Biology subjects

Zhao, L. P.

Publications and source records attributed to Zhao, L. P..

2 recordsLinked to original sources

bioRxiv2024

Generation of natural killer and myeloid cells in a 3D artificial marrow organoid system

The human bone marrow (BM) microenvironment involves hematopoietic and non-hematopoietic cell subsets organized in a complex architecture. Tremendous efforts have been made to model it in order to analyse normal or pathological hematopoiesis and its stromal counterpart. Herein, we report an original, fully-human in vitro 3D model of the BM microenvironment dedicated to study interactions taking place between mesenchymal stromal cells (MSC) and hematopoietic stem and progenitor cells (HSPC) during the hematopoietic differentiation. This artificial marrow organoid (AMO) model is highly efficient to support NK cell development from the CD34+ HSPC to the terminally differentiated NKG2A-KIR2D+CD57+ NK subset. In addition, myeloid differentiation can also be recapitulated in this model. Moreover, mature NK cell phenotype showed significant differences in the AMO compared to a conventional 2D coculture model for the expression of adhesion molecules and immune checkpoint receptors, thus better reflecting the NK cell behaviour in the BM microenvironment. Lastly, we proved that our model is suitable for evaluating anti-leukemic NK cell function in presence of treatments. Overall, the AMO is a versatile, low cost and simple model able to efficiently recapitulate hematopoiesis and granting better drug response taking into account both immune and non-immune BM microenvironment interactions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/575527v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@f9288corg.highwire.dtl.DTLVardef@1d51c8corg.highwire.dtl.DTLVardef@4563a9org.highwire.dtl.DTLVardef@1920e3c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs [~]91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

genomics↗