Biology subjects

Liu, Y.-Y.

Publications and source records attributed to Liu, Y.-Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Origins of Scaling Laws in Microbial Dynamics

Analysis of high-resolution time series data from the human and mouse gut microbiomes revealed that the gut microbial dynamics can be characterized by several robust and simple scaling laws. It is still unknown if those scaling laws are universal across different body sites, host species, or even free-living microbial communities. Moreover, the underlying mechanisms responsible for those scaling laws remain poorly understood. Here, we demonstrate that those scaling laws are not unique to gut microbiome, but universal across different habitats, from human skin and oral microbiome to marine plankton bacteria and eukarya communities. Since completely shuffled time series yield very similar scaling laws, we conjecture that the universal scaling laws in various microbiomes are largely driven by temporal stochasticity of the host or environmental factors. We leverage a simple population dynamics model with both deterministic inter-species interactions and stochastic noise to confirm our conjecture. In particular, we find that those scaling laws are jointly determined by inter-species interactions and linear multiplicative noises. The presented results deepen our understanding of the nature of scaling laws in microbial dynamics.

microbiology↗

bioRxiv2021

Establishment and Resilience of Transplanted Gut Microbiota in Aged Mice

Fecal microbiota transplantation (FMT), a procedure in which fecal material is transferred from a donor to a recipient, has been increasingly used as a treatment to restore healthy gut microbiota. There is a substantial difference in the composition of gut microbiota between young and aged hosts, but little is known about whether age matching between the FMT donor and recipient affects microbiota restoration and long-term maintenance. In the present investigation, we aimed to study the establishment and resilience of transplanted gut microbiota in aged recipients. We treated naturally aged mice (20 months old) with a broad-spectrum antibiotic cocktail and monitored the restoration of gut microbiota over 8 weeks. The diversity of gut microbiota in aged mice failed to reach the baseline level via spontaneous recovery; in contrast, FMT from either (age-)matched or unmatched donors facilitated the recovery of gut microbiota diversity. The microbiota transplanted from different donors successfully established in the aged recipients and had long-term effects on the gene expression profiles of the host colon. Finally, we evaluated the long-term maintenance of transplanted microbiota via intentional disruption of gut homeostasis. We found that lack of age matching between FMT donors and recipients may decrease the resilience of transplanted gut microbiota against colonic inflammation. The results from our study systematically examining the effects of FMT on the gut homeostasis of aged hosts suggest that the compatibility between donors and recipients should be taken into account when implementing FMT.

microbiology↗

bioRxiv2021

Mechanically Sensitive HSF1 is a Key Regulator of Left-Right Symmetry Breaking in Zebrafish Embryos

The left-right symmetry breaking of vertebrate embryos requires fluid flow (called nodal flow in zebrafish). However, the molecular mechanisms that mediate the asymmetric gene expression regulation under nodal flow remain elusive. In this paper, we report that heat shock factor 1 (HSF1) is asymmetrically activated in the Kuppfers vesicle at the early stage of zebrafish embryos in the presence of nodal flow. Deficiency in HSF1 expression caused a significant situs inversus and disrupted gene expression asymmetry of nodal signaling proteins in zebrafish embryos. Further studies demonstrated that HSF1 could be immediately activated by fluid shear stress. The mechanical sensation ability of HSF1 is conserved in a variety of mechanical stimuli in different cell types. Moreover, cilia and the Ca2+-Akt signaling axis are essential for the activation of HSF1 under mechanical stress in vitro and in vivo. Considering the conserved expression of HSF1 in organisms, these findings unveil a fundamental mechanism of gene expression regulation triggered by mechanical clues during embryonic development and other physiological and pathological transformations.

developmental biology↗

bioRxiv2021

Nutritional Redundancy in Human Diet

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as nutritional redundancy and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

systems biology↗

bioRxiv2021

Characterizing scaling laws in gut microbial dynamics from time series data: caution is warranted

Many studies have revealed that both host and environmental factors can impact the gut microbial compositions, implying that the gut microbiota is considerably dynamic1-5. In their Article, Ji et al.6 performed comprehensive analysis of multiple high-resolution time series data of human and mouse gut microbiota. They found that both human and mouse gut microbiota dynamics can be characterized by several robust scaling laws describing short- and long-term changes in gut microbiota abundances, distributions of species residence and return times, and the correlation between the mean and the temporal variance of species abundances. They claimed that those scaling laws characterize both short- and long-term dynamics of gut microbiota. However, we are concerned that their interpretation is quite misleading, because all the scaling laws can be reproduced by the shuffled time series with completely randomized time stamps of the microbiome samples.

microbiology↗

bioRxiv2020

A conserved ubiquitin- and ESCRT-dependent pathway to regulate human lysosomal membrane proteins

The lysosome is an essential organelle to recycle cellular materials and maintain nutrient homeostasis, but the mechanism to down-regulate lysosomal membrane proteins is poorly understood. In this study, we developed a cycloheximide chase assay to measure the half-lives of ~30 human lysosomal membrane proteins, and identified RNF152 as a short-lived protein. The degradation of RNF152 depends on ubiquitin and the endosomal sorting complexes required for transport (ESCRT) machinery. Ubiquitinated RNF152 is sorted and internalized by the ESCRT machinery into the lysosomal lumen for degradation. Strikingly, when expressed in budding yeast, human RNF152 is also degraded by the vacuole (yeast lysosome) in an ESCRT-dependent manner. Thus, our study uncovered a conserved mechanism to down-regulate lysosome membrane proteins.

cell biology↗

bioRxiv2020

Challenges in Benchmarking Metagenomic Profilers

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classifying metagenomic data and estimating taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Yet, benchmarking metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here, we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community should pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that was analyzed and interpreted, and clearly stating the strategy used for metagenomic profiling.

bioinformatics↗

bioRxiv2020

Impact of temporal pH fluctuations on the coexistence of nasal bacteria

To manipulate nasal microbiota for respiratory health, we need to better understand how this microbial community is assembled and maintained. Previous work has demonstrated that the pH in the nasal passage experiences temporal fluctuations. Yet, the impact of such pH fluctuations on nasal microbiota is not fully understood. Here, we examine how temporal fluctuations in pH might affect the coexistence of nasal bacteria. We take advantage of the cultivability of nasal bacteria to experimentally assess their responses to pH. Based on experimentally observed responses, we formulate a mathematical model to numerically investigate the impact of temporal pH fluctuations on species coexistence. Through extensive numerical simulations, we find that the composition of nasal communities is robust against pH fluctuations. Our results suggest that nasal microbiota could be more robust than expected against environmental fluctuations.

ecology↗

bioRxiv2020

Peripheral blood microbial signatures in COPD

BackgroundThe human microbiome has a role in the development of human diseases. Individual microbiome profiles are highly personalized, though many species are shared. Understanding the relationship between the human microbiome and disease may inform future individualized treatments. Specifically, the blood microbiome, once believed sterile, may be a surrogate for some lung and gut microbial characteristics. We sought associations between the blood microbiome and lung-relevant host factors. MethodsBased on reads not mapped to the human genome, we detected microbial nucleic acid signatures in peripheral blood RNA-sequencing for 2,590 current and former smokers with and without chronic obstructive pulmonary disease (COPD) from the COPDGene study. We used the GATK microbial pipeline PathSeq to infer microbial profiles. We tested associations between the inferred profiles and lung disease relevant phenotypes and examined links to host gene expression pathways. ResultsThe four phyla with highest abundance across all subjects were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. We observed associations between exacerbation phenotypes and the relative abundance of Staphylococcus, Acidovorax and Cupriavidus. The genus Flavobacterium was associated with emphysema and change in emphysema. Our host-microbiome interaction analysis revealed clustering of genera associated with emphysema, systemic inflammation, airway remodeling and exacerbations, through links to lung-relevant host pathways. ConclusionsThis study is the first to identify a bacterial microbiome signature in the peripheral blood of current and former smokers. Understanding the relationships between the systemic microbial populations and lung disease severity may inform novel interventions and aid in the understanding of exacerbation phenotypes.

genomics↗

bioRxiv2020

Dissimilarity-Overlap Analysis of Replicate Enrichment Communities

The taxonomic composition of microbial communities can vary substantially between individual hosts and within the same host over time. Efforts to build quantitative and predictive models of microbial population dynamics are underway, but fundamental questions remain. How different are population dynamics in different hosts? Do communities that share the same taxa also exhibit identical dynamics? In vitro communities can help establish baseline expectations that are critical towards resolving these questions in natural communities. Here, we applied a recently developed tool, Dissimilarity-Overlap Analysis (DOA), to a set of experimental in vitro communities that differed in nutrient composition. The Dissimilarity and Overlap of these communities are negatively correlated in replicate habitats, as one would expect if microbial population dynamics were on average strongly convergent (or "universal") across these replicate habitats. However, the existence of such a negative correlation does not necessarily imply that population dynamics are always universal in all communities. Even in replicate, identical habitats, two different communities may contain the same set of taxa at different abundances in equilibrium. The formation of alternative states in community assembly is strongly associated with the presence of specific taxa in the communities. Our results benchmark DOA, providing support for some of its core assumptions, and suggest that communities sharing the same taxa and external abiotic factors generally (but not necessarily) have a negative correlation between Dissimilarity and Overlap.

ecology↗

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↗

bioRxiv2019

TORC1 regulates vacuole membrane composition through ubiquitin- and ESCRT-dependent microautophagy

Cellular adaptation in response to nutrient limitation requires the induction of autophagy and lysosome biogenesis for the efficient recycling of macromolecules. Here, we discovered that starvation and TORC1 inactivation not only lead to the upregulation of autophagy and vacuole proteins involved in recycling, but also result in the downregulation of many vacuole membrane proteins to supply amino acids as part of a vacuole remodeling process. Downregulation of vacuole membrane proteins is initiated by ubiquitination, which is accomplished by the coordination of multiple E3 ubiquitin ligases, including Rsp5, the Dsc complex, and a newly characterized E3 ligase, Pib1. The Dsc complex is negatively regulated by TORC1 through the Rim15-Ume6 signaling cascade. After ubiquitination, vacuole membrane proteins are sorted into the lumen for degradation by ESCRT-dependent microautophagy. Thus, our study uncovered a complex relationship between TORC1 inactivation and vacuole biogenesis.

cell biology↗

bioRxiv2019

Pharmacodynamics of linezolid-plus-fosfomycin against vancomycin-susceptible and -resistant enterococci in vitro and in vivo of a Galleria mellonella larval infection model

ObjectiveTo explore the in vitro and in vivo antibacterial activity of linezolid/fosfomycin combination against vancomycin-susceptible and -resistant enterococci (VSE and VRE), providing theoretical basis for the treatment of VRE.\n\nMethodsThe checkerboard method and time-kill curve study were used to evaluate the synergistic effect of linezolid combined with fosfomycin against VSE and VRE. The transmission electron microscopy (TEM) was employed to observe the bacterial cell morphology followed by each drug alone and in combination, elucidating the possible result of antibiotic combination therapy. The Galleria mellonella infection model was constructed to demonstrate the in vivo efficacy of linezolid plus fosfomycin for VSE and VRE infection.\n\nResultsThe fractional inhibitory concentration index (FICI) values of all strains suggested that linezolid showed synergy or additivity in combination with fosfomycin against five of the six strains. Time-kill experiments demonstrated that the combination of linezolid-fosfomycin at 1xMIC or 2xMIC led to higher degree of bacterial killing without regrowth for all isolates tested than each monotherapy. TEM imaging showed that the combination treatment damaged the bacterial cell morphology more obviously than each drug alone. In the Galleria mellonella infection model, the enhanced survival rate of the combination treatment was revealed compared to linezolid monotherapy (P<0.05).\n\nConclusionsOur data manifest that the combination of linezolid and fosfomycin may be a possible therapeutic regimen for VRE infection. The combination displays excellent bacterial killing and inhibits amplification of fosfomycin-resistant subpopulations.

pharmacology and toxicology↗

bioRxiv2018

Identifying sensor species to predict critical transitions in complex ecosystems

Ecosystems provide key services needed for human well-being, such as purifying air and regulating our health, many of which are disrupted when these ecosystems undergo a critical transition to undesired states. Detecting early-warning signals of these critical transitions remains challenging for complex ecosystems with a large number of species. Here we built a mathematical formalism to identify minimal sets of \"sensor species\" from which we can determine the state of a whole ecosystem, allowing us to predict a critical transition in an ecosystem by monitoring a minimal subset of its species. We rigorously prove that minimal sets of sensor species can be generically identified knowing only the structure of the ecological network underlying the ecosystem, regardless of its population dynamics. We numerically validated our formalism to predict critical transitions in large complex ecosystems, and then we applied it to experimental data of a critical transition in a lake food-web. Our results contribute to better monitoring complex ecosystems, especially those with poorly known population dynamics such as host-associated microbial communities.

ecology↗

bioRxiv2018

Overcome Competitive Exclusion in Ecosystems

Explaining biodiversity in nature is a fundamental problem in ecology. One great challenge is embodied in the so-called competitive exclusion principle1-4: the number of species in steady coexistence cannot exceed the number of resources4-7. In the past five decades, various mechanisms have been proposed to overcome the limit on diversity set by the competitive exclusion principle8-25. Yet, none of the existing mechanisms can generically overcome competitive exclusion at steady state4,26. Here we show that by forming chasing triplets in the predation process among the consumers and resources, the number of coexisting species of consumers can exceed that of resources at steady state, naturally breaking the competitive exclusion principle. Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and deepen our understanding of biodiversity in nature.

ecology↗

bioRxiv2018

Link Prediction through Deep Learning

Inferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1-3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7-9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13-17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance. SummaryWe propose a new link prediction method based on deep generative models.

bioinformatics↗

bioRxiv2017

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways relationship to T2D.

systems biology↗

bioRxiv2017

Horizontal Gene Transfer Can Help Maintain the Equilibrium of Microbial Communities

Horizontal gene transfer and species coexistence are two focal points in the study of microbial communities. The evolutionary advantage of horizontal gene transfer has not been well-understood and is constantly being debated. Here we propose a simple population dynamics model based on the frequency-dependent interactions between different genotypes to evaluate the influence of horizontal gene transfer on microbial communities. We find that both structural stability and robustness of the microbial community are strongly affected by the gene transfer rate and direction. An optimal gene flux can stablize the ecosystem, helping it recover from disturbance and maintain the species coexistence.

evolutionary biology↗