Biology subjects

Ma, S.

Publications and source records attributed to Ma, S..

At least 217 records · Page 12Linked to original sources

bioRxiv2021

Serine 182 on RORγt regulates T helper 17 and regulatory T cell functions to resolve inflammation

Intestine homeostasis is maintained by the delicate balance of Th17 effector cells and Treg cells. Dysregulation of these cell populations contributes to inflammation, tissue damage, and chronic conditions. ROR{gamma}t is essential for the differentiation of Th17 and a subset of Treg (ROR{gamma}t+ Treg) cells involved in intestinal inflammation. ROR{gamma}t belongs to the nuclear receptor family of transcription factors with hinge regions that are highly flexible for co-activator/co-repressor interactions. Serine 182 at the hinge region of ROR{gamma}t is phosphorylated. This study aims to uncover how S182 on ROR{gamma}t contributes to mucosal homeostasis and diseases. We used CRISRP technology to generate a phosphor-null knock-in mutant mouse line (ROR{gamma}tS182A) to assess its role in intestine physiology. scRNA-seq was performed on WT and ROR{gamma}tS182A cohoused littermates to evaluate colonic T cell heterogeneity under steady state and colitis settings. Single-cell transcriptomics revealed that ROR{gamma}tS182 maintains colonic T cell heterogeneity under steady state, without interfering T cell development and differentiation. In inflamed tissues, ROR{gamma}tS182 simultaneously restricts IL-1{beta}-mediated Th17 activities and promotes anti-inflammatory cytokine IL-10 production in LT-like Treg cells. Phospho-null ROR{gamma}tS182A knock-in mice challenged with DSS induced colitis and EAE experienced delayed recovery and exacerbated pathology. The double switch role of ROR{gamma}tS182 is critical in resolving T cell-mediated inflammation and provides a potential therapeutic target to combat autoimmune diseases.

immunology↗

bioRxiv2021

A Chemical-Enhanced System for CRISPR-Based Nucleic Acid Detection

The CRISPR-based nucleic acid detection systems such as SHERLOCK, DETECTR and HOLMES have shown great potential for point-of-care testing of viral pathogens, especially in the context of COVID-19 pandemic. Here we optimize several key parameters of reaction chemistry and develop a Chemical Enhanced CRISPR Detection system for nucleic acid (termed CECRID). For the Cas12a/Cas13a-based signal detection phase, we determine buffer conditions and substrate range for optimal detection performance. By comparing several chemical additives, we find that addition of L-proline can secure or enhance Cas12a/Cas13a detection capability. For isothermal amplification phase with typical LAMP and RPA methods, inclusion of L-proline can also enhance specific target amplification as determined by CRISPR detection. Using SARS-CoV-2 pseudovirus, we demonstrate CECRID has enhanced detection sensitivity over chemical additive-null method with either fluorescence or lateral flow strip readout. Thus, CECRID provides an improved detection power and system robustness towards practical application of CRISPR-based diagnostics.

molecular biology↗

bioRxiv2021

A Statistical Model for Describing and Simulating Microbial Community Profiles

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, nonindependence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSAs model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbemicrobe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSAs performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSAs utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.

bioinformatics↗

bioRxiv2021

circMRPS35 promotes malignant progression and cisplatin resistance in hepatocellular cancer

Hepatocellular carcinoma (HCC), a common malignant tumor, is one of the main causes of cancer-related deaths worldwide. Circular RNAs (circRNAs), a novel class of non-coding RNA, have been reported to be involved in the etiology of various malignancy. However, the functions of circRNAs in HCC remain unclear. In this study, through mining the RNA sequencing databases from GEO datasets and subsequent experimental verification, we identified that hsa_circ_0000384 (circMRPS35) was highly expressed in HCC. Knockdown of circMRPS35 suppressed the proliferation, migration, invasion, clone formation and cell cycle of HCC cell lines both in vitro and in a xenograft mouse model. Mechanically, circMRPS35 sponged microRNA-148a-3p (miR-148a), which in turn regulated STX3-PTEN axis. Surprisingly, we detected a peptide encoded by circMRPS35 (circMRPS35-168aa), which was significantly induced by chemotherapeutic drugs and promoted cisplatin resistance in HCC cells. These results demonstrated that circMRPS35 might be a novel factor in HCC progress, and has a great potential as a new diagnosis and therapeutic target for treatment of HCC.

cell biology↗

bioRxiv2021

AGL16 negatively modulates stress response to balance with growth

Sensile plants constantly experience environmental stresses in nature. They must have evolved effective mechanisms to balance growth with stress response. Here we report the MADS-box transcription factor AGL16 acting as a negative regulator in stress response. Loss-of-AGL16 confers resistance to salt stress in seed germination, root elongation, and soil-grown plants, while elevated AGL16 expression confers the opposite phenotypes compared with wild type. However, the sensitivity to ABA in seed germination is inversely correlated with AGL16 expression level. Transcriptomic comparison revealed that the improved salt resistance of agl16 mutant was largely attributed to enhanced expression of stress responsive transcriptional factors and genes involved in ABA signaling and ion homeostasis. We further demonstrated that AGL16 directly binds to the CArG motifs in the promoter of HKT1;1, HsfA6a, and MYB102 and represses their expression. Genetic analyses with double mutants also support that HsfA6a and MYB102 are target genes of AGL16. Taken together, our results show that AGL16 acts as negative regulator transcriptionally suppressing key components in stress response and may play a critical role in balancing stress response with growth.

plant biology↗

bioRxiv2021

Comparative Chromatin Dynamics of Stem Cell Differentiation in Human and Rat

Differentiation of cell types homologous between species are controlled by conserved networks of regulatory elements driving gene expression. In order to identify conservation of gene expression and chromatin accessibility during cell differentiation in two different species. We collected a daily time-course of gene expression and chromatin accessibility in rat and human to quantify conserved and species-specific chromatin dynamics during embryonic stem cell differentiation to definitive endoderm (DE) as well as to neuronal progenitor cells (NPC). We identify shared and cell-type specific transient differentiation markers in each species, including key transcription factors that may regulate differentiation into each cell-type and their candidate cis-regulatory elements (cCREs). Our analysis shows that DE differentiation has higher conservation of gene expression and chromatin accessibility than NPC differentiation. We provide the first global comparison of transcriptional complexity and chromatin dynamics between human and rat for DE and NPC differentiation.

genomics↗

bioRxiv2021

NeuronMotif: Deciphering transcriptional cis-regulatory codes from deep neural networks

Discovering DNA regulatory sequence motifs and their relative positions are vital to understand the mechanisms of gene expression regulation. Such complicated motif grammars are difficult to be summarized from shallow models. Although Deep Convolutional Neural Network (DCNN) achieved great success in annotating cis-regulatory elements, few combinatorial motif grammars have been accurately interpreted due to the mixed signal in DCNN. To address this problem, we proposed NeuronMotif, a general backward decoupling algorithm, to reveal the homo-/hetero-typic motif combinations and arrangements embedded in convolutional neurons. We applied NeuronMotif on several widely-used DCNN models. Many uncovered motif grammars of deep convolutional neurons are supported by literature or ATAC-seq footprinting. We further diagnosed the sick neurons that are sensitive to adversarial noises, which can guide DCNN architecture optimization for better prediction performance and motif feature extraction. Overall, NeuronMotif enables decoding cis-regulatory codes from deep convolutional neurons and understanding DCNN from a novel perspective.

bioinformatics↗

bioRxiv2021

Machine learning reveals cryptic dialects that guide mate choice in a songbird

Culturally transmitted communication signals - such as human language or bird song - can change over time through a process of cultural drift, and may consequently enhance the separation of populations, potentially leading to reproductive isolation1-4. Local song dialects have been identified in bird species with relatively simple songs where individuals show high cultural conformity5-10. In contrast, the emergence of cultural dialects has been regarded as unlikely11-13 for species with more variable song, such as the zebra finch (Taeniopygia guttata). Instead, it has been proposed that selection for individual recognition and distinctiveness may lead to a complete spread across the space of acoustic and syntactical possibilities11-15. However, another possibility is that analytical limitations have meant that subtle but possibly salient group differences have not yet been discovered in such species. Here we show that machine learning can distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that these cryptic song dialects drive strong assortative mating in this species. We studied mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. Cross-fostering eggs within and between these populations and quantifying social interactions of the resulting offspring later in life revealed that mate choice primarily targets cultural traits that are transmitted during a short developmental time window. Detailed social networks showed that females preferentially approached males whose song resembled that of their adolescent peers. Our study shows that birds can be surprisingly sensitive to cultural traits for mating that have hitherto remained cryptic, even in this well-studied species that is used as a model for song-learning13,14,16-28.

animal behavior and cognition↗

bioRxiv2021

{-}Multivariable Association Discovery in Population-scale Meta-omics Studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

microbiology↗

bioRxiv2020

BMSCs differentiated into neurons, astrocytes and oligodendrocytesalleviatedthe inflammation and demyelination of EAE mice models

Multiple sclerosis (MS) is a complex, progressive neuroinflammatory disease associated with autoimmunity. Currently, effective therapeutic strategy was poorly found in MS. Experimental autoimmune encephalomyelitis (EAE) is widely used to study the pathogenesis of MS. Previous studies have shown that bone marrow mesenchymal stem Cells (BMSCs) transplantation could treat EAE animal models, but the mechanism was divergent. Here, we systematically evaluated whether BMSCs can differentiate into neurons, astrocytes and oligodendrocytes to alleviate the symptoms of EAE mice. We used Immunofluorescence staining to detect MAP-2 neurons marker, GFAP astrocytes marker, and MBP oligodendrocytes marker expression to evaluate whether BMSCs can differentiate. The effect of BMSCs transplantation on inflammatory cell invasion and demyelination in EAE mice were detected by Hematoxylin-Eosin (H&E) and Luxol Fast Blue (LFB) staining. Inflammatory factors expression was detected by ELISA and RT-qPCR. Our results showed that BMSCs could be induced to differentiate into neuron cells, astrocytes and oligodendrocyte in vivo and in vitro. In addition, BMSCs transplant improved the survival rate and weight, and reduced neurological function scores and disease incidence of EAE mice. Moreover, BMSCs transplant alleviated the inflammation and demyelination of EAE mice. Finally, we found that BMSCs transplantation down-regulated the expression levels of pro-inflammatory factors TNF-, IL-1{beta} and IFN-{gamma}, and up-regulated the expression levels of anti-inflammatory factors IL-10 and TGF-{beta}. In conclusion, this study found that BMSCs could alleviate the inflammatory response and demyelination in EAE mice, which may be achieved by the differentiation of BMSCs into neurons, astrocytes and oligodendrocytes in EAE mice.

neuroscience↗

bioRxiv2020

Evolution of Natural Lifespan Variation and Molecular Strategies of Extended Lifespan

To understand the genetic basis and selective forces acting on longevity, it is useful to examine lifespan variation among closely related species, or ecologically diverse isolates of the same species, within a controlled environment. In particular, this approach may lead to understanding mechanisms underlying natural variation in lifespan. Here, we analyzed 76 ecologically diverse wild yeast isolates and discovered a wide diversity of replicative lifespan. Phylogenetic analyses pointed to genes and environmental factors that strongly interact to modulate the observed aging patterns. We then identified genetic networks causally associated with natural variation in replicative lifespan across wild yeast isolates, as well as genes, metabolites and pathways, many of which have never been associated with yeast lifespan in laboratory settings. In addition, a combined analysis of lifespan-associated metabolic and transcriptomic changes revealed unique adaptations to interconnected amino acid biosynthesis, glutamate metabolism and mitochondrial function in long-lived strains. Overall, our multi-omic and lifespan analyses across diverse isolates of the same species shows how gene-environment interactions shape cellular processes involved in phenotypic variation such as lifespan.

genetics↗

bioRxiv2020

PIEZO1-induced cellular retraction controls keratinocyte migration in wound healing

Keratinocytes, the predominant cell type of the epidermis, migrate to reinstate the epithelial barrier during wound healing. Mechanical cues are known to regulate keratinocyte re-epithelization and wound healing however, the underlying molecular transducers and biophysical mechanisms remain elusive. Here, we show through molecular, cellular and organismal studies that the mechanically-activated ion channel PIEZO1 regulates keratinocyte migration and wound healing. Epidermal-specific Piezo1 knockout mice exhibited faster wound closure while gain-of-function mice displayed slower wound closure compared to littermate controls. By imaging the spatiotemporal localization dynamics of endogenous PIEZO1 channels we find that channel enrichment in sub-cellular regions induces a localized cellular retraction that slows keratinocyte migration. Our findings suggest a potential pharmacological target for wound treatment. More broadly, we show that nanoscale spatiotemporal dynamics of Piezo1 channels can control tissue-scale events, a finding with implications beyond wound healing to processes as diverse as development, homeostasis, disease and repair.

cell biology↗

bioRxiv2020

MiRNA-223 Inhibits MouseGallstone Formation by Targeting Key Transporters in Hepatobiliary Cholesterol Secretion Pathway

BackgroudMiRNA-223 has previously been reported to play an essential role in hepatic cholesterol homeostasis by suppressing cholesterol synthesis, attenuating cholesterol uptake by hepatocyte from and promoting cholesterol efflux into the blood. However, its role in regulation of biliary cholesterol secretion and gallstone formation remains unknown. MethodsMice with conventional knockout (KO), hepatocyte-specific knockout ({Delta}Hepa) / knockdown (KD) or gain expression of miRNA-223 were included in the study and were subjected to lithogenic diet (LD) for various weeks. The gallbladders were harvested and subjected to cholesterol crystal imaging and gallstone mass measurement. Liver tissues were collected for western blotting, RT-qPCR, and immunohistochemistry staining. Levels of cholesterol, bile salt, phospholipids, and triglyceride were determined in serum, liver tissues, and bile by enzyme color reactive assays. 3 UTR reporter gene assays were used to verify the direct target genes for miRNA-223. ResultsLD-induced gallstone formation was remarkably accelerated in miRNA-223 KO, {Delta}Hepa, and KD mice with concurrent enhancement in total cholesterol levels in liver tissue and bile. Key biliary cholesterol transporters ABCG5 and ABCG8 were identified as direct targets of miRNA-223. Reversely, AAV-mediated hepatocyte-specific miRNA-223 overexpression prevented gallstone progression with reduced targets protein expression. ConclusionThe present study demonstrates a novel role of miRNA-223 in the regulation of hepatic bile cholesterol secretion pathway and gallstone formation by targeting ABCG5 and ABCG8 expression. Therefore, elevating miRNA-223 would be a potentially novel approach to overcome the sternness of cholesterol gallstone disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/344580v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1c418bforg.highwire.dtl.DTLVardef@134e20dorg.highwire.dtl.DTLVardef@e4e0d2org.highwire.dtl.DTLVardef@641b40_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2020

Exploring the pH-dependent structure-dynamics function relationship of human renin

Renin is a pepsin-like aspartyl protease and an important drug target for the treatment of hypertension; despite three decades research, its pH-dependent structure-function relationship remains poorly understood. Here we employed the continuous constant pH molecular dynamics (CpHMD) simulations to decipher the acid/base roles of renins catalytic dyad and the conformational dynamics of the flap, which is a common structural feature among aspartyl proteases. The calculated pKas suggest that the catalytic Asp38 and Asp226 serve as the general base and acid, respectively, in agreement with experiment and supporting the hypothesis that renins neutral optimum pH is due to the substrate-induced pKa shifts of the aspartic dyad. The CpHMD data confirmed our previous hypothesis that hydrogen bond formation is the major determinant of the dyad pKa order. Additionally, our simulations showed that renins flap remains open regardless of pH, although a Tyr-inhibited state is occasionally formed above pH 5. These findings are discussed in comparison to the related aspartyl proteases, including {beta}-secretases 1 and 2, capthepsin D, and plasmepsin II. Our work represents a first step towards a systematic understanding of the pH-dependent structure-dynamics-function relationships of pepsin-like aspartyl proteases that play important roles in biology and human disease states.

biophysics↗

bioRxiv2020

RNA adenosine deaminase ADAR2 modulates T helper 17 cell effector function

ADAR1 and ADAR2 catalyze adenosine-to-inosine (A-to-I) editing, the most common post-transcriptional modification in RNA. While ADAR1 is ubiquitously expressed and plays a critical role in preventing activation of the host immune system, ADAR2 exhibits tissue-specific and inducible expression patterns, and its function in the immune system is not known. Here, we identify an intragenic super-enhancer involved in the dramatic induction of ADAR2 when naive helper T cells differentiate toward the Th17 lineage. By editing the inverted repeat sequences at the 3 untranslated region (UTR) of Malt1, which encodes a component of the NF-{kappa}B activation complex, ADAR2 promotes MALT1 expression and Th17 effector function. Interference with the ADAR2-MALT1 pathway dampens the production of Th17 cytokines and promotes T cell-mediated colitis. This study expands our understanding of RNA editing in adaptive immunity and identifies the ADAR2-MALT1-IL-17A axis as a potential therapeutic target for inflammatory conditions in the intestine.

immunology↗

bioRxiv2020

PAX8 orchestrates an angiogenic program through interaction with SOX17

Worldwide, the number of new ovarian cancer cases approaches 300,000 with more than 180,000 deaths every year. The low survival-rate reflects the limitations of current therapies and highlights the importance of identifying new therapeutic targets. Despite significant recent efforts to identify novel vulnerabilities in ovarian cancer, none have led to effective durable therapies with improvement in overall survival. PAX8, a lineage-transcription factor, whose expression is a major molecular feature of ovarian carcinomas, represents a novel therapeutic target. Herein, we have identified SOX17 as a bona fide PAX8-interacting partner and elucidated the impact of this interaction on the development of ovarian cancer. Importantly, we found that PAX8 and SOX17 regulate tumor angiogenesis in vitro and in vivo. The role of PAX8 and SOX17 in the regulation of angiogenesis reveals a novel function for these factors in regulating the tumor microenvironment and highlight this pathway as a viable therapeutic target.

cancer biology↗

bioRxiv2020

Population Structure Discovery in Meta-Analyzed Microbial Communities and Inflammatory Bowel Disease

Microbial community studies in general, and of the human microbiome in inflammatory bowel disease (IBD) in particular, have now achieved a scale at which it is practical to associate features of the microbiome with environmental exposures and health outcomes across multiple large-scale populations. This permits the development of rigorous meta-analysis methods, of particular importance in IBD as a means by which the heterogeneity of disease etiology and treatment response might be explained. We have thus developed MMUPHin (Meta-analysis Methods with a Uniform Pipeline for Heterogeneity in microbiome studies) for joint normalization, meta-analysis, and population structure discovery using microbial community taxonomic and functional profiles. Applying this method to ten IBD cohorts (5,151 total samples), we identified a single consistent axis of microbial associations among studies, including newly associated taxa such as Acinetobacter and Turicibacter detected due to the sensitivity of meta-analysis. Linear random effects models further revealed associations with medications, disease location, and interaction effects consistent within and between studies. Finally, multiple unsupervised clustering metrics and dissimilarity measures agreed on a lack of discrete microbiome "types" in the IBD gut microbiome. These results thus provide a benchmark for consistent characterization of the IBD gut microbiome and a general framework applicable to meta-analysis of any microbial community types.

genomics↗

bioRxiv2020

Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organs common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7-10. Conversely, Spatial Transcriptomics allows for in-situ measurements11-13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1-3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with [~]30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.

genomics↗