Biology subjects

Jiao, Y.

Publications and source records attributed to Jiao, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Genomic diversity and underlying metabolic variations of the human gut microbiome between populations at single nucleotide resolution

BackgroundA comprehensive and representative reference database is crucial for accurate taxonomic and functional profiling of the human gut microbiome in population-level studies. However, with 70% of current microbial reference data derived from Europeans and the Americans, East Asia, especially China, remain underrepresented. MethodsWe constructed the human Gut Microbiome Reference (GMR), comprising 478,588 high-quality microbial genomes from Chinese (247,134) and non-Chinese (231,454) populations. Species-level clustering and protein annotations were performed to characterize microbial diversity and function. We further integrated novel genomes into taxonomic profiling database and validated the improvements using independent cohort data. ResultsThe GMR dataset spans 6,664 species, including 26.4% newly classified species, and encodes over 20 million unique proteins, with 47% lacking known functional annotations. Notably, we observed that 35.35% and 32.46% of species unique to Chinese and non-Chinese populations, respectively. For 2,145 species shared between populations, 74% of 304 species with balanced prevalence between populations exhibited population-specific phylogenetic stratification, involving health relevant functionalities such as antibiotic resistance. Integration of novel genomes into taxonomic improved population-level species profiling by up to 23% and uncovered replicable associations between novel species and host physiological traits. ConclusionsOur study largely expands the compositional and functional landscape of the human gut microbiome, providing a crucial resource for studying the role of gut microbiome for regional health disparities.

genomics↗

bioRxiv2024

Zinc finger transcription factors BnaSTOP2s regulate sulfur metabolism and confer resistance to Sclerotinia sclerotiorum in Brassica napus

Rapeseed (Brassica napus L.) has a high sulfur requirement for optimal growth, development, and pathogen resistance. In this study, we identified zinc finger transcription factors, BnaSTOP2s, that play key roles in sulfur metabolism and Sclerotinia sclerotiorum resistance. First, our results suggested that BnaSTOP2s are involved in sulfur as evidenced from extensive protein interaction screening. Knockout of BnaSTOP2s reduced the response sensitivity in both sulfur-deficient and sulfur-excessive conditions by promoting the elongation of primary roots of seedlings. Furthermore, the content of essential sulfur-containing metabolites, including glucosinolate and glutathione, were substantially down-regulated in roots and leaves of Bnastop2 mutants, which is consistent with the significantly lowered transcriptional levels of key players of GSL synthesis and transportation, BnaMYB28s and BnaGTR2s, respectively. Through comprehensive RNA-seq analysis, we revealed the substantial effect of BnaSTOP2s on sulfur metabolism from source to sink. Additionally, we observed a significant decrease while increase in leaf lesion sizes of the BnaSTOP2-OE and Bnastop2 mutants, respectively, when compared to the wild type during Sclerotinia sclerotiorum infection, suggesting the vital role of BnaSTOP2 in plant defense response. Overall, our findings highlight that BnaSTOP2s seems to be global regulators of sulfur metabolism and confer resistance to Sclerotinia sclerotiorum infection in B. napus.

plant biology↗

bioRxiv2024

Microbial Community Structure of an Anaerobic Side-stream Coupled Anoxic-aerobic Membrane Bioreactor (AOMBR-ASSR) for an In-situ Sludge Reduction Process

The in-situ sludge reduction process via the insertion of an anaerobic side-stream reactor into the sludge return circuit is an efficient approach to reduce the sludge yield in the activated sludge process. In this study, with the anoxic-aerobic membrane bioreactor (AO-MBR,CP) as a reference, high-throughput sequencing technology was used to reveal the characteristics of the microbial community structure in the anaerobic side-stream anoxic-aerobic membrane bioreactor sludge reduction process (AOMBR-ASSR,SRP). After the stable operation of two processes for 120 days, the average removal efficiencies of TN and TP in the effluent of SRP were increased by 5.63% and 29.85%, respectively. However, there was no significant difference between the two processes in the removal effect of COD and ammonia nitrogen. It is worth noting that the observed sludge yields (Yobs) of the two processes were 0.14 and 0.17 gMLSS/(gCOD), respectively, and the sludge reduction rate of the AOMBR-ASSR reduction process was 19.5%. Compared to the CP, the microbial richness and diversity index of SRP increased significantly. Among 14 major phyla, Proteobacteria and Bacteroidetes were the dominant microorganisms. Chloroflexi, which is responsible for the degradation of organic substances under an anaerobic condition, seemed to be reduced in the SRP. Meanwhile, other phyla that involved in the nitrogen cycle, such as Nitrospirae and Planctomycetes, were found to be more abundant in the SRP than in the CP. A total of 21 identified classes were observed, and primarily hydrolyzed fermented bacteria (Sphingobacteriia, Betaproteobacteria, Actinobacteria, and Deltaproteobacteria) and slow-growing microorganisms (Bacilli) were accumulated in the SRP. At the genus level, the inserted anaerobic side-stream reactor favored the hydrolyzed bacteria (Saprospiraceae, Rhodobacter and Candidatus_Competibacter), fermented bacteria (Lactococcus and Trichococcus), and slow-growing microorganisms (Dechloromonas and Haliangium), which play a crucial role in the sludge reduction. Furthermore, the enrichment of bacterial species related to nitrogen (Nitrospir and Azospira) provided the potential for nitrogen removal, while the anaerobic environment of the side-stream reactor promoted the enrichment of phosphorus-accumulating organisms.

microbiology↗

bioRxiv2023

Interpretable and Generalizable Strategies for Stably Following Hydrodynamic Trails

Aquatic animals offer compelling evidence that flow sensing alone, without vision, is sufficient to guide a swimming organism to the source of an unsteady hydrodynamic trail. However, the sensory feedback strategies that allow these remarkable trail tracking abilities remain opaque. Here, by integrating mechanistic flow simulations with reinforcement learning techniques, we discovered two simple and equally effective strategies for hydrodynamic trail following. Though not a priori obvious, these strategies possess parsimonious interpretations, analogous to Braitenbergs simplest vehicles, where the agent senses local flow signals and turns away from or toward the direction of stronger signals. A rigorous stability analysis shows that the effectiveness of these strategies in robustly tracking flow currents is independent of the type of sensor but depends on sensor placement and the traveling nature of the flow signal. Importantly, these results inform a suite of versatile strategies for hydrodynamic trail following applicable to both vortical and turbulent flows. These insights support the future design and implementation of adaptive real-time sensory feedback strategies for autonomous robots in dynamic flow environments.

biophysics↗

bioRxiv2023

GENTANGLE: integrated computational design of gene entanglements

SummaryThe design of two overlapping genes in a microbial genome is an emerging technique for adding more reliable control mechanisms in engineered organisms for increased safety. The design of functional gene pairs is a challenging procedure and computational design tools are used to improve the efficiency to deploy successful designs in genetically engineered systems. GENTANGLE (Gene Tuples ArraNGed in overLapping Elements) is a high performance containerized pipeline for the computational design of two overlapping genes translated in different reading frames of the genome. This new software package can be used to design and test gene entanglements for microbial engineering projects using arbitrary sets of user specified gene pairs. Availability and ImplementationThe GENTANGLE source code and its submodules are freely available on GitHub at https://github.com/BiosecSFA/gentangle. The DATANGLE (DATA for genTANGLE) repository contains related data and results, and is freely available on GitHub at https://github.com/BiosecSFA/datangle. The GENTANGLE repository wiki contains detailed instructions on how to use the container and the different components of software and data, including reproducing the results. The code is licensed under the GNU Affero General Public License version 3 (https://www.gnu.org/licenses/agpl.html). Contactmartimartine1@llnl.gov and allen99@llnl.gov

bioinformatics↗

bioRxiv2023

Gut microbial genomes with paired isolates from China signify probiotic and cardiometabolic effects

The gut microbiome displays significant genetic differences between populations while systematic characterization of the genomic landscape of the gut microbiome in Asia populations remains limited. Here, we present the Chinese gut microbial reference (CGMR) set, comprising 101,060 high quality metagenomic assembled genomes (MAGs) of 3,707 non-redundant species paired with 1,376 live isolates from a national wide collection of 3,234 fecal samples across China. This improved reference set contains 987 novel species compared with existing resources worldwide. By associating MAGs with geographic and phenotypic characteristics, we observed regional-specific coexisting MAGs and MAGs with probiotic and cardiometabolic functionalities. We further conducted mice experiments to confirm the probiotic effects of two Faecalibacterium intestinalis isolates in alleviating constipation, the cardiometabolic influences of three Bacteroides fragilis_A isolates in obesity, and the functional potential of isolates from the two new species belonging to the genera Parabacteroides and Lactobacillus in host lipids metabolism. Our study not only expands the current microbial genomes with paired isolates but also demonstrates their probiotic and cardiometabolic effects on hosts, contributing to the mechanistic understanding of host-microbe interactions and the translation of microbiome-based personalized therapies.

microbiology↗

bioRxiv2023

GetPrimers: a generalized PCR-based genetic targeting primer designer enabling easy and standardized targeted gene modification across multiple systems

Genetic targeting (e.g., gene knockout and tagging) based on polymerase chain reaction (PCR) is a simple yet powerful approach for studying gene functions. Although originally developed in classic budding and fission yeast models, the same principle applies to other eukaryotic systems with efficient homologous recombination. One-step-PCR-based genetic targeting is conventionally used but the sizes of the homologous arms that it generates for recombination-mediated genetic targeting are usually limited. Alternatively, gene targeting can also be performed via fusion PCR, which can create homologous arms that are orders of magnitude larger, therefore substantially increases the efficiency of recombination-mediated genetic targeting. Here we present GetPrimers (https://www.evomicslab.org/app/getprimers/), a generalized computational framework and web tool to assist automatic targeting and verification primer design for both one-step-PCR-based and fusion-PCR-based genetic targeting experiments. Moreover, GetPrimers by design runs for any given genetic background of any species with full genome scalability. Therefore, GetPrimers is capable of empowering high-throughput functional genomics assays at multi-population and multi-species levels. Comprehensive experimental validations have been performed for targeting and verification primers designed by GetPrimers across multiple organism systems and experimental setups. We anticipate GetPrimers to become a highly useful and popular tool to facilitate easy and standardized gene modification across multiple systems. Take AwaysDeveloped GetPrimers for generalized primer design of PCR-based genetic targeting. GetPrimers shines with its high versatility and genome-wide scalability. GetPrimers was validated across multiple organisms and experimental setups.

genetics↗

bioRxiv2023

Telomere-to-telomere genome of the model plant Physcomitrium patens

The model plant Physcomitrium patens (P. patens) has played a pivotal role in enhancing our comprehension of plant evolution, growth, and development. However, the current genome harbors numerous intricate regions that remain unfinished and erroneous. To address these issues, we present an exemplary assembly of the P. patens nuclear genome, which incorporates telomeres and centromere regions, thereby establishing it as the telomere-to-telomere (T2T) genome in a non-seed plant. This T2T genome not only dispels the prevailing misconception regarding chromosome number in P. patens but also provides indispensable resources for conducting in-depth studies in moss genomics and biology.

genomics↗

bioRxiv2023

Temporal colonization of the gut microbiome in neonatal Bos taurus at single nucleotide resolution

BackgroundThe rumen of neonatal calves is underdeveloped and exhibits limited functionality during early life. Thus, the acquisition and colonization of microbes in the gut are key to establishing a healthy host-microbiome symbiosis for neonatal calves. Microbiome-linked health outcomes appear to be the consequences of individual strains of specific microbes. However, the temporal colonization of pioneering microbial strains and their linkages to the health and growth of neonatal calves are poorly understood. ResultsTo address this, we longitudinally profiled the gut microbiome of 36 neonatal calves from birth up to 2 months postpartum and carried out microbial transplantation (MT) to reshape their gut microbiome. Genomic reconstruction of deeply sequenced fecal samples resulted in a total of 3,931 metagenomic assembled genomes (MAGs), of which 397 were identified as new species when compared with existing databases of Bos taurus. Single nucleotide level metagenomic profiling shows a rapid influx of microbes after birth, followed by strong selection during the first few weeks of life. MT was found to reshape the genetic makeup of 33 MAGs (FDR<0.05), mainly from Prevotella and Bacteroides species. We further linked over 20 million microbial single nucleotide variations (SNVs) to 736 plasma metabolites, which enabled us to characterize 24 study-wide significant associations (P < 4.4x10-9) that identify the potential microbial genetic regulation of host immune and neuro-related metabolites, including glutathione and L-dopa. Our integration analyses further revealed that microbial genetic variations may influence the health status and growth performance of neonatal calves by modulating metabolites via structural regulation of their encoded proteins. For instance, we found that the albumin levels and total antioxidant capacity in neonatal calves were correlated with L-dopa, which was determined by SNVs via structural regulations of metabolic enzymes. ConclusionsThe current results indicate that the temporal colonization of microbial strains and MT-induced strain replacement are integral in the development of the gut microbiome of neonatal calves and may help to develop strategies that can improve the health status and growth performance of neonatal calves.

microbiology↗

bioRxiv2023

Early microbial intervention reduces diarrhea while improves later milk production in newborn calves

The rumen of neonatal calves is not well-developed and exhibits limited functionality. Therefore, the establishment of intestinal microbiota may play an instrumental role in their health and performance, but it has been rarely explored. Thus, we aim to explore the temporal colonization of the gut microbiome and the potential benefits of early microbial intervention in newborn calves. We followed up on the temporal dynamics of the gut microbiome and plasma metabolome in 36 newborn calves during the first two months of life and established their relationships with their health status and performance. We also evaluated whether microbiota transplantation (MT) could influence their phenotypes by modulating metabolism and its impact on later milk production performance.We showed that the composition and ecological interactions of the gut microbiome are likely to reach maturity one month after birth. Temporal changes in the gut microbiome of newborn calves are widely associated with changes in their physiological statuses, such as growth and fiber digestion. Importantly, we observed that MT reshapes the gut microbiome of newborns by altering the abundance and interaction of Bacteroides species, as well as amino acid pathways, such as arginine biosynthesis. Two-year follow-up of those calves further showed that MT improves their later milk production. Notably, MT improves fiber digestion, antioxidant capacity of newborns while reducing diarrhea. MT also contributes to significant changes in the metabolomic landscape, and with putative causal mediation analysis, we suggest that altered gut microbial composition in newborns may influence physiological status through microbial-derived metabolites. The data from the study may help develop strategies to manipulate the gut microbiota during early life, which may be significantly relevant to the health and production of newborn calves. HIGHLIGHTSO_LIThe gut microbial composition and ecological interaction in newborn calves reach maturity one month after birth C_LIO_LITemporal shifts in the gut microbiome of newborn calves are associated with phenotypic changes, including growth, digestion, and antioxidative capacity C_LIO_LIEarly microbial intervention in newborn calves reduces diarrhea while improving later milk production C_LIO_LIThe gut microbial impact on newborn calves is mediated by plasma metabolites C_LI

microbiology↗

bioRxiv2023

AI-Designed, Mutation-Resistant Broad Neutralizing Antibodies Against Multiple SARS-CoV-2 Strains

In this study, we generated a Digital Twin for SARS-CoV-2 by integrating data and meta-data with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein sequence language modeling. This approach enabled the computational design of broadly neutralizing antibodies against over 1300 different historical strains of SARS-COV-2 containing 64 mutations in the receptor binding domain (RBD) region. The AI-designed antibodies were experimentally validated in real-virus neutralization assays against multiple strains including the newer Omicron strains that were not included in the initial design base. Many of these antibodies demonstrate strong binding capability in ELISA assays against the RBD of multiple strains. These results could help shape future therapeutic design for existing strains, as well as predicting hidden patterns in viral evolution that can be learned by AI for developing future antiviral treatments.

bioinformatics↗

bioRxiv2023

Prolonging Genetic Circuit Stability through Adaptive Evolution of Overlapping Genes

The development of synthetic biological circuits that maintain functionality over application relevant timescales remains a significant challenge. Here, we employed synthetic overlapping sequences in which one gene is encoded or "entangled" entirely within an alternative reading frame of another gene. In this design, the toxin-encoding relE was entangled within ilvA, which encodes threonine deaminase, an enzyme essential for isoleucine biosynthesis. A functional entanglement construct was obtained upon modification of the ribosome binding site of the internal relE gene. Using this optimized design, we found that the selection pressure to maintain functional IlvA stabilized the production of burdensome RelE for over 130 generations, which compares favorably with the most stable kill-switch circuits developed to date. This stabilizing effect was achieved through a complete alteration of the mutational landscape such that mutations inactivating the entangled genes were disfavored. Instead, the majority of lineages accumulated mutations within the regulatory region of ilvA. By reducing baseline relE expression, these more benign mutations lowered circuit burden, which suppressed the accumulation of relE inactivating mutations, thereby prolonging kill-switch function. Overall, this work demonstrates the utility of sequence entanglement paired with an adaptive laboratory evolution campaign to increase the evolutionary stability of burdensome synthetic circuits.

synthetic biology↗

bioRxiv2023

Homocysteine accelerates hepatocyte autophagy by upregulation of TFEB via DNMT3b-mediated DNA hypomethylation

Autophagy plays a critical role in the physiology and pathophysiology of hepatocytes. High levels of homocysteine (Hcy) promote autophagy in hepatocytes, but the underlying mechanism is still unknown. Here, we investigated the relation between Hcy increased autophagy levels and the expression of nuclear transcription factor EB (TFEB). We demonstrate that Hcy increased autophagy levels is mediated by upregulation of TFEB. Silencing TFEB decreases the autophagy-related protein LC3BII/I and increases p62 expression levels in hepatocytes after exposure to Hcy. Moreover, the effect of Hcy on the expression of TFEB is regulated by hypomethylation of TFEB promoter catalyzed by DNA methyltransferase 3b (DNMT3b). In summary, this study shows that Hcy can activate autophagy by inhibiting DNMT3b-mediated DNA methylation and upregulating TFEB expression. These findings provide another new mechanism for Hcy-induced autophagy in hepatocytes.

cell biology↗

bioRxiv2023

Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

cancer biology↗

bioRxiv2023

Evaluating Evasion Strategies in Zebrafish Larvae

An effective evasion strategy allows prey to survive encounters with predators. Prey are generally thought to escape in a direction that is either random or serves to maximize the minimum distance from the predator. Here we introduce a comprehensive approach to determine the most likely evasion strategy among multiple hypotheses and the role of biomechanical constraints on the escape response of prey fish. Through a consideration of six strategies with sensorimotor noise and previous kinematic measurements, our analysis shows that zebrafish larvae generally escape in a direction orthogonal to the predators heading. By sensing only the predators heading, this orthogonal strategy maximizes the distance from fast-moving predators, and, when operating within the biomechanical constraints of the escape response, it provides the best predictions of prey behavior among all alternatives. This work demonstrates a framework for resolving the strategic basis of evastion in predator-prey interactions, which could be applied to a broad diversity of animals.

animal behavior and cognition↗

bioRxiv2022

Boosting wheat functional genomics via indexed EMS mutant library of KN9204

A better understanding of wheat functional genomics could facilitate the targeted breeding for agronomic traits improvement and environmental adaptation. With the release of reference genomes and extensive re-sequencing data of wheat and relatives, wheat functional genomics enters a new era. However, limited transformation efficiency in wheat hampers in-depth gene functional study and genetic manipulation for breeding. Here, we generated an EMS mutagenesis library of KN9204, a widely grown elite wheat variety in northern China, with available reference genome, transcriptome, and epigenome of various tissues. The library harbors enormous developmental diversity covering important tissues and transition stages. Exome capture sequencing of 2,090 mutant lines, with probes designed by KN9204 genome, revealed that 98.79% of coding genes have mutations and 1,383 EMS-type SNPs per line averagely. Novel allelic variations for important agronomic trait-related genes, such as Rht-D1, Q, TaTB1, and WFZP, were identified. We tested 100 lines with severe mutations in 80 NAC TFs under drought and salinity stresses, and found 13 lines with altered sensitivity. Three lines were further analyzed for the regulation insight of NAC TFs in stress response by combing transcriptome and available chromatin accessibility data. Hundreds of direct targets of NAC with altered transcriptional pattern in mutant lines under salt or drought stress induction were identified, including SNAC1, DREB2B, CML16 and ZFP182, knowing factors in abiotic stresses response. Thus, we have generated and indexed KN9204 EMS mutant library which would facilitate functional genomics study and provide resources for genetic manipulation in wheat.

plant biology↗

bioRxiv2022

Electrogenetic signaling and information propagation for controlling microbial consortia via programmed lysis

To probe signal propagation and genetic actuation in microbial consortia, we have coopted the components of both redox and quorum sensing (QS) signaling into a communication network for guiding composition by "programming" cell lysis. Here, we use an electrode to generate hydrogen peroxide as a redox cue that determines consortia composition. The oxidative stress regulon of Escherichia coli, OxyR, is employed to receive and transform this signal into a QS signal that coordinates the lysis of a subpopulation of cells. We examine a suite of information transfer modalities including "monoculture" and "transmitter-receiver" models, as well as a series of genetic circuits that introduce time-delays for altering information relay, thereby expanding design space. A simple mathematical model aids in developing communication schemes that accommodate the transient nature of redox signals and the "collective" attributes of QS signals. We suggest this platform methodology will be useful in understanding and controlling synthetic microbial consortia for a variety of applications, including biomanufacturing and biocontainment.

synthetic biology↗

bioRxiv2022

Deep-learning based bioactive therapeutic peptides generation and screening

Many bioactive peptides demonstrated therapeutic effects over-complicated diseases, such as antiviral, antibacterial, anticancer, etc. Similar to the generating de novo chemical compounds, with the accumulated bioactive peptides as a training set, it is possible to generate abundant potential bioactive peptides with deep learning. Such techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. However, there are very few deep learning-based peptide generating models. Here, we have created an LSTM model (named LSTM_Pep) to generate de novo peptides and finetune learning to generate de novo peptides with certain potential therapeutic effects. Remarkably, the Antimicrobial Peptide Database has fully utilized in this work to generate various kinds of potential active de novo peptide. We proposed a pipeline for screening those generated peptides for a given target, and use Main protease of SARS-COV-2 as concept-of-proof example. Moreover, we have developed a deep learning-based protein-peptide prediction model (named DeepPep) for fast screening the generated peptides for the given targets. Together with the generating model, we have demonstrated iteratively finetune training, generating and screening peptides for higher predicted binding affinity peptides can be achieved. Our work sheds light on to the development of deep learning-based methods and pipelines to effectively generating and getting bioactive peptides with a specific therapeutic effect, and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

bioinformatics↗