Biology subjects

Ma, B.-G.

Publications and source records attributed to Ma, B.-G..

13 recordsLinked to original sources

bioRxiv2026

DDTRN: Predicting Bacterial Transcriptional Regulatory Networks Based on Gene Sequences using Dual Descriptor

Accurate computational reconstruction of bacterial transcriptional regulatory network (TRN) from sequence information alone remains a fundamental challenge in systems biology, particularly for non-model organisms lacking extensive transcriptomic data. We present DDTRN, a sequence-driven framework that formulates TRN inference as a binary classification task over concatenated regulator-target gene sequence pairs and employs a Dual Descriptor (DD) model to predict regulatory interactions. The DD architecture represents a sequence into two learnable components: Composition Weight Map (CWM) and Position Weight Function (PWF). We comprehensively evaluate DDTRN against six conventional machine learning baselines across eight benchmark bacterial datasets, including E. coli (DREAM5, RegulonDB), B. subtilis, S. enterica, C. glutamicum, M. tuberculosis, P. aeruginosa, and S. coelicolor. DDTRN achieves superior overall performance, attaining average AUROC and AUPR scores of 0.869 and 0.868, respectively, with particularly pronounced advantages at lower descriptor ranks where positional weighting compensates for limited sequence context. Systematic sensitivity analyses of rank, embedding dimension, and basis function count reveal stable optimal operating regimes, while subsampling experiments demonstrate strong robustness even with limited training data. Interpretability analyses show that PWF learns distinct periodic contributions across different rank granularities and that CWM preferentially weights meaningful k-mers. A case study on E. coli dataset further illustrates that DDTRN identifies method-specific candidate targets complementary to those proposed by conventional approaches. By operating solely on genomic sequence, DDTRN provides a scalable, interpretable, and data-efficient framework for bacterial TRN inference in species where expression data are scarce, and it establishes a foundation for future multimodal integration with condition-specific regulatory information.

bioinformatics↗

bioRxiv2025

Intelligent Design of Escherichia coli Terminators

Terminators are specific nucleotide sequences located at the 3 end of a gene and contain transcription termination information. As a fundamental genetic regulatory element, terminators play a crucial role in the design of gene circuits. Accurately characterizing terminator strength is essential for improving the precision of gene circuit designs. Experimental characterization of terminator strength is time-consuming and labor-intensive; therefore, there is a need to develop computational tools capable of accurately predicting terminator strength. Current prediction methods do not fully consider sequence or thermodynamic information related to terminators, lacking robust models for accurate prediction. Meanwhile, deep generative models have demonstrated tremendous potential in the design of biological sequences and are expected to be applied to terminator sequence design. This study focuses on intelligent design of Escherichia coli terminators and primarily conducts the following research: (1) To construct an intrinsic terminator strength prediction model for E. coli, this study extracts sequence features and thermodynamic features from E. coli intrinsic terminators. Machine learning models based on the selected features achieved a prediction performance of R2 = 0.72. (2) This study employs a generative adversarial network (GAN) to learn from intrinsic terminator sequence training data and generate terminator sequences. Evaluation reveals that the generated terminators exhibit similar data distributions to intrinsic terminators, demonstrating the reliability of GAN-generated terminator sequences. (3) This study uses the constructed terminator strength prediction model to screen for strong terminators from the generated set. Experimental verification shows that among the 18 selected terminators, 72% exhibit termination efficiencies greater than 90%, confirming the reliability of the intelligent design approach for E. coli terminators. In sum, this study constructs a terminator strength prediction model and a terminator generation model for E. coli, providing model support for terminator design in gene circuits. This enhances the modularity of biological component design and promotes the development of synthetic biology.

synthetic biology↗

bioRxiv2024

Spatial chromosome organization and adaptation of Escherichia coli under heat stress

The spatial organization of bacterial chromosomes is crucial for cellular functions. It remains unclear how bacterial chromosomes adapt to high temperature stress. This study delves into the 3D genome architecture and transcriptomic responses of Escherichia coli under heat stress condition to unravel the intricate interplay between chromosome structure and environmental cues. By examining the role of macrodomains, chromosome interaction domains (CIDs), and nucleoid-associated proteins (NAPs), this work unveils the dynamic changes in chromosome conformation and gene expression patterns induced by high temperature stress. It was observed that under heat stress, short-range interaction frequency of chromosome decreased, while the long-range interaction frequency of the Ter macrodomain increased. Furthermore, two metrics, namely, Global Compactness (GC) and Local Compactness (LC), were devised to measure and compare the compactness of chromosomes based on their 3D structure models. The findings in this work shed light on the molecular mechanisms underlying thermal adaptation and chromosomal organization in bacterial cells, offering valuable insights into the complex interrelationships between environmental stimuli and genomic responses.

systems biology↗

bioRxiv2024

PGBTR: A powerful and general method for inferring bacterial transcriptional regulatory networks

Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR, which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD and the deep learning model CNNBTR. On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC, AUPR, and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data.

bioinformatics↗

bioRxiv2023

Spatial chromosome organization and adaptation of the radiation-resistant extremophile Deinococcus radiodurans

Radiation-resistant Deinococcus radiodurans is an extremophilic microorganism capable of withstanding high levels of ionizing radiation and chemical mutagens. It possesses remarkable DNA repair capability and serves as a model organism for studying stress resistance mechanism. However, our understanding on the spatial chromosome organization of this species remains limited. In this study, we employed chromosome conformation capture (3C) technology to determine the 3D genome structure of D. radiodurans and to further investigate the changes of chromosome conformation induced by ultraviolet (UV) irradiation. We observed that UV irradiation reduced short-range chromosome interactions, and smaller chromosomal interaction domains (CIDs) merged to form larger CIDs. Integrating transcriptomic data analysis, we found that the majority of upregulated differentially expressed genes were significantly enriched near specific CID boundaries. Specially, we comprehensively elucidated that the nucleoid-associated protein Dr_ebfC may serve as a global regulator for gene expression by altering chromosome structure, thereby influencing the physiological state of the bacterium. Overall, our study revealed the chromosome conformations of D. radiodurans under different conditions, and offered valuable insights into the molecular responses of this extremophile to environmental stresses.

systems biology↗

bioRxiv2023

Evaluation of chromosome structure modelling tools in bacteria

The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulating datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.

bioinformatics↗

bioRxiv2023

Independent component analysis reveals the transcriptional regulatory modules in Bradyrhizobium diazoefficiens USDA110

The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK2-FixK1 regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK2-FixK1 and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK2 cascade as a mediator connecting the FixK2-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.

systems biology↗

bioRxiv2023

EVRC: Reconstruction of chromosome 3D structure models using Error-Vector Resultant algorithm with Clustering coefficient

Reconstruction of 3D structure models is of great importance for the study of chromosome function. In this paper, we present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real human Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions.

bioinformatics↗

bioRxiv2023

OptoCRISPRi-HD: engineering a green-light activated CRISPRi system with high dynamic range

The ability to modulate gene expression is crucial for studying gene function and programming cell behaviors. Combining the reliability of CRISPRi and the precision of optogenetics, the opto-CRISPRi technique is emerging as an advanced tool for live-cell gene regulation. Since previous versions of opto-CRISPRi often exhibit a no more than 10 folds dynamic range due to the leakage activity, they are not suitable for targets that are sensitive to such leakage or critical for cell growth. Here, we describe a green-light activated CRISPRi system with high dynamic range (40-fold) and the flexibility of changing targets in Escherichia coli. Our optoCRISPRi-HD system can efficiently repress essential genes, non-essential genes or inhibit the initiation of DNA replication. Providing a regulative system with high resolution over space-time and extensive targets, our study would facilitate further researches involving complex gene networks, metabolic flux redirection or bioprinting.

synthetic biology↗

bioRxiv2022

The Spatial Organization of Bacterial Transcriptional Regulatory Networks

Transcriptional regulatory network (TRN) is the central pivot of a prokaryotic organism to receive, process and respond to internal and external environmental information. However, little is known about its spatial organization so far. In recent years, chromatin interaction data of bacteria such as Escherichia coli and Bacillus subtilis have been published, making it possible to study the spatial organization of bacterial transcriptional regulatory networks. By combining TRNs and chromatin interaction data of E. coli and B. subtilis, we explored the spatial organization characteristics of bacterial TRNs in many aspects such as regulation directions (positive and negative), central nodes (hubs, bottlenecks), hierarchical levels (top, middle, bottom) and network motifs (feed-forward loops and single input modules) of the TRNs and found that the bacterial TRNs have a variety of stable spatial organization features under different physiological conditions which may be closely related with basic life activities. Our findings provided new insights into the connection between transcriptional regulation and the spatial organization of chromosome in bacteria, and might serve as a foundation for spatial-distance-based gene circuit design in synthetic biology.

systems biology↗

bioRxiv2021

Computationally reconstructed interactome of Bradyrhizobium diazoefficiens USDA110 reveals novel functional modules and protein hubs for symbiotic nitrogen fixation

Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Detailed panorama about the molecular mechanism of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand on new studies integrating multi-omics information. Here we present a comprehensive computational framework, integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data, to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may owe to the requirement of more protein modifications and further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and 9 novel functional modules and 11 key protein hubs playing key roles for symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.

systems biology↗

bioRxiv2019

Spatial organization of the transcriptional regulatory network of Saccharomyces cerevisiae

Transcriptional regulatory network (TRN) is a directed complex network composed of all regulatory interactions between transcription factors and corresponding target genes. Recently, the three-dimensional (3D) genomics studies have shown that the 3D structure of the genome makes a difference to the regulation of gene transcription, which provides us with a novel perspective. In this study, we constructed the TRN of the budding yeast Saccharomyces cerevisiae and placed it in the context of 3D genome model. We analyzed the spatial organization of the yeast TRN on four levels: global feature, central nodes, hierarchical structure and network motifs. Our results suggested that the TRN of S. cerevisiae presents an optimized structure in space to adapt to functional requirement.

systems biology↗

bioRxiv2018

EVR: Reconstruction of Bacterial Chromosome 3D Structure Using Error-Vector Resultant Algorithm

More and more 3C/Hi-C experiments on prokaryotes have been published. However, most of the published modeling tools for chromosome 3D structures are targeting at eukaryotes. How to transform prokaryotic experimental chromosome interaction data into spatial structures is an important task and in great need. We have developed a new reconstruction program for bacterial chromosome 3D structures called EVR that exploits a simple Error-Vector Resultant (EVR) algorithm. This software tool is particularly optimized for the closed-loop structural features of prokaryotic chromosomes. EVR can be used to reconstruct the bacterial 3D chromosome structure based on the contact frequency matrix derived from 3C/Hi-C experimental data quickly and precisely.

bioinformatics↗