Biology subjects

Guo, B.

Publications and source records attributed to Guo, B..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages ({phi}KZ and{phi} PA3) in Pseudomonas aeruginosa. This enabled the detection >6000 unique host-pathogen and >200 pathogen-pathogen interactions for each phage, encompassing >50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a{phi} PA3 complex showing strong structural and sequence similarity to{phi} KZ nvRNAp, suggesting{phi} PA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction (https://phagemap.ucsf.edu/). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.

systems biology↗

bioRxiv2023

Genetics of Latin American Diversity (GLAD) Project: insights into population genetics and association studies in recently admixed groups in the Americas

Latin America is underrepresented in genetic studies, which can exacerbate disparities in personalized genomic medicine. However, genetic data of thousands of Latin Americans are already publicly available, but require a bureaucratic maze to navigate all the data access and consenting issues. We present the Genetics of Latin American Diversity (GLAD) Project, a platform that compiles genome-wide information of 54,077 Latin Americans from 39 studies representing 45 geographical regions. Through GLAD, we identified heterogeneous ancestry composition and recent gene-flow across the Americas. Also, we developed a simulated-annealing-based algorithm to match the genetic background of external samples to our database and share summary statistics without transferring individual-level data. Finally, we demonstrate the potential of GLAD as a critical resource for evaluating statistical genetic softwares in the presence of admixture. By making this resource available, we promote genomic research in Latin Americans and contribute to the promises of personalized medicine to more people.

genetics↗

bioRxiv2022

Cell type-specific attenuation of brassinosteroid signalling drives stomatal asymmetric cell division

In Arabidopsis thaliana, the negative brassinosteroid (BR) signalling regulator, BR INSENSITIVE2 (BIN2) promotes and restricts stomatal asymmetric cell division (ACD) depending on its subcellular localization, which is regulated by the stomatal lineage-specific scaffolding protein POLAR. BRs inactivate BIN2, but how they govern stomatal development remains unclear. Mapping the single-cell transcriptome of stomatal lineages with either exogenous BRs or the specific BIN2 inhibitor revealed that the two modes of BR activation triggered spatiotemporally distinct transcriptional responses. We established that when in a complex with POLAR and its closest homolog POLAR-LIKE1, BIN2 is insulated from BR-mediated inactivation, nevertheless, it remains sensitive to the inhibitor. Subsequently, BR signalling is attenuated in ACD precursors, whereas it remains active in epidermal cells that would undergo differentiation. Our study demonstrates how scaffold proteins contribute to cellular signal specificity of hormonal responses in plants.

plant biology↗

bioRxiv2022

Wastewater Influent Microbial Immigration and Contribution to Resource Consumption in Activated Sludge Using Taxon-Specific Mass-Flow Immigration Model

Wastewater influent microorganisms are part of the total chemical oxygen demand (COD) and affect the activated sludge (AS) microbial community. Precise modeling of AS processes requires accurate quantification of influent microorganisms, which is missing in many AS models (ASMs). In this study, influent microorganisms in COD unit were determined using a fast quantification method based on DNA yield and was compared with conventional respirometry method. The actively growing influent microorganisms were identified. A mass-flow immigration model was developed to quantify the influent-to-AS immigration efficiency (mi) of specific taxon i using mass balance and 16S rRNA gene high-throughput sequencing data. The modelled average m was 0.121-0.257 in site 1 (LaPrairie), and 0.050-0.126 in site 2 (Pincourt), which were corrected to 0.111-0.186 and 0.048-0.109 respectively using a constrain of mi [≤] 1. The model was further developed to calculate contributions to organic substrate consumption by specific taxa. Those genera with zero or negative net growth rates were not completely immigration dependent (mi < 1) and contributed to 2.4% - 5.4% of the substrate consumption. These results suggest that influent microbiome may be important contributors to AS microbiome assembly and system performance (substrate consumption), which may help to improve future AS process modelling and design. SynopsisInfluent microbial immigration lacks detailed taxon-specific quantification. This study presents quantitative methods and models for influent biomass, mass-flow immigration model, and resource consumption in activated sludge. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/504022v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1d6b43forg.highwire.dtl.DTLVardef@d3b7eborg.highwire.dtl.DTLVardef@1cfaef5org.highwire.dtl.DTLVardef@1e75bdb_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Porous microneedle patch with sustained exosomes delivery repairs severe spinal cord injury

1.Mesenchymal stem cell-derived exosome (MSC-EXO) transplantation has been suggested as an efficacious treatment to suppress spinal cord injury (SCI)-triggered neuroinflammation. However, an ethically acceptable method to continuously deliver MSC-EXOs to acute spinal lesions, without damaging nearby tissues/axons, has never been achieved. In this study, we fabricated a device comprising a patch containing MSCs and a microneedle array (MN-MSC patch) to treat severe SCI. When topically applied to an acute spinal lesion beneath the spinal dura, the soft microneedle (MN) array with reasonable mechanical strength avoided damaging the nearby spinal tissues, and the porous microstructure of MNs facilitated highly efficient MSC-EXO delivery. With the capacity for sustained delivery of MSC-EXOs, the MN-MSC patch was evaluated in a contusive rat SCI model. The MSCs encapsulated in the patch could survive for at least 7 days, encompassing the optimal time window for downregulating SCI-triggered neuroinflammation. As a result, MN-MSC patch treatment led to reduced cavity and scar tissue formation, greater angiogenesis, and improved survival of nearby tissues/axons. Remarkably, rats treated by this method achieved superior muscle control and exhibited robust hindlimb locomotion functional recovery. Conclusively, the MN-MSC patch device proposed here overcomes the current dilemma between treatment efficacy and ethical issues in treating acute SCI.

bioengineering↗

bioRxiv2022

Combined omics unravels the molecular mechanism of golden-leaf coloration in Koelreuteria paniculata 'jinye'

Koelreuteria paniculata is widely distributed in Asia and introduced to Europe and North America. K. paniculata jinye is a mutant variety used in landscaping that has a golden leaf color phenotype. Although similar leaf color variants occur in plants, little is known of the underlying mechanism. We performed physiological, anatomical, microRNA sequencing, transcriptomic, and metabolomic analyses of the golden leaf variation in the mutant. Compared with the original green cultivar, the golden leaf mutant exhibited 76.05% and 44.32% decreased chlorophyll a (Chl a) and chlorophyll b (Chl b) contents, respectively, and significantly increased carotenoid content. Analysis of leaf ultrastructure revealed an abnormal chloroplast morphology and fewer lamellae in the mutant. Fifty-nine differentially expressed genes (DEGs), forty transcription factors (TFs) and forty-nine differentially expressed miRNAs (DEmiRs) involved in pigment metabolism, chloroplast development, and photosynthesis were identified. The GLK and petC genes were downregulated and are involved in chloroplast development and chlorophyll synthesis, respectively. The upregulated PSY and PDS genes, and the downregulated NCED gene promote carotenoid accumulation. A variety of chalcones and flavonols were upregulated in the mutant. Consequently, the carotenoid to chlorophyll ratio increased by more than 75%, and the accumulation of chalcones and flavonols was responsible for the golden leaf phenotype of the mutant K. paniculata.

molecular biology↗

bioRxiv2022

Protein 3D structure-based neural networks highly improve the accuracy in compound-protein binding affinity prediction

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug screening tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

bioinformatics↗

bioRxiv2022

TFIIB-related factor 1 is a nucleolar protein that promotes RNA polymerase I-directed transcription and tumour cell growth

RNA polymerase I (Pol I) products play pivotal roles in ribosomal assembly, protein synthesis and cell growth. Dysregulation of Pol I product synthesis closely correlates with tumourigenesis. However, the factors and pathways governing Pol I-dependent transcription remain to be identified. Here, we report that TFIIB-related factor 1 (BRF1), a subunit of TFIIIB required for Pol III-mediated transcription, is a nucleolar protein and modulates the rRNA synthesis directed by Pol I. We show that BRF1 can be co-localized with nucleolar protein markers in several human cell types. BRF1 expression positively correlates with Pol I product levels and tumour cell growth in vitro and in vivo. Mechanistically, BRF1 binds to the Pol I transcription machinery factors and is recruited to the rDNA promoter with them. Alteration of BRF1 expression affected the recruitment of Pol I transcription machinery at the rDNA promoter, the rDNA promoter activity and expression of TBP and TAF1A, indicating that BRF1 modulates Pol I-direct transcription by controlling the expression of selective factor 1 subunits. Collectively, we demonstrate that BRF1 acts as a positive factor to regulate Pol I-directed transcription, suggesting that BRF1 can concurrently modulate Pol I and Pol III transcription and acts as a key coordinator between them.

molecular biology↗

bioRxiv2022

Improving functional recovery after severe spinal cord injury by a noninvasive dual functional approach of neuroprotection and neuromodulation

Despite tremendous unmet medical needs, there is no effective pharmacological treatment to promote functional recovery after spinal cord injury (SCI). Although multiple pathological events have been implicated in SCI, the development of a noninvasive pharmacological approach to simultaneously target the different mechanisms involved in SCI remains a formidable challenge. In this study, we report the development of a noninvasive nanodrug delivery system that consists of ROS-responsive amphiphilic copolymers and an encapsulated neurotransmitter-conjugated KCC2 agonist. We show that upon intravenous administration, the nanodrugs were able to enter the injured spinal cord due to blood spinal cord barrier disruption and ROS-responsive disassembly. Remarkably, once in the injured spinal cord, these nanodrugs exhibited dual functions: scavenging ROS accumulated in the lesion to protect spared connections and increasing neuronal excitability in the injured spinal cord through targeted delivery of the KCC2 agonist to inhibitory neurons. Thus, the noninvasive treatment led to significant functional recovery in the rats with contusive SCI. Together, these findings provide a much-needed translational pharmacological approach for treating severe SCI.

neuroscience↗

bioRxiv2021

A spatiotemporal steroidogenic regulatory network in human fetal adrenal glands and gonads

Human fetal adrenal glands produce substantial amounts of dehydroepiandrosterone (DHEA), which is one of the most important precursors of sex hormones. However, the underlying biological mechanism remains largely unknown. Herein, we sequenced human fetal adrenal glands and gonads from 7 to 14 GW via the 10x Genomics single-cell transcriptome techniques and reconstructed their location information by Spatial Transcriptome, conducted COOL-seq for the MC2R+ inner zone steroidogenic cells during the time window of sex differentiation (8-12GW). We found that relative to gonads, adrenal glands begin to synthesize steroids early. The coordination among steroidogenic cells and multiple nonsteroidogenic cells promotes adrenal cortex construction and steroid synthesis. Notably, during the time window of sex differentiation (8-12 GW), key enzyme gene expression shifts to accelerate DHEA synthesis in males and cortisol synthesis in females. Furthermore, high SST+ expressions in the adrenal gland and testis amplify androgen synthesis in males. Our research highlights the robustness of the action of fetal adrenal glands on gonads to modify the process of sexual differentiation.

systems biology↗

bioRxiv2021

Impact of pH and removed filtrate on E. coli regrowth and microbial community during storage of electro-dewatered biosolids

Residual biosolids can be land applied if they meet microbiological requirements at the time of application. Electro-dewatering technology is shown to reduce biosolids bacterial counts to detection limits with little potential for bacterial regrowth during incubations. Here, we investigated the impacts on Escherichia coli regrowth and microbial communities of biosolids pH, removed nutrients via the filtrate, and inhibitory compounds produced in electro-dewatered biosolids. Findings suggest pH as the primary mechanism impacting E. coli regrowth in electro-dewatered biosolids. Propidium monoazide treatments were effective at removing DNA from dead cells, based on the removal of obligate anaerobes observed after anaerobic incubation. Analyses of high throughput sequenced data showed lower alpha-diversities associated with electro-dewatering treatment and incubation time. Moreover, biosolids pH and incubation period were the main factors contributing to the variations in microbial community compositions after incubation. Results highlight the role of electro-dewatered biosolids low pH on inhibiting the regrowth of culturable bacteria as well as reducing the microbial community variance.

microbiology↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2021

Reorganizations of latency structure within white matter from wakefulness to sleep

Previous studies based on resting-state fMRI (rsfMRI) data have revealed the existence of highly reproducible latency structure, reflecting the propagation of BOLD fMRI signals, in white matter (WM). Here, based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data collected from 35 healthy subjects who were instructed to sleep, we explored the alterations of propagations in WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxel-wise time series, followed by parabolic interpolation to determine the actual latency value in-between. In WM, regions including cerebellar peduncle, internal capsule, posterior thalamic radiation, genu of corpus callosum, and corona radiata, were found to change their temporal roles drastically, as revealed by applying linear mixed-effect model on voxel-wise latency projections across wakefulness and NREM sleep stages. Using these regions as seeds, further seed-based latency analysis revealed that variations of latency projections across different stages were underlain by inconsistent temporal shifts between each seed and the remaining part of WM. Finally, latency analysis on resting-state networks (RSNs), obtained by applying k-means clustering technique on group-level functional connectivity matrix, identified a path of signal propagations similar to previous findings in EEG during wakefulness, which propagated mainly from the brainstem upward to internal capsule and further to corona radiata. This path showed inter-RSN temporal reorganizations depending on the paired stages between which the brain transitioned, e.g., it changed, between internal capsule and corona radiata, from mainly unidirectional to clearly reciprocal when the brain transitioned from wakefulness to N3 stage. These findings suggested the functional role of BOLD signals in white matter as a slow process, dynamically modulated across wakefulness and NREM sleep stages, and involving in maintaining different levels of consciousness and cognitive processes.

neuroscience↗

bioRxiv2021

Correlated Functional Connectivity and Glucose Metabolism in Brain White Matter Revealed by Simultaneous MRI/PET

Blood oxygenation level-dependent (BOLD) signals in white matter (WM) have usually been ignored or undetected, consistent with the lower vascular density and metabolic demands in WM than in gray matter (GM). Despite converging evidence demonstrating the reliable detection of BOLD signals in WM evoked by neural stimulation and in a resting state, few studies have examined the relationship between BOLD functional signals and tissue metabolism in WM. By analyzing simultaneous recordings of MRI and PET data, we found that the correlations between low frequency resting state BOLD signals in WM are spatially correlated with local glucose uptake, which also covaried with the amplitude of spontaneous low frequency fluctuations in BOLD signals. These results provide further evidence that BOLD signals in WM reflect variations in metabolic demand associated with neural activity, and suggest they should be incorporated into more complete models of brain function.

neuroscience↗

bioRxiv2021

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/436603v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a95337org.highwire.dtl.DTLVardef@966d5aorg.highwire.dtl.DTLVardef@1e55b18org.highwire.dtl.DTLVardef@bfb5d7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling C_LIO_LI73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics data C_LIO_LIPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76 C_LIO_LITop models of whole signaling response models perform almost as well as a biological replicate C_LIO_LICell-line specific variation in dynamics can be predicted from basal omics C_LI

bioinformatics↗

bioRxiv2021

Lag structure of BOLD signals within white matter in resting-state fMRI

PurposePrevious studies have demonstrated that BOLD signals in gray matter in resting-state functional MRI (RSfMRI) have variable time lags, representing apparent propagations of fMRI BOLD signals in gray matter. We complemented existing findings and explored the corresponding variations of signal latencies in white matter. MethodsWe used data from the Brain Genomics Superstruct Project, consisting of 1412 subjects (both sexes included) and divided the dataset into ten equal groups to study both the patterns and reproducibility of latency estimates within white matter. We constructed latency matrices by computing cross-correlations between voxel pairs. We also applied a clustering analysis to identify functional networks within white matter, based on which latency analysis was also performed to investigate lead/lag relationship at network level. A dataset consisting of various sensory states (eyes closed, eyes open and eyes open with fixation) was also included to examine the relationship between latency structure and different states. ResultsProjections of voxel latencies from the latency matrices were highly correlated (average Pearson correlation coefficient = 0.89) across the subgroups, confirming the reproducibility and structure of signal lags in white matter. Analysis of latencies within and between networks revealed a similar pattern of inter- and intra-network communication to that reported for gray matter. Moreover, a dominant direction, from inferior to superior regions, of BOLD signal propagation was revealed by higher resolution clustering. The variations of lag structure within white matter are associated with different sensory states. ConclusionsThese findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.

neuroscience↗

bioRxiv2021

Withaferin A promotes white adipose browning and prevents obesity through sympathetic nerve-activated Prdm16-FATP1 axis

The increasing prevalence of obesity has resulted in demands for the development of new effective strategies for obesity treatment. The Withaferin A (WA) shows a great potential for prevention of obesity by sensitizing leptin signaling in the hypothalamus. However, the mechanism underlying the weight- and adiposity-reducing effects of WA remains to be elucidated. Here, we report that WA treatment induced white adipose tissue (WAT) browning, elevated energy expenditure (EE), decreased respiratory exchange ratio (RER), and prevented high-fat diet (HFD)-induced obesity. The sympathetic chemical denervation dampened the WAT browning and also impeded the reduction of adiposity in WA-treated mice. WA markedly up-regulated the levels of Prdm16 and FATP1 (Slc27a1) in the inguinal WAT (iWAT), and this was blocked by sympathetic denervation. Prdm16 or FATP1 knockdown in iWAT abrogated the WAT browning-inducing effects of WA, and restored the weight gain and the adiposity in WA-treated mice. Together, these findings suggest that WA induces WAT browning through the sympathetic nerve-adipose axis; and the adipocytic Prdm16-FATP1 pathway mediates the promotive effects of WA on white adipose browning.

physiology↗

bioRxiv2021

Multi Task deep learning for genomic predictions

Genomic predictions have been recognized as a new promising technique in animal and plant breeding. Linear mixed model is a widely used statistical technique, but it may not be desirable for large training sets and number of molecular markers, because it is intensive in computation. Deep learning is a subfield of machine learning and it can be used for complex predictions on a large scale. Multi task deep learning (MT-DL) incorporates related tasks(labels or traits) into one learning process to enable the learning model to perform better than single task deep learning (ST-DL). I applied MT-DL to genotype by environment genomic predictions to predict the performances of breeding lines at multiple environments. I compared MT-DL with linear mixed model-based Bayesian genotype x environment method (BGGE) and separate genomic predictions on single environments with widely used rrBLUP, ridge regression and ST-DL using cross validations. Compared with rrBLUP, MT-DL and non-linear BGGE showed a moderate increase of 9.4 and 7.6%, respectively, ST-DL has a small increase of 5.4%, ridge regression had a similar prediction accuracy and linear BGGE had a small decrease of -2.0% for prediction accuracy. I also found that all methods including rrBLUP had an overfitting, this is likely because yield genomic predictions are complex and the data set used in this study are small. rrBLUP, ridge regression, ST-DL and MT-DL has similar overfitting. Difference between training and test set prediction accuracies was between 0.344 and 0. 387. Linear and nonlinear BGGE methods seem to have much worse overfitting than other methods. Difference between training and test set prediction accuracies were 0.429 and 0.472, respectively. I also discussed the potential applications of ST-DL and MT-DL in genomic predictions of hybrid crops such as maize

genetics↗