Biology subjects

Tao, F.

Publications and source records attributed to Tao, F..

14 recordsLinked to original sources

bioRxiv2025

Universal principles of protein resource allocation among cellular organelles revealed by yeast large-scale absolute quantitative proteomics

The presence of organelles is a hallmark distinguishing eukarya from bacteria and archaea, and this culminates in compartmentalization of cellular metabolism and subsequent metabolic specialization. Here we established a dataset encompassing over 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning models, quantitative correlations among protein abundance, organelle-level resource distribution, and cellular phenotypes were elucidated at a system level. We found that protein resources always exhibit robust and precise distribution at the organelle level across distinct conditions. Specifically, at high specific growth rates, the protein mass fraction from some main organelles, i.e., peroxisome and nucleus, is consistently reduced to offset the increasing protein resource demand from the ribosome. Meanwhile, we found that the nutrition limitation could induce resource recycling by upregulating protein resources within the vacuole and lipid droplets to sustain stress adaptation. Importantly, our integrative analysis demonstrates that protein mass fraction from less than 4 organelles (e.g., nucleus and ribosomes) can accurately predict diverse yeast physiological parameters (e.g., specific growth rate, oxygen uptake rate), and a core set of 37 proteins could predict resource allocation among 24 main organelles and sub-organelles with high accuracy (average R^2 > 0.9). Finally, we found organelle resource allocation reflects the divergence of yeast species. For example, anaerobic conditions and respiratory suppression have less influence on Crabtree-positive yeast, i.e., Saccharomyces cerevisiae, with respect to organelle resource allocation but have a larger effect on the Crabtree-negative yeast Issatchenkia orientalis, thus suggesting that cellular resources have facilitated adaptive evolution. In summary, the high-quality, genome-scale quantitative proteomic dataset for yeast species offers an unprecedented opportunity for understanding the basic principles underlying resource allocation at the organelle level, laying theoretical foundations for precision engineering of cell factories in synthetic biology. The resource used in this study is available at https://yeast-proteome-database.streamlit.app/.

systems biology↗

bioRxiv2025

Alphavirus replicon particle expressing IL-12 reprograms tumor-associated macrophages and neutrophils and induces anti-tumor immunity

Cancers evade anti-tumor immunity by the infiltration of immunosuppressive myeloid cells such as macrophages and neutrophils into the tumor microenvironment (TME). The limited efficacy of current immunotherapy is attributed to the presence of these immunosuppressive cells. Here, we describe a novel immunotherapy approach, using a single-cycle alphavirus replicon particle (VRP) that carries a self-amplifying RNA encoding interleukin-12 (IL-12). Leveraging the natural tropism of alphavirus to myeloid cells, single cell transcriptomic analyses of mouse tumor tissues demonstrated that intratumoral (i.t.) injection of the vector effectively reprogrammed tumor-associated macrophages and neutrophils toward a pro-inflammatory, anti-tumor phenotype via interferon signaling, further enhanced by IL-12 expression. The treatment also induced cytotoxic NK and T cell responses, resulting in a significant reduction of tumor growth and effectively preventing nodal and distant metastases in solid tumor mouse models. By targeting tumor-supporting myeloid cells to transform the TME into an immune-active state, our myeloid modulation approach has the potential to enhance the efficacy of current immunotherapies.

cancer biology↗

bioRxiv2025

Engineering low pH-tolerant Komagataella phaffii for industrial-ready itaconic acid production via methanol biorefinery

Methanol biorefinery stands as a cornerstone strategy in the pursuit of sustainable biochemical production from non-food feedstocks. Despite its vast potential, bioconverting methanol into high titer biochemical production remains a formidable challenge. In this study, we have successfully engineered Komagataella phaffii to efficiently produce itaconic acid (IA), one of the top 12 building block chemicals, utilizing methanol as the sole carbon source. Our approach involved strategic metabolic engineering interventions--such as enhancing the supply of cis-aconitate and citrate precursors, minimizing isocitrate diversion, facilitating efficient formaldehyde assimilation, and optimizing IA export mechanisms--culminating in an unprecedented IA titer of 103.8 g/L in a 5-L bioreactor through fed-batch fermentation at pH 5.5. Remarkably, even at a reduced pH of 3.5, the strain still yielded 50.4 g/L of IA, highlighting the industrial viability of methanol biorefinery. This systematic work lays a robust foundation for high-titer production via methanol biorefinery, offering valuable insights for future research and applications.

synthetic biology↗

bioRxiv2024

Reliable prediction of protein-protein binding affinity changes upon mutations with Pythia-PPI

Protein-protein interactions are essential for numerous biological functions, and predicting binding affinity changes caused by mutations is crucial for understanding the impact of genetic variations and advancing protein engineering. Although machine learning-based methods show promise in improving the prediction accuracy, the scarcity of experimental data remains a significant bottleneck. Here, we utilized multi-task learning and self-distillation to overcome the data limitation and improved the accuracy of deep learning-based protein binding affinity prediction. By incorporating a mutation stability prediction task, the model achieved state-of-the-art accuracy on the SKEMPI dataset and was subsequently used to predict binding affinity changes for millions of mutations, generating an expanded dataset for self-distillation. Compared with prevalent methods, Pythia-PPI increased the Pearson correlation between predictions and experimental data from 0.6447 to 0.7850 on the SKEMPI dataset, and from 0.3654 to 0.6051 on the virial-receptor dataset. These findings demonstrated Pythia-PPI to be a valuable tool for analyzing the fitness landscape of protein-protein interactions. We provided a web server at https://pythiappi.wulab.xyz for easy access to Pythia-PPI.

bioinformatics↗

bioRxiv2024

Combining transformer and 3DCNN models to achieve co-design of structures and sequences of antibodies in a diffusional manner

Antibody drugs are among the fastest growing therapeutic modalities in modern drug research and development. Due to the huge search space of antibody sequences, the traditional experimental screening method cannot fully meet the needs of antibody discover. More and more rational design methods have been proposed to improve the success rate of antibody drugs. In recent years, artificial intelligence methods have increasingly become an important means of rational design. We have proposed an algorithm for antibody design, called AlphaPanda (AlphaFold2 inspired Protein-specific antibody design in a diffusional manner). The algorithm mainly combines the transformer model, the 3DCNN model and the diffusion generative model, use the transformer model to capture the global information and uses the 3DCNN model to capture the local structural characteristics of the antibody-antigen complexes, and then uses the diffusion model to generate sequences and structures of antibodies. The 3DCNN model can capture pairwise interactions in antibody-antigen complex, as well as non-pairwise interactions in antibody-antigen complex, and it requires less training sample data, while avoiding the defects of the generation progress by the autoregressive model and by the self-consistent iterative model. Diffusion generative model can generate sequence and structure effectively and with high quality. By combining 3DCNN method and diffusion model method, we have achieved the integration of 3DCNN model to the protein design with flexible main chains. By utilizing the advantages of these aspects, a good performance has been achieved by the AlphaPanda algorithm. The algorithm we propose can not only be applied to antibody design, but also be more widely applied to various fields of other protein design. The source code can be get from github (https://github.com/YueHuLab/AlphaPanda).

bioinformatics↗

bioRxiv2023

Reply to: Contribution of carbon inputs to soil carbon accumulation cannot be neglected

In the accompanying Comment1, He et al. argue that the determinant role of microbial carbon use efficiency in global soil organic carbon (SOC) storage shown in Tao et al. (2023)2 was overestimated because carbon inputs were neglected in our data analysis while they suggest that our model-based analysis could be biased and model-dependent. Their argument is based on a different choice of independent variables in the data analysis and a sensitivity analysis of two process-based models other than that used in our study. We agree that both carbon inputs and outputs (as mediated by microbial processes) matter when predicting SOC storage - the question is their relative contributions. While we encourage further studies to examine how the evaluation of the relative importance of CUE to global SOC storage may vary with different model structures, He et al.s claims about Tao et al. (2023) need to be taken as an alternative, unproven hypothesis until empirical data support their specific parameterization. Here we show that an additional literature assessment of global data does not support He et al.s argument, in contrast to our study, and that further study on this topic is essential.

ecology↗

bioRxiv2023

Reprogramming a high robust Geobacillus thermoglucosidasius for efficient synthesis of polymer-grade lactic acid under extremely high temperature (60℃)

Optically pure lactic acid is an important precursor for the synthesis of biodegradable plastic polylactic acid. Geobacillus thermoglucosidasius is a thermophilic bacterial chassis with industrial potential. In this study, we reprogrammed this chassis to efficiently produce polymer-grade lactic acid by combining rational and semi-rational design strategies. We obtained optically pure L-lactic acid- and D-lactic acid-producing strains, GTD17 and GTD7, respectively, via rational strategies: constructing a lactic acid synthesis module, deleting by-product synthesis, and enhancing lactic acid production. At an extremely high temperature (60{degrees}C), the engineered strain GTD17 produced 94.2 g L-1 of L-lactic acid with an overall yield and productivity of 91.5% and 2.0 g L-1 h-1, respectively, and the optical purity of L-lactic acid was 99.5%. We then performed semi-rational adaptive evolution on the engineered strain GTD7 to obtain an optically pure D-lactic acid-producing strain GTD7-144; at the extremely high temperature (60{degrees}C), it produced 153.1 g L-1 of D-lactic acid with an overall yield and productivity of 93.0% and 3.2 g L-1 h-1, respectively. The optical purity of the D-lactic acid was 99.6%. Genome resequencing and analysis of the strain GTD7-144 revealed potential genetic targets that may improve production performance. This study represents an advancement in the high-temperature production of polymer-grade lactic acid by G. thermoglucosidasius.

bioengineering↗

bioRxiv2022

Harnessing cellular perception apparatus for smart metabolic reprogramming

Metabolic reprogramming (MRP) is a fundamental approach in synthetic biology that involves redirecting metabolic flux and remodeling metabolic networks. However, only few approaches have been made in effective metabolic operations, especially at global level of metabolic networks. Naturally existing cellular perception apparatuses (CPAs), such as histidine kinases (HKs), are considered to sit on sensitive nodes of the metabolic network, which can trigger natural MRP upon perceiving environmental fluctuations. We develop a plateform for global MRP by natural environmental stimulation based on the combinational interference of CPAs. The plateform consists of a CRISPRi-mediated dual-gene combinational knockdown (CDCK) strategy and survivorship-based metabolic interaction analysis (SMIA). A total of 35 histidine kinase (HK) genes and 24 glycine metabolism genes were selected as targets to determine effectiveness of our approach for fast-growing chassis Vibrio FA2. Combined knockdown of several genes of HKs and glycine metabolism increased the glycine production. Other other hand, effects of CDCK on bacterial antibiotic resistance were assessed by targeting HKs. Many HKs were identified to be associated with antibiotic resistance in Vibrio FA2, of which combinational knockdown of two HK genes sasA_8 and 04288 reduced the ampicillin resistance. This MRP strategy is powerful and cost-effective, and can be considered as a smart strategy capable of operating a broad range of metabolic networks in microorganisms.

synthetic biology↗

bioRxiv2021

Single-cell metabolomics reveals the metabolic heterogeneity among microbial cells

In microbial research, the heterogeneity phenomenon is closely associated with microbial physiology in multiple dimensions. For now, A few studies were proposed in transcriptome and proteome analysis to discover the heterogeneity among single cells. However, microbial single cell metabolomics has not been possible yet. Herein, we developed a method, RespectM, based on discontinuous mass spectrometry imaging, which can detect more than 700 metabolites at a rate of 500 cells per hour. While ensuring the high throughput of RespectM, it integrates matrix sublimation, QC-based peak filtering, and batch correction strategies to improve accuracy. The results show that RespectM can distinguish single microbial cells from the blank matrix with an accuracy of 98.4%, depending on classification algorithms. Furthermore, to verify the accuracy of RespectM for distinguishing different single cells, we performed a classification test on Chlamydomonas reinhardtii single cells among allelic strains. The results showed an accuracy of 93.1%, which provides RespectM with enough confidence to perform microbial single cell metabolomics analysis. As we expected, untreated microbial cells will spontaneously undergo metabolic grouping coherence with genetic and biochemical similarities. Interestingly, the pseudo-time analysis also provided intuitive evidence on the metabolic dimension, indicating the cell grouping is based on microbial population heterogeneity. We believe that the RespectM can offer a powerful tool in the microbial study. Researchers can now directly analyze the changes in microbial metabolism at a single-cell level with high efficiency.

systems biology↗

bioRxiv2021

Direct carbon capture for production of high-performance biodegradable plastic by cyanobacterial cell factory

Plastic pollution has become one of the most pressing environmental issues today, leading to an urgent need to develop biodegradable plastics1-3. Polylactic acid (PLA) is one of the most promising biodegradable materials because of its potential applications in disposable packaging, agriculture, medicine, and printing filaments for 3D printers4-6. However, current biosynthesis of PLA entirely uses edible biomass as feedstock, which leads to competition for resources between material production and food supply7,8. Meanwhile, excessive emission of CO2 that is the most abundant carbon source aggravates global warming, and climate instability. Herein, we first developed a cyanobacterial cell factory for the de novo biosynthesis of PLA directly from CO2, using a combinational strategy of metabolic engineering and high-density cultivation (HDC). Firstly, the heterologous pathway for PLA production, which involves engineered D-lactic dehydrogenase (LDH), propionate CoA-transferase (PCT), and polyhydroxyalkanoate (PHA) synthase, was introduced into Synechococcus elongatus PCC7942. Subsequently, different metabolic engineering strategies, including pathway debottlenecking, acetyl-CoA self-circulation, and carbon-flux redirection, were systematically applied, resulting in approximately 19-fold increase to 15 mg/g dry cell weight (DCW) PLA compared to the control. In addition, HDC increased cell density by 10-fold. Finally, the PLA titer of 108 mg/L (corresponding to 23 mg/g DCW) was obtained, approximately 270 times higher than that obtained from the initially constructed strain. Moreover, molecular weight (Mw, 62.5 kDa; Mn, 32.8 kDa) of PLA produced by this strategy was among the highest reported levels. This study sheds a bright light on the prospects of plastic production from CO2 using cyanobacterial cell factories.

bioengineering↗

bioRxiv2021

Crop loss identification at field parcel scale using satellite remote sensing and machine learning

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of 0.688 {+/-} 0.059 over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring.

bioinformatics↗

bioRxiv2020

Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

neuroscience↗

bioRxiv2019

A cold shock protein from a thermophile bacterium promotes the high-temperature growth of bacteria and fungi through binding to diverse RNA species

High temperatures deleteriously affect cells by damaging cellular structures and changing the behavior of diverse biomolecules, and extensive research about thermophilic microorganisms has elucidated some of the mechanisms that can overcome these effects and allow thriving in high-temperature ecological niches. We here used functional genomics methods to screen out a cold-shock protein (CspL) from a high-productivity lactate producing thermophile strain (Bacillus coagulans strain 2-6) grown at 37{degrees}C and 60{degrees}C. We subsequently made the highly striking finding that transgenic expression of CspL conferred massive increases in high temperature growth of other organisms including E. coli (2.4-fold biomass increase at 45{degrees}C) and the eukaryote S. cerevisiae (a 2.7-fold biomass increase at 34{degrees}C). Pursuing these findings, we used bio-layer interferometry assays to characterize the nucleotide-binding function of CspL in vitro, and used proteomics and RNA-Seq to characterize the global effects of CspL on mRNA transcript accumulation and used RIP-Seq to identify in vivo RNA targets of this nucleotide-binding protein (e.g. rpoE, and rmf, etc.). Finally, we confirmed that a nucleotide-binding-dead variant form of CspL does not have increased growth rates or biomass accumulation effects at high temperatures. Our study thus establishes that CspL can function as a global RNA chaperone.

microbiology↗

bioRxiv2019

PSS: An enabling QTY server for designing water-soluble α-helical transmembrane proteins

Membrane proteins, especially the -helical ones such as G-protein coupled receptors (GPCRs), are considered extremely important owing to their significant biological roles. However, their expression and purification pose difficulties because of their poor solubility in water, which seriously impedes research progress in this field. Recently, QTY method, a revolutionary code-based protein engineering approach, was developed for the purpose of producing soluble transmembrane proteins. Here we describe a web server built for QTY design and certain analyses related to it (pss.sjtu.edu.cn). Typically, the Simple Design model is expected to take only 2-4 min, and the Library Design 2-5 h, of computer time, depending on target protein size and the number of transmembrane helices. Further, we describe a protocol for using the server with both Simple and Library Design modules. Protocols for experiments based on QTY design are also included. In summary, utilization of the web server, and associated protocols, will enable QTY-based protein-engineering to be implemented in a convenient, fast, accurate, and rational manner.

bioinformatics↗