studies have shown that microbes are closely related to the occurrence of diseases in the human body. It is beneficial for treating diseases by means of microbes to modulate the activity and toxicity of drugs. Therefore, it is significant in predicting associations between drugs and microbes. Recently, there are several computational models for addressing the issue. However, most of them only focus on drug-related microbes and neglect related diseases, which can lead to insufficient training. Here we introduce a new model (called MDMD) is proposed to predict drug-related microbes based on the Metapaths from a heterogeneous network constructed by using the data of Diseases, Microbes, Drugs, the associations of microbe-disease and disease-drug. The MDMD uses an aggregation of the metapath features that can effectively abundance the embedding of the features for different types of nodes and edges in the heterogeneous networks. Then, the MDMD uses the attention mechanism to mark the importance of the metapath vector for each node type which can improve the quality of feature embedding. Experimental results demonstrate that the MDMD improves accuracy by 1.9% compared with other models. The MDMD is also used to predict the microbes of two drugs Lamivudine and Tenofovir which are the antiretroviral drugs used to treat the Acquired Immune Deficiency Syndrome(AIDS). The results show that 90-95% of microbes are reported in the PubMed. Mycobacterium tuberculosis(Mtb) is a specific microbe only predicted by the MDMD. An online platform of the MDMD is available in https://mdmd2023.bit1024.top/, in which the source code of the MDMD and the data in the work can be downloaded. Author summaryMicrobes inhabit multiple organs of the human body that consist of bacteria, fungi, and viruses. Extensive research shows that the microbes can adjust the efficacy and toxicity of drugs to treat the disease. The efficient and accurate selection of drug-related microbes is important for drug research and disease treatment. However, screening of drug-related microbes relies on traditional lab experiments that are labor-intensive and costly. With the growth of high-throughput data, the research of drug-related microbes urgently needs a computational method in bioinformatics. However, most of them only focus on drug-related microbes and neglect related diseases, which can lead to insufficient training. Therefore, we propose a new method (called MDMD) based on the aggregation of the metapath to efficiently and accurately predict potential drug-related microbes within the microbes-disease-drug network.

bioinformatics↗
bioRxiv2023

Integrative analysis of multi-omics and machine learning highlighted an m6A-related mRNA signature as a robust AAA progression predictor

ObjectiveAbdominal aortic aneurysm (AAA) is a life-threatening disease in vascular surgery with significant morbidity and mortality rates upon rupture. Despite surgical interventions, effective targeted drugs for non-surgical candidates are lacking. M6A methylation, a dynamic RNA modification, has been implicated in various diseases, but its role in AAA remains poorly understood. In this study, we aimed to explore the participation of M6A in the progression of AAA progression through multi-omics and machine learning. Approach and Resultswe conducted methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-seq) to profile the m6A methylome in AAA tissues, identifying differentially methylated genes (DMGs). Integrating multi-omics data from RNA-sequencing (RNA-seq) in GEO databases, we developed a machine learning-based AAA m6A-related mRNA signature (AMRMS) to predict AAA dilation risk. The AMRMS demonstrated robust predictive performance in distinguishing AAA patients with large AAA and small AAA. Notably, the AMRMS highlighted FKBP11 as a key gene with a significant impact on the predicted model. Subsequent single-cell RNA sequencing (ScRNA-seq) revealed the pivotal role of FKBP11-positive plasma cells in AAA progression. ConclusionsOur study provides novel insights into the regulatory role of m6A modification in AAA pathogenesis, and further develop a promising AMRMS for risk evaluation in AAA patients. Furthermore, the identification of FKBP11 positive plasma cells as significant contributors to AAA progression opens new avenues for targeted therapeutic interventions.

bioinformatics↗
bioRxiv2023

Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell data

Modeling cellular processes in the framework of dynamical systems theories is a focused area in systems and mathematical biology, but a bottleneck to extend the efforts to genome-wide modeling is lack of quantitative data to constrain model parameters. With advances of single cell techniques, learning dynamical information from high throughput snapshot single cell data emerges as an exciting direction in single cell studies. Our previously developed dynamo framework reconstructs generally nonlinear genome-wide gene regulation relations from single cell expression state and either splicing- or metabolic labeling-based RNA velocity data. In this work, we first developed a graph-based machine learning procedure that imposes a mathematical constraint that the RNA velocity vectors lie in the tangent space of the low-dimensional manifold formed by the single cell expression data. Unlike a popular cosine correlation kernel used in literature, this tangent space projection (TSP) preserves the magnitude information of a vector when one transforms between different representations of the data manifold. Next, we formulated a data-driven graph Fokker-Planck (FPE) equation formalism that models the full cellular state transition dynamics as a convection-diffusion process on a data-formed graph network. The formalism is invariant under representation transformation and preserves the topological and dynamical properties of the system dynamics. Numerical tests on synthetic data and experimental scRNA-seq data demonstrate that the graph TSP/FPE formalism built from snapshot single cell data can recapitulate system dynamics. Significance StatementA cell is a dynamical system, which responds to extracellular and intracellular cues and changes its internal state. In practice the internal state of a cell is often characterized by its gene expression profile such as its transcriptome measured through destructive single cell techniques. Just like one can use Newtons equations to describe motions of the celestial bodies in the solar system, the state change of a cell in principle can also be described by a set of mathematical equations. Determining the form and associated parameters of such equations, however, is challenging. This work presented a general framework of reconstructing dynamical equations from snapshot single cell data.

biophysics↗
bioRxiv2023

Prokaryotic microbial diversity analysis and metabolic function prediction of salt lakes on the Qinghai-Tibet Plateau

The Dong Taijinar Salt Lake (DT) and Xi Taijinar Salt Lake (XT) have been widely studied as mineral-rich areas. However, little is known about the composition and distribution of the microbial communities in these two lakes. In this study, metagenomics sequencing was used to analyze the diversity and potential functions of the microbial communities in DT and XT. According to our report, the salinity of DT (332.18-358.30 g/L) was 10 times higher than that of XT (20.09-36.83 g/L). Interestingly, the dominant domain in DT was Archaea (96.16%), while that in XT was Bacteria (93.09%). The distribution of Bacteria in the DT revealed 33 phyla and 1717 genera. The dominant genus in DT was Marinobacillus, which was positively correlated with total phosphorus content. There were four main phyla and 153 genera identified in the Archaea of DT. The most abundant Archaea genera in DT were Natronomonas (24.61%) and Halorubrum (23.69%), which were mainly positively correlated with the Na+, Ca2+, and Cl- contents. Similarly, there were 33 phyla and 1906 genera of Bacteria in XT, and Loktanella was the dominant genus. The archaeal taxonomy in XT mainly included four phyla and 149 genera. Proteobacteria and Euryarchaeota were the most abundant bacterial and archaeal phyla in the two salt lakes. Analysis of the halophilic mechanisms of the microorganisms identified in these two salt lakes revealed that the Bacteria in XT preferred to synthesize compatible solutes, whereas the Archaea in DT preferred a "salt-in" adaptation strategy in salt-stressed environments. IMPORTANCEThe Qinghai-Tibet Plateau is the origin of many lakes and mountains in China. Among them, the Dong Taijinar and Xi Taijinar salt lakes are important biological resources with unknown microbial community compositions and functional potentials. The results of this study revealed significant differences in the distribution of Bacteria and Archaea between the two salt lakes. Salinity mainly drives lower biodiversity and restricted bacterial growth and metabolism in the high-salinity and near-saturated Dong Taijinar Salt Lake. This study not only identifies the key microorganisms in two penetrating salt lakes, but also provides insights into the mechanisms of salinity tolerance and the unknown ecological functions of microorganisms in extreme environments.

microbiology↗
bioRxiv2023

Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging

Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, mutual information-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection. We found that selecting regions within a multiplex image with low spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging.

bioengineering↗
bioRxiv2023

Deep learning and host variable embedding augment microbiome-based simultaneous detection of multiple diseases

Microbiome has emerged as a promising indicator or predictor of human diseases. However, previous studies typically labeled each specimen as either healthy or with a specific disease, ignoring the prevalence of complications or comorbidities in actual cohorts, which may confound the microbial-disease associations. For instance, a patient may suffer from multiple diseases, making it challenging to detect their health status accurately. Furthermore, host phenotypes such as physiological characteristics and lifestyles can alter the microbiome structure, but this information has not yet been fully utilized in data models. To address these issues, we propose a highly explainable deep learning (DL) method called Meta-Spec. Using a deep neural network (DNN) based approach, it encodes and embeds the refined host variables with microbiome features, enabling the detection of multiple diseases and their correlations simultaneously. Our experiments showed that Meta-Spec outperforms regular machine learning (ML) strategies for multi-label disease screening in several cohorts. More importantly, Meta-Spec can successfully detect comorbidities that are often missed by regular ML approaches. In addition, due to its high interpretability, Meta-Spec captures key factors that shape disease patterns from host variables and microbial members. Hence, these efforts improve the feasibility and sensitivity of microbiome-based disease screening in practical scenarios, representing a significant step towards personalized medicine and better health outcomes.

bioinformatics↗
bioRxiv2023

Whole Transcriptome and Functional Analyses Identify Novel Genes Involved in Meiosis and Fertility in Drosophila melanogaster

Reproductive success requires the development of viable oocytes and the accurate segregation of chromosomes during meiosis. Failure to segregate chromosomes properly can lead to infertility, miscarriages, or developmental disorders. A variety of factors contribute to accurate chromosome segregation and oocyte development, such as spindle assembly and sister chromatid cohesion. However, many proteins required for meiosis remain unknown. In this study, we aimed to identify and characterize novel meiotic and fertility genes using the genome of Drosophila melanogaster. To accomplish this goal, genes upregulated within meiotically active tissues were identified. About 200 genes with no known function were silenced using RNA interference (RNAi), and the effects on meiosis and fertility were assessed. We identified 65 genes that when silenced caused infertility and/or high levels of chromosomal nondisjunction. The vast majority of these genes have human and mouse homologs that are also poorly studied. Through this screening process, we identified novel genes that are crucial for meiosis and oocyte development but have not been extensively studied in human or model organisms. Understanding the function of these genes will be an important step towards the understanding of their biological significance during reproduction. Author SummaryIn this study, we aimed to identify and characterize novel meiotic and fertility genes within the genome of Drosophila melanogaster. We identified 65 genes that when silenced caused infertility and/or high levels of chromosomal nondisjunction. The vast majority of these genes have human and mouse homologs that are also poorly studied. Through this screening process, we identified novel genes that are crucial for meiosis and oocyte development, making them strong candidates for future studies to characterize their functions.

genetics↗
bioRxiv2023

Origin and functional diversification of PAS domain, a ubiquitous intracellular sensor

Signal perception is a key function in regulating biological activities and adapting to changing environments. PAS domains are ubiquitous sensors found in diverse receptors in bacteria, archaea, and eukaryotes, but their origins, distribution across the tree of life, and extent of their functional diversity remain unknown. Here, we show that using sequence conservation and structural information it is possible to propose specific and potential functions for a large portion of nearly 3 million PAS domains. Our analysis suggests that PAS domains originated in bacteria and were horizontally transferred to archaea and eukaryotes. We reveal that gas sensing via a heme cofactor evolved independently in several lineages, whereas redox and light sensing via FAD and FMN cofactors have the same origin. The close relatedness of human PAS domains to those in bacteria provides an opportunity for drug design by exploring potential natural ligands and cofactors for bacterial homologs. TeaserSignaling domains that originated in bacteria hold a potential as drug targets in humans.

evolutionary biology↗
bioRxiv2023

Dynamical modeling reveals RNA decay mediates the effect of matrix stiffness on aged muscle stem cell fate

Loss of muscle stem cell (MuSC) self-renewal with aging reflects a combination of influences from the intracellular (e.g., post-transcriptional modifications) and extracellular (e.g., matrix stiffness) environment. Whereas conventional single cell analyses have revealed valuable insights into factors contributing to impaired self-renewal with age, most are limited by static measurements that fail to capture nonlinear dynamics. Using bioengineered matrices mimicking the stiffness of young and old muscle, we showed that while young MuSCs were unaffected by aged matrices, old MuSCs were phenotypically rejuvenated by young matrices. Dynamical modeling of RNA velocity vector fields in silico revealed that soft matrices promoted a self-renewing state in old MuSCs by attenuating RNA decay. Vector field perturbations demonstrated that the effects of matrix stiffness on MuSC self-renewal could be circumvented by fine-tuning the expression of the RNA decay machinery. These results demonstrate that post-transcriptional dynamics dictate the negative effect of aged matrices on MuSC self-renewal. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/529950v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1a840corg.highwire.dtl.DTLVardef@d354cforg.highwire.dtl.DTLVardef@650556org.highwire.dtl.DTLVardef@41587c_HPS_FORMAT_FIGEXP M_FIG Graphical abstract description: The balance of self-renewal and differentiation in young muscle stem cells (MuSCs) is robust to perturbations of the biophysical microenvironment. In contrast, aged MuSCs are highly sensitive to extrinsic perturbations, and exposure to a youthful microenvironment rejuvenates the self-renewing potential of aged MuSCs by modulating post-transcriptional RNA dynamics. C_FIG

cell biology↗
bioRxiv2023

CircPLXNA2 affects the proliferation and apoptosis of myoblast through circPLXNA2/gga-miR-12207-5P/MDM4 axis

BackgroundcircRNAs are new-identified special endogenous RNA molecules that covalently close a loop by backsplicing with pre-mRNA. In the cytoplasm, circRNAs would act as molecular sponges to bind with specific miRNA to promote the expression of target genes. However, there is still in its fancy of knowing circRNA functional alternation in skeletal myogenesis. In this study, we favor a model to identify the circRNA-miRNA-mRNA interaction network in which the axis may be implicated in the progression of chicken primary myoblasts (CPMs) myogenesis by a combination of multi-omics (i.e., circRNA-seq and ribo-seq). ResultsIn total, 314 circRNA-miRNA-mRNA regulatory axis containing 66 circRNAs, 70 miRNAs, and 24 mRNAs that may be relevant to myogenesis were collected. With these, the circPLXNA2-gga-miR-12207-5P-MDM4 axis aroused our research interest. The circPLXNA2 is highly differentially expressed during differentiation versus proliferation. It was demonstrated that circPLXNA2 inhibited the process of apoptosis while at the same time stimulating cell proliferation. Furthermore, we demonstrated that circPLXNA2 could inhibit the repression of gga-miR-12207-5p to MDM4 by directing binding to gga-miR-12207-5p, thereby restoring MDM4 expression. ConclusionsCircPLXNA2 could function as a competing endogenous RNA (ceRNA) to inhibit the repression of gga-miR-12207-5p to MDM4 by directing binding to gga-miR-12207-5p, thereby recovering the expression of MDM4.

molecular biology↗
bioRxiv2022

Dysregulation of mTOR Signaling Mediates Common Neurite and Migration Defects in Both Idiopathic and 16p11.2 Deletion Autism Neural Precursor Cells

Autism spectrum disorder (ASD) is defined by common behavioral characteristics, raising the possibility of shared pathogenic mechanisms. Yet, vast clinical and etiological heterogeneity suggests personalized phenotypes. Surprisingly, our iPSC studies find that six individuals from two distinct ASD-subtypes, idiopathic and 16p11.2 deletion, have common reductions in neural precursor cell (NPC) neurite outgrowth and migration even though whole genome sequencing demonstrates no genetic overlap between the datasets. To identify signaling differences that may contribute to these developmental defects, an unbiased phospho-(p)-proteome screen was performed. Surprisingly despite the genetic heterogeneity, hundreds of shared p-peptides were identified between autism subtypes including the mTOR pathway. mTOR signaling alterations were confirmed in all NPCs across both ASD-subtypes, and mTOR modulation rescued ASD phenotypes and reproduced autism defects in controls. Thus, our studies demonstrate that genetically distinct ASD subtypes have common defects in neurite outgrowth and migration which are driven by the shared pathogenic mechanism of mTOR signaling dysregulation.

neuroscience↗
bioRxiv2022

Tumor removal limits prostate cancer cell dissemination in bone cortex and osteoblasts induce cancer cell dormancy

BackgroundDisseminated tumor cells (DTCs) can enter a dormant state and cause no symptoms in cancer patients. On the other hand, the dormant DTCs can reactivate and cause metastases progression and lethal relapses. In prostate cancer (PCa), relapse can happen after curative treatments such as primary tumor removal. The impact of surgical removal on PCa dissemination and dormancy remains elusive. Furthermore, as dormant DTCs are asymptomatic, dormancy-inducing can be an operational cure for preventing metastases and relapse of PCa patients. MethodsWe used a PCa subcutaneous xenograft model and species-specific PCR to survey the DTCs in various organs at different time points of tumor growth and in response to tumor removal. We developed in vitro 2D and 3D co-culture models to recapitulate the dormant DTCs in the bone microenvironment. Proliferation assays, fluorescent cell cycle reporter, qRT-PCR, and Western Blot were used to characterize the dormancy phenotype. We performed RNA sequencing to determine the dormancy signature of PCa. A drug repurposing algorithm was applied to predict dormancy-inducing drugs and a top candidate was validated for the efficacy and the mechanism of dormancy induction. ResultsWe found DTCs in almost all mouse organs examined, including bones, at week 2 post-tumor cell injections. Surgical removal of the primary tumor reduced the overall DTC abundance, but the DTCs were enriched only in the bones. We found that osteoblasts, but not other cells of the bones, induced PCa cell dormancy. RNA-Seq revealed the suppression of mitochondrial-related biological processes in osteoblast-induced dormant PCa cells. Importantly, the mitochondrial-related biological processes were found up-regulated in both circulating tumor cells and bone metastases from PCa patients data. We predicted and validated the dormancy-mimicking effect of PF-562271, an inhibitor of focal adhesion kinase (FAK) in vitro. Decreased FAK phosphorylation and increased nuclear translocation were found in both co-cultured and PF-271-treated C4-2B cells, suggesting that FAK plays a key role in osteoblast-induced PCa dormancy. ConclusionsOur study provides the first insights into how primary tumor removal enriches PCa cell dissemination in the bones, defines a unique osteoblast-induced PCa dormancy signature, and identifies FAK as a PCa cell dormancy gatekeeper.

cancer biology↗
bioRxiv2022

A 4-bp natural deletion of maize Na+/H+ exchanger gene alters maize salt stress tolerance

Soil salinity is a major environmental constraint severely reducing plant growth and crop productivity worldwide. Knowledge of salt tolerance-related genes can facilitate improving crop salt tolerance and alleviating the threat of increasing saline area to world food security. Here, we identified a major locus SALT TOLERANCE 1 (qST1) conferring maize salt tolerance via bulked segregant RNA-Seq (BSR-Seq). qST1 encodes a plasma membrane Na+/H+ exchanger ZmSOS1 which is the ortholog of Arabidopsis thaliana SOS1 gene. In salt-sensitive variety D9H, the natural variation of 4-bp deletion in the coding sequence of ZmSOS1 gene was the causal allele for salt sensitivity. We identified two ethyl methanesulfonate-induced mutants, zmsos1-1 and zmsos1-2, which were sensitive to salt stress and cant complement salt-sensitive variety under salt stress within an allelism test. Overexpression of ZmSOS1 enhanced maize seedling salt tolerance. ZmSOS1 can increase the salt tolerance of Arabidopsis sos1-1 mutant and can be activated by AtSOS2 and AtSOS3 in yeast cells, suggesting that ZmSOS1 confers salt tolerance through the conserved SOS signaling pathway in maize. The detrimental allele harboring the 4-bp deletion was rarely found in the natural population but appeared in an important heterotic group composed of x1132x-derived inbred lines which have been used widely for breeding dozens of hybrid varieties in China. The 4-bp deletion-based molecular marker has been successfully used to improve salt-sensitive varieties in a backcross and marker-assisted breeding program by screening and purging this deleterious allele.

plant biology↗
bioRxiv2022

TransCell: In silico characterization of genomic landscape and cellular responses from gene expressions through a two-step deep transfer learning

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. We evaluate commonly used machine learning methods and present TransCell, a two-step deep transfer learning framework that utilizes the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell has the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and has comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigate the predictive power of gene expression in six molecular measurement types and develop a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles. Key PointsO_LIWe provide a systematic investigation on evaluating the predictive power of gene expression in six molecular measurement types including protein expression, copy number variation, mutation, metabolite, gene effect score, and drug sensitivity. C_LIO_LITransCell took advantage of the transfer learning technique, showing how to learn knowledge from the source tumors, and transfer learned weight initializations to the downstream tasks in cell lines. C_LIO_LICompared to the baseline methods, TransCell outperformed in metabolite, gene effect score, and drug sensitivity predictions. C_LIO_LITwo cases studies demonstrate that TransCell could identify new repurposing candidates for pediatric cancer cell lines as well as capture the differences of genetic dependencies in melanoma resistant cell lines. C_LI

bioinformatics↗
bioRxiv2022

Genomic and phylogenetic analysis of the first myovirus isolated from Oceanospirillaceae, representing a novel viral cluster prevalent in polar oceans

The marine bacterial family Oceanospirillaceae, which is abundant in the deep-seas and polar oceans, is closely associated with algal blooms and petroleum hydrocarbons degradation. However, only a few Oceanospirillaceae-infecting phages have so far been reported. Here we report on a novel Oceanospirillum phage, vB_OsaM_PD0307, which is the first myovirus to be found that infects Oceanospirillaceae. vB_OsaM_PD0307 with a 44,421 bp linear dsDNA genome. Phylogenetic analysis and average nucleotide sequence identities suggest that vB_OsaM_PD0307 is different from other phage isolates and represents a novel genus-level myoviral cluster with two high-quality uncultured viral genomes, designed as Oceanospimyovirus. Additionally, the biogeographical distribution of the vB_OsaM_PD0307 cluster suggests that they are widespread in the oceans and abundant in polar areas. In summary, our findings expand the current understanding of the phylogenetic diversity, genomic characteristic and function of Oceanospimyovirus phages, and highlight the role of the vB_OsaM_PD0307 phage as a major ecological agent that can infect certain key bacterial groups associated with polar algal blooms. ImportanceOceanospirillumphage vB_OsaM_PD0307 is the first myovirus found to infect Oceanospirillaceae and represents a novel viral genus, Oceanospimyovirus. This study provides insights into the genomic, phylogenetic, and ecological characteristics of myoviruses infecting Oceanospirillaceae and improves our understanding of the interactions between Oceanospirillaceae and their phages in the oceans.

genomics↗
bioRxiv2021

Post-injury born oligodendrocytes integrate into the glial scar and inhibit growth of regenerating axons by premature myelination

Pathologies of the central nervous system (CNS) white matter often result in permanent functional deficits because mature mammalian projection neurons fail to regenerate long-distance axons after injury. A major barrier to axonal regenerative research is that the CNS axons that regenerate in response to experimental treatments stall growth before reaching their post-synaptic targets. Here, we test the hypothesis that premature, de novo, myelination of regenerating axons stalls their growth, even after bypassing the glial scar. To test this hypothesis, first, we used single cell RNA-seq (scRNA-seq) and immunohistological analysis to investigate whether post-injury born oligodendrocytes integrate into the glial scar after optic nerve injury. Then, we used a multiple sclerosis model of demyelination concurrently with the stimulation of axon regeneration by Pten knockdown (KD) in projection neurons after optic nerve injury. We found that post-injury born oligodendrocytes integrate into the glial scar, where they are susceptible to the demyelination treatment, which prevented premature myelination, and thereby enhanced Pten KD-stimulated axon regeneration. We also present a website for comparing the gene expression of scRNA-seq-profiled optic nerve oligodendrocytes under physiological and pathophysiological conditions. SIGNIFICANCE STATEMENTMyelin debris from degenerating axons along with reactive astrocytes in the glial scar inhibit CNS axon regeneration. However, even with the recently developed experimental approaches which activate axons to regenerate passed the glial scar, almost all axons still stall growth before reaching their post-synaptic targets. Here, we show that post-injury born oligodendrocytes integrate into the glial scar, and that other than myelin debris, live oligodendrocytes prematurely myelinating the regenerating axons inhibit growth, even if the axons have already regenerated passed the glial scar.

neuroscience↗