Biology subjects

Zhao, N.

Publications and source records attributed to Zhao, N..

107 records · Page 6Linked to original sources

bioRxiv2020

Orthosteric-allosteric dual inhibitors of PfHT1 as selective anti-malarial agents

Artemisinin-resistant malaria parasites have emerged and been spreading, posing a significant public health challenge. Anti-malarial drugs with novel mechanisms of action are therefore urgently needed. In this report, we exploit a "selective starvation" strategy by selectively inhibiting Plasmodium falciparum hexose transporter 1 (PfHT1), the sole hexose transporter in Plasmodium falciparum, over human glucose transporter 1 (hGLUT1), providing an alternative approach to fight against multidrug-resistant malaria parasites. Comparison of the crystal structures of human GLUT3 and PfHT1 bound to C3361, a PfHT1-specific moderate inhibitor, revealed an inhibitor binding-induced pocket that presented a promising druggable site. We thereby designed small-molecules to simultaneously block the orthosteric and allosteric pockets of PfHT1. Through extensive structure-activity relationship (SAR) studies, the TH-PF series was identified to selectively inhibit PfHT1 over GLUT1 and potent against multiple strains of the blood-stage P. falciparum. Our findings shed light on the next-generation chemotherapeutics with a paradigm-shifting structure-based design strategy to simultaneously targeting the orthosteric and allosteric sites of a transporter. Significance statementBlocking sugar uptake in P. falciparum by selectively inhibiting the hexose transporter PfHT1 kills the blood-stage parasites without affecting the host cells, indicating PfHT1 as a promising therapeutic target. Here, we report the development of novel small-molecule inhibitors that are selectively potent to the malaria parasites over human cell lines by simultaneously targeting the orthosteric and the allosteric binding sites of PfHT1. Our findings established the basis for the rational design of next-generation anti-malarial drugs.

pharmacology and toxicology↗

bioRxiv2020

Exploiting KRAS-driven Ferroaddiction in Cancer Through Ferrous Iron-Activatable Drug Conjugates (FeADC)

KRAS mutations cause a quarter of cancer mortality and most are undruggable. Several inhibitors of the MAPK pathway are FDA approved but poorly tolerated at dosages required to adequately extinguish RAS/RAF/MAPK signaling. We found that oncogenic KRAS signaling induces ferrous iron (Fe2+) accumulation early in and throughout KRAS-mediated transformation. We used an FDA-approved MEK inhibitor to produce a prototypical Ferrous Iron-Activatable Drug Conjugate (FeADC) which achieved potent MAPK blockade in tumor cells while sparing normal tissues. This innovation allowed sustainable, effective treatment of tumor bearing animals, with tumor-selective drug activation producing superior systemic tolerability. Ferrous iron accumulation is an exploitable feature of KRAS transformation and FeADCs hold promise for improving treatment of KRAS-driven solid tumors.

cancer biology↗

bioRxiv2020

Protein manipulation using single copies of short peptide tags in cultured cells and in Drosophila melanogaster

Cellular development and specialized cellular functions are regulated processes which rely on highly dynamic molecular interactions among proteins, distributed in all cell compartments. Analysis of these interactions and their mechanisms of action has been one of the main topics in cellular and developmental research over the last fifty years. Studying and understanding the functions of proteins of interest (POIs) has been mostly achieved by their alteration at the genetic level and the analysis of the phenotypic changes generated by these alterations. Although genetic and reverse genetic technologies contributed to the vast majority of information and knowledge we have gathered so far, targeting specific interactions of POIs in a time- and space-controlled manner or analyzing the role of POIs in dynamic cellular processes such as cell migration or cell division would require more direct approaches. The recent development of specific protein binders, which can be expressed and function intracellularly, together with several improvements in synthetic biology techniques, have contributed to the creation of a new toolbox for direct protein manipulations. We selected a number of short tag epitopes for which protein binders from different scaffolds have been developed and tested whether these tags can be bound by the corresponding protein binders in living cells when they are inserted in a single copy in a POI. We indeed find that in all cases, a single copy of a short tag allows protein binding and manipulation. Using Drosophila, we also find that single short tags can be recognized and allow degradation and relocalization of POIs in vivo.

cell biology↗

bioRxiv2020

Reconstruction and dynamics of human intestinal microbiome observed in situ

Gut microbiome are studied primarily using fecal samples in humans and we gained vital knowledge of compositional and functional capacities of gastro-intestinal microbial communities. Yet, fecal materials limit our ability to investigate microbial dynamics in different locations along GI-tract (in situ), nor in finer temporal scales as they are infrequent. With a technology developed originally for fecal material transplantation, colonic transendoscopic enteral tubing, we were able to sample ileocecal microbiome twice daily, and carried out metagenomic as well as metatranscriptomic analyses. Ileocecal and fecal microbiome are similar in metagenomic profiling, yet their active genes (in metatranscriptomes) are highly distinct. Both were perturbed after laxatives and then became more similar to microbiome prior to treatment, demonstrating resilience as an innate property of gut microbiome. Ileocecal microbiome transcriptomes sampled during day and night revealed diurnal rhythmes exist in certain bacterial species and functional pathways, in particular those related to short-chain fatty acid production. Lastly, metabolomic analysis in fecal and urine samples mirrored the perturbance and recovery in gut microbiome, indicating crucial contribution of gut microbiome to many of the key metabolites involved in host health. Our study provides interesting novel insights into human gut microbiome, and demonstrates the inner resilience, diurnal rhythmes and potential consequences to the host.

microbiology↗

bioRxiv2020

The impact of colonization history on the composition of ecological systems

Observational studies of ecological systems have shown that different species compositions can arise from distinct species arrival orders during community assembly--also known as colonization history. However, it is still unclear under which conditions colonization history will dominate community composition. Yet, this is important in order to understand and anticipate the impact of species arrivals on the biodiversity that we observe in both nature and experiments. To address this fundamental question, here we develop a testable theory linking colonization history and community composition. First, we prove two general theorems to determine whether the composition of a community will depend on its colonization history. For communities governed by Lotka-Volterra dynamics, we further simplify the two theorems into a corollary that is easy for numerical test. Second, we show, via extensive numerical simulations, that the probability that community composition is dominated by colonization history increases monotonically with community size and species connectivity. Third, we show that this probability significantly increases with the variation of intrinsic growth rates across species. These results reveal that community composition is a probabilistic process mediated by ecological dynamics via the interspecific variation and the size of regional pools.

ecology↗

bioRxiv2020

Profiling of Human Gut Virome with Oxford Nanopore Technology

Human gut virome play critical roles in maintaining gut microbial composition and functionality, as well as host physiology and immunology. Yet, there are insufficient amount of studies on this topic mainly due to methodological limitations, including enrichment of viruses (phages and host viruses) as well as short read-length from current sequencing technology. Here we developed a full working protocol for analyzing human gut virome using physical enrichment, reverse transcription and random amplification, and eventually the state-of-art single-molecule real-time sequencing (SMRT) platform of Oxford Nanopore Technology (ONT). We demonstrate that sequencing viral DNA directly, or viral DNA/cDNA after amplification using ONT achieves much longer reads and provides more information regarding virome diversity, many of the virome sequences do not have match in current databases. Moreover, direct DNA sequencing of virome provides first insights into the epigenetic modifications on phages, where signals of methylations can be directly detected. Our study demonstrates that progressing sequencing technology and bioinformatic improvements will bring more knowledge into virome composition, diversity and potentially their important functions. HighlightsO_LIVirus-like particles were enriched from human stool samples; C_LIO_LIViral nucleotides were sequenced with Oxford Nanopore Technology with and without amplification; C_LIO_LIGut virome in humans showed highly individualized composition; C_LIO_LINovel sequences and contigs were found to be the majority in the resulted sequences; C_LIO_LIEpigenetic modifications were detected directly on virus genomes. C_LI

bioinformatics↗

bioRxiv2020

Spreading predictability in complex networks

Spreading dynamics analysis is an important and interesting topic since it has many applications such as rumor or disease controlling, viral marketing and information recommending. Many state-of-the-art researches focus on predicting infection scale or threshold. Few researchers pay attention to the predicting of infection nodes from a snapshot. With developing of precision marketing, recommending and, controlling, how to predict infection nodes precisely from snapshot becomes a key issue in spreading dynamics analysis. In this paper, a probability based prediction model is presented so as to estimate the infection nodes from a snapshot of spreading. Experimental results on synthetic and real networks demonstrate that the model proposed could predict the infection nodes precisely in the sense of probability.

bioinformatics↗

bioRxiv2020

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burdens at the national sub-national scale in Colombia

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed for the national and departmental levels in Colombia to predict weekly dengue cases at 12-weeks ahead. A national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, population counts, income inequality, and education. Our RF model trained on the national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the departments data. Additionally, the forecast errors of the national RF model were smaller to those of the national ANN model and were increased with the forecast horizon increasing from one-week ahead (mean absolute error, MAE: 5.80; root mean squared error, RMSE: 11.10) to 12-weeks ahead (MAE: 13.38; RMSE: 26.82). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study showed the potential of RF in dengue forecasting with also demonstrating the feasibility of using a national model to forecast at finer spatial scales. Furthermore, sociodemographic predictors are important to include to capture longer-term trends in dengue. Author summaryDengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 million people annually in 128 countries. Forecasting is an important warning mechanism that can help with proactive planning and response for clinical and public health services. In this study, we compare two different machine learning approaches to dengue forecasting: random forest (RF) and neural networks (NN). National and local (departmental-level) models were compared and used to predict dengue cases in the future. The results showed that the counts of future dengue cases were more accurately estimated by RF than by NN. It was also shown that environmental and meteorological predictors were more important for forecast accuracy for shorter-term forecasts while socio-demographic predictors were more important for longer-term forecasts. Finally, the national model applied to local data was more accurate in dengue forecasting compared to the local model. This research contributes to the field of disease forecasting and highlights different considerations for future forecasting studies.

immunology↗

bioRxiv2019

Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P<5.0x10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate <0.05). Five loci showed associations (P<0.05) in opposite directions between luminal- and non-luminal subtypes. In-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

genetics↗

bioRxiv2019

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites

Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.

microbiology↗

bioRxiv2019

CELF1 is an EIF4E binding protein that promotes translation of epithelial-mesenchymal transition effector mRNAs

Mounting evidence is revealing a granularity within gene regulation that occurs at the level of mRNA translation. Within mammalian cells, canonical cap-dependent mRNA translation is dependent upon the interaction between the m7G cap-binding protein eukaryotic initiation factor 4E (eIF4E) and the scaffolding protein eukaryotic initiation factor 4G (eIF4G), the latter of which facilitates pre-translation initiation complex assembly, mRNA circularization, and ultimately ribosomal scanning. In breast epithelial cells, we previously demonstrated that the CELF1 RNA-binding protein promotes the translation of epithelial to mesenchymal transition (EMT) effector mRNAs containing GU-rich elements (GREs) within their 3 untranslated regions (UTRs). Here we show that within this context, CELF1 directly binds to both the eIF4E cap-binding protein and Poly(A) binding protein (PABP), promoting translation of GRE-containing mRNAs in mesenchymal cells. Disruption of this CELF1/eIF4E interaction inhibits both EMT induction and experimental metastasis. Our findings illustrate a novel way in which non-canonical mechanisms of translation initiation underlie transitional cellular states within the context of development or human disease.

cancer biology↗

bioRxiv2019

Discovery Of UFO Proteins: Human-Virus Chimeric Proteins Generated During Influenza Virus Infection

Influenza A virus (IAV) is a threat to mankind because it generates yearly epidemics and poorly predictable sporadic pandemics with catastrophic potential. IAV has a small RNA genome composed of 8 mini-chromosomes (segments) that constitute a 5UTR followed by a coding region and a 3UTR. Transcription of IAV RNA into mRNA depends on host mRNA, as the viral polymerase cleaves 5m7G-capped nascent transcripts to use as primers to initiate viral mRNA synthesis. We hypothesized that captured host transcripts bearing AUG could drive the expression of upstream ORFs in the viral segments, a phenomenon that would depend on the translatability of the viral 5UTRs. Here we report the existence of this mechanism, which generates host-virus chimeric proteins. We label these proteins as Upstream Flu ORFs (UFO). Depending on the frame, two types of host-virus UFO proteins are made: canonical viral proteins with human-derived N term extensions or novel uncharacterized proteins. Here we show that both types are made during IAV infection. Sequences that enable chimeric protein synthesis are conserved across IAV strains, indicating that selection allowed the expansion of the proteome diversity of IAV in infected cells to include multiple human-virus proteins.

microbiology↗

bioRxiv2018

A genetically encoded probe for imaging HA-tagged protein translation, localization, and dynamics in living cells and animals

To expand the toolbox of imaging in living cells, we have engineered a new single chain variable fragment (scFv) that binds the classic linear HA epitope with high affinity and specificity in vivo. The resulting probe, which we call the HA frankenbody, is capable of lighting up in multiple colors HA-tagged nuclear, cytoplasmic, and membrane proteins in diverse living cell types. The HA frankenbody also enables state-of-the-art single-molecule experiments, which we demonstrate by tracking single mRNA translation dynamics in living U2OS cells and neurons. In combination with the SunTag, we track two mRNA species simultaneously to demonstrate comparative single-molecule studies of translation can now be done with genetically encoded tools alone. Finally, we use the HA frankenbody to precisely quantify the expression of HA tagged proteins in developing zebrafish embryos. The versatility of the HA frankenbody makes it a powerful new tool for imaging protein dynamics in vivo.\n\nOne-sentence summaryA genetically encodable intracellular single-chain variable fragment that selectively binds the HA epitope (YPYDVPDYA) with high affinity in living cells and organisms can be used to quantify HA-tagged protein translation, localization, and dynamics.

cell biology↗

bioRxiv2018

A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics

AO_SCPLOWBSTRACTC_SCPLOWCancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible sub-types defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization (EM) algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).

genetics↗

bioRxiv2018

Multilayer view of pathogenic SNVs in human interactome through in-silico edgetic profiling

Non-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present an systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, \"edgetic\", effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus, and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations.

systems biology↗

bioRxiv2018

Small effect size leads to reproducibility failure in resting-state fMRI studies

Thousands of papers using resting-state functional magnetic resonance imaging (RS-fMRI) have been published on brain disorders. Results in each paper may have survived correction for multiple comparison. However, since there have been no robust results from large scale meta-analysis, we do not know how many of published results are truly positives. The present meta-analytic work included 60 original studies, with 57 studies (4 datasets, 2266 participants) that used a between-group design and 3 studies (1 dataset, 107 participants) that employed a within-group design. To evaluate the effect size of brain disorders, a very large neuroimaging dataset ranging from neurological to psychiatric isorders together with healthy individuals have been analyzed. Parkinsons disease off levodopa (PD-off) included 687 participants from 15 studies. PD on levodopa (PD-on) included 261 participants from 9 studies. Autism spectrum disorder (ASD) included 958 participants from 27 studies. The meta-analyses of a metric named amplitude of low frequency fluctuation (ALFF) showed that the effect size (Hedges g) was 0.19 - 0.39 for the 4 datasets using between-group design and 0.46 for the dataset using within-group design. The effect size of PD-off, PD-on and ASD were 0.23, 0.39, and 0.19, respectively. Using the meta-analysis results as the robust results, the between-group design results of each study showed high false negative rates (median 99%), high false discovery rates (median 86%), and low accuracy (median 1%), regardless of whether stringent or liberal multiple comparison correction was used. The findings were similar for 4 RS-fMRI metrics including ALFF, regional homogeneity, and degree centrality, as well as for another widely used RS-fMRI metric namely seed-based functional connectivity. These observations suggest that multiple comparison correction does not control for false discoveries across multiple studies when the effect sizes are relatively small. Meta-analysis on un-thresholded t-maps is critical for the recovery of ground truth. We recommend that to achieve high reproducibility through meta-analysis, the neuroimaging research field should share raw data or, at minimum, provide un-thresholded statistical images.

neuroscience↗

bioRxiv2018

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macromolecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plants sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to lignin-related lignin-phenotypes across the network layers. The resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance.

bioinformatics↗