Biology subjects

Ju, F.

Publications and source records attributed to Ju, F..

34 records · Page 2Linked to original sources

bioRxiv2022

Hydrodynamic and anthropogenic disturbances co-shape microbiota rhythmicity and community assembly within intertidal groundwater-surface water continuum

Tidal hydrodynamics drive the groundwater-seawater exchange and shifts in microbiota structure in the coastal zone. However, how the coastal water microbiota structure and assembly patterns respond to periodic tidal fluctuations and anthropogenic disturbance remain unexplored in the intertidal groundwater-surface water (GW-SW) continuum, although it affects biogeochemical cycles and coastal water quality therein. Here, through hourly time-series sampling in the saltmarsh tidal creek, rhythmic patterns of microbiota structure in response to daily and monthly tidal fluctuations in intertidal surface water are disentangled for the first time. The similarity in archaeal community structures between groundwater and ebb-tide surface water (R2=0.06, p=0.2) demonstrated archaeal transport through groundwater discharge, whereas multi-source transport mechanisms led to unique bacterial biota in ebb-tide water. Homogeneous selection (58.6%-69.3%) dominated microbiota assembly in the natural intertidal GW-SW continuum and the presence of 157 rhythmic ASVs identified at ebb tide and 141 at flood tide could be attributed to different environmental selection between groundwater and seawater. For intertidal groundwater in the tidal creek affected by anthropogenically contaminated riverine inputs, higher microbial diversity and shift in community structure were primarily controlled by increased co-contribution of dispersal limitation and drift (jointly 57.8%) and enhanced microbial interactions. Overall, this study fills the knowledge gaps in the tide-driven water microbial dynamics in coastal transition zone and the response of intertidal groundwater microbiota to anthropogenic pollution of overlying waters. It also highlights the potential of microbiome analysis in enhancing coastal water quality monitoring and identifying anthropogenic pollution sources (e.g., aquaculture pathogenic Vibrio) through the detection of rhythmic microbial variances associated with intertidal groundwater discharge and seawater intrusion.

ecology↗

bioRxiv2022

Deduplication Improves Cost-Efficiency and Yields of De novo Assembly and Binning of Shot-Gun Metagenomes in Microbiome Research

Metagenomics has in the last decade greatly revolutionized the study of microbial communities. However, the presence of artificial duplicate reads mainly raised from the preparation of metagenomic DNA sequencing library and their impacts on metagenomic assembly and binning have never brought to the attention. Here, we explicitly investigated the effects of duplicate reads on metagenomic assembly and binning, based on analyses of four groups of representative metagenomes with distinct microbiome complexity. Our results showed that deduplication considerably increased the binning yields (by 3.5% to 80%) for most of the metagenomic datasets examined thanks to improved contig length and coverage profiling of metagenome-assembled contigs. Specifically, 411 versus 397, 331 versus 317, 104 versus 88 and 9 versus 5 metagenome-assembled genomes (MAGs) were recovered from MEGAHIT assemblies of bioreactor sludge, surface water, lake sediment, and forest soil metagenomes, respectively. Noticeably, deduplication reduced the computational costs of metagenomic assembly including elapsed time (by 9.0% to 29.9%) and maximum memory requirement (by 4.3% to 37.1%). Collectively, it is recommended to remove duplicate reads in metagenomic data before assembly and binning analyses, particularly for complex environmental samples, such as forest soils examined in this study. ImportanceDuplicated reads are usually considered as technical artefacts. Their presence in metagenomes would theoretically not only introduce bias in the quantitative analysis, but also result in mistakes in coverage profile, leading to negative effects or even failures on metagenomic assembly and binning, as the widely used metagenome assemblers and binners all need coverage information for graph partitioning and assembly binning, respectively. However, this issue was seldomly noticed and its impacts on the downstream key bioinformatic procedures (e.g., assembly and binning) still remained unclear. In this study, we comprehensively evaluated for the first time the impacts of duplicate reads on de novo assembly and binning of real metagenomic datasets by comparing assembly quality, binning yields and the requirements of computational resources with and without the removal of duplicate reads. It was revealed that deduplication considerably increased the binning yields and significantly reduced the computational costs including elapsed time and maximum memory requirement. The results provide empirical reference for more cost-efficient metagenomic analyses in microbiome research.

bioinformatics↗

bioRxiv2022

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of Knot-Fold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using a self-attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially-designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely-used dynamic programming algorithms, thus facilitating the identification of pseudoknots. Using a total of 1605 RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy significantly higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

bioinformatics↗

bioRxiv2022

Establishment Strategy and Temporal Dynamics of Tetrasphaera-Enriched Microbiome for Enhanced Biological Phosphorus Removal and Recovery

Tetrasphaera were recently identified based on the 16S rRNA gene as among the most abundant polyphosphate-accumulating organisms (PAOs) in global full-scale wastewater treatment plants (WWTPs) with enhanced biological phosphorus removal (EBPR). However, it is unclear how Tetrasphaera PAOs are selectively enriched in the context of the EBPR microbiome. In this study, an EBPR microbiome enriched with Tetrasphaera (accounting for 40% of 16S sequences on day 113) was built using a top-down design approach featuring multicarbon sources and a low dosage of allylthiourea. The microbiome showed enhanced nutrient removal (P removal ~85% and N removal ~80%) and increased P recovery (up to 23.2 times) compared with the seeding activated sludge from a local full-scale WWTP. The supply of 1 mg/L allylthiourea promoted the coselection of Tetrasphaera PAOs and Microlunatus PAOs and sharply reduced the relative abundance of both ammonia oxidizer Nitrosomonas and putative competitors Brevundimonas and Paracoccus, facilitating the establishment of the EBPR microbiome. Based on 16S rRNA gene analysis, a putative novel PAO species, EBPR-ASV0001, was identified with Tetrasphaera japonica as its closest relative. This study provides new knowledge on the establishment of a Tetrasphaera-enriched microbiome facilitated by allylthiourea, which can be further exploited to guide future process upgrading and optimization to achieve and/or enhance simultaneous biological phosphorus and nitrogen removal from high-concentration wastewater.

bioengineering↗

bioRxiv2022

Hydrological dynamics drive the transition of antibiotic resistance genes between particle-attached and free-living lifestyles in a deep freshwater reservoir

Despite the growing awareness of antibiotic resistance genes (ARGs) spreading in the environment, there is a knowledge gap on the fate and transport of ARGs in particle-attached (PA) and free-living (FL) lifestyles in deep freshwater ecosystems experiencing seasonal hydrological changes. Here, we examined the ARG profiles using high-throughput quantitative PCR in PA and FL lifestyles at four seasons representing two hydrological seasons (i.e., vertical mixing and thermal stratification) in the Shuikou Reservoir (SR), Southern China. The results indicated that seasonal hydrological dynamics were critical for influencing ARGs in PA and FL fractions, and the transition of ARGs between the two lifestyles. Although both PA and FL ARG profiles were likely to be shaped by horizontal gene transfer, PA and FL ARGs had different responses to the changes in physico-chemicals (e.g., nutrients and dissolved oxygen) caused by seasonal hydrological dynamics. The particle-associated niche (PAN) index revealed that there were 94 non-conservative ARGs (i.e., no preferences for PA and FL), 23 conservative ARGs that preferred PA lifestyle, and 16 conservative ARGs for FL lifestyle. A sharp decline in the number of conservative ARGs in stratified seasons suggests a hydrological dynamics-dependent transition of ARGs between two lifestyles. Remarkably, the conservative ARGs (in PA or FL lifestyle) were more closely related to bacterial OTUs in their preferred lifestyle compared to their counterpart lifestyle, suggesting a lifestyle-dependent ARG enrichment. Altogether, these findings enhance our understanding of the role of seasonal hydrological changes in the dissemination of ARGs in different size fractions in deep aquatic ecosystems.

ecology↗

bioRxiv2022

Using Culture-Enriched Phenotypic Metagenomics for Targeted High-Throughput Monitoring of Clinically-Important Fraction of Beta-Lactam Resistome

The high bacterial community complexity greatly hinders the monitoring of clinically prevalent extended-spectrum beta-lactam resistant bacteria, which are usually present as rare but key populations involving in the environmental dissemination of clinical resistance. Here, we introduce culture-enriched phenotypic metagenomics that integrates culture enrichment, phenotypic screening and metagenomic analyses as an emerging methodology to profile beta-lactam resistome in a municipal wastewater treatment plant (WWTP) and its receiving river. The results showed that clinically prevalent carbapenemase genes (e.g., NDM and KPC family) and extended-spectrum beta-lactamase genes (e.g., CTX-M, TEM and OXA family) considerably existed in the WWTP and showed prominent potential in horizontal dissemination. Strikingly, carbapenem and polymyxin resistance genes co-occurred in highly virulent Enterobacter kobei (MCR-1 and NDM-6) and Citrobacter freundii (ArnA and NDM-16) genomes. Overall, this study exemplifies phenotypic metagenomics for high-throughput surveillance of targeted fraction of clinically-important resistome and substantially expands current knowledge on extended-spectrum beta-lactam resistance in WWTPs.

microbiology↗

bioRxiv2022

Long-term two-photon imaging of spinal cord in freely behaving mice

The spinal cord is critical to the perception of peripheral information under sensory-guided motor behaviors in health and disease. However, the cellular activity underlie spinal cord function in freely behaving animals is not clear. Here, we developed a new method for imaging the spinal cord at cellular and subcellular resolution over weeks under naturalistic conditions. The method involves an improved surgery to reduce spinal movement, and the installation of a miniaturized two-photon microscope to obtain high-resolution imaging in moving mice. In vivo calcium imaging demonstrated that dorsal horn neurons show a sensorimotor program-dependent synchronization and heterogeneity under distinct cutaneous stimuli in behaving mice. The long-term imaging of sensory neurons revealed that in the spinal cord, healthy mice demonstrated stereotyped responses. However, in a neuropathic pain model, plasticity changes and neuronal sensitization were observed. We provide a practical method to study the function of spinal cord on sensory perception and disorders in freely behaving mice.

neuroscience↗

bioRxiv2022

Cyanopeptides Restriction and Degradation Co-mediate Microbiota Assembly During a Freshwater Cyanobacterial Harmful Algal Bloom (CyanoHAB)

Cyanobacterial harmful algal blooms (CyanoHABs) are globally intensifying and exacerbated by climate change and eutrophication. However, microbiota assembly mechanisms underlying CyanoHABs remain scenario specific and elusive. Especially, cyanopeptides, as a group of bioactive secondary metabolites of cyanobacteria, could affect microbiota assembly and ecosystem function. Here, the trajectory of cyanopeptides were followed and linked to microbiota during Microcystis-dominated CyanoHABs in lake Taihu, China. The most abundant cyanopeptide classes detected included microginin, spumigin, microcystin, nodularin and cyanopeptolin with total MC-LR-equivalent concentrations between 0.23 and 2051.54 ppb, of which cyanotoxins beyond microcystins (e.g., cyanostatin B and nodularin_R etc.) far exceeded reported organismal IC50 and negatively correlated with microbiota diversity, exerting potential collective eco-toxicities stronger than microcystins alone. The microbial communities were differentiated by size fraction and sampling date throughout CyanoHABs, and surprisingly, their variances were better explained by cyanopeptides (19-38%) than nutrients (0-16%). Cyanopeptides restriction (e.g., inhibition) and degradation are first quantitatively verified as the deterministic drivers governing community assembly, with stochastic processes being mediated by interplay between cyanopeptide dynamics and lake microbiota. This study presents an emerging paradigm in which cyanopeptides restriction and degradation co-mediate lake water microbiota assembly, unveiling new insights about the ecotoxicological significance of CyanoHABs to freshwater ecosystems.

ecology↗

bioRxiv2021

Polyvinyl Chloride Biodegradation Fuels Survival of Invasive Insect Larva and Intestinal Degrading Strain of Klebsiella

Microbial degradation of polyvinyl chloride (PVC) is eco-friendly and economically attractive but extremely challenging due to the lack of a molecular understanding of the degrading strains and enzymes. Motivated by the serendipitous discovery that the larva of an agricultural invasive insect pest, Spodoptera frugiperda, effectively survived PVC film alone, we profiled the intestinal microbiota of S. frugiperda larva and screened for PVC-degrading strains. Feeding on PVC film significantly changed the larval intestinal microbiota through selective enrichment of Enterococcus, Ochrobactrum, Falsochrobactrum, Microbaterium, Sphingobacterium and Klebsiella. From the larval intestine, we isolated the biofilm-forming Klebsiella sp. EMBL-1 and experimentally verified it as the first Klebsiella bacterium known to actively degrade and utilize PVC by various classic physicochemical and morphological analyses. We further used multiomic analyses, complementarily integrating genomic, transcriptomic, proteomic, and metabolomic insights, to identify enzyme-coding genes responsible for PVC degradation and proposed a biodegradation pathway for the bacterial strain. Overall, both S. frugiperda and strain EMBL-1 are first found to survive effectively on PVC film by using the polymer as the sole energy source. Moreover, this work exemplifying PVC biodegradation provides a reference for discovering more microbes and enzymatic resources for degrading other recalcitrant plastics.

microbiology↗

bioRxiv2021

Untangling Microbiota Diversity and Assembly Patterns in the World's Largest Water Diversion Canal

Large water diversion projects are important constructions for reallocation of human-essential water resources. Deciphering microbiota dynamics and assembly mechanisms underlying canal water ecosystem services especially during long-distance diversion is the prerequisite for water quality monitoring, biohazard warning and sustainable management. Using a 1432-km canal of the South-to-North Water Diversion Projects as a model system, we answer three central questions: how bacterial and micro-eukaryotic communities spatio-temporally develop, how much ecological stochasticity contributes to microbiota assembly, and which immigrating populations better survive and navigate across the canal. We applied quantitative ribosomal RNA gene sequence analyses to investigate canal water microbial communities sampled over a year, as well as null model- and neutral model-based approaches to disentangle the microbiota assembly processes. Our results showed clear microbiota dynamics in community composition driven by seasonality more than geographic location, and seasonally dependent influence of environmental parameters. Overall, bacterial community was largely shaped by deterministic processes, whereas stochasticity dominated micro-eukaryotic community assembly. We defined a local growth factor (LGF) and demonstrated its innovative use to quantitatively infer microbial proliferation, unraveling taxonomically dependent population response to local environmental selection across canal sections. Using LGF as a quantitative indicator of immigrating capacities, we also found that most micro-eukaryotic populations (82%) from the source lake water sustained growth in the canal and better acclimated to the hydrodynamical water environment than bacteria (67%). Taxa inferred to largely propagate include Limnohabitans sp. and Cryptophyceae, potentially contributing to water auto-purification. Combined, our work poses first and unique insights into the microbiota assembly patterns and dynamics in the worlds largest water diversion canal, providing important ecological knowledge for long-term sustainable water quality maintenance in such a giant engineered system.

microbiology↗

bioRxiv2020

ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly-related templates are available. Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently-occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build structure model according to the alignment. Tested on three independent datasets with in total 6,688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods including HHpred, CNFpred, CEthreader and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.

bioinformatics↗

bioRxiv2020

Sewage-Borne Pathogens and Indigenous Denitrifiers are Active and Key Multi-Antibiotic Resistant Players in Wastewater Treatment Plants

The global rise and spread of antibiotic resistance greatly challenge the treatment of bacterial infections. Wastewater treatment plants (WWTPs) harbor and discharge antibiotic resistance genes (ARGs) as environmental contaminants. However, the knowledge gap on the host identity, activity and functionality of ARGs limits transmission and health risk assessment of WWTPs resistome. Hereby, a genome-centric quantitative metatranscriptomic approach was exploited to realize high-resolution qualitative and quantitative analyses of bacterial hosts of ARGs (i.e., multi-resistance, pathogenicity, activity and niches) throughout 12 urban WWTPs. We found that [~]45% of 248 recovered genomes expressed ARGs against multiple classes of antibiotics, among which bacitracin and aminoglycoside resistance genes in Proteobacteria was the most prevalent scenario. Both potential pathogens and indigenous denitrifying bacteria were transcriptionally active hosts of ARGs. The almost unchanged relative expression levels of ARGs in the most resistant populations (66.9%) and the surviving ARG hosts including globally emerging pathogens (e.g., Aliarcobacter cryaerophilus) in treated WWTP effluent prioritizes future examination on the health risks related with resistance propagation and human exposure in the receiving environment.

microbiology↗

bioRxiv2020

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

Protein functions are largely determined by the final details of their tertiary structures, and the structures could be accurately reconstructed based on inter-residue distances. Residue co-evolution has become the primary principle for estimating inter-residue distances since the residues in close spatial proximity tend to co-evolve. The widely-used approaches infer residue co-evolution using an indirect strategy, i.e., they first extract from the multiple sequence alignment (MSA) of query protein some handcrafted features, say, co-variance matrix, and then infer residue co-evolution using these features rather than the raw information carried by MSA. This indirect strategy always leads to considerable information loss and inaccurate estimation of inter-residue distances. Here, we report a deep neural network framework (called CopulaNet) to learn residue co-evolution directly from MSA without any handcrafted features. The CopulaNet consists of two key elements: i) an encoder to model context-specific mutation for each residue, and ii) an aggregator to model correlations among residues and thereafter infer residue co-evolutions. Using the CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrated the successful application of CopulaNet for estimating inter-residue distances and further predicting protein tertiary structure with improved accuracy and efficiency. Head-to-head comparison suggested that for 24 out of the 31 free modeling CASP13 domains, ProFOLD outperformed AlphaFold, one of the state-of-the-art prediction approaches.

bioinformatics↗

bioRxiv2020

Metformin chlorination byproducts in drinking water exhibit marked toxicity to nematode worms, human cells, and mice

Metformin (MET), a worldwide used drug for type 2 diabetes, has been found with the largest amount by weight among all drugs in aquatic environment, including the drinking water sources where chlorination inevitably transforms MET into chlorination byproducts. Although MET has health-promoting properties, whether or how its chlorination byproducts affect health remains largely unknown. Here we reveal that MET chlorination byproducts Y (C4H6ClN5) and C (C4H6ClN3) exhibit marked toxicity, even higher than that of the well-known poisonous arsenic, to live worms and human cells. Moreover, both byproducts are harmful to mice and Y at 250 ng/L destroys the mouse small intestine integrity. Strikingly, we detected MET and byproduct C in worldwide drinking water. Both byproducts are increasingly produced with more MET present during chlorination process. Unprecedentedly, we unveil boiling and activated carbon adsorption as effective solutions that are in urgent demand globally for removing these byproducts from water.

pharmacology and toxicology↗

bioRxiv2020

Distinct growth stages shaped by an interplay of deterministic and neutral processes are indispensable for functional anammox biofilms

Complex microbial biofilms orchestrating mainstream anaerobic ammonium oxidation (anammox) represent one of the most promising energy-efficient mechanisms of fixed nitrogen elimination from anthropogenic waste waters. However, little is known about the ecological processes that are driving microbial community assembly leading to functional anammox biofilms in engineered ecosystems. Here, we use fluorescence in situ hybridization and 16S rRNA sequencing combined with network modelling to elucidate the contribution of stochastic and deterministic processes during anammox biofilm development from first colonization to maturation in a carrier-based anammox reactor. We find that distinct stages of biofilm development emerge naturally in terms of structure and community composition. These stages are characterized by dynamic succession and an interplay of stochastic and deterministic processes. The staged process of biofilm establishment appears to be the prerequisite for the anticipated growth of anammox bacteria and for reaching a biofilm community structure with the desired metabolic capacities. We discuss the relevance of this improved understanding of anammox community ecology and biofilm development concerning its practical application in the start-up and configuration of anammox biofilm reactors.

microbiology↗

bioRxiv2020

Genome-centric portrait of the microbes' cellulolytic competency

Neither the abundance of the exo/endoglucase GH modules nor the taxonomy affiliation is informative enough in inferring whether a genome is of a potential cellulolytic microbe or not. By interpreting the complete genomes of 2642 microbe strains whose phenotypes have been well documented, we are trying to reveal a more reliable genotype and phenotype correlation on the specific function niche of cellulose hydrolysis. By incorporating into the annotation approach an automatic recognition of the potential synergy machineries, a more reliable prediction on the corresponding microbes cellulolytic competency could be achieved. The potential cellulose hydrolyzing microbes could be categorized into 5 groups according to the varying synergy machineries among the carbohydrate active modules/genes annotated. Results of the meta-analysis on the 2642 genomes revealed that some cellulosome gene clusters were in lack of the surface layer homology module (SLH) and microbe strains annotated with such cellulosome gene clusters were not certainly cellulolytic. Hypothesized in this study was that cellulosome-independent genes harboring both the SLH module and the cellulose-binding carbohydrate binding module (CBM) were likely an alternative gene apparatus initiating the formation of the cellulose-enzyme-microbe (CEM) complexes; and their role is important especially for the cellulolytic anaerobes without cellulosome gene clusters. ImportanceIn the genome-centric prediction on the corresponding microbes cellulolytic activity, recognition of the synergy machineries that include but are not limited to the cellulosome gene clusters is equally important as the annotation of the individual carbohydrate active modules or genes. This is the first time that a pipeline was developed for an automatic recognition of the synergy among the carbohydrate active units annotated. With promising resolution and reliability, this pipeline should be a good add to the bioinformatic tools for the genome-centric interpretations on the specific function niche of cellulose hydrolysis.

genomics↗