Biology subjects

Tang, J.

Publications and source records attributed to Tang, J..

At least 253 records · Page 14Linked to original sources

bioRxiv2019

Induction of long-term allogeneic cell acceptance and formation of immune privileged tissue in immunocompetent Hosts

A vast number of diseases could be treated with therapeutic cells derived from pluripotent stem cells (PSCs). However, cell products that come from non-autologous sources can be immune rejected by the recipients immune system. Here, we show that forced expression of eight immunomodulatory transgenes, including Ccl21, Pdl1, Fasl, Serpinb9, H2-M3, Cd47, Cd200, and Mfge8, allows mouse embryonic stem cells (mESCs) and their derivatives to escape immune rejection in fully immunocompetent, allogeneic recipients. Despite no genetic alterations to major histocompatibility complex (MHC) genes, immune-modified C57BL/6 mESCs could generate long-term, allogeneic tissues in inbred FVB/N, C3H, and BALB/c, as well as outbred CD-1 recipients. Due to the tandem incorporation of our safe-cell suicide system, which allows tight and drug-inducible control over proliferation in vivo, these allotolerated cells can generate safe and dormant ectopic tissues in the host. We show that these ectopic tissues maintain high expression of all eight immunomodulatory transgenes and are immune-privileged sites that can host and protect unmodified mouse and human cells from rejection in allogeneic and xenogeneic settings, respectively. If translated to human clinical settings, we envision the development of a single pluripotent cell line that can be used to generate allo-tolerated, off-the-shelf cell products to serve all humankind, as well as immune-privileged ectopic tissues to host and immune-protect any kind of therapeutic cell product.

bioengineering↗

bioRxiv2019

Predicting Meridian in Chinese Traditional Medicine Using Machine Learning Approaches

Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.\n\nAuthor SummaryIn East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.

pharmacology and toxicology↗

bioRxiv2019

A Single-Cell Immune Atlas of Triple Negative Breast Cancer Reveals Novel Immune Cell Subsets

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, which recently attracts great interest for immune therapeutic development. In this context, in-depth understanding of TNBC immune landscape is highly demanded. Here we report single-cell RNA sequencing results of 9683 tumor-infiltrated immune cells isolated from 14 treatment naive TNBC tumors, where 22 immune cell subsets, including T cells, macrophages, B cells, and DCs have been characterized. We identify a new T cell subset, CD8+CXCL8+ naive T cell, which associates with poor survival. A novel immune cell subset comprised of TCR+ macrophages, is found to be widely distributed in TNBC tumors. Further analyses reveal an up-regulation of molecules associated with TCR signaling and cytotoxicity in these immune cells, indicating TCR signaling activation. Altogether, our study provides a valuable resource to understand the immune ecosystem of TNBC. The novel immune cell subsets reported herein might be functionally important in cancer immunity.\n\nSIGNIFICANCEThis work demonstrates a single-cell transcriptome atlas of immune cells in treatment naive TNBC tumors, revealing novel immune cell subsets. This study provides a valuable resource to understand the immune ecosystem of TNBC, which will be helpful for the immunotherapeutic strategy design of TNBC.

immunology↗

bioRxiv2019

Functional ultrasound speckle decorrelation-based velocimetry of the brain

A high-speed, contrast free, quantitative ultrasound velocimetry (vUS) for blood flow velocity imaging throughout the rodent brain is developed based on the normalized first order temporal autocorrelation function of the ultrasound field signal. vUS is able to quantify blood flow velocity in both transverse and axial directions, and is validated with numerical simulation, phantom experiments, and in vivo measurements. The functional imaging ability of vUS is demonstrated by monitoring blood flow velocity changes during whisker stimulation in awake mice. Compared to existing power Doppler and color Doppler-based functional ultrasound imaging techniques, vUS shows quantitative accuracy in estimating both axial and transverse flow speeds and resistance to acoustic attenuation and high frequency noise.

bioengineering↗

bioRxiv2019

Serum-free culture system for spontaneous human mesenchymal stem cell spheroids formation

Human mesenchymal stem cells (hMSCs) are widely used in clinical research because of their multipotential, immunomodulatory, and reparative properties. Previous studies determined that hMSC spheroids from three-dimensional (3D) culture possess higher therapeutic efficacy than conventional hMSCs from monolayer (2D) culture. To date, various 3D culture methods have been developed to form hMSC spheroids, but most of them used culture medium containing fetal bovine serum (FBS), which is not suitable for further clinical use. Here, we demonstrate that dissociated single MSCs seeded in induced pluripotent stems medium (MiPS), adhere loosely to the dish and spontaneously migrate to form spheroids during day 3 to day 6. Through component deletion screening and complementation experiments, the knockout serum replacement (KSR) was identified as necessary and sufficient for hMSC spheroid formation. Transcriptome analysis showed that the overall expression profiles were highly similar between 2D culture with FBS and KSR derived spheroids. Interestingly, genes related to inflammatory response, immune response, and angiogenesis were up-regulated in spheroids at day 6, and qPCR results further validated the increased expression level of related genes, including STC1, CCL7, HGF, IL24, and TGFB3. When spheroids were re-plated in normal FBS medium, cells formed a typical spindle-shaped morphology, and FACS results showed that the recovered cells retained MSC-specific surface markers, such as CD73, CD90, and CD105. In summary, we developed a practical and convenient method to generate hMSC spheroids for clinical research and therapy.

cell biology↗

bioRxiv2019

Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony?

Studying relationships among gene-products by gene expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed correlation of transcript and protein expression levels. All these efforts partook in the current understanding of signaling network models and expanded the signaling databases. In fact, due to the unavailability or high-cost of the experiments, most of the studies do not usually look for direct interactions, and some parts of these networks are contradictory. Besides, it is now a standard step to accomplish enrichment analysis on biological annotations, to make claims about the potentially implicated biological pathways in any perturbation. Explicitly, upon identifying differentially expressed genes, they are spontaneously presumed the corresponding dysregulated pathways. Then, molecular mechanistic insights are proposed for disease etiology and drug discovery based on statistically enriched biological processes. In this study, using four common and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level and the causal relationships among the gene pairs. We illustrated that the signaling network was not more consistent or coherent with the recorded expression profile compared to the random relationships. Finally, we provided the pieces of evidence and concluded that gene-product expression data, especially at the transcript level, are not reliable or at least insufficient to infer causal biological relationships among genes and in turn, describe cellular behavior.

systems biology↗

bioRxiv2019

The maternal-fetal interface of successful pregnancies and impact of fetal sex using single cell sequencing

The first trimester is a critical window of maternal-fetal communication for pregnancy. Therefore, we characterized crosstalk in ongoing human pregnancies at 11-13 weeks gestation. RNA-sequencing of matched maternal decidua and placenta identified 818 receptors and 3502 ligands, including 126 differentially expressed receptor-ligand pairs. Using single cell RNA-sequencing to further dissect placenta heterogeneity, we identified five major cell types (trophoblasts, stromal cells, hofbauer cells, antigen presenting cells and endothelial cells) with unique crosstalk at the maternal-fetal interface. We identified seven unique trophoblast subclusters, including new subtypes that transition into the terminal cell types, extra-villous trophoblasts and syncytiotrophoblasts. As fetal sex impacts pregnancy, we analyzed sex differences in each cell type and identified differences in immune cell function. TGF{beta}1, {beta}-estradiol, and dihydrotestosterone emerge as upstream regulators of sexually dimorphic genes in a cell type specific manner. Thus, the fetal contribution at the maternal-fetal interface is cell and sex specific.

developmental biology↗

bioRxiv2019

Dynamic Laser Speckle Imaging

Utilizing a high-speed camera and recording back-scattered laser light at more than 20,000 frames per second, we introduce the first wide-field dynamic laser speckle imaging (DLSI) in which we are able to quantify the laser speckleintensity temporal auto-correlation function g2({tau}) for every pixel individually to obtain a quantitative image of the dynamics of the light scattering particles in the sample. The ability to directly and quantitatively measure the intensity auto-correlation function allows us to solve the problem of how to quantitatively interpret data measured by laser speckle contrast imaging (LSCI), multi-exposure laser speckle imaging (MESI) and laser Doppler flowmetry (LDF). The intensity auto-correlation function is related to the field temporal auto-correlation function g1({tau}), which has been quantitatively related to the dynamics of the light scattering particles including flowing red blood cells. The form of g1({tau}) depends on the amount of light scattering (i.e. single or multiple scattering) and the type of particle motion (i.e. ordered or unordered). Although these forms of the field correlation functions have been established for over 30 years, there is no agreement nor experimental support on what scattering and motion regimes are relevant for the varied biomedical applications. We thus apply DLSI to image cerebral blood flow in mouse through a cranial window and show that the generally accepted form of g1({tau}), is applicable only to visible surface vessels of a specific size (20 - 200m). We demonstrate that for flow in smaller vessels and in parenchymal regions that the proper g1({tau}) form corresponds with multiple scattering light and unordered motion which was never considered to be relevant for these techniques. We show that the wrong assumption for the field auto-correlation model results in a severe underestimation of flow changes when measuring blood flow changes during ischemic stroke. Finally, we describe how DLSI can be integrated with other laser speckle methods to guide model selection, or how it can be used by itself as a quantitative blood flow imaging technique.

bioengineering↗

bioRxiv2019

Stxbp1/Munc18-1 haploinsufficiency in mice recapitulates key features of STXBP1 encephalopathy and impairs cortical inhibition

Mutations in genes encoding synaptic proteins cause many neurodevelopmental disorders, but the underlying pathogeneses are poorly understood. Syntaxin-binding protein 1 (STXBP1) is an essential component of the neurotransmitter release machinery. Its de novo heterozygous mutations are among the most frequent causes of neurodevelopmental disorders including intellectual disabilities and epilepsies. These disorders, collectively referred to as STXBP1 encephalopathy, affect a broad spectrum of neurological and neuropsychiatric features common among neurodevelopmental disorders. To gain insight into STXBP1 encephalopathy pathogenesis, we generated new Stxbp1 null alleles in mice and found that Stxbp1 haploinsufficiency impaired cognitive, psychiatric, and motor functions and caused cortical hyperexcitability and seizures. Surprisingly, Stxbp1 haploinsufficiency reduced neurotransmission from cortical parvalbumin- and somatostatin-expressing GABAergic interneurons by differentially decreasing the synaptic strength and connectivity, respectively. These results demonstrate that Stxbp1 haploinsufficient mice recapitulate key features of STXBP1 encephalopathy and indicate that inhibitory dysfunction is likely a key contributor to the disease pathogenesis.

neuroscience↗

bioRxiv2019

Spontaneous isomerization of long-lived proteins provides a molecular mechanism for the lysosomal failure observed in Alzheimer’s disease

Proteinaceous aggregation is a well-known observable in Alzheimers disease (AD), but failure and storage of lysosomal bodies within neurons is equally ubiquitous and actually precedes bulk accumulation of extracellular amyloid plaque. In fact, AD shares many similarities with certain lysosomal storage disorders though establishing a biochemical connection has proven difficult. Herein, we demonstrate that isomerization and epimerization, which are spontaneous chemical modifications that occur in long-lived proteins, prevent digestion by the proteases in the lysosome (namely the cathepsins). For example, isomerization of aspartic acid into L-isoAsp prevents digestion of the N-terminal portion of A{beta} by cathepsin L, one of the most aggressive lysosomal proteases. Similar results were obtained after examination of various target peptides with a full series of cathepsins, including endo-, amino-, and carboxy-peptidases. In all cases peptide fragments too long for transporter recognition or release from the lysosome persisted after treatment, providing a mechanism for eventual lysosomal storage and bridging the gap between AD and lysosomal storage disorders. Additional experiments with microglial cells confirmed that isomerization disrupts proteolysis in active lysosomes. These results are easily rationalized in terms of protease active sites, which are engineered to precisely orient the peptide backbone and cannot accommodate the backbone shift caused by isoaspartic acid or side chain dislocation resulting from epimerization. Although A{beta} is known to be isomerized and epimerized in plaques present in AD brains, we further establish that the rates of modification for aspartic acid in positions 1 and 7 are fast and could accrue prior to plaque formation. Spontaneous chemistry can therefore provide modified substrates capable of inducing gradual lysosomal failure, which may play an important role in the cascade of events leading to the disrupted proteostasis, amyloid formation, and tauopathies associated with AD.

biochemistry↗

bioRxiv2019

Unbiased metagenomic sequencing for pediatric meningitis in Bangladesh reveals neuroinvasive Chikungunya virus outbreak and other unrealized pathogens

The disease burden due to meningitis in low and middle-income countries remains significant and failure to determine an etiology impedes appropriate treatment for patients and evidence-based policy decisions for populations. Broad-range pathogen surveillance using metagenomic next-generation sequencing (mNGS) of RNA isolated from cerebral spinal fluid (CSF) provides an unbiased assessment for possible infectious etiologies. In this study, our objective was to use mNGS to identify etiologies of pediatric meningitis in Bangladesh.\n\nWe conducted a retrospective case-control mNGS study on CSF from patients with known neurologic infections (n=36), idiopathic meningitis (n=25), without infection (n=30) and six environmental samples collected between 2012-2018. Using an open-access, cloud-based bioinformatics pipeline (IDseq) and machine learning, we identified potential pathogens which were confirmed through qPCR and Sanger sequencing. These cases were followed-up through phone/home-visits. The CSF samples were collected from children with WHO-defined meningeal signs during prospective meningitis surveillance at the largest pediatric referral hospital in Bangladesh.\n\nThe 91 participants (42% female) ranged in age from 0-160 months (median: 9 months). In samples with known infectious causes of meningitis and without infections (n=66), there was 83% concordance between mNGS and conventional testing. In idiopathic cases (n=25), mNGS identified a potential etiology in 40% (n=10), including bacterial and viral pathogens. There were three instances of neuroinvasive Chikungunya virus (CHIKV). The CHIKV genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause systemic illness in 2017. CHIKV qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital (n=472) revealed 17 additional CHIKV meningitis cases. Orthogonal molecular confirmation of each mNGS-identified infection, case-based clinical data, and follow-up of patients substantiated the key findings.\n\nUsing mNGS, we obtained a microbiological diagnosis for 40% of idiopathic meningitis cases and identified a previous unappreciated pediatric CHIKV meningitis outbreak. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis and facilitate informed policy decisions.

genomics↗

bioRxiv2019

Manure substitution of chemical fertilizer affect soil microbial community diversity, structure and function in greenhouse vegetable production systems

Soil microbial community and enzyme activities together affect various ecosystem functions of soils. Fertilization, as important agricultural management practices, are known to modify soil microbial characteristics; however, inconsistent results have been reported. The aim of this research therefore was to make a comparative study of the effects of different fertilization patterns (No N inputs (No N), 100% chemical fertilizer-N (CN) inputs (4/4CN) and different substitution rates of CN by organic manure-N (MN) (3/4CN+1/4MN, 2/4CN+2/4MN and 1/4CN+3/4MN)) on soil physicochemical properties, enzyme activities and microbial attributes in a GVP of Tianjin, China. Manure substitution of chemical fertilizer, especially at higher substitution rate (2/4CN+2/4MN and 1/4CN+3/4MN), improved soil physicochemical properties (higher soil organic C (SOC) and nutrient contents; lower bulk densities), promoted microbial growth (higher total phospholipid fatty acids and microbial biomass C contents) and activity (higher soil hydrolase activities). Manure addition caused a remarkable increase of the fungi/bacteria ratio and a distinct shift in the fungal (bacterial) community to greater abundance of arbuscular mycorrhizal fungi (G+ bacteria) compared with saprotrophic fungi (G- bacteria). These changes drove shifts toward fungal-dominated soil microbial communities and then optimized microbial community structure. Also, manure application increased soil biodiversity (microbial community and enzyme function), indicated by increased Shannon-Wiener diversity. Redundancy analysis indicated that the most possible mechanism of the impacts of different fertilization patterns on soil microbial characteristics may be the mediation of SOC and nutrient (N) availability (especially SOC) in this GVP of China. In conclusion, manure substitution of chemical fertilizer, especially at higher substitution rate, was more efficient for improving soil quality and biological functions.

microbiology↗

bioRxiv2019

DrugComb - an integrative cancer drug combination data portal

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users own drug combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

pharmacology and toxicology↗

bioRxiv2019

Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

High-throughput drug sensitivity screening has been utilized for facilitating the discovery of drug combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of drug combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a drug pair. We developed a drug combination sensitivity score (CSS) to summarize the drug combination dose-response curves. Using a high-throughput drug combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic drug combinations. The cross drug combination design coupled with the CSS scoring facilitated the evaluation of drug combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.\n\nAuthor summaryBeing a complex disease, cancer is one of the main death causes worldwide. Although new treatment strategies have been achieved with cancers, they still have limited efficacy. Even when there is an initial treatment response, cancer cells can develop drug resistance thus cause disease recurrence. To achieve more effective and safe therapies to treat cancer, patients critically need multi-targeted drug combinations that will kill cancer cells at reduced dosages and thereby avoid side effects that are often associated with the standard treatment. However, the increasing number of possible drug combinations makes a pure experimental approach unfeasible, even with automated drug screening instruments. Therefore, we have proposed a new experimental set up to get the drug combination sensitivity data cost-efficiently and developed a score to quantify the efficiency of the drug combination, called drug combination sensitivity score (CSS). Using public datasets, we have shown that the CSS robustness and its highly predictive nature with an accuracy comparable to the experimental replicates. We have also defined a CSS-based synergy score as a metric of drug interaction and justified its relevance. Thus, we expect the proposed computational techniques to be easily applicable and beneficial in the field of drug combination discovery.

pharmacology and toxicology↗

bioRxiv2018

Optimization of DNA extraction from human urinary samples for mycobiome community profiling

IntroductionRecent data suggest the urinary tract hosts a microbial community of varying composition, even in the absence of infection. Culture-independent methodologies, such as next-generation sequencing of conserved ribosomal DNA sequences, provide an expansive look at these communities, identifying both common commensals and fastidious organisms. A fundamental challenge has been the isolation of DNA representative of the entire resident microbial community, including fungi.\n\nMaterials and MethodsWe evaluated multiple modifications of commonly-used DNA extraction procedures using standardized male and female urine samples, comparing resulting overall, fungal and bacterial DNA yields by quantitative PCR. After identifying protocol modifications that increased DNA yields (lyticase/lysozyme digestion, bead beating, boil/freeze cycles, proteinase K treatment, and carrier DNA use), all modifications were combined for systematic confirmation of optimal protocol conditions. This optimized protocol was tested against commercially available methodologies to compare overall and microbial DNA yields, community representation and diversity by next-generation sequencing (NGS).\n\nResultsOverall and fungal-specific DNA yields from standardized urine samples demonstrated that microbial abundances differed significantly among the eight methods used. Methodologies that included multiple disruption steps, including enzymatic, mechanical, and thermal disruption and proteinase digestion, particularly in combination with small volume processing and pooling steps, provided more comprehensive representation of the range of bacterial and fungal species. Concentration of larger volume urine specimens at low speed centrifugation proved highly effective, increasing resulting DNA levels and providing greater microbial representation and diversity.\n\nConclusionsAlterations in the methodology of urine storage, preparation, and DNA processing improve microbial community profiling using culture-independent sequencing methods. Our optimized protocol for DNA extraction from urine samples provided improved fungal community representation. Use of this technique resulted in equivalent representation of the bacterial populations as well, making this a useful technique for the concurrent evaluation of bacterial and fungal populations by NGS.

microbiology↗

bioRxiv2018

Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers

The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450K BeadChip (Illumina 450K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in the majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, TRIM59, showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets.

bioinformatics↗

bioRxiv2018

Seedling leaves allocate lower fractions of nitrogen to photosynthetic apparatus in nitrogen fixing trees (Dalbergia odorifera and Erythrophleum fordii) than in non-nitrogen fixing trees (Betula alnoides and Castanopsis hystrix) in subtropical China

Photosynthetic-nitrogen use efficiency (PNUE) is a useful trait to characterize leaf economics, physiology, and strategy. In this study, we investigated the differences in PNUE, leaf nitrogen (N) allocation, and mesophyll conductance (gm) in Dalbergia odorifera and Erythrophleum fordii (N-fixing trees), and Betula alnoides and Castanopsis hystrix (non-N-fixing trees). Seedlings of the four species were cultured in pots and received the same nutrient solution, water volume, and light. LiCor-6400 was used to determine fluorescence yield, photosynthetic response to light, and intercellular CO2 concentration (Ci). N allocation fractions in the photosynthetic apparatus were calculated according to Niinemets and Tenhunen method; gm was calculated according to variable J, EDO, and A-Ci curve fitting methods. PNUE of D. odorifera and E. fordii were significantly lower than those of B. alnoides and C. hystrix because of their allocation of a lower fraction of leaf N to Rubisco (PR) and bioenergetics (PB). Mesophyll conductance had a significant positive correlation with PNUE in D. odorifera, E. fordii, and B. alnoides. The fraction of leaf N to cell wall (PCW) had a significant negative correlation with PR in B. alnoides and C. hystrix. We conclude that B. alnoides and C. hystrix optimized their leaf N allocation toward photosynthesis, with the trade-off being N allocation to the cell wall and Rubisco. Thus, these two species may have a higher competitive ability in natural ecosystems with fertile soil.

ecology↗

bioRxiv2018

Re-expression of SynGAP Protein in Adulthood Improves Translatable Measures of Brain Function and Behavior in a Model of Neurodevelopmental Disorders

BackgroundNeurodevelopmental disorder (NDD) risk genes have pleiotropic biological functions, such as control over both developmental and non-developmental processes that influence disease-related phenotypes. Currently, it remains unclear how developmental versus non-developmental processes influence the duration and/or effectiveness of permissive treatment windows for NDDs. SYNGAP1 haploinsufficiency causes an NDD defined by autistic traits, cognitive impairment, and epilepsy. Syngap1 heterozygosity in mice disrupts a developmental critical period, and, consistent with this, certain behavioral abnormalities are resistant to gene therapy initiated in adulthood. However, the Syngap1 endophenotype is extensive and this protein has diverse cell biological functions. Therefore, SynGAP pleiotropy may influence the permissive treatment window for previously untested disease-relevant phenotypes.\n\nMethodsA whole-body gene restoration technique was used to determine how restoration of SynGAP protein in adult heterozygous mice impacted previously untested phenotypes, such as memory, seizure susceptibility, systems-level cortical hyperexcitability, and hippocampal oscillations linked to mnemonic processes.\n\nResultsAdult restoration of SynGAP protein in haploinsufficient mice reversed long-term contextual memory deficits and behavioral measures of seizure susceptibility. Moreover, SynGAP re-expression in adult mice eliminated brain state-dependent, patient-linked paroxysmal interictal spiking and increased the amplitude of hippocampal theta oscillations.\n\nConclusionsSynGAP protein in the mature brain dynamically regulates neural circuit function and influences disease-relevant phenotypes. The impact of these findings is that treatments targeting certain debilitating aspects of SYNGAP1-related disorders may be effective throughout life. Moreover, the efficacy of experimental treatments for SYNGAP1 patients may be quantifiable through changes in species-conserved, state-dependent pathological electroencephalogram signals.

neuroscience↗