Biology subjects

Dai, Z.

Publications and source records attributed to Dai, Z..

122 records · Page 7Linked to original sources

bioRxiv2019

Label-retention expansion microscopy

Expansion microscopy (ExM) increases the effective resolving power of any microscope by expanding the sample with swellable hydrogel. Since its invention, ExM has been successfully applied to a wide range of cell, tissue and animal samples. Still, fluorescence signal loss during polymerization and digestion limits molecular-scale imaging using ExM. Here we report the development of label-retention ExM (LR-ExM) with a set of trifunctional anchors that not only prevent signal loss but also enable high-efficiency labeling using SNAP and CLIP tags. We have demonstrated multicolor LR-ExM for a variety of subcellular structures. Combining LR-ExM with super-resolution Stochastic Optical Reconstruction Microscopy (STORM), we have achieved molecular resolution in the visualization of polyhedral lattice of clathrin-coated pits in situ.

bioengineering↗

bioRxiv2019

Disinfectant residuals in drinking water systems select for mycobacterial populations with intrinsic antimicrobial resistance.

Antimicrobial resistance (AMR) in drinking water has received less attention than counterparts in the urban water cycle. While culture-based techniques or gene-centric PCR have been used to probe the impact of treatment approaches (e.g., disinfection) on AMR in drinking water, to our knowledge there is no systematic comparison of AMR traits between disinfected and disinfectant residual-free drinking water systems. We use metagenomics to assess the associations between disinfectant residuals and AMR prevalence and its host association in full-scale drinking water distribution systems (DWDSs). The differences in AMR profiles between DWDSs are associated with the presence or absence of disinfectant. Further, AMR genes and mechanisms enriched in disinfected systems are associated with drug classes primarily linked to nontuberculous mycobacteria (NTM). Finally, evaluation of metagenome assembled genomes (MAGs) of NTM indicates that they possess AMR genes conferring intrinsic resistance to key antibiotics, whereas such NTM genomes were not detected in disinfectant residual free DWDSs. Thus, disinfection may not only influence the AMR profiles of the drinking water microbiome but also select for NTM with intrinsic AMR.

microbiology↗

bioRxiv2019

Microbial Nitrogen Metabolism in Chloraminated Drinking Water Reservoirs

Nitrification is a common concern in chloraminated drinking water distribution systems. The addition of ammonia promotes the growth of nitrifying organisms, causing the depletion of chloramine residuals and resulting in operational problems for many drinking water utilities. Therefore, a comprehensive understanding of the microbially mediated processes behind nitrogen metabolism together with chemical water quality data, may allow water utilities to better address the undesirable effects caused by nitrification. In this study, a metagenomic approach was applied to characterise the microbial nitrogen metabolism within chloraminated drinking water reservoirs. Samples from two geographically separated but connected chloraminated reservoirs within the same drinking water distribution system (DWDS) were collected within a 2-year sampling campaign. Spatial changes in the nitrogen compounds (ammonium (NH4+), nitrites (NO2-) and nitrates (NO3-)) across the DWDS were observed, where nitrate concentrations increased as the distance from the site of chloramination increased. The observed dominance of Nitrosomonas and Nitrospira-like bacteria, together with the changes in the concentration of nitrogen species, suggests that these bacteria play a significant role in contributing to varying stages of nitrification in both reservoirs. Functionally annotated protein sequences were mined for the genes associated with nitrogen metabolism and the community gene catalogue contained mostly genes involved in nitrification, nitrate and nitrite reduction and nitric oxide reduction. Furthermore, based on the construction of Metagenome Assembled Genomes (MAGs), a highly diverse assemblage of bacteria (i.e., predominately Alpha- and Betaproteobacteria in this study) was observed among the draft genomes. Specifically, 5 MAGs showed high coverage across all samples including two Nitrosomonas, Nitrospira, Sphingomonas and a Rhizobiales-like MAGs. The role of these MAGs in nitrogen metabolism revealed that the fate nitrate may be linked to changes in ammonia concentrations, that is, when ammonia concentrations are low, nitrate may be assimilated back to ammonia for growth. Alternatively, nitrate may be reduced to nitric oxide and potentially used in the regulation of biofilm formation. Therefore, this study provides insight into the genetic network behind microbially mediated nitrogen metabolism and together with the water chemistry data improves our understanding nitrification in chloraminated DWDSs.

microbiology↗

bioRxiv2019

Influence of genetic interactions on polygenic prediction

Prediction of phenotypes from genotypes is an important objective to fulfill the promises of genomics, precision medicine and agriculture. Although its now possible to account for the majority of genetic variation through model fitting, prediction of phenotypes remains a challenge, especially across populations that have diverged in the past. In this study, we designed simulation experiments to specifically investigate the role of genetic interactions in failure of polygenic prediction. We found that non-additive genetic interactions can significantly reduce the accuracy of polygenic prediction. Our study demonstrated the importance of considering genetic interactions in genetic prediction.

genetics↗

bioRxiv2019

Dietary methionine restriction targets one carbon metabolism in humans and produces broad therapeutic responses in cancer

Nutrition exerts profound effects on health and dietary interventions are commonly used to treat diseases of metabolic etiology. Although cancer has a substantial metabolic component, the principles that define whether nutrition may be used to influence tumour outcome are unclear. Nevertheless, it is established that targeting metabolic pathways with pharmacological agents or radiation can sometimes lead to controlled therapeutic outcomes. In contrast, whether specific dietary interventions could influence the metabolic pathways that are targeted in standard cancer therapies is not known. We now show that dietary restriction of methionine (MR), an essential amino acid, and the reduction of which has aging and obesogenic properties, influences cancer outcome through controlled and reproducible changes to one carbon metabolism. This pathway metabolizes methionine and further is the target of a host of cancer interventions involving chemotherapy and radiation. MR produced therapeutic responses in chemoresistant RAS-driven colorectal cancer patient derived xenografts and autochthonous KRASG12D+/-;TP53-/- -driven soft tissue sarcomas resistant to radiation. Metabolomics revealed the therapeutic mechanisms to occur through tumor cell autonomous effects on the flux through one carbon metabolism that impacted redox and nucleotide metabolism, thus interacting with the antimetabolite or radiation intervention. Finally, in a controlled and tolerated feeding study in humans, MR resulted in similar effects on systemic metabolism as obtained in responsive mice. These findings provide evidence that a targeted dietary manipulation can affect specific tumor cell metabolism to mediate broad aspects of cancer outcome.

cancer biology↗

bioRxiv2019

Evolved resistance to GAPDH inhibition results in loss of the Warburg Effect but retains a different state of glycolysis

Aerobic glycolysis or the Warburg Effect (WE) is characterized by increased glucose uptake and incomplete oxidation to lactate. Although ubiquitous, the biological role of the WE remains controversial and whether glucose metabolism is functionally different during fully oxidative glycolysis or during the WE is unknown. To investigate this question, we evolved resistance to koningic acid (KA), a natural product shown to be a specific inhibitor of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a rate-controlling glycolytic enzyme during the WE. We find that KA-resistant cells lose the WE but conduct glycolysis and surprisingly remain dependent on glucose and central carbon metabolism. Consequentially this altered state of glycolysis leads to differential metabolic activity and requirements including emergent activities in and dependencies on fatty acid metabolism. Together, these findings reveal that, contrary to some recent reports, aerobic glycolysis is a functionally distinct entity from conventional glucose metabolism and leads to distinct metabolic requirements and biological functions.

systems biology↗

bioRxiv2019

Oligotyping and Genome-Resolved Metagenomics Reveal Distinct Candidatus Accumulibacter Communities in Full-Scale Side-Stream versus Conventional Enhanced Biological Phosphorus Removal (EBPR) Configurations

Candidatus Accumulibacter phosphatis (CAP) and its sub-clades-level diversity has been associated and implicated in successful phosphorus removal performance in enhanced biological phosphorus removal (EBPR). Development of high-throughput untargeted methods to characterize clades of CAP in EBPR communities can enable a better understanding of Accumulibacter ecology at a higher-resolution beyond OTU-level in wastewater resource recovery facilities (WRRFs). In this study, for the first time, using integrated 16S rRNA gene sequencing, oligotyping and genome-resolved metagenomics, we were able to reveal clade-level differences in Accumulibacter communities and associate the differences with two different full-scale EBPR configurations. The results led to the identification and characterization of a distinct and dominant Accumulibacter oligotype - Oligotype 2 (belonging to Clade IIC) and its matching MAG (RC14) associated with side-stream EBPR configuration. We are also able to extract MAGs belonging to CAP clades IIB (RCAB4-2) and II (RC18) which did not have representative genomes before. This study demonstrates and validates the use of a high-throughput approach of oligotyping analysis of 16S rRNA gene sequences to elucidate CAP clade-level diversity. We also show the existence of a previously uncharacterized diversity of CAP clades in full-scale EBPR communities through extraction of MAGs, for the first time from full-scale facilities.

microbiology↗

bioRxiv2019

Metabolic landscape of the tumor microenvironment

The tumor milieu consists of numerous cell types with each cell existing in a different nutritional environment. However, a characterization of intratumoral metabolic heterogeneity at the single-cell level in human cancer is not established. Here, we develop a computational pipeline to analyze metabolic gene expression programs of single cells within human tumors. In two representative cancer types, melanoma and head and neck, we apply this algorithm to define the single-cell metabolic landscape of human tumors. We find that malignant cells in general have higher metabolic activity and higher metabolic variation than previously observed from studies of bulk tumor comparisons. Indeed, most of the observed metabolic variation of single tumor and normal cells were found to be inconsistent with comparisons with bulk tumor samples. Variation in the expression of mitochondrial programs is the major contributor to intratumoral metabolic heterogeneity. Surprisingly, the expression of both glycolytic and mitochondrial programs strongly correlates with hypoxia in almost all cell types. Immune and stromal cells could also be distinguished by their metabolic features. Taken together this analysis establishes a computational framework for characterizing metabolism using single cell expression data and defines principles of intratumoral metabolic heterogeneity.

systems biology↗

bioRxiv2018

Model-Aware Deep Learning Based Strategies for Clutter Suppression in Contrast Enhanced Ultrasound

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of a low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.

bioengineering↗

bioRxiv2018

Genetically modified pigs are protected from classical swine fever virus

Classical swine fever (CSF) caused by classical swine fever virus (CSFV) is among the most detrimental diseases, and leads to significant economic losses in the swine industry. Despite efforts by many government authorities try to stamp out the disease from national pig populations, the disease remains widespread. Here, antiviral small hairpin RNAs (shRNAs) were selected and then inserted at the porcine ROSA26 (pROSA26) locus via a CRISPR/Cas9-mediated knock-in strategy. Finally, anti-CSFV transgenic (TG) pigs were produced by somatic nuclear transfer (SCNT). Importantly, in vitro and in vivo viral challenge assays demonstrated that these TG pigs could effectively limit the growth of CSFV and reduced CSFV-associated clinical signs and mortality, and the disease resistance was stably transmitted to F1-generation. The use of these TG pigs can improve the well-being of livestock and substantially reduce virus-related economic losses. Additionally, this antiviral approach may provide a reference for future antiviral research.\n\nAuthor summaryClassical swine fever (CSF), caused by classical swine fever virus (CSFV), and is a highly contagious, often fatal porcine disease with significant economic losses. Due to its economic importance to the pig industry, the biology and pathogenesis of CSFV have been investigated extensively. Despite efforts by many government authorities to stamp out the disease from national pig populations, the disease remains widespread in some regions and seems to be waiting for the reintroduction and the next round of disease outbreaks. These highlight the necessity and urgency of developing more effective approaches to eradicate the challenging CSFV. In this study, we successfully produced anti-CSFV transgenic pigs and confirmed that these transgenic pigs could effectively limit the growth of CSFV in vivo and in vitro and that the disease resistance traits in the TG founders can be stably transmitted to their F1-generation offspring. This study suggests that these TG pigs can improve the well-being of livestock and contribute to offer potential benefits over commercial vaccination. The use of these TG pigs can improve the well-being of livestock and substantially reduce CSFV-related economic losses.

genomics↗

bioRxiv2018

Thermodynamic constraints on the regulation of metabolic fluxes

Nutrition and metabolism are fundamental to cellular function in physiological and pathological contexts. Metabolic activity (i.e. rates, flow, or most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis in several representative topological structures of metabolic pathways as models, we derive exact results and conduct computer simulations that define relationships between thermodynamics, enzyme activity, and flux control. We confirm that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, metabolic pathways have a more adaptable pattern of regulation, controlled minimally by thermodynamics and not necessarily by the specific enzyme that generates the given reaction. These findings show how the control of metabolic pathways, which are rarely very far from equilibrium, is largely set by the overall flux through a pathway rather than by the enzyme which generates the flux or by thermodynamics.

biophysics↗

bioRxiv2018

Environmental factors shape methionine metabolism in p16/MTAP deleted cells

The co-deletion of a common tumor suppressor locus and neighboring metabolic gene is an attractive possible synthetic dependency of tumor suppression on metabolism. However, the general impact that these co-deletions have on metabolism, which also dependent on nutrient availability and the tissue of origin, is unknown. As a model to investigate this question, we considered a set of tissue-matched cancer cells with homozygous co-deletions in CDKN2a and MTAP, genes respectively encoding the most commonly deleted tumor suppressor p16 and an enzyme involved in methionine metabolism. A comparative metabolomics analysis revealed that while there exists a definite pan-cancer metabolic signature of MTAP-deletion, this signature was not preserved when cells were subjected to changes in the availability of methionine, serine, or cysteine, nutrients related to methionine metabolism. Notably, the heterogeneity exhibited by these cells in their responsiveness to nutrient availability dominated both MTAP status and tissue-of-origin. Furthermore, re-expression of MTAP exerted a modest effect on metabolism. Together these findings demonstrate that environmental factors, particularly nutrition and tissue identity, may overwhelm the genetic effects of collateral deletions of metabolic genes.

molecular biology↗

bioRxiv2018

Methionine metabolism influences the genomic architecture of H3K4me3 with the link to gene expression encoded in peak width

Nutrition and metabolism are known to influence chromatin biology and epigenetics by modifying the levels of post-translational modifications on histones, yet how changes in nutrient availability influence specific aspects of genomic architecture and connect to gene expression is unknown. To investigate this question we considered, as a model, the metabolically-driven dynamics of H3K4me3, a histone methylation mark that is known to encode information about active transcription, cell identity, and tumor suppression. We analyzed the genome-wide changes in H3K4me3 and gene expression in response to alterations in methionine availability under conditions that are known to affect the global levels of histone methylation in both normal rodent physiology and in human cancer cells. Surprisingly, we found that the location of H3K4me3 peaks at specific genomic loci was largely preserved under conditions of methionine restriction. However, upon examining different geometrical features of peak shape, it was found that the response of H3K4me3 peak width encoded almost all aspects of H3K4me3 biology including changes in expression levels, and the presence of cell identity and cancer associated genes. These findings reveal simple yet new and profound principles for how nutrient availability modulates specific aspects of chromatin dynamics to mediate key biological features.

genetics↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗