Biology subjects

Zhu, J.

Publications and source records attributed to Zhu, J..

At least 343 records · Page 19Linked to original sources

bioRxiv2023

Tumor-derived exosomal miR-222-3p induce cancer-associated fibroblasts activation to foster progression of renal cancer

The interaction between tumor-derived exosomes and stroma is crucial for tumor progression. However, the mechanisms by which tumor cells influence stromal changes are not yet fully understood. Our study revealed that high-metastatic renal cancer cells are more effective in converting normal fibroblasts into cancer-associated fibroblasts (CAFs) compared to low-metastatic renal cancer cells. Meanwhile, high-metastatic renal cancer cells secrete more exosomal miR-222-3p, which can directly target PANK3, activate NF-kB signaling pathway in fibroblasts and induce intracellular metabolic reprogramming to produce more lactic acid through Warburg effect. The activated CAFs further promote renal cancer progression by secreting lactic acid and inflammatory cytokines, including IL-6 and IL-8. Patients with renal cancer who have high levels of serum exocrine miR-222-3p are more likely to experience progression. These findings suggest that the intercellular communication between renal cancer cells and fibroblasts is facilitated by tumor exosomes. Targeting this communication may hold promise for the prevention and treatment of renal cancer.

cancer biology↗

bioRxiv2023

GRAS1 non-coding RNA protects against DNA damage and cell death by binding NKAP

Non-coding RNA (ncRNA) gene products are involved in diverse biological processes including splicing, epigenetic regulation, gene expression, proliferation, and metabolism. The biological mechanisms by which ncRNAs contribute to cell survival remain poorly understood. We found that the Growth Regulator Antisense 1 (GRAS1) long non-coding RNA (lncRNA) transcript promotes growth in multiple human cell types by protecting against DNA damage. Knockdown of GRAS1 induced DNA damage and cell death, along with significant expression changes in DNA damage response, intrinsic apoptotic signaling, and cellular response to environmental stimulus genes. Extensive DNA damage occurred after GRAS1 knockdown, with numerous double strand breaks occurring in each cell. The number of cells undergoing apoptosis and with fragmented nuclei increased significantly after GRAS1 knockdown. We used RNA antisense purification and mass spectrometry (RAP-MS) to identify the NF-{kappa}B activating protein (NKAP) as a direct protein interaction partner of GRAS1 lncRNA. NKAP protein was degraded after GRAS1 knockdown, in a proteasome-dependent manner. Overexpression of GRAS1 or NKAP mitigated the DNA damage effects of GRAS1 knockdown. In summary, GRAS1 and NKAP directly interact to protect against DNA damage and cell death in multiple human cell lines.

molecular biology↗

bioRxiv2023

Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis.

Hepatic ischemia/reperfusion injury (HIRI) is a common and inevitable factor leading to poor prognosis in various liver diseases, making the outcomes of current treatments in clinic unsatisfactory. Metformin has been demonstrated to be beneficial to alleviate HIRI in recent studies, however, the underpinning mechanism remains unclear. In this study, we found metformin mitigates HIRI-induced ferroptosis through reshaped gut microbiota in mice, which was confirmed by the results of fecal microbiota transplantation (FMT) treatment but showed the elimination of the beneficial effects when gut bacteria were depleted using antibiotics. Detailedly, through 16S rRNA and metagenomic sequencing, we identified that the metformin-reshaped microbiota was characterized by the increase of gamma-aminobutyric acid (GABA) producing bacteria. This increase was further confirmed by the elevation of GABA synthesis key enzymes, glutamic acid decarboxylase (GAD) and putrescine aminotransferase (PAT), in gut microbes of metformin-treated mice and healthy volunteers. Furthermore, the benefit of GABA against HIRI-induced ferroptosis was demonstrated in GABA-treated mice. Collectively, our data indicate that metformin can mitigate HIRI-induced ferroptosis by reshaped gut microbiota, with GABA identified as a key metabolite.

microbiology↗

bioRxiv2023

Single-component multilayered self-assembling protein nanoparticles displaying extracellular domains of matrix protein 2 as a pan-influenza A vaccine

The development of a cross-protective pan-influenza A vaccine remains a significant challenge. In this study, we designed and evaluated single-component self-assembling protein nanoparticles (SApNPs) presenting the conserved extracellular domain of matrix protein 2 (M2e) as vaccine candidates against influenza A viruses. The SApNP-based vaccine strategy was first validated for human M2e (hM2e) and then applied to tandem repeats of M2e from human, avian and swine hosts (M2ex3). Vaccination with M2ex3 displayed on SApNPs demonstrated higher survival rates and lower weight loss compared to the soluble M2ex3 antigen against lethal challenges of H1N1 and H3N2 in mice. M2ex3 I3-01v9a SApNPs formulated with a squalene-based adjuvant were retained in the lymph node follicles over eight weeks and induced long-lived germinal center reactions. Notably, a single low dose of M2ex3 I3-01v9a SApNP formulated with a potent adjuvant, either a Toll-like receptor 9 (TLR9) agonist or a stimulator of interferon genes (STING) agonist, conferred 90% protection against a lethal H1N1 challenge in mice. With the ability to induce robust and durable M2e-specific functional antibody and T cell responses, the M2ex3-presenting I3-01v9a SApNP provides a promising pan-influenza A vaccine candidate. ONE-SENTENCE SUMMARYSingle-component self-assembling protein nanoparticles (SApNPs) displaying tandem M2e elicit robust and durable immunity that may protect against influenza A viruses of diverse origins.

microbiology↗

bioRxiv2023

Tuning methylation-dependent silencing dynamics by synthetic modulation of CpG density

Methylation of cytosines in CG dinucleotides (CpGs) within promoters has been shown to lead to gene silencing in mammals in natural contexts. Recently, engineered recruitment of methyltransferases (DNMTs) at specific loci was shown to be sufficient to silence synthetic and endogenous gene expression through this mechanism. A critical parameter for DNA methylation-based silencing is the distribution of CpGs within the target promoter. However, how the number or density of CpGs in the target promoter affects the dynamics of silencing by DNMT recruitment has remained unclear. Here we constructed a library of promoters with systematically varying CpG content, and analyzed the rate of silencing in response to recruitment of DNMT. We observed a tight correlation between silencing rate and CpG content. Further, methylation-specific analysis revealed a constant accumulation rate of methylation at the promoter after DNMT recruitment. We identified a single CpG site between TATA box and transcription start site (TSS) that accounted for a substantial part of the difference in silencing rates between promoters with differing CpG content, indicating that certain residues play disproportionate roles in controlling silencing. Together, these results provide a library of promoters for synthetic epigenetic and gene regulation applications, as well as insights into the regulatory link between CpG content and silencing rate.

synthetic biology↗

bioRxiv2023

Functional interrogation of twenty type 2 diabetesassociated genes using isogenic hESC-derived β-like cells

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined {beta}-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-{beta} cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes (CP, RNASE1, PCSK1N and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.

genetics↗

bioRxiv2023

Externally provided rewards increase internal preference, but not as much as preferred ones without extrinsic rewards

It is well known that preferences are formed through choices, known as choice-induced preference change (CIPC). However, whether value learned through externally provided rewards influences the preferences formed through CIPC remains unclear. To address this issue, we used tasks for decision-making guided by reward provided by the external environment (externally guided decision-making; EDM) and for decision-making guided by ones internal preference (internally guided decision-making; IDM). In the IDM task, we presented stimuli with learned value in the EDM and novel stimuli to examine whether the value in the EDM affects preferences. Stimuli reinforced by rewards given in the EDM were reflected in the IDMs initial preference and further increased through CIPC in the IDM. However, such stimuli were not as strongly preferred as the most preferred novel stimulus in the IDM, indicating the superiority of intrinsically learned values (SIV). The underlying process of this phenomenon is discussed in terms of the fundamental self-hypothesis. Author SummaryWe make decisions based on internal value criteria, which are individual preferences, or based on external value criteria, which are the values learned from the external environment. Although it is known that values are learned in both types of decisions, is there a difference in the nature of these values? Our study uses simulation and fits human behavioral data to address this question. The results showed that stimuli that were learned to be highly valued because of external feedback became preferred in subsequent preference judgments. However, it is interesting to note that such stimuli were not chosen as much as stimuli that were preferred without influence from the external environment. This finding suggests that values formed through ones own criteria have characteristics distinct from those formed through external environmental influence. Our findings promote an integrated understanding of the decision-making process.

animal behavior and cognition↗

bioRxiv2023

Family-wide analysis of integrin structures predicted by AlphaFold2

Recent advances in protein structure prediction using AlphaFold2, known for its high efficiency and accuracy, have opened new avenues for comprehensive analysis of all structures within a single protein family. In this study, we evaluated the capabilities of AphaFold2 in analyzing integrin structures. Integrins are heterodimeric cell surface receptors composed of a combination of 18 and 8 {beta} subunits, resulting in a family of 24 different members. Both and {beta} subunits consist of a large extracellular domain, a short transmembrane domain, and typically, a short cytoplasmic tail. Integrins play a pivotal role in a wide range of cellular functions by recognizing diverse ligands. Despite significant advances in integrin structural studies in recent decades, high-resolution structures have only been determined for a limited subsets of integrin members, thus limiting our understanding of the entire integrin family. Here, we first analyzed the single-chain structures of 18 and 8 {beta} integrins in the AlphaFold2 protein structure database. We then employed the newly developed AlphaFold2-multimer program to predict the /{beta} heterodimer structures of all 24 human integrins. The predicted structures show a high level of accuracy for the subdomains of both and {beta} subunits, offering high-resolution structure insights for all integrin heterodimers. Our comprehensive structural analysis of the entire integrin family unveils a potentially diverse range of conformations among the 24 members, providing a valuable structure database for studies related to integrin structure and function. We further discussed the potential applications and limitations of the AlphaFold2-derived integrin structures.

cell biology↗

bioRxiv2023

A neural model of modified excitation/inhibition and feedback levels in schizophrenia

The strength of certain visual illusions is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. To investigate whether and how these factors influence the perceived illusions in individuals with schizophrenia, we propose a two-layer network that can model visual receptive fields (RFs), their inhibitory and excitatory subfields, and the top-down feedback. Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups.

neuroscience↗

bioRxiv2023

Cancer heterogeneity is defined by normal cellular trade-offs

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2-breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

cancer biology↗

bioRxiv2023

Discovery and Engineering of the L-Threonine Aldolase from Neptunomonas Marine for Efficient Synthesis of β-Hydroxy-α-Amino Acids via C-C Formation

O_SCPLOWLC_SCPLOW-Threonine aldolases (LTAs) are attractive biocatalysts for synthesizing {beta}-hydroxy--amino acids (HAAs) via C-C bond formation in pharmaceuticals, although their industrial applications suffer from low activity and diastereoselectivity. Herein, we describe the discovery of a new LTA from Neptunomonas marine (NmLTA) that displays both ideal enzymatic activity (64.8 U/mg) and diastereoselectivity (89.5% diastereomeric excess; de) for the desired product O_SCPLOWLC_SCPLOW-threo-4-methylsulfonylphenylserine (O_SCPLOWLC_SCPLOW-threo-MPTS). Using X-ray crystallography, site-directed mutagenesis, and computational modeling, we propose a "dual-conformation" mechanism for the diastereoselectivity control of NmLTA, whereby the incoming 4-methylsulfonylbenzaldehyde (4-MTB) could potentially bind at the NmLTA active site in two distinct orientations, potentially forming two diastereoisomers (threo- or erythro-form products). Importantly, two key NmLTA residues H140 and Y319 play critical roles in fine-tuning the binding mode of 4-MTB, supported by our site-mutagenesis assays. Uncovering of the catalytic mechanism in NmLTA guides us to further improve the diastereoselectivity of this enzyme. A triple variant of NmLTA (N18S/Q39R/Y319L; SRL) exhibited both improved diastereoselectivity (de value > 99%) and enzymatic activity (95.7 U/mg) for the synthesis of O_SCPLOWLC_SCPLOW-threo-MPTS compared with that of wild type. The preparative gram-scale synthesis for O_SCPLOWLC_SCPLOW-threo-MPTS with the SRL variant produced a space-time yield of up to 9.0 g L-1h-1, suggesting a potential role as a robust C-C bond synthetic tool for industrial synthesis of HAAs at a preparative scale. Finally, the SRL variant accepted a wider range of aromatic aldehyde derivatives as substrates and exhibited improved diastereoselectivity toward para-site substituents. This work provides deep structural insights into the molecular mechanism underlying the catalysis in NmLTA and pinpoints the key structural motifs responsible for regulating the diastereoselectivity control, thereby guiding future attempts for protein engineering of various LTAs from different sources.

biochemistry↗

bioRxiv2023

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival. STATEMENT OF SIGNIFICANCEMultiple myeloma has heterogenous clinical outcomes which are not well predicted by current prognostic scoring systems. Global assessment of gene-protein interactions using Ollivier-Ricci Curvature produces clusters of patients with defined prognostic significance, with high-risk groups harboring complex gene dysregulation impacting immune function.

cancer biology↗

bioRxiv2023

Modeling cross-cell type cis-regulatory patterns via hierarchical deep neural network and gene expression prediction

Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of RNA-seq and ChIP-seq data only, allows for the readily generalization of CREaTor to a broad range of cell types. Evaluation reveals that CREaTor outperforms existing methods in capturing cCRE-gene interactions across various distance ranges in held-out cell types. Further analysis indicates that the superior performance of CREaTor can be attributed to its capacity to model regulatory interactions at multiple levels, including the higher-order genome organizations that govern cCRE activities as well as cCRE-gene interactions. Collectively, our findings highlight CREaTor as a powerful tool for systematically investigating cis-regulatory programs across various cell types, both in normal developmental processes and disease-associated contexts.

genetics↗

bioRxiv2023

Metabolic regulation of misfolded protein import into mitochondria

Mitochondria are the cellular energy hub and central target of metabolic regulation. Mitochondria also facilitate proteostasis through pathways such as the mitochondria as guardian in cytosol (MAGIC) whereby cytosolic misfolded proteins (MPs) are imported into and degraded inside mitochondria. In this study, a genome-wide screen in yeast uncovered that Snf1, the yeast AMP-activated protein kinase (AMPK), inhibits the import of MPs into mitochondria while promoting mitochondrial biogenesis under glucose starvation. We show that this inhibition requires a downstream transcription factor regulating mitochondrial gene expression and is likely to be conferred through substrate competition and mitochondrial import channel selectivity. We further show that Snf1/AMPK activation protects mitochondrial fitness in yeast and human cells under stress induced by MPs such as those associated with neurodegenerative diseases.

cell biology↗

bioRxiv2023

Learning Perturbation-specific Cell Representations for Prediction of Transcriptional Response across Cellular Contexts

High-throughput screens (HTS) are widely utilized to profile transcriptional states across multiple cell types and perturbations, and are often the first step on the bridge to the patient. However, their representative capacity to encompass all the cellular contexts encountered in a patient is limited. Thus, we present PerturbX, a novel deep learning model that leverages the rich information obtained from HTS to predict transcriptional responses to chemical or genetic perturbations in unobserved cellular contexts, and demonstrate its effectiveness in an experimental setting. Further-more, we show that the model is able to uncover interpretable genetic signatures associated with the predicted response, which can ultimately be translated into the clinical setting.

systems biology↗

bioRxiv2023

Cryo-EM structure of the human Sirtuin 6-nucleosome complex

Sirtuin 6 (SIRT6) is a multifaceted protein deacetylase/deacylase and a major target for small-molecule modulators of longevity and cancer. In the context of chromatin, SIRT6 removes acetyl groups from histone H3 in nucleosomes, but the molecular basis for its nucleosomal substrate preference is unknown. Our cryo-electron microscopy structure of human SIRT6 in complex with the nucleosome shows that the catalytic domain of SIRT6 pries DNA from the nucleosomal entry-exit site and exposes the histone H3 N-terminal helix, while the SIRT6 zinc-binding domain binds to the histone acidic patch using an arginine anchor. In addition, SIRT6 forms an inhibitory interaction with the C-terminal tail of histone H2A. The structure provides insights into how SIRT6 can deacetylate both H3 K9 and H3 K56. TeaserThe structure of the SIRT6 deacetylase/nucleosome complex suggests how the enzyme acts on both histone H3 K9 and K56 residues.

biochemistry↗

bioRxiv2023

Vaccination with mycobacterial lipid loaded nanoparticle leads to lipid antigen persistence and memory differentiation of antigen-specific T cells

Mycobacterium tuberculosis (Mtb) infection elicits both protein and lipid antigen-specific T cell responses. However, the incorporation of lipid antigens into subunit vaccine strategies and formulations has been underexplored, and the characteristics of vaccine-induced Mtb lipid-specific memory T cells have remained elusive. Mycolic acid (MA), a major lipid component of the Mtb cell wall, is presented by human CD1b molecules to unconventional T cell subsets. These MA-specific CD1b-restricted T cells have been detected in the blood and disease sites of Mtb-infected individuals, suggesting that MA is a promising lipid antigen for incorporation into multicomponent subunit vaccines. In this study, we utilized the enhanced stability of bicontinuous nanospheres (BCN) to efficiently encapsulate MA for in vivo delivery to MA-specific T cells, both alone and in combination with an immunodominant Mtb protein antigen (Ag85B). Pulmonary administration of MA-loaded BCN (MA-BCN) elicited MA-specific T cell responses in humanized CD1 transgenic mice. Simultaneous delivery of MA and Ag85B within BCN activated both MA- and Ag85B-specific T cells. Notably, pulmonary vaccination with MA-Ag85B-BCN resulted in the persistence of MA, but not Ag85B, within alveolar macrophages in the lung. Vaccination of MA-BCN through intravenous or subcutaneous route, or with attenuated Mtb likewise reproduced MA persistence. Moreover, MA-specific T cells in MA-BCN-vaccinated mice differentiated into a T follicular helper-like phenotype. Overall, the BCN platform allows for the dual encapsulation and in vivo activation of lipid and protein antigen-specific T cells and leads to persistent lipid depots that could offer long-lasting immune responses.

immunology↗

bioRxiv2023

Uni-Fold MuSSe: De Novo Protein Complex Prediction with Protein Language Models

AO_SCPLOWBSTRACTC_SCPLOWAccurately solving the structures of protein complexes is crucial for understanding and further modifying biological activities. Recent success of AlphaFold and its variants shows that deep learning models are capable of accurately predicting protein complex structures, yet with the painstaking effort of homology search and pairing. To bypass this need, we present Uni-Fold MuSSe (Multimer with Single Sequence inputs), which predicts protein complex structures from their primary sequences with the aid of pre-trained protein language models. Specifically, we built protein complex prediction models based on the protein sequence representations of ESM-2, a large protein language model with 3 billion parameters. In order to adapt the language model to inter-protein evolutionary patterns, we slightly modified and further pre-trained the language model on groups of protein sequences with known interactions. Our results highlight the potential of protein language models for complex prediction and suggest room for improvements.

bioinformatics↗