Biology subjects

Song, Y.

Publications and source records attributed to Song, Y..

At least 343 records · Page 19Linked to original sources

bioRxiv2020

Machine Learning-Based Protein Microarray Digital Assay Analysis

Serial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, we developed a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay achieved 14-plexed biomarker detection at concentrations < 10pg/mL with a sample volume < 10 L, including all processes from sampling to analyzed data delivery within 30 min, while only requiring a 5-min assay incubation. The assay procedure applied both a spatial-spectral microfluidic encoding scheme and an image data analysis algorithm based on machine learning with a convolutional neural network (CNN) for pre-equilibrated single-molecule protein digital counting. This unique approach remarkably reduced errors facing the high-capacity multiplexing of digital immunoassay at low protein concentrations. Longitudinal data obtained for a panel of 14 serum cytokines in human patients receiving chimeric antigen receptor-T (CAR-T) cell therapy manifested the powerful biomarker profiling capability and great potential of the assay for its translation to near-real-time bedside immune status monitoring.

bioengineering↗

bioRxiv2020

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation

Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature. The significance of including third plane information for low-resolution volumetric images is investigated by comparing the performance of 3D texture descriptor with its respective pseudo 3D form that ignores the interslice intensity correlations. Following classification, changes in chromatin pattern are estimated by computing the ratio of heterochromatin and euchromatin corresponding to their respective intensities and image gradient obtained by 3D SRP. The proposed method is evaluated on two publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines in two phenotypic states obtained from the public Statistics Online Computational Resource. Experimental results show that 3D SRP and 3D Local Binary Pattern provide better results than other utilised handcrafted descriptors and deep learning features extracted using a pre-trained model. The results also show the advantage of utilising 3D feature descriptor for classification over its corresponding pseudo version. In addition, the proposed method validates that as the cell passes to another phenotypic state, there is a change in intensity and aggregation of heterochromatin. Author SummaryAutomated classification and measurement of cellular phenotypic traits can significantly impact clinical decision making. Early detection of diseases requires an accurate description of low-level cellular features to detect small-scale abnormalities in the few abnormal cells in the tissue microenvironment. The challenge is the development of a computational approach for 3D textural feature description that can capture the heterogeneous information in multiple dimensions and characterise the cells in their ultimate and intermediate phenotypic states effectively. Our work has proposed the method and metrics to measure chromatin condensation pattern and classify the phenotypic states. Experimental evaluation on the 3D image set of human fibroblast and human prostate cancer cell collections validates the proposed method for the classification of cell states. Results also signify the credibility of proposed metrics to characterise the cellular phenotypic states and contributes to studies related to early diagnosis, prognosis and drug resistance.

bioinformatics↗

bioRxiv2020

A Small interfering RNA lead targeting RNA-dependent RNA-polymerase effectively inhibit the SARS-CoV-2 infection in Golden Syrian hamster and Rhesus macaque

A small interfering RNA (siRNA) inhibitors have demonstrated the novel modality for suppressing infectious diseases. Sixty-one siRNA molecules, predicted by the bioinformatics programs, were screened for the possibility of treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an in vitro plaque assay. Among six siRNA leads with the efficacy of reducing plaque number, the siRNA targeting RNA-dependent RNA polymerase (RdRp) showed a reduction in SARS-CoV-2 infection-induced fever and virus titer in the Golden Syrian hamster and rhesus macaque. These results suggest the potential for RdRp targeting siRNA as a new treatment for the coronavirus disease 2019 (COVID-19).Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗

bioRxiv2020

Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation

ABSTRACTGenotype imputation is a fundamental step in genomic data analysis such as GWAS, where missing variant genotypes are predicted using the existing genotypes of nearby ‘tag’ variants. Imputation greatly decreases the genotyping cost and provides high-quality estimates of common variant genotypes. As population panels increase, e.g., the TOPMED Project, genotype imputation is becoming more accurate, but it requires high computational power. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. To address this problem, we developed the first fully secure genotype imputation by utilizing ultra-fast homomorphic encryption (HE) techniques that can evaluate millions of imputation models in seconds. In HE-based methods, the genotype data is end-to-end encrypted, i.e., encrypted in transit, at rest, and, most importantly, in analysis, and can be decrypted only by the data owner. We compared secure imputation with three other state-of-the-art non-secure methods under different settings. We found that HE-based methods provide full genetic data security with comparable or slightly lower accuracy. In addition, HE-based methods have time and memory requirements that are comparable and even lower than the non-secure methods. We provide five different implementations and workflows that make use of three cutting-edge HE schemes (BFV, CKKS, TFHE) developed by the top contestants of the iDASH19 Genome Privacy Challenge. Our results provide strong evidence that HE-based methods can practically perform resource-intensive computations for high throughput genetic data analysis. In addition, the publicly available codebases provide a reference for the development of secure genomic data analysis methods.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

Population Pharmacokinetics of an Anti-PD-1 Antibody Camrelizumab in Patients with Multiple tumor types and model informed dosing strategy

Objective Camrelizumab, a programmed cell death 1 (PD-1) inhibitor, has been approved for the treatment of relapsed or refractory classical Hodgkin lymphoma. The aim of this study was to perform a population pharmacokinetics (PK) analysis of camrelizumab to quantify the impact of patient characteristics on PK and to investigate the appropriateness of flat dose in the dosing regimen.Methods A total of 3298 camrelizumab concentrations from 133 patients from four studies were analyzed using nonlinear mixed effects modeling. Covariate model building was conducted using stepwise forward addition and backward elimination. Monte Carlo simulation was conducted to compare exposures of 200 mg and 3 mg/kg every 2-week regimens.Results The PK of camrelizumab were adequately described by a two-compartment model with parallel linear and nonlinear clearances. Baseline albumin had significant effects on linear clearance, and weight had effects on inter-compartmental clearance. Moreover, 200 mg and 3 mg/kg regimens provide similar exposure distributions with no advantage to either dosing approach.Conclusion Population PK analysis provided an integrated evaluation of the impact of albumin and weight on the PK of camrelizumab. It also provided evidence that neither the flat-dose nor the weight-based dose regimen was advantageous over the other for most patients with tumors.Competing Interest StatementThis study was sponsored by Jiangsu Hengrui Medicine Co. Ltd. Guang-li Ma, Da Xu and Yu-ya Wang are employees of Jiangsu Hengrui Medicine Co. Ltd.AbbreviationsAICAkaike’s information criterion;AUCarea under the concentration-time curve;AUCsssteady-state area under the concentration-time curve;BSVBetween-subject variability;Ccamrelizumab concentration;C1concentration of central compartment;Caverage,sssteady-state average concentration;CLclearance;CLlinearclearance of linear elimination;Cmax,sssteady-state peak concentration;CLnonlinearclearance of nonlinear elimination;Cmin,sssteady-state trough concentration;CWRESconditional weighted residuals;DVobserved concentration;IPREDindividual predicted concentrations;k0infusion rate;k23elimination rate from central compartment to peripheral compartment;k32elimination rate from peripheral compartment to central compartment;klinearlinear elimination rate;knonlinearnonlinear elimination rate;KmMichaelis–Menten constant;mAbmonoclonal antibody;NONMEMnonlinear mixed effect modeling;PD-1programmed cell death 1 receptor;PKpharmacokinetics;PREDpopulation predicted concentration;Qinter-compartmental clearance;Q2Wevery 2 weeks;Vmmaximum elimination rate;VPCvisual predictive check;V1distribution volume of central compartment;V2distribution volume of peripheral compartment;WBCwhite blood cell.View Full Text

cancer biology↗

bioRxiv2020

Implementation-independent representation for deep convolutional neural networks and humans in processing faces

Deep convolutional neural networks (DCNN) nowadays can match and even outperform human performance in challenging complex tasks. However, it remains unknown whether DCNNs achieve human-like performance through human-like processes; that is, do DCNNs use similar internal representations to achieve the task as humans? Here we applied a reverse-correlation method to reconstruct the internal representations when DCNNs and human observers classified genders of faces. We found that human observers and a DCNN pre-trained for face identification, VGG-Face, showed high similarity between their "classification images" in gender classification, suggesting similar critical information utilized in this task. Further analyses showed that the similarity of the representations was mainly observed at low spatial frequencies, which are critical for gender classification in human studies. Importantly, the prior task experience, which the VGG-Face was pre-trained for processing faces at the subordinate level (i.e., identification) as humans do, seemed necessary for such representational similarity, because AlexNet, a DCNN pre-trained to process objects at the basic level (i.e., categorization), succeeded in gender classification but relied on a completely different representation. In sum, although DCNNs and humans rely on different sets of hardware to process faces, they can use a similar representation, possibly from similar prior task experiences, to achieve the same computation goal. Therefore, our study provides the first empirical evidence supporting the hypothesis of implementation-independent representation.

neuroscience↗

bioRxiv2020

"3G" Trial: An RNA Editing Signature for Guiding Gastric Cancer Chemotherapy

Background & AimsGastric cancer (GC) cases are often diagnosed at an advanced stage with poor prognosis. Platinum-based chemotherapy has been internationally accepted as first-line therapy for inoperable or metastatic GC. To achieve greater benefits, it is critical to select patients who are eligible for the treatment. Albeit gene expression profiling has been widely used as a genomic classifier to identify molecular subtypes of GC and stratify patients for different chemotherapy regimens, the prediction accuracy remains to be improved. More recently, adenosine-to-inosine (A-to-I) RNA editing has emerged as a new player contributing to GC development and progression, offering potential clinical utility for diagnosis and treatment. MethodsWe conducted a transcriptome-wide RNA editing analysis of a cohort of 104 patients with advanced GC and identified an RNA editing (GCRE) signature to guide GC chemotherapy, using a systematic computational approach followed by both in vitro validations and in silico validations in TCGA. ResultsWe found that RNA editing events alone stand as a prognostic and predictive biomarker in advanced GC. We developed a GCRE score based on the GCRE signature consisting of 50 editing sites associated with 29 genes and achieved a high accuracy (84%) of predicting patient response to chemotherapy. Of note, patients demonstrating higher editing levels of this panel of sites present a better overall response. Consistently, GC cell lines with higher editing levels showed higher chemosensitivity. Applying the GCRE score on TCGA dataset confirmed that responders had significantly higher levels of editing in advanced GC. ConclusionsOverall, the GCRE signature reliably stratifies patients with advanced GC and predicts response from chemotherapy. SignificanceDespite the increasing documentation of RNA editing and its functional regulation, the translational potential of RNA editome in cancer remains largely under-investigated. This study reports for the first time an RNA editing signature in advanced GC, to reliably stratify patients with advanced disease to predict response from chemotherapy independently of gene expression profiling and other genomic and epigenetic changes. For this purpose, a bioinformatics approach was used to develop a GCRE score based on a panel of 50 editing sites from 29 unique genes (GCRE signature), followed by an experimental evaluation of their clinical utility as predictive biomarker in GC cell lines and in silico validation in using RNA sequencing (RNA-Seq) datasets from TCGA. The applied methodology provides a robust means of an RNA editing signature to be investigated in patients with advanced GC. Overall, this study provides insights into the translation of RNA editing process into predictive clinical applications to direct chemotherapy against GC.

cancer biology↗

bioRxiv2020

MCL1 dependence across MDS subtypes and dual inhibition of MCL1 and BCL2 in MISTRG6 mice

Treatment for myelodysplastic syndromes (MDS) remains insufficient due to clonal heterogeneity and clinical complexity. Dysregulation of apoptosis is observed across MDS subtypes regardless of mutations and represents an attractive therapeutic opportunity. Venetoclax (VEN), a selective inhibitor of anti-apoptotic protein B-cell lymphoma-2 (BCL2), has yielded impressive responses in older patients with acute myeloid leukemia (AML). BCL2 family anti-apoptotic proteins BCL-XL and induced myeloid cell leukemia 1 (MCL1) are implicated in leukemia survival, and upregulation of MCL1 is seen in VEN-resistant AML and MDS. Here, we determined the in vitro sensitivity of MDS patient samples to selective inhibitors of BCL2, BCL-XL and MCL1. While VEN response positively correlated with increasing blast counts, all MDS subtypes responded to the MCL1 inhibitor, S63845. Treatment with combined VEN+S63845 was synergistic in all MDS subtypes and reduced MDS engraftment in MISTRG6 mice supporting the pursuit of clinical trials with combined BCL2+MCL1 inhibition in MDS.

cancer biology↗

bioRxiv2020

The Atr-Chek1 pathway inhibits axon regeneration in response to Piezo-dependent mechanosensation

Atr is a serine/threonine kinase, known to sense single-stranded DNA breaks and activate the DNA damage checkpoint by phosphorylating Chek1, which inhibits Cdc25, causing cell cycle arrest. This pathway has not been implicated in neuroregeneration. We show that in Drosophila sensory neurons, removing Atr or Chek1, or overexpressing Cdc25 promotes regeneration, whereas Atr or Chek1 overexpression, or Cdc25 knockdown impedes regeneration. Inhibiting the Atr-associated checkpoint complex in neurons promotes regeneration and improves synapse/behavioral recovery after CNS injury. Independent of DNA damage, Atr responds to the mechanical stimulus elicited during regeneration, via the mechanosensitive ion channel Piezo and its downstream NO signaling. Sensory neuron-specific knockout of Atr in adult mice, or pharmacological inhibition of Atr-Chek1 in mammalian neurons in vitro and in flies in vivo enhance regeneration. Our findings reveal the Piezo-Atr-Chek1-Cdc25 axis as an evolutionarily conserved inhibitory mechanism for regeneration, and identify potential therapeutic targets for treating nervous system trauma.

neuroscience↗

bioRxiv2020

Computational analysis of the adaptability of H9N2 avian influenza virus: A comparative docking simulation study

Influenza A virus, the H9N2 subtype, is an avian influenza virus that has long been circulating in the worldwide poultry industry and is occasionally found to be transmissible to humans. Evidence from genomic analysis suggests that H9N2 provides the genes for the H5N1 and H7N9 subtypes, which have been found to infect mammals and pose a threat to human health. However, due to the lack of a structural model of the interaction between H9N2 and host cells, the mechanism of the extensive adaptability and strong transformation capacity of H9N2 is not fully understood. In this paper, we collected 40 representative H9N2 virus samples reported recently, mainly in China and neighboring countries, and investigated the interactions between H9N2 hemagglutinin and the mammalian receptor, the polysaccharide -2,6-linked lactoseries tetrasaccharide c, at the atomic level using docking simulation tools. We categorized the mutations of studied H9N2 hemagglutinin according to their effects on ligand-binding interactions and the phylogenetic analysis. The calculations indicated that all the studied H9N2 viruses can establish a tight binding with LSTc although the mutations caused a variety of perturbations to the local conformation of the binding pocket. Our calculations suggested that a marginal equilibrium is established between the conservative ligand-receptor interaction and the conformational dynamics of the binding pocket, and it might be this equilibrium that allows the virus to accommodate mutations to adapt to a variety of environments. Our results provided a way to understand the adaptive mechanisms of H9N2 viruses, which may help predict its propensity to spread in mammals.

bioinformatics↗

bioRxiv2020

Montane species and communities track recent warming more closely in the tropics

Many species are responding to global warming by shifting their distributions upslope to higher elevations, but the observed rates of shifts vary considerably among studies. Here we test the hypothesis that this variation is in part explained by latitude, with tropical species being generally more responsive to warming temperatures than are temperate species. We find support for this hypothesis in each of two independent empirical datasets--shifts in species elevational ranges, and changes in composition of forest inventory tree plots. Tropical species are tracking rising temperatures 2.1-2.4 times (range shift dataset) and 10 times (tree plot dataset) better than their temperate counterparts. Models predict that for a 100 m upslope shift in temperature isotherm, species at the equator have shifted their elevational ranges 93-96 m upslope, while species at 45{degrees} latitude have shifted only 37-42 m upslope. For tree plots, models predict that a 1{degrees}C increase in temperature leads to an increase in community temperature index (CTI), a metric of the average temperature optima of tree species within a plot, of 0.56 {degrees}C at the equator but no change in CTI at 45{degrees} latitude (-0.033). This latitudinal gradient in temperature tracking suggests that tropical montane biotas may be on an "escalator to extinction" as global temperatures continue to rise.

ecology↗

bioRxiv2020

PSGL-1 inhibits HIV-1 infection by restricting actin dynamics and sequestering HIV envelope proteins

PSGL-1 has recently been identified as an HIV restriction factor that inhibits HIV DNA synthesis and more potently, virion infectivity. But the underlying mechanisms of these inhibitions are unknown. Here we show that PSGL-1 directly binds to cellular actin filaments (F-actin) and restricts actin dynamics, which leads to inhibition of HIV DNA synthesis. PSGL-1 is incorporated into nascent virions and restricts actin dynamics in the virions, which partially accounts for the inhibition of virion infectivity. More potently, PSGL-1 inhibits incorporation of Env proteins into nascent virions, leading to loss of envelope spikes on the virions as shown by Cryo-electron microscopy and super-resolution imaging. This loss is associated with a profound defect in viral entry. Mechanistically, PSGL-1 binds gp41 and sequesters gp41 at the plasma membrane, explaining the inhibition of Env incorporation in nascent virions. PSGL-1s dual anti-HIV mechanisms represent novel strategies of human cells to defend against HIV infection.

microbiology↗

bioRxiv2020

Optical control of ERK and AKT signaling promotes axon regeneration and functional recovery of PNS and CNS in Drosophila

Neuroregeneration is a dynamic process synergizing the functional outcomes of multiple signaling circuits. Channelrhodopsin-based optogenetics shows feasibility of stimulating neural repair but does not pin down specific signaling cascades. Here, we utilized optogenetic systems, optoRaf and optoAKT, to delineate the contribution of the ERK and AKT signaling pathways to neuroregeneration in live Drosophila larvae. We showed that optoRaf or optoAKT activation not only enhanced axon regeneration in both regeneration competent and incompetent sensory neurons in the peripheral nervous system, but also allowed temporal tuning and proper guidance of axon regrowth. Furthermore, optoRaf and optoAKT differ in their signaling kinetics during regeneration, showing a gated versus graded response, respectively. Importantly in the central nervous system, their activation promotes axon regrowth and functional recovery of the thermonociceptive behavior. We conclude that non-neuronal optogenetics target damaged neurons and signaling subcircuits, providing a novel strategy in the intervention of neural damage with improved precision.

neuroscience↗

bioRxiv2020

Generation of cardiac valve endocardial like cells from human pluripotent stem cells

The cardiac valvular endothelial cells (VECs) are an ideal cell source that could be used for the fabrication of the next generation tissue-engineered cardiac valves (TEVs). However, few studies have been focused on the derivation of this important cell type. Here we describe a chemically defined xeno-free method for generating VEC-like cells from human pluripotent stem cells (hPSCs) through an intermediate endocardium stage. HPSCs were initially specified to KDR+/ISL1+ multipotent cardiac progenitor cells (CPCs), followed by differentiation into endocardial progenitors under the combined treatment with VEGFA, BMP4 and bFGF. In the presence of VEGFA, BMP4 and TGFb, valve endocardial progenitor cells (VEPs) were efficiently derived from endocardial progenitors without a sorting step. Mechanistically, administration of TGFb and BMP4 may specify the VEP fate by facilitating the expression of key transcription factors ETV2 and NFATc1 at the immediate early stage and by activating Notch signaling at the later stage. Notch activation is likely an important part of VEP induction. HPSC-derived VEPs exhibited morphological, molecular and functional similarities to that of the primary VECs isolated from normal human aortic valves. When hPSC-derived VEPs were seeded onto the surface of the de-cellularized porcine aortic valve (DCV) matrix scaffolds, they exhibited higher proliferation and survival potential than the primary VECs. Our results suggest that hPSC-derived VEPs could serve as as a potential platform for the study of valve development, and as starting materials for the construction of the next generation TEVs. HighlightsO_LIValve endocardial progenitor cells (VEPs) could be efficiently derived from hPSCs without a sorting step C_LIO_LIThe combined treatment with TGFb and BMP4 induce VEP fate by enhancing the expression of ETV2 and NFATc1 C_LIO_LIHPSC-derived VEPs resemble the isolated primary VECs molecularly, morphologically and functionally C_LIO_LIHPSC-derived VEPs exhibit proliferative and functional potential similar to the primary VECs when seeded onto the DCVs C_LI

developmental biology↗

bioRxiv2020

Comparative genomics identified a genetic locus in plant-associated Pseudomonas spp. that is necessary for induced systemic susceptibility

Plant root-associated microbes promote plant growth and induce systemic resistance (ISR) to foliar pathogens. In an attempt to find novel growth-promoting and ISR-inducing strains, we previously identified strains of root-associated Pseudomonas spp. that promote plant growth but unexpectedly induced systemic susceptibility (ISS) rather than ISR to foliar pathogens. Here we demonstrate that the ISS-inducing phenotype is common among root-associated Pseudomonas spp. Using comparative genomics, we identified a single P. fluorescens locus that is unique to ISS strains. We generated a clean deletion of the 11-gene ISS locus and found that it is necessary for the ISS phenotype. Although the functions of the predicted genes in the locus are not apparent based on similarity to genes of known function, the ISS locus is present in diverse bacteria and a subset of the genes have previously been implicated in pathogenesis in animals. Collectively these data show that a single bacterial locus contributes to modulation of systemic plant immunity. ImportanceMicrobiome-associated bacteria can have diverse effects on health of their hosts, yet the genetic and molecular basis of these effects have largely remained elusive. This work demonstrates that a novel bacterial locus can modulate systemic plant immunity. Additionally, this work demonstrates that growth promoting strains may have unanticipated consequences on plant immunity and this is critical to consider when engineering the plant microbiome for agronomic improvement.

microbiology↗

bioRxiv2020

Paxillin Promotes ATP-induced Activation of P2X7 Receptor and NLRP3 Inflammasome

The stimulation of P2X7 receptor by extracellular ATP leads to activation of NLRP3 inflammasome and release of pro-inflammatory cytokines. Here, we reveal a distinct mechanism by which Paxillin promotes ATP-induced activation of P2X7 receptor and NLRP3 inflammasome. Extracellular ATP induces Paxillin phosphorylation and facilitates Paxillin-NLRP3 interaction. Interestingly, Paxillin enhances NLRP3 deubiquitination and activates NLRP3 inflammasome upon ATP treatment and K+ efflux. Moreover, we reveal that UPS13 is a key enzyme for Paxillin-mediated NLRP3 deubiquitination upon ATP treatment. Notably, extracellular ATP promotes Paxillin and NLRP3 migration from cytosol to plasma membrane and facilitates P2X7-Paxillin interaction and Paxillin-NLRP3 association, resulting in the formation of P2X7-Paxillin-NLRP3 complex. Functionally, Paxillin is essential for ATP-induced NLRP3 inflammasome activation in mouse BMDMs and BMDCs as we as in human PBMCs and THP-1-differentiated macrophages. Thus, Paxillin plays key roles in ATP-induced activation of P2X7 receptor and NLRP3 inflammasome by facilitating the formation of the P2X7-Paxillin-NLRP3 complex.

immunology↗

bioRxiv2020

Prediction of protein mutational free energy: benchmark and sampling improvements increase classification accuracy

Software to predict the change in protein stability upon point mutation is a valuable tool for a number of biotechnological and scientific problems. To facilitate the development of such software and provide easy access to the available experimental data, the ProTherm database was created. Biases in the methods and types of information collected has led to disparity in the types of mutations for which experimental data is available. For example, mutations to alanine are hugely overrepresented whereas those involving charged residues, especially from one charged residue to another, are underrepresented. ProTherm subsets created as benchmark sets that do not account for this often underrepresented certain mutational types. This issue introduces systematic biases into previously published protocols ability to accurately predict the change in folding energy on these classes of mutations. To resolve this issue, we have generated a new benchmark set with these problems corrected. We have then used the benchmark set to test a number of improvements to the point mutation energetics tools in the Rosetta software suite.

bioengineering↗

bioRxiv2020

Functional insights from biophysical study of TREM2 interactions with ApoE and Aβ1-42

INTRODUCTIONTREM2 is an innate immune receptor expressed on myeloid cells including microglia in the brain. How TREM2 engages different ligands remains poorly understood. METHODSWe used comprehensive BLI analysis to investigate the TREM2 interactions with ApoE and monomeric amyloid beta (mA{beta}42). RESULTSTREM2 binding did not depend on ApoE lipidation, and there were only slight differences in affinity observed between ApoE isoforms (E4 > E3 > E2). Surprisingly, disease-linked TREM2 variants within a "basic patch" minimally impact ApoE binding. Instead, TREM2 has a unique hydrophobic surface that can bind to ApoE. This direct engagement requires the hinge region of ApoE. TREM2 directly binds mA{beta}42 and can potently inhibit A{beta}42 polymerization, suggesting a potential mechanism for soluble TREM2 (sTREM2) in preventing AD pathogenesis. DISCUSSIONThese findings demonstrate that TREM2 has at least two separate surfaces to engage ligands and uncovers a potential function for sTREM2 in directly inhibiting A{beta} polymerization.

biophysics↗