Biology subjects

Peng, J.

Publications and source records attributed to Peng, J..

At least 163 records · Page 9Linked to original sources

bioRxiv2022

Proximal Exploration for Model-guided Protein Sequence Design

Designing protein sequences with a particular biological function is a long-lasting challenge for protein engineering. Recent advances in machine-learning-guided approaches focus on building a surrogate sequence-function model to reduce the burden of expensive in-lab experiments. In this paper, we study the exploration mechanism of model-guided sequence design. We leverage a natural property of protein fitness landscape that a concise set of mutations upon the wild-type sequence are usually sufficient to enhance the desired function. By utilizing this property, we propose Proximal Exploration (PEX) algorithm that prioritizes the evolutionary search for high-fitness mutants with low mutation counts. In addition, we develop a specialized model architecture, called Mutation Factorization Network (MuFacNet), to predict low-order mutational effects, which further improves the sample efficiency of model-guided evolution. In experiments, we extensively evaluate our method on a suite of in-silico protein sequence design tasks and demonstrate substantial improvement over baseline algorithms.

bioinformatics↗

bioRxiv2022

Safety assessment of insecticidal proteins: A case study utilizing His-tagged protein to determine the safety of Mpp75Aa1.1, a new ETX_MTX2 protein that controls western corn rootworm

The recently discovered insecticidal protein Mpp75Aa1.1 from Brevibacillus laterosporus is one member of the ETX_MTX family of beta-pore forming proteins ({beta}-PFPs) expressed in genetically modified (GM) maize to control western corn rootworm (WCR; Diabrotica virgifera virgifera LeConte). In this paper, bioinformatic analysis establishes that although Mpp75Aa1.1 shares varying degrees of similarity to members of the ETX_MTX2 protein family, it is unlikely to contribute to any allergenic, toxic, or otherwise adverse biological effects. The safety of Mpp75Aa1.1 is further supported by a weight of evidence including evaluation of history of safe use (HOSU) of ETX_MTX2 proteins. Assessments using either purified Mpp75Aa1.1 protein or a poly-histidine-tagged (His-tagged) variant of the Mpp75Aa1.1 protein demonstrate that both forms of the protein are heat labile at temperatures at or above 55 {degrees}C, degraded by gastrointestinal proteases within 0.5 min, and have no adverse effects in acute mouse oral toxicity studies at a dose level of 1920 or 2120 mg/kg body weight. Results also indicate that the domain-based protein characterization should be considered as part of the weight of evidence for the safe consumption of food or feed. Furthermore, these results support the use of His-tagged proteins as suitable surrogates for assessing the safety of their non-tagged parent proteins.

pharmacology and toxicology↗

bioRxiv2022

Global Spatial Transcriptome of Macaque Brain at Single-Cell Resolution

Global profile of gene expression at single-cell resolution remains to be determined for primates. Using a recently developed technology ("Stereo-seq"), we have obtained a comprehensive single-cell spatial transcriptome map at the whole-brain level for cynomolgus monkeys, with [~]600 genes per cell for 10 m-thick coronal sections (up to 15 cm2 in size). Large-scale single-nucleus RNA-seq analysis for [~]1 million cells helped to identify cell types corresponding to Stereo-seq gene expression profiles, providing a 3-D cell type atlas of the monkey brain. Quantitative analysis of Stereo-seq data revealed molecular fingerprints that mark distinct neocortical layers and subregions, as well as domains within subcortical structures including hippocampus, thalamus, striatum, cerebellum, hypothalamus and claustrum. Striking whole-brain topography and coordinated patterns were found in the expression of genes encoding receptors and transporters for neurotransmitters and neuromodulators. These results pave the way for cellular and molecular understanding of organizing principles of the primate brain.

cell biology↗

bioRxiv2021

Metformin abrogates pathological TNF-α-producing B cells through mTOR-dependent metabolic reprogramming in polycystic ovary syndrome

B cells contribute to the pathogenesis of polycystic ovary syndrome (PCOS). Clinically, metformin is used to treat PCOS, but it is unclear whether metformin exerts its therapeutic effect by regulating B cells. Here, we showed that the expression level of TNF- in peripheral blood B cells from PCOS patient was increased. Metformin used in vitro and in vivo was able to reduce the production of TNF- in B cells from PCOS patient. Administration of metformin improved mouse PCOS phenotypes induced by dehydroepiandrosterone (DHEA) and also inhibited TNF- expression in splenic B cells. Further, metformin induced metabolic reprogramming of B cells in PCOS patients, including the alteration in mitochondrial morphology, the decrease in mitochondrial membrane potential, ROS production and glucose uptake. In DHEA-induced mouse PCOS model, metformin altered metabolic intermediates in splenic B cells. Moreover, the inhibition of TNF- expression and metabolic reprogramming in B cells of PCOS patients and mouse model by metformin were associated with decreased mTOR phosphorylation. Together, TNF--producing B cells are involved in the pathogenesis of PCOS, and metformin inhibits mTOR phosphorylation and affects metabolic reprogramming, thereby inhibiting TNF- expression in B cells, which may be a new mechanism of metformin in the treatment of PCOS.

cell biology↗

bioRxiv2021

A druggable addiction to de novo pyrimidine biosynthesis in diffuse midline glioma

Diffuse midline glioma (DMG) is a uniformly fatal pediatric cancer driven by oncohistones that do not readily lend themselves to drug development. To identify druggable targets for DMG, we conducted a genome-wide CRISPR screen that reveals a DMG selective dependency on the de novo pathway for pyrimidine biosynthesis. This metabolic vulnerability reflects an elevated rate of uridine/uracil degradation that depletes DMG cells of substrates for the alternate salvage pathway for pyrimidine biosynthesis. A clinical stage inhibitor of DHODH (rate limiting enzyme in the de novo pathway) diminishes UMP pools, generates DNA damage, and induces apoptosis through suppression of replication forks--an "on target" effect, as shown by uridine rescue. MALDI mass spectroscopy imaging demonstrates that this DHODH inhibitor (BAY2402234) accumulates in brain at therapeutically relevant concentrations, suppresses de novo pyrimidine biosynthesis in vivo, and prolongs survival of mice bearing intracranial DMG xenografts, highlighting BAY2402234 as a promising therapy against DMGs.

cancer biology↗

bioRxiv2021

A large-scale systematic survey of SARS-CoV-2 antibodies reveals recurring molecular features

In the past two years, the global research in combating COVID-19 pandemic has led to isolation and characterization of numerous human antibodies to the SARS-CoV-2 spike. This enormous collection of antibodies provides an unprecedented opportunity to study the antibody response to a single antigen. From mining information derived from 88 research publications and 13 patents, we have assembled a dataset of [~]8,000 human antibodies to the SARS-CoV-2 spike from >200 donors. Analysis of antibody targeting of different domains of the spike protein reveals a number of common (public) responses to SARS-CoV-2, exemplified via recurring IGHV/IGK(L)V pairs, CDR H3 sequences, IGHD usage, and somatic hypermutation. We further present a proof-of-concept for prediction of antigen specificity using deep learning to differentiate sequences of antibodies to SARS-CoV-2 spike and to influenza hemagglutinin. Overall, this study not only provides an informative resource for antibody and vaccine research, but fundamentally advances our molecular understanding of public antibody responses to a viral pathogen.

immunology↗

bioRxiv2021

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that human complex traits are determined by poorly understood interactions between our genomes and daily environments. These "genotype x environment" (GxE) interactions remain difficult to map at the organismal level, but can be uncovered using molecular phenotypes. To do so at large-scale, we profiled transcriptomes across 12 cellular environments using 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression across environments and revealed a context-dependent genetic architecture: the average heritability of gene expression levels increased in treatment relative to control conditions and, on average, each treatment revealed expression quantitative trait loci (eQTL) at 11% of genes. In total, 22% of all eQTL were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses revealed that positive selection has shaped GxE loci involved in responding to immune challenges and hormones, but not man-made chemicals, suggesting there is reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposures evolutionary history when studying and interpreting GxE interactions, and provides new insight into the evolutionary mechanisms that maintain GxE loci in human populations.

genomics↗

bioRxiv2021

SMAP: A pipeline for sample matching in proteogenomics

Integration of genomics and proteomics (proteogenomics) offers unprecedented promise for in-depth understanding of human diseases. However, sample mix-up is a pervasive, recurring problem, due to complex sample processing in proteogenomics. Here we present a pipeline for Sample Matching in Proteogenomics (SMAP) for verifying sample identity to ensure data integrity. SMAP infers sample-dependent protein-coding variants from quantitative mass spectrometry (MS), and aligns the MS-based proteomic samples with genomic samples by two discriminant scores. Theoretical analysis with simulation data indicates that SMAP is capable of uniquely match proteomic and genomic samples, when [≥]20% genotypes of individual samples are available. When SMAP was applied to a large-scale proteomics dataset from 288 biological samples generated by the PsychENCODE BrainGVEX project, we identified and corrected 18.8% (54/288) mismatched samples. The correction was further confirmed by ribosome profiling and assay for transposase-accessible chromatin sequencing data from the same set of samples. Thus our results demonstrate that SMAP is an effective tool for sample verification in a large-scale MS-based proteogenomics study. The source code, manual, and sample data of the SMAP are publicly available at https://github.com/UND-Wanglab/SMAP, and a web-based SMAP can be accessed at https://smap.shinyapps.io/smap/.

bioinformatics↗

bioRxiv2021

Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches

A major challenge in the application of the CRISPR-Cas13d (RfxCas13d, or CasRx) RNA editing system is to accurately predict its guide RNA (gRNA) dependent on-target and off-target effect. Here, we performed CRISPR-Cas13d proliferation screens that target protein-coding genes and long non-coding RNAs (lncRNAs), followed by a systematic modeling of Cas13d on-target efficiency and off-target viability effect. We first designed a deep learning model, named DeepCas13, to predict the on-target activity of a gRNA with high accuracy from its sequence and secondary structure. DeepCas13 outperforms existing methods and accurately predicts the efficiency of guides targeting both protein-coding and non-coding RNAs (e.g., circRNAs and lncRNAs). Next, we systematically studied guides targeting non-essential genes, and found that the off-target viability effect, defined as the unintended effect of guides on cell viability, is closely related to their on-target RNA cleavage efficiency. This finding suggests that these gRNAs should be used as negative controls in proliferation screens to reduce false positives, possibly coming from the unwanted off-target viability effect of efficient guides. Finally, we applied these models to our screens that included guides targeting 234 lncRNAs, and identified lncRNAs that affect cell viability and proliferation in multiple cell lines. DeepCas13 is freely accessible via http://deepcas13.weililab.org.

bioinformatics↗

bioRxiv2021

Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic analysis of ovarian cancer

To unravel the mechanism of immune activation and suppression within tumors, a critical step is to identify transcriptional signals governing cell-cell communication between tumor and immune/stromal cells in the tumor microenvironment. Central to this communication are interactions between secreted ligands and cell-surface receptors, creating a highly connected signaling network among cells. Recent advancement in in situ-omics profiling, particularly spatial transcriptomic (ST) technology, provide unique opportunities to directly characterize ligand-receptor signaling networks that powers cell-cell communication. In this paper, we propose a novel statistical method, LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and stroma cells based on ST data. LRnetST utilizes a directed acyclic graph (DAG) model with a novel treatment to handle the zero-inflated distribution observed in the ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. Application of LRnetST to ST data of high-grade serous ovarian tumor samples revealed both common and distinct ligand-receptor regulations across different tumors. Some of these interactions were validated through a MERFISH data set of independent ovarian tumor samples. These results cast light on biological processes relating to the communication between tumor and immune/stromal cells in ovarian tumors. An open-source R package of LRnetST is available on GitHub at https://github.com/jie108/LRnetST.

systems biology↗

bioRxiv2021

Based on the identification of tumor antigens and immune subtypes in renal clear cell carcinoma for mRNA vaccine development

BackgroundCancer vaccine based on mRNA is considered as a promising strategy and has become a new hot spot in cancer immunotherapy. However, its application to KIRC is not clear. A growing body of research has shown that immunotyping can reflect the comprehensive immune status and immune microenvironment of tumor, which is closely related to treatment response and vaccination potential. The aim of this study was to identify the potential antigens of KIRC for the development of anti-KIRC mRNA vaccines, and to further differentiate the immune subtypes of KIRC to construct an immune landscape for the selection of appropriate patients for vaccination. MethodsGene expression profiles and corresponding clinical information of 265 KIRC patients and RNA-seq data of 539 KIRC patients were retrieved from were collected from GEO and TCGA. cBioPortal was used to visualize and compare genetic alterations, while GEPIA2 was used to calculate the prognostic index of selected antigens. The relationship between the infiltration of antigen presenting cells and the expression of the identified antigen was visualized with TIMER, and consensus clustering analysis was used to determine the immune subtypes. Finally, the immune landscape of KIRC is visualized through the dimensionality reduction analysis based on graph learning. ResultsTwo tumor antigens associated with prognostic and antigen-presenting infiltrating cells were identified in KIRC, including LRP2, and DOCK8. KIRC patients were classified into six immune subtypes based on different molecular, cellular, and clinical characteristics. Patients with IS5 and IS6 tumors had an immune "hot" and immunosuppressive phenotype, which was associated with better survival compared to other subtypes, whereas patients with IS1-4 tumors had an immune "cold" phenotype, which was associated with a higher tumor mutation burden. In addition, the expression of immune checkpoints and immunogenic cell death modulators differed significantly in different immunosubtypes of tumors. Finally, the immune landscape of KIRC shows a high degree of heterogeneity across patients. ConclusionsLRP2 and FEM2 are potential KIRC antigens for mRNA vaccine development, and patients with immune subtypes IS1-4 are suitable for vaccination.

bioinformatics↗

bioRxiv2021

The Prediction of Active Ingredients and Polypharmacological Mechanisms of Orthosiphon stamineus Benth. (Lamiaceae) of Dai medicine based on Network Pharmacology

BackgroundOrthosiphon stamineus Benth. was a dietary supplement or a traditional folk herb with widespread clinical application, but it was lack of comprehensive understanding of its active ingredients and polypharmacological mechanisms. The aim of this work was to systematically study its natural compounds and the molecular mechanisms of the O. stamineus via network pharmacology. MethodsCompounds from O. stamineus were collected by literature retrieval and evaluated by SwissADME with the physicochemical property (ADMET model) and the likelihood for a natural medicine. The connection of active ingredients and target genes was built and confirmed by Cytoscape and AutoDock vina. Then, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were separately performed to obtain a more in-depth understanding of O. stamineus. Finally, the relationship among active ingredients, targets, and diseases was built to clarify the polypharmacological mechanisms and found relevant active substances for further drug discovery. ResultsA total of 159 compounds of O. stamineus were collected, and 22 potentially active ingredients were screened out. The Ingredient-Target Interaction Network was built with 12 flavonoids, 3 diterpenes, 3 phenols, and 4 volatile oils, and 65 targets. The Docking analysis indicated that the ingredient-target interaction network was reliable; most ligand-receptor had a strong binding affinity (lowest binding energy: -6.9 kcal/mol). After pathway analysis, 185 significant biological processes and 36 signal pathways were found, and the ingredient-target-disease network of O. stamineus was constructed for polypharmacological mechanisms. ConclusionOur study clarified the polypharmacological mechanisms via the relationship among active ingredients, targets, and diseases and provided better guidance for subsequent experiments and potential active ingredients for drug discovery or health promotion.

systems biology↗

bioRxiv2021

Golden pompano genome resource enables discovery of valuable gene determining growth traits

One important goal of fish genetic breeding is to identify valuable loci and genes that can facilitate growth and thereby productivity. Few such loci or genes have been identified in golden pompano (Trachinotus ovatus), a species of significant economic value. In this study, we produced a high-quality chromosome-level genome assembly of the golden pompano by de novo sequencing and assemblies for 2 parents and 200 F1 offspring by genome re-sequencing. We exploited these assemblies to identify loci and genes by QTL mapping, Kompetitive Allele Specific PCR (KASP) genotyping, and haplotype-based regional association analysis based on growth records of a 64 biparental and 147 individuals from a naturally occurring population. At a locus 291kb from BSNP21031, we identified a somatostatin receptor type 1-like (designated as gpsstr1) gene in which the BSNP1369 of the promoter region was highly associated with growth. Loss of sstr1a, the homolog of gpsstr1 in zebrafish, caused growth retardation. Sstr1a mediated growth via sstr2 and Wnt-gsk-3{beta} signaling pathways. Our findings provide new insights into the underlying mechanisms controlling growth. Our strategy can serve as an effective way to uncover novel genomic information and facilitate improvement of fish growth.

genomics↗

bioRxiv2021

High-throughput mapping of single-cell molecular and projection architecture of neurons by retrograde barcoded labelling

Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene of neurons extending collateral branches to dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.

neuroscience↗

bioRxiv2021

A novel framework for single-cell Hi-C clustering based on graph-convolution-based imputation and two-phase-based feature extraction

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Singlecell Hi-C technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. However, few methods are well designed for single-cell Hi-C clustering, because of high sparsity, noise and heterogeneity of single-cell Hi-C data. In this manuscript, we propose a novel framework, named ScHiC-Rep, for singlecell Hi-C data representation and clustering. ScHiC-Rep mainly contains two parts: data imputation and feature extraction. In the imputation part, a novel imputation workflow is proposed, including graph convolution-based, random walk with restart-based and genomic neighbor-based imputation. In the feature extraction part, a two-phase feature extraction method is proposed, including linear phase for chromosome level and non-linear phase for cell level feature extraction. The evaluation results show that the proposed framework outperforms existing state-of-the-art approaches on both human and mouse datasets.

bioinformatics↗

bioRxiv2021

Ubiquitination is essential for recovery of cellular activities followingheat shock

Eukaryotic cells respond to stress via adaptive programs that include reversible shutdown of key cellular processes, the formation of stress granules, and a global increase in ubiquitination. The primary function of this ubiquitination is generally considered to be tagging damaged or misfolded proteins for degradation. Here we show that different types of stress generate distinct ubiquitination patterns. For heat stress, ubiquitination correlates with cellular activities that are downregulated during stress, including nucleocytoplasmic transport and translation, as well as with stress granule constituents. Ubiquitination is not required for the shutdown of these processes or for stress granule formation, but is essential for resumption of cellular activities and for stress granule disassembly. These findings indicate that stress-induced ubiquitination primes the cell for recovery following heat stress. One Sentence SummaryStress-induced ubiquitination is essential for recovery of cellular activities following heat stress.

cell biology↗

bioRxiv2021

Large-Scale Deep Multi-Layer Analysis of Alzheimer's Disease Brain Reveals Strong Proteomic Disease-Related Changes Not Observed at the RNA Level

The biological processes that are disrupted in the Alzheimers disease (AD) brain remain incompletely understood. We recently performed a proteomic analysis of >2000 brains to better understand these changes, which highlighted alterations in astrocytes and microglia as likely key drivers of disease. Here, we extend this analysis by analyzing >1000 brain tissues using a tandem mass tag mass spectrometry (TMT-MS) pipeline, which allowed us to nearly triple the number of quantified proteins across cases. A consensus protein co-expression network analysis of this deeper dataset revealed new co-expression modules that were highly preserved across cohorts and brain regions, and strongly altered in AD. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism, and a module related to the matrisome. Analysis of paired genomic and proteomic data within subjects showed that expression level of the matrisome module was influenced by the APOE {varepsilon}4 allele, but was not related to the rate of cognitive decline after adjustment for neuropathology. In contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.

systems biology↗

bioRxiv2021

Exceptional origin activation revealed by comparative analysis in two laboratory yeast strains

We performed a comparative analysis of replication origin activation by genome-wide single-stranded DNA mapping in two common laboratory strains of Saccharomyces cerevisiae challenged by hydroxyurea (HU), an inhibitor of the ribonucleotide reductase. By doing so we gained understanding of the impact on origin activation by three factors: replication checkpoint control, DNA sequence polymorphisms, and relative positioning of origin and transcription unit. Our analysis recapitulated the previous finding that the majority of origins are subject to checkpoint control by the Rad53 kinase when cells were treated with HU. In addition, origins either subject to Rad53 checkpoint control or impervious to it are largely concordant between the two strains. However, these two strains also produced different dynamics of origin activation. First, the W303-RAD53 cells showed a significant reduction of fork progression than A364a-RAD53 cells. This phenotype was accompanied by an elevated level of Rad53 phosphorylation in W303-RAD53 cells. Second, W303-rad53K227A checkpoint-deficient cells activated a greater number of origins accompanied by global reduction of ssDNA across all origins compared to A364a-rad53K227A cells; and this is correlated with lower expression level of the mutant protein in W303 than in A364a. We also show that sequence polymorphism in the consensus motifs of the replication origins plays a minor role in determining origin usage. Remarkably, eight strain-specific origins lack the canonical 11-bp consensus motif for autonomously replicating sequences in either strain background. Finally, we identified a new class of origins that are only active in checkpoint-proficient cells, which we named "Rad53-dependent origins". The only discernible feature of these origins is that they tend to overlap with an open reading frame, suggesting previously unexplored connection between transcription and origin activation. Our study presents a comprehensive list of origin usage in two diverse yeast genetic backgrounds, fine-tunes the different categories of origins with respect to checkpoint control, and provokes further exploration of the interplay between origin activation and transcription. Author SummaryComparative analysis of origins of replication in two laboratory yeast strains reveals new insights into origin activation, regulation and dependency on the Rad53 checkpoint kinase.

genomics↗