Biology subjects

Meng, S.

Publications and source records attributed to Meng, S..

28 records · Page 2Linked to original sources

bioRxiv2021

Killed whole genome-reduced bacteria surface-expressed coronavirus fusion peptide vaccines protect against disease in a porcine model

As the coronavirus disease 2019 (COVID-19) pandemic rages on, it is important to explore new evolution-resistant vaccine antigens and new vaccine platforms that can produce readily scalable, inexpensive vaccines with easier storage and transport. We report here a synthetic biology-based vaccine platform that employs an expression vector with an inducible Gram-negative autotransporter to express vaccine antigens on surface of genome-reduced bacteria to enhance interaction of vaccine antigen with immune system. As a proof of principle, we utilized genome-reduced E. coli to express SARS-CoV-2 and porcine epidemic diarrhea virus (PEDV) fusion peptide (FP) on the cell surface, and evaluated their use as a killed whole cell vaccine. The FP sequence is highly conserved across coronaviruses; the 6 FP core amino acid residues along with the 4 adjacent residues upstream and the 3 residues downstream the core are identical between SARS-CoV-2 and PEDV. We tested the efficacy of PEDV FP and SARS-CoV-2 FP vaccines in a PEDV challenge pig model. We demonstrated that both vaccines induced potent anamnestic responses upon virus challenge, potentiated IFN-{gamma} responses, reduced viral RNA loads in jejunum tissue, and provided significant protection against clinical disease. However, neither vaccines elicited sterilizing immunity. Since SARS-CoV-2 FP and PEDV FP vaccines provided similar clinical protection, the coronavirus FP could be a target for a broadly-protective vaccine using any platform. Importantly, the genome-reduced bacterial surface-expressed vaccine platform, when using a vaccine appropriate bacterial vector, has potential utility as an inexpensive, readily manufactured, and rapid vaccine platform for other pathogens. Significance StatementWe report a new vaccine platform to express vaccine antigens on surface of genome-reduced bacteria to enhance vaccine immunogenicity. We demonstrated the utility of this vaccine platform by expressing the highly conserved fusion peptide (FP) of SARS-CoV-2 and porcine epidemic diarrhea virus on the surface of E.coli to produce killed whole cell bacterial vaccines. The vaccine primes a potent anamnestic response, potentiates IFN-{gamma} responses, and provides significant protection in pigs against disease following virus challenge. The FP could be a target for a broadly-protective coronavirus vaccine since a Betacoronavirus SARS-CoV-2 FP vaccine provided cross-protection against Alphacoronavirus PEDV. When using a vaccine appropriate bacteria vector, this inexpensive new vaccine platform offers the potential for use in developing countries.

microbiology↗

bioRxiv2021

Newcastle disease virus induced ferroptosis through p53-SLC7A11-GPX4 axis mediated nutrient deprivation in tumor cells

A number of new cell death processes have been discovered in recent years, including ferroptosis, which is characterized by the accumulation of lipid peroxidation products derived from iron metabolism. The evidence suggests that ferroptosis has a tumor-suppressor function. However, the mechanism by which ferroptosis mediates the response of tumor cells to oncolytic viruses remains poorly understood. Newcastle disease virus can selectively replicate in tumor cells. We show that NDV-induced ferroptosis acts through p53-SLC7A11-GPX4 pathway. The expression of tumor suppressor gene p53 increased after NDV infection, and the expressions of SLC7A11 and SLC3A2 were down-regulated, leading to the inhibition of glutathione synthesis and a decrease in glutathione peroxidase 4 expression. The chemical compound erastin, which induces ferroptosis, also down-regulated glutathione synthase expression and caused lipid peroxide accumulation and cell death. Meanwhile, the levels of intracellular reactive oxygen species and lipid peroxides increased in tumor cells. Ferritinophagy was induced by NDV promotion of ferroptosis through the release of ferrous iron and an enhanced Fenton reaction. Collectively, these observations demonstrated that NDV can kill tumor cells through ferroptosis. Our study provides novel insights into the mechanisms of NDV-induced ferroptosis and highlights the critical role of viruses in treating therapy-resistant cancers.

cancer biology↗

bioRxiv2020

An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology

The use of computational techniques to analyze viral sequence data and ultimately inform public health intervention has become increasingly common in the realm of epidemiology. These methods typically attempt to make epidemiological inferences based on multiple sequence alignments and phylogenies estimated from the raw sequence data. Like all estimation techniques, multiple sequence alignment and phylogenetic inference tools are error-prone, and the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly-used workflows for conducting viral phylogenetic analyses on simulated viral sequence data modeling HIV, HCV, and Ebola, and we computed multiple methods of accuracy motivated by transmission clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was orders of magnitude faster than the other tools, and when the other tools were used to optimize branch lengths along a fixed topology provided by FastTree 2 (i.e., no tree search), the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime. Our results indicate that an ideal workflow for viral phylogenetic inference is to (1) use MAFFT to perform MSA, (2) use FastTree 2 under the GTR model with discrete gamma-distributed site-rate heterogeneity to quickly obtain a reasonable tree topology, and (3) use RAxML-NG to optimize branch lengths along the fixed FastTree 2 topology.

bioinformatics↗

bioRxiv2020

RNF219 interacts with CCR4-NOT in regulating stem cell differentiation

Regulation of RNA stability plays a crucial role in gene expression control. Deadenylation is the initial rate-limiting step for the majority of RNA decay events. Here, we show that RING Finger Protein 219 (RNF219) interacts with the CCR4-NOT deadenylase complex. RNF219-CCR4-NOT exhibits deadenylation activity in vitro. RNA-seq analyses identify some of the 2-cell specific genes and the neuronal genes significantly down-regulated upon RNF219 knockdown, while up-regulated after depletion of the CCR4-NOT subunit CNOT10 in mouse embryonic stem (ES) cells. RNF219 depletion leads to impaired neuronal lineage commitment during ES cell differentiation. Our study suggests that RNF219 is a novel interacting partner of CCR4-NOT, and required for maintenance of ES cell pluripotency.View Full Text

molecular biology↗

bioRxiv2020

Evaluation validation of a qPCR curve analysis method and conventional approaches

Real-time quantitative polymerase chain reaction (qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle). We used the large clinical biomaker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method----CqMAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare various publicly available curve analysis methods with our method and established a suitable evaluation index system. The results show that CqMAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis.

biochemistry↗

bioRxiv2020

Clinical Characteristics and Risk of Second Primary Lung Cancer After Cervix Cancer: A Population-Based Study

BackgroundLung cancer as a second primary malignancy is increasingly common, but the clinical characteristics of second primary non-small cell lung cancer after cervix cancer (CC-NSCLC) in comparison with first primary non-small cell lung cancer (NSCLC1) is unknown. MethodsThe Surveillance, Epidemiology and EndResults (SEER) cancer registry between 1998 and 2010 was used to conduct a large population-based cohort analysis. Demographic and clinical characteristics as well as prognostic data were systematically analyzed. We further compared overall survival (OS) in the two cohorts. Risk factors of secondary primary lung cancer in cervical cancer patients were also analyzed. Results557 (3.52%) had developed second primary lung cancer after cervix cancer and 451 were eligible for inclusion in the final analyses. In comparison to NSCLC1, patients with CC-NSCLC had a higher rate of squamous cell carcinoma (SCC) (36.59% vs. 19.07%, p<0.01). Median OS was longer for CC-NSCLC than for NSCLC1 before propensity score matching (PSM) (16 vs. 13months) but there was no significant difference after PSM. High-risk factors in cervical cancer to developing CC-NSCLC include: 50-79years old, black race (OR 1.417; 95%CI 1.095-1.834; p<0.05)and history of radiotherapy (OR 1.392; 95%CI 1.053-1.841; p<0.05). Conclusion50-79years old, black race and history of radiotherapy were independent risk factors of second primary lung cancer in cervical cancer patient. CC-NSCLC patients had distinctive clinical characteristics and a better prognosis compared with NSCLC1 patients.

cancer biology↗

bioRxiv2020

PD-L1 upregulation by IFN-α/γ-mediated Stat1 suppresses anti-HBV T cell response

Programmed death ligand 1 (PD-L1) has been recently shown to be a major obstacle to antiviral immunity by binding to its receptor programmed death 1 (PD-1) on specific IFN-{gamma} producing T cells in chronic hepatitis B. Currently, IFN- is widely used to treat hepatitis B virus(HBV) infection, but its antiviral effect vary greatly and the mechanism is not totally clear. We found that IFN-/{gamma} induced a marked increase of PD-L1 expression in hepatocytes. Signal and activators of transcription (Stat1) was then identified as a major transcription factor involved in IFN-/{gamma}-mediated PD-L1 elevation both in vitro and in mice. Blockage of the PD-L1/PD-1 interaction by a specific mAb greatly enhanced HBV-specific T cell activity by the gp96 adjuvanted therapeutic vaccine, and promoted HBV clearance in HBV transgenic mice. Our results demonstrate the IFN-/{gamma}-Stat1-PD-L1 axis plays an important role in mediating T cell hyporesponsiveness and inactivating liver-infiltrating T cells in the hepatic microenvironment. These data raise further potential interest in enhancing the anti-HBV efficacy of IFN- and therapeutic vaccines.

molecular biology↗

bioRxiv2019

Nuclear actin regulates inducible transcription by enhancing RNA polymerase II clustering

Gene expression in response to external stimuli underlies a variety of fundamental cellular processes. However, how the transcription machinery is regulated under these scenarios is largely unknown. Here, we discover a novel role of nuclear actin in inducible transcriptional regulation using next-generation transcriptome sequencing and super-resolution microscopy. The RNA-seq data reveal that nuclear actin is required for the establishment of the serum-induced transcriptional program. Using super-resolution imaging, we found a remarkable enhancement of RNA polymerase II (Pol II) clustering upon serum stimulation and this enhancement requires the presence of nuclear actin. To study the molecular mechanisms, we firstly observed that Pol II clusters co-localized with the serum-response genes and nuclear actin polymerized in adjacent to Pol II clusters upon serum stimulation. Furthermore, N-WASP and Arp2/3 are reported to interact with Pol II, and we demonstrated N-WASP is required for serum-enhanced Pol II clustering. Importantly, using an optogenetic tool, we revealed that N-WASP phase-separated with the carboxy-terminal domain of Pol II and nuclear actin. In addition to serum stimulation, we found nuclear actin also essential in enhancing Pol II clustering upon interferon-{gamma} treatment. Taken together, our work unveils nuclear actin promotes the formation of transcription factory on inducible genes, acting as a general mechanism underlying the rapid response to environmental cues.

cell biology↗

bioRxiv2019

Deep learning enables therapeutic antibody optimization in mammalian cells

Therapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (103 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massively diverse sequence space to identify globally optimized antibody variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (104) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence. Deep learning is then used to predict millions of antigen binders from an in silico library of ~108 variants, where experimental testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set of in silico predicted binders is then subjected to multiple developability filters, resulting in thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate high-dimensional protein sequence space, deep learning offers great potential for antibody engineering and optimization.

synthetic biology↗

bioRxiv2018

Synthetic standards combined with error and bias correction improves the accuracy and quantitative resolution of antibody repertoire sequencing in human and naive memory B cells

High-throughput sequencing of immunoglobulin repertoires (Ig-seq) is a powerful method for quantitatively interrogating B cell receptor sequence diversity. When applied to human repertoires, Ig-seq provides insight into fundamental immunological questions, and can be implemented in diagnostic and drug discovery projects. However, a major challenge in Ig-seq is ensuring accuracy, as library preparation protocols and sequencing platforms can introduce substantial errors and bias that compromise immunological interpretation. Here, we have established an approach for performing highly accurate human Ig-seq by combining synthetic standards with a comprehensive error and bias correction pipeline. First, we designed a set of 85 synthetic antibody heavy chain standards (in vitro transcribed RNA) to assess correction workflow fidelity. Next, we adapted a library preparation protocol that incorporates unique molecular identifiers (UIDs) for error and bias correction which, when applied to the synthetic standards, resulted in highly accurate data. Finally, we performed Ig-seq on purified human circulating B cell subsets (naive and memory), combined with a cellular replicate sampling strategy. This strategy enabled robust and reliable estimation of key repertoire features such as clonotype diversity, germline segment and isotype subclass usage, and somatic hypermutation (SHM). We anticipate that our standards and error and bias correction pipeline will become a valuable tool for researchers to validate and improve accuracy in human Ig-seq studies, thus leading to potentially new insights and applications in human antibody repertoire profiling.

immunology↗