Biology subjects

Mi, Z.

Publications and source records attributed to Mi, Z..

4 recordsLinked to original sources

bioRxiv2025

Restoration of tumor suppressor protein with enhanced activity using engineered tRNAs to induce tumor regression

Cancer develops through a gradual accumulation of mutations including nonsense mutations in tumor suppressor genes (TSGs), which drive tumor proliferation and progression due to the impairment or complete loss of tumor suppressor protein function. The ability to restore functional tumor suppressor protein from mutant driver TSGs is a longstanding aspiration in cancer therapy, however, current strategies face formidable challenges in recovering proteins harboring heterogeneous mutations and circumventing dominant-negative effects exerted by endogenous mutant proteins. Therefore, the development of therapeutic interventions that are effective and generalizable across diverse nonsense mutations in cancer, to rescue endogenous mutant tumor suppressor proteins remains an urgent yet unmet need. Here we find that engineered suppressor transfer RNA (sup-tRNA) could overcome nonsense mutations in key driver tumor suppressor genes, such as TP53, thereby avoiding the dominant-negative effects through the restoration of endogenous mutant p53 with its original functions. Optimized sup-tRNA that we named super tRNA, enabling high-fidelity incorporation of desired amino acids, can further enhanced the transcriptional activity of p53 to suppress tumor cell proliferation. Moreover, we also establish a computational model to guide super tRNAs design, and demonstrate that super tRNA could effectively inhibit tumor growth by targeted readthrough of nonsense mutations in different tumor suppressor genes. In two xenograft models harboring nonsense mutant TP53, we observed up to a 70% reduction in tumor volume after lipid nanoparticle (LNP)-based sup-tRNA treatment, along with robust recovery of p53 expression and function, and no hematological or histological abnormalities detected. Our findings demonstrate the potential of sup-tRNA as a feasible and effective strategy to rescue and enhance tumor suppressor protein function, thereby preventing cancer development. As a novel therapeutic RNA, sup-tRNA provides a flexible strategy for targeting driver mutations in cancer, thereby expanding the potential of tRNA-based therapy in precision oncology.

bioengineering↗

bioRxiv2021

KIM-1-mediated anti-inflammatory activity is preserved by MUC1 induction in the proximal tubule during ischemia-reperfusion injury.

Cell-associated kidney injury molecule-1 (KIM-1) exerts an anti-inflammatory role following kidney injury by mediating efferocytosis and downregulating the NF-{kappa}B pathway. KIM-1 cleavage blunts its anti-inflammatory activities. We reported that Mucin 1 (MUC1) is protective in a mouse model of ischemia-reperfusion injury (IRI). As both KIM-1 and MUC1 are induced in the proximal tubule (PT) during IRI and are ADAM17 substrates, we tested the hypothesis that MUC1 protects KIM-1 activity. Muc1 KO mice and wild-type (WT) littermates were subjected to IRI. KIM-1, MUC1 and ADAM17 levels (and signaling pathways) were assessed by immunoblotting. PT localization was assessed by confocal microscopy and in situ proximity ligation assay. Findings were extended using human kidneys and urine, and KIM-1-mediated efferocytosis assays in mouse PT cultures. In response to tubular injury in mouse and human kidneys, we observed induction and co-expression of KIM-1 and MUC1 in the PT. Compared to WT, Muc1 KO mice had higher urinary KIM-1 and lower kidney KIM-1. KIM-1 was apical in PT of WT kidneys, but predominately with luminal debris in Muc1 KO mice. Efferocytosis was reduced in Muc1 KO PT cultures when compared to WT cells, while inflammation was increased in Muc1 KO kidneys when compared to WT mice. MUC1 was cleaved by ADAM17 in PT cultures, and blocked KIM-1 shedding in MDCK cells. We conclude that KIM-1-mediated efferocytosis and thus anti-inflammatory activity during IRI is preserved in the injured kidney by MUC1 inhibition of KIM-1 shedding. NEW & NOTEWORTHYKIM-1 plays a key role in the recovery of the tubule epithelium during renal IRI by mediating efferocytosis and associated signaling that suppresses inflammation. Excessive cleavage of KIM-1 by ADAM17 provides decoy receptor that aggravates efferocytosis and subsequent signaling. Our data from studies in mice, patients and cultured cells show that MUC1 is also induced during IRI and competes with KIM-1 for cleavage by ADAM17. Consequently, MUC1 protects KIM-1 anti-inflammatory activity in the damaged kidney.

pathology↗

bioRxiv2020

DReSS: A difference measurement based on reachability between state spaces of Boolean networks

Researches on dynamical features of biological systems are mostly based on fixed network structure. However, both biological factors and data factors can cause structural perturbations to biological regulatory networks. There are researches focus on the influence of such structural perturbations to the systems dynamical features. Reachability is one of the most important dynamical features, which describe whether a state can automatically evolve into another state. However, there is still no method can quantitively describe the reachability differences of two state spaces caused by structural perturbations. DReSS, Difference based on Reachability between State Spaces, is proposed in this research to solve this problem. First, basic properties of DReSS such as non-negativity, symmetry and subadditivity are proved based on the definition. And two more indexes, diagDReSS and iDReSS are proposed based on the definition of DReSS. Second, typical examples like DReSS = 0 or 1 are shown to explain the meaning of DReSS family, and the differences between DReSS and traditional graph distance are shown based on the calculation steps of DReSS. Finally, differences of DReSS distribution between real biological regulatory network and random networks are compared. Multiple interaction positions in real biological regulatory network show significant different DReSS value with those in random networks while none of them show significant different diagDReSS value, which illustrates that the structural perturbations tend to affect reachability inside and between attractor basins rather than to affect attractor set itself. Author summaryBoolean network is a kind of networks which is widely used to model biological regulatory systems. There are structural perturbations in biological systems based on both biological factors and data-related factors. We propose a measurement called DReSS to describe the difference between state spaces of Boolean networks, which can be used to evaluate the influence of specific structural perturbations of a network to its state space quantitively. We can use DReSS to detect the sensitive interactions in a regulatory network, where structural perturbations can influence its state space significantly. We proved properties of DReSS including non-negativity, symmetry and subadditivity, and gave examples to explain the meaning of some special DReSS values. Finally, we present an example of using DReSS to detect sensitive vertexes in yeast cell cycle regulatory network. DReSS can provide a new perspective on how different interactions affect the state space of a specific regulatory network differently.

bioinformatics↗

bioRxiv2019

Advanced Whole Genome Sequencing Using a Complete PCR-free Massively Parallel Sequencing (MPS) Workflow

BackgroundSystematic errors can be introduced from DNA amplification during massively parallel sequencing (MPS) library preparation and sequencing array formation. Polymerase chain reaction (PCR)-free genomic library preparation methods were previously shown to improve whole genome sequencing (WGS) quality on the Illumina platform, especially in calling insertions and deletions (InDels). We hypothesized that substantial InDel errors continue to be introduced by the remaining PCR step of DNA cluster generation. In addition to library preparation and sequencing, data analysis methods are also important for the accuracy of the output data.In recent years, several machine learning variant calling pipelines have emerged, which can correct the systematic errors from MPS and improve the data performance of variant calling. ResultsHere, PCR-free libraries were sequenced on the PCR-free DNBSEQ arrays from MGI Tech Co., Ltd. (referred to as MGI) to accomplish the first true PCR-free WGS which the whole process is truly not only PCR-free during library preparation but also PCR-free during sequencing. We demonstrated that PCR-based WGS libraries have significantly (about 5 times) more InDel errors than PCR-free libraries.Furthermore, PCR-free WGS libraries sequenced on the PCR-free DNBSEQ platform have up to 55% less InDel errors compared to the NovaSeq platform, confirming that DNA clusters contain PCR-generated errors.In addition, low coverage bias and less than 1% read duplication rate was reproducibly obtained in DNBSEQ PCR-free using either ultrasonic or enzymatic DNA fragmentation MGI kits combined with MGISEQ-2000. Meanwhile, variant calling performance (single-nucleotide polymorphisms (SNPs) F-score>99.94%, InDels F-score>99.6%) exceeded widely accepted standards using machine learning (ML) methods (DeepVariant or DNAscope). ConclusionsEnabled by the new PCR-free library preparation kits, ultra high-thoughput PCR-free sequencers and ML-based variant calling, true PCR-free DNBSEQ WGS provides a powerful solution for improving WGS accuracy while reducing cost and analysis time, thus facilitating future precision medicine, cohort studies, and large population genome projects.

genomics↗