Biology subjects

Zeng, E.

Publications and source records attributed to Zeng, E..

12 recordsLinked to original sources

bioRxiv2026

New paradigm of CRISPR spacerome for classification of global Escherichia coli lineages

Our research team has recently identified a proof-of-concept for a previously unrecognized spacerome signature in a subset of globally distributed E. coli. Motivated by our initial observation, we pursued a more in-depth CRISPR spacerome analysis, which revealed a novel insight in discrimination of global E. coli sequence type (ST) lineages. We systematically retrieved publicly available E. coli complete genomes, analyzed and compared CRISPR spacerome of globally distributed strains. We found that global strains harbored spacerome that has remained conserved spaceromes for decades across multiple continents. Strains with the conserved spacerome belong to specific ST lineages of clinical importance. In addition, most protospacers were assigned to Gammaproteobacteria and Caudoviricetes. Our findings reveal unexpected long-term conservation of CRISPR spaceromes and their potential as high-resolution markers for E. coli epidemiological tracking.

microbiology↗

bioRxiv2025

Sensorineural regulation of skull healing implicates FGF1 signaling in non-healing bone

Bone injuries demonstrate rapid peripheral nerve ingrowth followed by nerve pruning as healing ensues. However, in non-healing bone injuries, peripheral innervation remains elevated, the implications of which remain unknown. Therefore, we investigated the neuroskeletal microenvironment in subcritical-and critical-sized calvarial defects using quantitative 3D fluorescence imaging. We identified elevated densities of peripheral nerves and Osterix positive (Osx+) osteoprogenitors in critical-sized defects, while osteogenic differentiation markers were severely diminished. Moreover, Osx+ osteoprogenitors in critical-sized defects exhibited enhanced proximity to peripheral nerves, which in turn was associated with increased osteoprogenitor cell proliferation. Using retrograde tracing in conjunction with single cell RNA-sequencing of sensory nerves from the innervating trigeminal ganglia, genes encoding for nerve-secreted proliferative and anti-differentiation factors were identified. Specifically, FGF-1/FGFR-1 signaling was identified as a significant neuroskeletal interaction with critical-sized defects demonstrating higher FGF-1 expression in fluorescence imaging. Presence of FGF-1 in neurons innervating the calvarial was confirmed, and neural conditioned media depleted for FGF1 showed enhanced induction of osteogenesis when placed on parietal bone cells. Collectively, we identify a sensorineural-skeletal signaling interaction elevated in critical-sized defects that can be leveraged as a potential therapeutic target for enhancing bone healing.

bioengineering↗

bioRxiv2025

DeepBioSim: Efficient and Versatile Methods for Microbiome Data Simulation with Minimal Statistical Assumptions

BackgroundThe human microbiome profoundly influences health and disease. Robust computational and statistical tools for identifying causal microbe-disease links are therefore critical to uncovering the mechanistic basis of these associations. Yet benchmarking such tools remains difficult: microbiome datasets are sparse, high-dimensional, and contain complex dependencies, and no gold-standard reference set exists. Realistic simulated data with embedded ground truth are essential for fair evaluation of analytical tools. Current simulators often impose strong assumptions, require hard-to-obtain auxiliary information, or fail to scale to large, high-dimensional datasets. ResultsWe introduce DeepBioSim, a DEEP-learning framework for BIOlogical SIMulation of microbiome data. DeepBioSim uses variational autoencoders (VAEs) to generate realistic microbiome datasets by sampling directly from the latent distribution of metagenomic or metatranscriptomic count data. ConclusionsThe approach is fast, accurate, and scalable, generating highly realistic synthetic microbiome datasets without extensive hyper-parameter tuning or phylogenetic input. Tests on human RNA-seq data confirm versatility of DeepBioSim, showing it can also reliably simulate single-organism omics profiles.

bioinformatics↗

bioRxiv2025

Metatranscriptomic analysis reveals toxin-antitoxin system shifts in caries-associated oral microbiomes

BackgroundToxin-antitoxin (TA) systems are bacterial regulatory elements involved in persistence, dormancy, and biofilm stability under stress conditions such as limited nutrients, acid exposure, and antimicrobial treatment. Although their roles are well-characterized in isolated pathogens, TA systems remain largely unexamined in the context of the oral microbiome. Given their potential involvement in microbial adaptation during oral diseases and treatment, we aimed to identify transcriptionally active TA systems and evaluate their condition-specific expression patterns across oral health states using metatranscriptomic data. ResultsWe re-analyzed two publicly available supragingival metatranscriptomic datasets: a longitudinal dataset by Carda-Dieguez et.al. (Dieguez) capturing treatment transitions (fluoride (Fl), fluoride-arginine (Fl-Ar)) in caries-active and caries-free individuals, and a cross-sectional dataset by Ev et.al., (Ev) profiling caries-active lesions (CA), caries-inactive (CI), and caries-free (CF) states. After quality control, host sequences removal, rRNA removal, and HUMAnN3-based functional profiling, we identified 1,189 unique UniRef90 gene clusters with known TA associations. Differential analysis using ANCOM-BC revealed 197 TA-related gene clusters as significantly modulated in at least one condition. Of these, 22 gene clusters were differentially expressed in Dieguez, 38 in Ev, with 77 shared across both datasets. Distinct TA expression signatures were observed across healthy (CF, CI, baseline), lesion types (CAa, CAi, CAs), and treatment stages (Fl, Fl-Ar), suggesting condition-specific regulatory activity. Functional annotation of differentially expressed TA genes using the Toxin-antitoxin system database, eggNOG-mapper, and InterProScan, revealed 18 genes with strong evidence for their involvement in key bacterial stress-response pathways. These included members of the ParD-ParE, RelE/StbE-RelB/StbD, FitA, and YafQ families, which are annotated with mRNA catabolism, transcriptional repression, and prokaryotic defense-associated pathways. ConclusionThis is the first metatranscriptomic analysis profiling functional shifts in toxin-antitoxin systems across oral healthy, caries, and treatment states. TA systems showed dynamic and condition-specific expression, with pathway annotations suggesting their roles in microbial persistence, stress adaptation, and ecological remodeling during caries progression. These findings open new avenues for targeting microbial stress modules in precision microbiome therapeutics.

bioinformatics↗

bioRxiv2025

Cross-order detection of bacteriophage transduction in communities using ribosomal RNA barcoding

Bacteriophages (phages) facilitate gene transfer and microbial evolution in all ecosystems and have applications as tools for engineering microbiomes and as antimicrobials. Historic efforts to map phage hosts, such as plaque assays, are limited to culturable bacteria, are low throughput, and are hard to apply in environmentally-relevant contexts. To overcome these limitations, a synthetic ribozyme that stores information about DNA uptake in 16S ribosomal RNA (rRNA) was used to identify phage-host interactions by integrating the ribozyme into phage-plasmid P1 and performing targeted 16S rRNA sequencing following transduction. Experiments in synthetic and wastewater communities revealed Aeromonadales as a novel P1 host order and transduction of P1 into pathogens. Host range varied across phagemids with different origins of replication and phage particles with different tail fibers. This work shows how autonomous barcoding can be used in phages to identify the molecular controls on their host range in communities.

synthetic biology↗

bioRxiv2024

Critical analysis of polycyclic tetramate macrolactam biosynthetic cluster phylogeny and functional diversity

Polycyclic tetramate macrolactams (PTMs) are bioactive natural products commonly associated with certain actinobacterial and proteobacterial lineages. These molecules have been the subject of numerous structure-activity investigations since the 1970s. New members continue to be pursued in wild and engineered bacterial strains, and advances in PTM biosynthesis suggests their outwardly simplistic biosynthetic gene clusters (BGCs) belie unexpected product complexity. Towards addressing the origins of this complexity and understanding its influence on PTM discovery, we engaged in a combination of bioinformatics to systematically classify PTM BGCs, and PTM-targeted metabolomics to compare the products of select BGC types. By comparing groups of producers and BGC mutants, we exposed knowledge gaps that complicate bioinformatics-driven product predictions. In sum, we provide new insights into the evolution of PTM BGCs while systematically accounting for the PTMs discovered thus far. The combined computational and metabologenomic findings presented here should prove useful for guiding future discovery. IMPORTANCEPolycyclic tetramate macrolactam (PTM) pathways are frequently found within the genomes of biotechnologically-important bacteria, including Streptomyces and Lysobacter spp. Their molecular products are typically bioactive, having substantial agricultural and therapeutic interest. Leveraging bacterial genomics for the discovery of new related molecules is thus desirable, but drawing accurate structural predictions from bioinformatics alone remains challenging. This difficulty stems from a combination of previously underappreciated biosynthetic complexity and remaining knowledge gaps, compounded by a stream of yet-uncharacterized PTM biosynthetic loci gleaned from recently sequenced bacterial genomes. We engaged in the following study to create a useful framework for cataloging historic PTM clusters, identifying new cluster variations, and tracing evolutionary paths for these molecules. Our data suggests new PTM chemistry remains discoverable in nature. However, our metabolomic and mutational analyses emphasize practical limitations to genomics-based discovery by exposing hidden complexity.

microbiology↗

bioRxiv2022

Inflammasome mediated neuronal-microglial crosstalk: a therapeutic substrate in C9orf72-FTD/ALS.

Intronic G4C2 hexanucleotide repeat expansions (HRE) of C9orf72 are the most common cause of familial variants of frontotemporal dementia/amyotrophic lateral sclerosis (FTD/ALS). G4C2 HREs in C9orf72 undergo non-canonical repeat-associated translation, producing dipeptide repeat (DPR) proteins, with various deleterious impacts on cellular homeostasis. While five different DPRs are produced, poly(glycine-arginine) (GR) is amongst the most toxic and is the only DPR to accumulate in the associated clinically relevant anatomical locations of the brain. Previous work has demonstrated the profound effects of a poly(GR) model of C9orf72 FTD/ALS, including motor impairment, memory deficits, neurodegeneration, and neuroinflammation. Neuroinflammation is hypothesized to be a driving factor in the disease course; microglia activation is present prior to symptom onset and persists throughout the disease. Here, using an established mouse model of C9orf72 FTD/ALS we investigate the contributions of the nod-like receptor pyrin-containing 3 (NLRP3) inflammasome in the pathogenesis of FTD/ALS. We find that inflammasome-mediated neuroinflammation is increased with microglial activation, cleavage of caspase-1, production of IL-1{beta} and upregulation of Cxcl10 in the brain of C9orf72 FTD/ALS mice. Excitingly, we find that genetic ablation of Nlrp3 significantly improved survival, protected behavioral deficits and prevented neurodegeneration suggesting a novel mechanism involving HRE-mediated induction of innate immunity. The findings provide experimental evidence of the integral role of HRE in inflammasome-mediated innate immunity in the C9orf72 variant of FTD/ALS pathogenesis and suggest the NLRP3 inflammasome as a therapeutic target.

neuroscience↗

bioRxiv2021

Gene network module changes associated with the vertebrate fin to limb transition

Evolutionary phenotypic transitions, such as the fin to limb transition in vertebrate evolution, result from changes in associated genes and their interactions, often in response to changing environment. Identifying the associated changes in gene networks is vital to achieve a better understanding of these transitions. Previous experimental studies have been typically limited to manipulating a small number of genes. To expand the number of analyzed genes and hence, biological knowledge, we computationally isolated and compared the gene modules for paired fins (pectoral fin, pelvic fin) of fishes (zebrafish) to those of the paired limbs (forelimb, hindlimb) of mammals (mouse) using quality-enhanced gene networks from zebrafish and mouse. We ranked module genes according to their weighted-degrees and identified the highest-ranking hub genes, which were important for the module stability. Further, we identified genes conserved during the fin to limb transition and investigated the fates of zebrafish-specific and mouse-specific module genes in relation to their involvements in newly emerged or lost anatomical structures during the aquatic to terrestrial vertebrate transition. This paper presents the results of our investigations and demonstrates a general network-based computational workflow to study evolutionary phenotypic transitions involving diverse model organisms and anatomical entities.

bioinformatics↗

bioRxiv2020

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

BackgroundIdentification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet-lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. This is because PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes for anatomical entities. We developed an integrative framework to predict candidate genes for anatomical entities by combining existing experimental knowledge about gene-anatomy relationships with PPI networks using anatomy ontology annotations. We expected this integration to improve the quality of the PPI networks and be better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomy entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks are semantic networks, as they are constructed based on the Uberon anatomy ontology annotations that are obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database, and we compared the performance of their network-based candidate gene predictions. ResultsAccording to candidate gene prediction performance evaluations tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks showed better receiver operating characteristic (ROC) and precision-recall curve performances than PPI networks for both zebrafish and mouse. ConclusionIntegration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improves the network quality, which makes them better optimized for predicting candidate genes for anatomical entities.

bioinformatics↗

bioRxiv2019

Gene regulatory networks associated with lateral root and nodule development in soybean

Legume plants such as soybean produce two major types of root lateral organs, lateral roots and root nodules. A robust computational framework was developed to predict potential gene regulatory networks (GRNs) associated with root lateral organ development in soybean. A genome-scale expression dataset was obtained from soybean root nodules and lateral roots and subjected to biclustering using QUBIC. Biclusters (BCs) and transcription factor (TF) genes with enriched expression in lateral root tissues were converged using different network inference algorithms to predict high confident regulatory modules that are repeatedly retrieved in different methods. The ranked combination of results from all different network inference algorithms into one ensemble solution identified 21 GRN modules of 182 co-regulated genes networks potentially involved in root lateral organ development stages in soybean. The pipeline correctly predicted previously known nodule- and LR-associated TFs including the expected hierarchical relationships. The results revealed high scorer AP2, GRF5, and C3H co-regulated GRN modules during early nodule development; and GRAS, LBD41, and ARR18 co-regulated GRN modules late during nodule maturation. Knowledge from this work supported by experimental validation in the future is expected to help determine key gene targets for biotechnological strategies to optimize nodule formation and enhance nitrogen fixation.

plant biology↗

bioRxiv2019

A Deficiency in SUMOylation Activity Disrupts Multiple Pathways Leading to Neural Tube and Heart Defects in Xenopus Embryos

BackgroundAdenovirus protein, Gam1, triggers the proteolytic destruction of the E1 SUMO-activating enzyme. Microinjection of an empirically determined amount of Gam1 mRNA into one-cell Xenopus embryos can reduce SUMOylation activity to undetectable, but nonlethal, levels, enabling an examination of the role of this post-translational modification during early vertebrate development. ResultsWe find that SUMOylation-deficient embryos consistently exhibit defects in neural tube and heart development. We have measured differences in gene expression between control and embryos injected with Gam1 mRNA at three developmental stages: early gastrula (immediately following the initiation of zygotic transcription), late gastrula (completion of the formation of the three primary germ layers), and early neurula (appearance of the neural plate). Although changes in gene expression are widespread and can be linked to many biological processes, three pathways, non-canonical Wnt/PCP, snail/twist, and Ets-1, are especially sensitive to the loss of SUMOylation activity and can largely account for the predominant phenotypes of Gam1 embryos. SUMOylation appears to generate different pools of a given transcription factor having different specificities with this post-translational modification involved in the regulation of more complex, as opposed to housekeeping, processes. ConclusionsWe have identified changes in gene expression that underlie the neural tube and heart phenotypes resulting from depressed SUMOylation activity. Notably, these developmental defects correspond to the two most frequently occurring congenital birth defects in humans, strongly suggesting that perturbation of SUMOylation, either globally or of a specific protein, may frequently be the origin of these pathologies.

genomics↗

bioRxiv2018

Temporal Small RNA Expression Profiling Under Drought Reveals a Potential Regulatory Role of snoRNAs in Drought Responses of Maize

Small RNAs (sRNAs) are short noncoding RNAs that play roles in many biological processes, including drought responses in plants. However, how the expression of sRNAs dynamically changes with the gradual imposition of drought stress in plants is largely unknown. We generated time-series sRNA sequence data from maize seedlings under drought stress and under well-watered conditions at the same time points. Analyses of length, functional annotation, and abundance of 736,372 non-redundant sRNAs from both drought and well-watered data, as well as genome copy number and chromatin modifications at the corresponding genomic regions, revealed distinct patterns of abundance, genome organization, and chromatin modifications for different sRNA classes of sRNAs. The analysis identified 6,646 sRNAs whose regulation was altered in response to drought stress. Among drought-responsive sRNAs, 1,325 showed transient down-regulation by the seventh day, coinciding with visible symptoms of drought stress. The profiles revealed drought-responsive microRNAs, as well as other sRNAs that originated from ribosomal RNAs (rRNAs), splicing small nuclear RNAs, and small nucleolar RNAs (snoRNA). Expression profiles of their sRNA derivers indicated that snoRNAs might play a regulatory role through regulating stability of rRNAs and splicing small nuclear RNAs under drought condition.

genomics↗