Biology subjects

Dai, L.

Publications and source records attributed to Dai, L..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Multi-omics reveals AKR1B1-regulated galactose metabolic as a driver of gastrointestinal stromal tumor progression

The underlying mechanism of malignant progression in gastrointestinal stromal tumors (GISTs) is not fully understood. Despite recent advancements, a comprehensive profile of metabolome, transcriptome, and proteome of GISTs is lacking. This study conducted an integrated multi-omics analysis of GISTs across different risk classifications. By integrating metabolomics, transcriptomics, and proteomics, we identify distinct metabolic patterns and associated biological pathways implicated in the malignant progression of GISTs. Moreover, we identified galactose metabolism and the pivotal rate-limiting enzyme AKR1B1 is dysregulated in GISTs progression. AKR1B1 was upregulated and predicted poor prognosis in GISTs. In addition, AKR1B1 knockdown resulted in trehalose accumulation in GIST cells, thereby inhibiting cell proliferation and mitosis. These findings not only enhance our comprehension of the underlying mechanisms governing GIST progression from a metabolic reprogramming standpoint but also furnish prognostic biomarkers and potential therapeutic targets for GISTs.

cancer biology↗

bioRxiv2024

Ultrasensitive amplification-free quantification of a methyl CpG-rich cancer biomarker by single-molecule kinetic fingerprinting

The most well-studied epigenetic marker in humans is the 5-methyl modification of cytosine in DNA, which has great potential as a disease biomarker in liquid biopsies of cell-free DNA. Currently, quantification of DNA methylation relies heavily on bisulfite conversion followed by PCR amplification and NGS or microarray analysis. PCR is subject to potential bias in differential amplification of bisulfite-converted methylated versus unmethylated sequences. Here, we combine bisulfite conversion with single-molecule kinetic fingerprinting to develop an amplification-free assay for DNA methylation at the branched-chain amino acid transaminase 1 (BCAT1) promoter. Our assay selectively responds to methylated sequences with a limit of detection below 1 fM and a specificity of 99.9999%. Evaluating complex genomic DNA matrices, we reliably distinguish 2-5% DNA methylation at the BCAT1 promoter in whole blood DNA from completely unmethylated whole-genome amplified DNA. Taken together, these results demonstrate the feasibility and sensitivity of our amplification-free, single-molecule quantification approach to improve the early detection of methylated cancer DNA biomarkers.

bioengineering↗

bioRxiv2024

CRISPR/Cas9 gene editing in Drosophila via visual selection in a summer classroom

CRISPR/Cas9 methods are a powerful in vivo approach to edit the genome of Drosophila melanogaster. To convert existing Drosophila GAL4 lines to LexA driver lines in a secondary school classroom setting, we applied the CRISPR-based genetic approach to a collection of Gal4 driver lines. The integration of the yellow+ coat color marker into homology-assisted CRISPR knock-in (HACK) enabled visual selection of Gal4-to-LexA conversions using brightfield stereo-microscopy available in a broader set of standard classrooms. Here, we report the successful conversion of eleven Gal4 lines with expression in neuropeptide-expressing cells into corresponding, novel LexA drivers. The conversion was confirmed by LexA- and Gal4-specific GFP reporter gene expression. This curriculum was successfully implemented in a summer course running 16 hours/week for seven weeks. The modularity, flexibility, and compactness of this course should enable development of similar classes in secondary schools and undergraduate curricula, to provide opportunities for experience-based science instruction, and university-secondary school collaborations that simultaneously fulfill research needs in the community of science.

genetics↗

bioRxiv2024

Molecular basis of inhibition of the amino acid transporter B0AT1 (SLC6A19)

The epithelial neutral amino acid transporter B0AT1 (SLC6A19) is the major transporter for the absorption of neutral amino acids in the intestine and their reabsorption in the kidney. Mouse models have demonstrated that lack of B0AT1 can normalize elevated plasma amino acids in rare disorders of amino acid metabolism such as phenylketonuria and urea-cycle disorders. This discovery has resulted in a quest to identify high-affinity selective inhibitors of B0AT1 for the treatment of disorders of amino acid metabolism. Here we employed a medicinal chemistry approach to improve lead compounds that were identified in a previous high-throughput screen. This effort yielded a group of compounds that inhibited B0AT1 with IC50-values of 31-90 nM. High-resolution cryo-EM structures of B0AT1 in the presence of two compounds from this series identified an allosteric binding site in the vestibule of the transporter. Mechanistically, binding of these inhibitors prevents a substantial movement of TM1 and TM6 that is required for the transporter to make a conformational change from an outward open state to the occluded state.

biochemistry↗

bioRxiv2024

Degradation of indole-3-acetic acid by plant-associated microbes

Plant-associated microbiota affect pant growth and development by regulating plant hormones homeostasis. Indole-3-acetic acid (IAA), a well-known plant hormone, can be produced by various plant-associated bacteria. However, the prevalence of microbes with the capacity to degrade IAA in the rhizosphere has not been systematically studied. In this study, we analyzed the IAA degradation capabilities of bacterial isolates from the roots of Arabidopsis and rice. Using genomics analysis and in vitro assays, we found that 21 out of 189 taxonomically diverse bacterial isolates possess the ability to degrade IAA. Through comparative genomics and transcriptomic assays, we identified iac-like or iad-like operon in the genomes of these IAA degraders. Additionally, the regulator of the operon was found to be highly conserved among these strains through protein structure similarity analysis. Some of the IAA degraders could utilize IAA as their sole carbon and energy source. In planta, most of the IAA degrading strains mitigated Arabidopsis seedling root growth inhibition (RGI) triggered by exogenous IAA. Importantly, we observed increased colonization preference of IAA degraders from soil to root according to the frequency of the biomarker genes in metagenome-assembled genomes (MAGs) collected from different habitats, suggesting that there is a close association between IAA degraders and IAA-producers. In summary, our findings further the understanding of the functional diversity and roles of plant-associated microbes.

microbiology↗

bioRxiv2024

Integrative Omics Uncovers Low Tumorous Magnesium Content as A Driver Factor of Colorectal Cancer

Magnesium (Mg) deficiency is associated with increased risk and malignancy of colorectal cancer (CRC), yet the underlying mechanisms remain elusive. Here we used genomic, proteomic, and phosphoproteomic data to elucidate the impact of Mg deficiency on CRC. Genomic analysis identified 160 genes with higher mutation frequencies in Low-Mg tumors, including key driver genes such as KMT2C and ERBB3. Unexpectedly, initiation driver genes of CRC, such as TP53 and APC, displayed higher mutation frequencies in High-Mg tumors. Additionally, proteomics and phosphoproteomics indicated that low tumorous Mg content may activate epithelial-mesenchymal transition (EMT) by modulating inflammation or remodeling the phosphoproteome of cancer cells. Notably, we observed a negative correlation between the phosphorylation of DBN1 at S142 (DBN1S142p) and Mg content. A mutation in S142 to D (DBN1S142D) mimicking DBN1S142p upregulated MMP2 and enhanced cell migration, while treatment with MgCl2 reduced DBN1S142p, thereby reversing this phenotype. Mechanistically, Mg2+ attenuated the DBN1-ACTN4 interaction by decreasing DBN1S142p, which, in turn, enhanced the binding of ACTN4 to F-actin and promoted F-actin polymerization, ultimately reducing MMP2 expression. These findings shed new light on the crucial role of Mg deficiency in CRC progression and suggest that Mg supplementation may offer a promising preventive and therapeutic strategy for CRC.

cancer biology↗

bioRxiv2023

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.

bioinformatics↗

bioRxiv2023

SARS-CoV-2 Omicron BA.2.86: less neutralization evasion compared to XBB sub-variants

The continual emergence and circulation of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have caused a great challenge for the coronavirus disease 2019 (COVID-19) pandemic control. Recently, Omicron BA.2.86 was identified with more than 30 amino acid changes on the spike (S) protein, compared to Omicron BA.2 or XBB.1.5. The immune evasion potential of BA.2.86 is of great concern. In this study, we evaluated the neutralizing activities of sera collected from participants and mice. Participants were divided into five groups according to their vaccination (inactivated vaccine, protein subunit vaccine ZF2001 or ZF2202-A) and infection (Omicron BF.7/BA.5.2) status. ZF2202-A is ZF2001 vaccines next-generation COVID-19 vaccine with updated bivalent Delta-BA.5 RBD-heterodimer immunogen. BALB/c mice were immunized with XBB.1.5 RBD-homodimer, BA.5-BA.2, Delta-XBB.1.5 or BQ.1.1-XBB.1.5 RBD-heterodimers protein vaccine candidates for evaluating the neutralizing responses. We found that Omicron BA.2.86 shows stronger immune evasion than BA.2 due to >30 additional mutations on S protein. Compared to XBB sub-variants, BA.2.86 does not display more resistance to the neutralizing responses induced by ZF2001-vaccination, BF.7/BA.5.2 breakthrough infection or a booster dose of ZF2202-A-vaccination. In addition, the mouse experiment results showed that BQ.1.1-XBB.1.5 RBD-heterodimer and XBB.1.5 RBD-homodimer induced high neutralizing responses against XBB sub-variants and BA.2.86, indicating that next-generation COVID-19 vaccine should be developed to enhance the protection efficacy against the circulating strains in the future.

immunology↗

bioRxiv2023

Heterologous naringenin production in the filamentous fungus Penicillium rubens

Naringenin is a natural product with several reported bioactivities and is the key intermediate for the entire class of plant flavonoids. The translation of flavonoids into modern medicine as pure compounds is often hampered by their low abundance in nature and difficult chemical synthesis. Here, we investigated new avenues toward producing high levels of naringenin in microbial hosts. Penicillium rubens is a well characterized and highly engineered traditional "workhorse" for the production of {beta}-lactam antibiotics and cholesterol-lowering statins. We explored a secondary metabolite deficient P. rubens strain, P. rubens 4xKO, that was derived from an earlier industrial production strain as a promising microbial host for a recombinant flavonoid pathway. By integrating two plant genes encoding for enzymes in the naringenin biosynthesis pathway into the genome of this strain, we achieved a high naringenin titer in flask fermentations 36 h after feeding the precursor p-coumaric acid. Along with the rapid product accumulation of up to an 88% molar yield, we also observed rapid degradation of naringenin. Based on high-resolution mass spectrometric analysis, we identified the degradation products and proposed a naringenin degradation pathway in P. rubens 4xKO, which is distinct from other flavonoid-converting pathways reported in fungi. Our approach combines fundamental research with application-oriented microbial engineering, and our findings will pave the way to the more sustainable and economically feasible production of flavonoids for pharmaceutical and nutraceutical applications.

biochemistry↗

bioRxiv2023

Neutralization of SARS-CoV-2 EG.5/EG.5.1 by sera from ZF2001 RBD-dimer and its next-generation vaccines

SARS-CoV-2 Omicron EG.5 and EG.5.1 are surging in several areas of the world, including China. Compared with XBB.1, EG.5 contains additional mutations of F456L and S486P in the spike protein receptor binding domain (RBD) and its subvariant EG.5.1 carries a further spike mutation Q52H. The immune escape potential of EG.5/EG.5.1 is of great concern. In this study, we evaluated the neutralization activities of sera from participants who received COVID-19 inactivated vaccines, protein subunit vaccine ZF2001 or a booster vaccination of Delta-BA.5 RBD-heterodimer protein vaccine, and participants who had a breakthrough infection during a wave of BF.7/BA.5.2 circulation in December 2022. Neutralization profiles elicited by bivalent RBD-heterodimer vaccine candidates containing XBB.1.5 antigen were evaluated in a murine model. We found that EG.5 and EG.5.1 displayed similar immune evasion potential to XBB.1 and XBB.1.5. The Delta-BA.5 RBD-heterodimer booster induced higher neutralizing titers against the tested XBB subvariants, including EG.5 and EG.5.1, than breakthrough infection by BF.7 or BA.5.2. In addition, Delta-XBB.1.5 and BQ.1.1-XBB.1.5 RBD-heterodimer vaccines induced high neutralizing activities against XBB sub-variants in a murine model, suggesting that next-generation COVID-19 vaccines with updated components must be developed to enhance the protection efficacy against the circulating SARS-CoV-2 strains.

immunology↗

bioRxiv2023

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

BackgroundGlioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. MethodAfter collecting 18 RCD-related signatures from the opening literatures, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. ResultHere, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed a RCD gene pair scoring system, named RCD.GP signature. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified the SLC43A3 by q-PCR in glioma grades and glioma cell lines. ConclusionOur study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

bioinformatics↗

bioRxiv2023

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium. The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

ecology↗

bioRxiv2023

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

microbiology↗

bioRxiv2023

Neutralization of SARS-CoV-2 BQ.1.1 and XBB.1.5 by Breakthrough Infection Sera from Previous and Current Waves in China

SARS-CoV-2 is continuing to evolve and diversify, with an array of various Omicron sub-lineages, including BA.5, BA.2.75, BN.1, BF.7, BQ.1, BQ.1.1, XBB and XBB.1.5, now circulating globally at recent time. In this study, we evaluated the neutralization sensitivity of a comprehensive panel of Omicron subvariants to sera from different clinical cohorts, including individuals who received homologous or heterologous booster vaccinations, vaccinated people who had Delta or BA.2 breakthrough infection in previous waves, and patients who had BA.5 or BF.7 breakthrough infection in the current wave in China. All the Omicron subvariants exhibited substantial neutralization evasion, with BQ.1, BQ.1.1, XBB.1, and XBB.1.5 being the strongest escaped subvariants. Sera from Omicron breakthrough infection, especially the recent BA.5 or BF.7 breakthrough infection, exhibited higher neutralizing activity against all Omicron sub-lineages, indicating the chance of BA.5 and BF.7 being entirely replaced by BQ or XBB subvariants in China in a short-term might be low. We also demonstrated that the BQ and XBB subvariants were the most resistant viruses to monoclonal antibodies. Continuing to monitor the immune escape of SARS-CoV-2 emerging variants and developing novel broad-spectrum vaccines and antibodies are still crucial.

immunology↗

bioRxiv2023

Label-free quantitative proteomics analyses of mouse astrocytes provides insight into the host response mechanism at different developmental stages of Toxoplasma gondii

Toxoplasma gondii (T. gondii) is an opportunistic parasite that can infect the central nervous system (CNS), causing severe toxoplasmosis and behavioral cognitive impairment. Mortality is high in immunocompromised individuals with toxoplasmosis, most commonly due to reactivation of infection in the CNS. There are still no effective vaccines and drugs for the prevention and treatment of toxoplasmosis. There are five developmental stages for T. gondii to complete life cycle, of which the tachyzoite and bradyzoite stages are the key to the acute and chronic infection. In this study, to better understanding of how T. gondii interacts with the host CNS at different stages of infection, we constructed acute and chronic infection models of T. gondii in astrocytes, and used label-free proteomics to detect the proteome changes before and after infection, respectively. A total of 4676 proteins were identified, among which 163 differentially expressed proteins (DEPs) (fold change[≥]1.5 or [≤]0.67 and p-value[≤]0.05) including 109 up-regulated proteins and 54 down-regulated proteins in C8-TA vs C8 group, and 719 DEPs including 495 up-regulated proteins and 224 down-regulated proteins in C8-BR vs C8-TA group. After T. gondii tachyzoites infected astrocytes, DEPs were enriched in immune-related biological processes to promote the formation of bradyzoites and maintain the balance of T. gondii, CNS and brain. After T. gondii bradyzoites infected astrocytes, the DEPs up-regulated the hosts glucose metabolism, and some up-regulated DEPs were closely related to neurodegenerative diseases. These findings not only provide new insights into the psychiatric pathogenesis of T. gondii, but also provide potential targets for the treatment of acute and chronic Toxoplasmosis.

microbiology↗

bioRxiv2022

Different tissues in the maternal-fetal interface harbor distinct microbiomes showing associations related to their anatomical position or function

The human placenta was thought to be sterile in healthy pregnancies which has been challenged by the development of DNA sequence-based techniques, although it is still open to controversy. Nonetheless, little is known whether different parts of fetal appurtenances contain district microbiome profiles. Here, DNA 16S rRNA sequencing was performed of the amniotic fluid cells (AC), amnion membrane (AM), the placenta of fetal surface (remove the amniotic membrane, PL), maternal blood (MB), and umbilical cord blood (UCB) at V3-V4 hypervariable region from participants with cesarean delivery. Then sequence raw data were followed by taxonomic classification at 97% similarity and diversity analysis at the genus level. The differences and associations among the five tissues were analyzed. At the phylum composition level, the most abundant microorganisms were Proteobacteria in all five tissues, and followed by Firmicutes in AC, AM, and MB groups, Actinobacteria in UCB and Bacteroidetes in PL, respectively. As the maternal-fetal barrier, PL and AM had the lower OUT number and weaker co-occurrence network compared with the other three tissues. At the beta diversity clustering level, the microbiota constituents in the MB and UCB were highly similar; the microbiota profiles of PL and AM were also remarkably alike; AC was immensely different from those two clusters. Therefore, the five tissues were distinctly separated into three clusters. Our study reveals that different pregnancy-related anatomical sites harbor unique microbial compositions and show different degrees of correlation with other tissues.

microbiology↗

bioRxiv2022

Relative enrichment of oral bacteria in feces indicates loss of intestinal commensals with implications for host health

The increased relative abundance of oral bacteria detected in fecal samples has been associated with intestinal diseases and digestive disorders. This observation raises two competing hypotheses: either oral bacteria invade the gut bacterial population and expand in the intestine (the Expansion hypothesis), or oral bacteria transit through and their relative increase in feces marks a depletion of the gut bacterial population (the Marker hypothesis). To address this, we conducted a comprehensive analysis of quantitative microbiome data from mouse experiments and diverse patient cohorts. Our findings consistently support the Marker hypothesis as the primary explanation. We further establish a robust inverse correlation between the total fraction of oral bacteria and decreased total bacterial abundance in feces. This correlation underlies the associations between the oral bacterial fraction and multiple patient outcomes consistent with a depleted gut microbiota. By distinguishing between the two hypotheses, our study guides the interpretation of microbiome compositional data and their links with human health.

microbiology↗

bioRxiv2022

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor and investigating the immunorelated genes is of great significance. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, a immunoprognosis model was constructed to identify 4 hub genes with moderate predictive values for the prognosis of ccRCC patients. Interestingly, these 4 genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltrate and could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of 3 hub genes were associated with tumor progression (P<0.1). In conclusion, 4 genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

cancer biology↗