Biology subjects

Du, B.

Publications and source records attributed to Du, B..

24 records · Page 2Linked to original sources

bioRxiv2020

Cross-neutralization antibodies against SARS-CoV-2 and RBD mutations from convalescent patient antibody libraries

The emergence of coronavirus disease 2019 (COVID-19) pandemic led to an urgent need to develop therapeutic interventions. Among them, neutralizing antibodies play crucial roles for preventing viral infections and contribute to resolution of infection. Here, we describe the generation of antibody libraries from 17 different COVID-19 recovered patients and screening of neutralizing antibodies to SARS-CoV-2. After 3 rounds of panning, 456 positive phage clones were obtained with high affinity to RBD (receptor binding domain). Then the positive clones were sequenced and reconstituted into whole human IgG for epitope binning assays. After that, all 19 IgG were classified into 6 different epitope groups or Bins. Although all these antibodies were shown to have ability to bind RBD, the antibodies in Bin2 have more superiority to inhibit the interaction between spike protein and angiotensin converting enzyme 2 receptor (ACE2). Most importantly, the antibodies from Bin2 can also strongly bind with mutant RBDs (W463R, R408I, N354D, V367F and N354D/D364Y) derived from SARS-CoV-2 strain with increased infectivity, suggesting the great potential of these antibodies in preventing infection of SARS-CoV-2 and its mutations. Furthermore, these neutralizing antibodies strongly restrict the binding of RBD to hACE2 overexpressed 293T cells. Consistently, these antibodies effectively neutralized pseudovirus entry into hACE2 overexpressed 293T cells. In Vero-E6 cells, these antibodies can even block the entry of live SARS-CoV-2 into cells at only 12.5 nM. These results suggest that these neutralizing human antibodies from the patient-derived antibody libraries have the potential to become therapeutic agents against SARS-CoV-2 and its mutants in this global pandemic.

immunology↗

bioRxiv2020

Long Noncoding RNA Derived from LncRNA-mRNA Co-expression Networks Modulates the Locust Phase Change

Long noncoding RNAs (lncRNAs) regulate various biological processes from gene expression to animal behavior. Although protein-coding genes, microRNAs, and neuropeptides play important roles in the regulation of phenotypic plasticity in migratory locust, empirical studies on the function of lncRNAs in the process remain limited. Here, we applied high-throughput RNA-seq to characterize the expression patterns of lncRNAs and mRNAs in the time course of the locust phase change. LncRNAs displayed more rapid response at the early stages of the time-course treatments than mRNA expression. Functional annotations demonstrated that early changed lncRNAs employed different pathways in isolation and crowding processes to cope up with the changes in population density. Finally, two overlapping hub lncRNA loci in the crowding and isolation networks were screened to be functionally verified. Experimental validation indicated that LNC1010057 could act as a potential regulator to modulate the locust phase change. This work offers new insights into the mechanism underlying the locust phase change and expands the scope of lncRNA functions in animal behavior.

bioinformatics↗

bioRxiv2019

Adaptive laboratory evolution of Escherichia coli under acid stress

The ability of Escherichia coli to tolerate acid stress is important for its survival and colonization in the human digestive tract. Here, we performed adaptive laboratory evolution of the laboratory strain E. coli K-12 MG1655 at pH 5.5 in glucose minimal medium. By 800 generations, six independent populations under evolution reached 18.0% higher growth rates than their starting strain at pH 5.5, while maintaining comparable growth rates to the starting strain at pH 7. We characterized the evolved strains to find that: (1) whole genome sequencing of isolated clones from each evolved population revealed mutations in rpoC appearing in 5 of 6 sequenced clones; (2) gene expression profiles revealed different strategies to mitigate acid stress, that are related to amino acid metabolism and energy production and conversion. Thus, a combination of adaptive laboratory evolution, genome resequencing, and expression profiling reveals, on a genome-scale, the strategies that E. coli deploys to mitigate acid stress.

microbiology↗

bioRxiv2019

Phanerochaete chrysosporium strain B-22, a parasitic fungus infecting Meloidogyne incognita

We characterized the parasitism by strain B-22 of Phanerochaete chrysosporium on the eggs, second stage juveniles (J2), and adult females of the root-knot nematode (Meloidogyne Incognita). Strain B-22 had a strong lethal effect against M. incognita J2. The highest corrected mortality was 71.9% at 3 x 108 conidia mL-1. The estimated LC50 value was 0.96 x 108 conidia mL-1. Strain B-22 parasitized M. incognita eggs 2 days after treatment, causing the depression and dissolution of egg shells. The fungal spores parasitized J2 by gathering in the body wall, germinating to develop hyphae, and crossing the juvenile cuticle to dissolve it, thereby causing the shrinkage and deformation of the juvenile body wall. The spores and hyphae also attacked adult females, causing the shrinkage and dissolution of their bodies and leakage of contents in 5 days. Results of greenhouse experiments showed that different concentrations of P. chrysosporium effectively controlled different life stages of M. incognita and root knot symptoms in tomato plants. Moreover, the control efficacy increased with increasing conidial concentration; the best results were achieved with 3 x 108 cfu mL-1. In the roots, the highest inhibition rate was 84.61% for adult females, 78.91% for juveniles, 84.25% for the egg mass, and 79.48% for the gall index. The highest juvenile inhibition rate was 89.18% in the soil. Meanwhile, strain B-22 improved the plant growth. Thus, P. chrysosporium strain B-22 is safe for tomato plants while effectively parasitizing M. incognita, making it a promising biocontrol agent against M. incognita.

pathology↗

bioRxiv2018

Temperature-dependent estimation of Gibbs energies using an updated group contribution method

Reaction equilibrium constants determine the mass action ratios necessary to drive flux through metabolic pathways. Group contribution methods offer a way to estimate reaction equilibrium constants at wide coverage across the metabolic network. Here, we present an updated group contribution method with: 1) additional curated thermodynamic data used in fitting; and 2) capabilities to calculate equilibrium constants as a function of temperature. We first collected and curated aqueous thermo-dynamic data, including reaction equilibrium constants, enthalpies of reaction, Gibbs free energies of formation, enthalpies of formation, entropies change of formation of compounds, and proton and metal ion binding constants. We further estimated magnesium binding constants for 618 compounds using a linear regression model validated against measured data. Next, we formulated the calculation of equilibrium constants as a function of temperature and calculated necessary parameters, including standard entropy change of formation ({Delta}f S{circ}) and standard entropy change of reaction ({Delta}rS{circ}), using a model based on molecular properties. The median absolute errors in estimating {Delta}f S{circ} and {Delta}rS{circ} were 0.010 kJ/K/mol and 0.018 kJ/K/mol, respectively. The efforts here fill in gaps for thermodynamic calculations under various conditions, specifically different temperatures and metal ion concentrations. These results support the study of thermodynamic driving forces underlying the metabolic function of organisms living under diverse conditions.

biophysics↗

bioRxiv2017

Topological and Kinetic Determinants of the Modal Matrices of Dynamic Models of Metabolism

Linear analysis of kinetic models of metabolism can help in understanding the dynamic response of metabolic networks. Central to linear analysis of these models are two key matrices: the Jacobian matrix (J) and its modal matrix (M-1). The modal matrix contains dynamically independent motions of the kinetic model, and it is sparse in practice. Understanding the sparsity structure of the modal matrix provides insight into metabolic network dynamics. In this study, we analyze the relationship between J and M-1. First, we show that diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures within M-1. Dominant diagonal elements in J approximate the eigenvalues corresponding to these simple modes, in which a single metabolite is driven back to its reference state on a characteristic timescale. Second, we analyze more complicated mode structures in M-1, in which two or more variables move in a certain ratio relative to one another on defined time scales. We show that complicated modes originate from sub-matrices of topologically connected elements of similar magnitude in J. Third, we describe the origin of these mode structure features based on the network stoichiometric matrix S and the reaction kinetic gradient matrix G. We demonstrate that the topologically-connected reaction sensitivities of similar magnitude in G play a central role in determining the mode structure. Ratios of these reaction sensitivities represent equilibrium balances of half reactions that are defined by linearization of the bilinear mass action rate laws followed by enzymatic reactions. These half-reaction equilibrium ratios are key determinants of modal structure for both simple and complicated modes. The work presented here helps to establish a foundation for understanding the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions.

systems biology↗