Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation

BACKGROUND

Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging.

RESULTS

Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters.

CONCLUSION

Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Snowprint: a predictive tool for genetic biosensor discovery

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies

Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.

DiMo: discovery of microRNA motifs using deep learning and motif embedding

MicroRNAs are small regulatory RNAs that decrease gene expression after transcription in various biological disciplines. In bioinformatics, identifying microRNAs and predicting their functionalities is critical. Finding motifs is one of the most well-known and important methods for identifying the functionalities of microRNAs. Several motif discovery techniques have been proposed, some of which rely on artificial intelligence-based techniques. However, in the case of few or no training data, their accuracy is low. In this research, we propose a new computational approach, called DiMo, for identifying motifs in microRNAs and generally macromolecules of small length. We employ word embedding techniques and deep learning models to improve the accuracy of motif discovery results. Also, we rely on transfer learning models to pre-train a model and use it in cases of a lack of (enough) training data. We compare our approach with five state-of-the-art works using three real-world datasets. DiMo outperforms the selected related works in terms of precision, recall, accuracy and f1-score.

Orchestrated translation specializes dinoflagellate metabolism three times per day

Many cells specialize for different metabolic tasks at different times over their normal ZT cycle by changes in gene expression. However, in most cases, circadian gene expression has been assessed at the mRNA accumulation level, which may not faithfully reflect protein synthesis rates. Here, we use ribosome profiling in the dinoflagellate Lingulodinium polyedra to identify thousands of transcripts showing coordinated translation. All of the components in carbon fixation are concurrently regulated at ZT0, predicting the known rhythm of carbon fixation, and many enzymes involved in DNA replication are concurrently regulated at ZT12, also predicting the known rhythm in this process. Most of the enzymes in glycolysis and the TCA cycle are also regulated together, suggesting rhythms in these processes as well. Surprisingly, a third cluster of transcripts show peak translation at approximately ZT16, and these transcripts encode enzymes involved in transcription, translation, and amino acid biosynthesis. The latter has physiological consequences, as measured free amino acid levels increase at night and thus represent a previously undocumented rhythm in this model. Our results suggest that ribosome profiling may be a more accurate predictor of changed metabolic state than transcriptomics.

Base-resolution prediction of transcription factor binding signals by a deep learning framework

Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level (named as FCNsignal). The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs. Besides, FCNsignal can also be used to predict opening regions across the whole genome. The experimental results on 53 TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets show that our proposed framework outperforms some existing state-of-the-art methods. In addition, we explored to use the trained FCNsignal to locate all potential TF-DNA binding regions on a whole chromosome and predict DNA sequences of arbitrary length, and the results show that our framework can find most of the known binding regions and accept sequences of arbitrary length. Furthermore, we demonstrated the potential ability of our framework in discovering causal disease-associated single-nucleotide polymorphisms (SNPs) through a series of experiments.

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.

Organic Hydroperoxide Resistance Gene ohr (VPA1681) Confers Protection against Organic Peroxides in the Presence of Alkyl Hydroperoxide Reductase Genes in Vibrio parahaemolyticus

The marine foodborne enteropathogen Vibrio parahaemolyticus contains the chief organic peroxide reductases AphC1-AhpC2 and a putative organic hydroperoxide resistance enzyme (Ohr; VPA1681) against different peroxides. This study investigated the function of the Ohr under the presence of AhpC1-AhpC2 in this pathogen by gene mutation. Experimental results demonstrated that the ohr gene product was a weak scavenger of H₂O₂ only in the mutant strains that lacked the peroxide sensor/regulator oxyR and ahpC1-ahpC2 genes. The Ohr of V. parahaemolyticus was highly effective at scavenging organic peroxide, as demonstrated by assaying the defective changes in the Δohr mutant strain and determining the detoxifying activity of the purified recombinant V. parahaemolyticus Ohr^vp protein in the reduced form. The Ohr and AhpC1-AhpC2 exhibited similar functions against organic peroxides; however, only the ΔahpC1ΔahpC2 mutant strain showed a significant increase in susceptibility to several disinfectants, organic acids, and antibiotics compared with the wild-type strain. The transcription of the ohr gene depended on exogenous cumene hydroperoxide (cumene) stress and was markedly enhanced in the ΔohrR (VPA1682) mutant strains. This study revealed the organic hydroperoxide reductase activity of the Ohr in V. parahaemolyticus, and its role probably depends on sophisticated regulation by OhrR. IMPORTANCE Vibrio parahaemolyticus is the most prevalent foodborne pathogen in Taiwan and some other coastal Asian countries, and its antioxidative activity contributes to the tolerance of this bacterium to different environmental stresses. This study reports on the function of the organic hydroperoxide resistance gene (ohr; VPA1681) and its gene regulator, ohrR (VPA1682), in this pathogen. The strain with the ohr gene had effective protection against organic peroxide, and the recombinant Ohr^vp was active in its reduced form. The function of Ohr was significant mostly in strains in which the function of AhpC1-AhpC2 was limited. The ohrR repressor of the ohr gene was effective at low concentrations of organic peroxide. Other common Vibrio species that contain homologous ohr, ohrR, ahpC1, and ahpC2 genes, which are phylogenetically close to those of V. parahaemolyticus, may share similar functions to those revealed in this study.

A novel computational framework for genome-scale alternative transcription units prediction

Alternative transcription units (ATUs) are dynamically encoded under different conditions and display overlapping patterns (sharing one or more genes) under a specific condition in bacterial genomes. Genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques because of the complexity and dynamic nature of ATUs. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in Escherichia coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. In addition, the proportion of 5'- or 3'-end genes of the predicted ATUs, having documented transcription factor binding sites and transcription termination sites, was three times greater than that of no 5'- or 3'-end genes. We further evaluated the predicted ATUs by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. The results suggested that gene pairs frequently encoded in the same ATUs are more functionally related than those that can belong to two distinct ATUs. Overall, these results demonstrated the high reliability of predicted ATUs. We expect that the new insights derived by SeqATU will not only improve the understanding of the transcription mechanism of bacteria but also guide the reconstruction of a genome-scale transcriptional regulatory network.

Computational analysis of LexA regulons in Proteus species

To gain a general understanding of the SOS system in Proteus species, in this study LexA-binding sites and the LexA regulons in 23 Proteus genomes were first predicted by phylogenetic footprinting server, then with Proteus vulgaris as an example, the expression of LexA regulon in iron limitation was investigated by proteomic analysis and quantitative reverse transcription polymerase chain reaction (RT-qPCR) method. The results showed that LexA proteins were highly conserved in Proteus species, and were in a close phylogenetic relationship with those in Gram-negative bacteria; the core SOS response genes lexA and recA were found in all the 23 genomes, indicating that this system was widely distributed in this genus; besides that, putative LexA-binding sites were also found in the upstream sequences of some genes involved in other biological processes such as biosynthesis, drug resistance, and stress response. Proteomic and RT-qPCR analyses showed that under iron deficient condition, the expression of lexA, recA and sulA was transcriptionally upregulated (p < 0.05), lexA was also translationally upregulated but recA was on the contrary (p < 0.05), whereas another SOS response gene dinI was transcriptionally downregulated (p < 0.01). These results indicated that in response to iron deficiency, the members of LexA regulon were not regulated by the same way, suggesting the existence of a precise regulation mechanism of SOS response in P. vulgaris. In conclusion, this study provided a preliminary understanding of the SOS system in Proteus species, which laid the foundation for further investigation of its roles in SOS response and other biological processes.

Pharmacologic Activation of LXR Alters the Expression Profile of Tumor-Associated Macrophages and the Abundance of Regulatory T Cells in the Tumor Microenvironment

Liver X receptors (LXR) are transcription factors from the nuclear receptor family that are activated by oxysterols and synthetic high-affinity agonists. In this study, we assessed the antitumor effects of synthetic LXR agonist TO901317 in a murine model of syngeneic Lewis Lung carcinoma. Treatment with TO901317 inhibited tumor growth in wild-type, but not in LXR-deficient mice, indicating that the antitumor effects of the agonist depends on functional LXR activity in host cells. Pharmacologic activation of the LXR pathway reduced the intratumoral abundance of regulatory T cells (Treg) and the expression of the Treg-attracting chemokine Ccl17 by MHCII^high tumor-associated macrophages (TAM). Moreover, gene expression profiling indicated a broad negative impact of the LXR agonist on other mechanisms used by TAM for the maintenance of an immunosuppressive environment. In studies exploring the macrophage response to GM-CSF or IL4, activated LXR repressed IRF4 expression, resulting in subsequent downregulation of IRF4-dependent genes including Ccl17. Taken together, this work reveals the combined actions of the LXR pathway in the control of TAM responses that contribute to the antitumoral effects of pharmacologic LXR activation. Moreover, these data provide new insights for the development of novel therapeutic options for the treatment of cancer. SIGNIFICANCE: This study reveals unrecognized roles of LXR in the transcriptional control of the tumor microenvironment and suggests use of a synthetic LXR agonist as a novel therapeutic strategy to stimulate antitumor activity.

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.

Expanded roles of pyruvate-sensing PdhR in transcription regulation of the Escherichia coli K-12 genome: fatty acid catabolism and cell motility

The transcription factor PdhR has been recognized as the master regulator of the pyruvate catabolism pathway in Escherichia coli, including both NAD-linked oxidative decarboxylation of pyruvate to acetyl-CoA by PDHc (pyruvate dehydrogenase complex) and respiratory electron transport of NADH to oxygen by Ndh-CyoABCD enzymes. To identify the whole set of regulatory targets under the control of pyruvate-sensing PdhR, we performed genomic SELEX (gSELEX) screening in vitro. A total of 35 PdhR-binding sites were identified along the E. coli K-12 genome, including previously identified targets. Possible involvement of PdhR in regulation of the newly identified target genes was analysed in detail by gel shift assay, RT-qPCR and Northern blot analysis. The results indicated the participation of PdhR in positive regulation of fatty acid degradation genes and negative regulation of cell mobility genes. In fact, GC analysis indicated an increase in free fatty acids in the mutant lacking PdhR. We propose that PdhR is a bifunctional global regulator for control of a total of 16–23 targets, including not only the genes involved in central carbon metabolism but also some genes for the surrounding pyruvate-sensing cellular pathways such as fatty acid degradation and flagella formation. The activity of PdhR is controlled by pyruvate, the key node between a wide variety of metabolic pathways, including generation of metabolic energy and cell building blocks.

The functional determinants in the organization of bacterial genomes

Bacterial genomes are now recognized as interacting intimately with cellular processes. Uncovering organizational mechanisms of bacterial genomes has been a primary focus of researchers to reveal the potential cellular activities. The advances in both experimental techniques and computational models provide a tremendous opportunity for understanding these mechanisms, and various studies have been proposed to explore the organization rules of bacterial genomes associated with functions recently. This review focuses mainly on the principles that shape the organization of bacterial genomes, both locally and globally. We first illustrate local structures as operons/transcription units for facilitating co-transcription and horizontal transfer of genes. We then clarify the constraints that globally shape bacterial genomes, such as metabolism, transcription and replication. Finally, we highlight challenges and opportunities to advance bacterial genomic studies and provide application perspectives of genome organization, including pathway hole assignment and genome assembly and understanding disease mechanisms.

Genomic architecture of endogenous ichnoviruses reveals distinct evolutionary pathways leading to virus domestication in parasitic wasps

BACKGROUND

Polydnaviruses (PDVs) are mutualistic endogenous viruses inoculated by some lineages of parasitoid wasps into their hosts, where they facilitate successful wasp development. PDVs include the ichnoviruses and bracoviruses that originate from independent viral acquisitions in ichneumonid and braconid wasps respectively. PDV genomes are fully incorporated into the wasp genomes and consist of (1) genes involved in viral particle production, which derive from the viral ancestor and are not encapsidated, and (2) proviral segments harboring virulence genes, which are packaged into the viral particle. To help elucidating the mechanisms that have facilitated viral domestication in ichneumonid wasps, we analyzed the structure of the viral insertions by sequencing the whole genome of two ichnovirus-carrying wasp species, Hyposoter didymator and Campoletis sonorensis.

RESULTS

Assemblies with long scaffold sizes allowed us to unravel the organization of the endogenous ichnovirus and revealed considerable dispersion of the viral loci within the wasp genomes. Proviral segments contained species-specific sets of genes and occupied distinct genomic locations in the two ichneumonid wasps. In contrast, viral machinery genes were organized in clusters showing highly conserved gene content and order, with some loci located in collinear wasp genomic regions. This genomic architecture clearly differs from the organization of PDVs in braconid wasps, in which proviral segments are clustered and viral machinery elements are more dispersed.

CONCLUSIONS

The contrasting structures of the two types of ichnovirus genomic elements are consistent with their different functions: proviral segments are vehicles for virulence proteins expected to adapt according to different host defense systems, whereas the genes involved in virus particle production in the wasp are likely more stable and may reflect ancestral viral architecture. The distinct genomic architectures seen in ichnoviruses versus bracoviruses reveal different evolutionary trajectories that have led to virus domestication in the two wasp lineages.

iRNAD: a computational tool for identifying D modification sites in RNA sequence

MOTIVATION

Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA.

RESULTS

We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model.

AVAILABILITY AND IMPLEMENTATION

A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

Remarks on Computational Method for Identifying Acid and Alkaline Enzymes

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

MOTIVATION

Cardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction.

RESULTS

In this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6-7% in both benchmarking and independent datasets.

AVAILABILITY AND IMPLEMENTATION

The user-friendly online prediction tool, mAHTPred is freely accessible at http://thegleelab.org/mAHTPred.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DOOR: a prokaryotic operon database for genome analyses and functional inference

The rapid accumulation of fully sequenced prokaryotic genomes provides unprecedented information for biological studies of bacterial and archaeal organisms in a systematic manner. Operons are the basic functional units for conducting such studies. Here, we review an operon database DOOR (the Database of prOkaryotic OpeRons) that we have previously developed and continue to update. Currently, the database contains 6 975 454 computationally predicted operons in 2072 complete genomes. In addition, the database also contains the following information: (i) transcriptional units for 24 genomes derived using publicly available transcriptomic data; (ii) orthologous gene mapping across genomes; (iii) 6408 cis-regulatory motifs for transcriptional factors of some operons for 203 genomes; (iv) 3 456 718 Rho-independent terminators for 2072 genomes; as well as (v) a suite of tools in support of applications of the predicted operons. In this review, we will explain how such data are computationally derived and demonstrate how they can be used to derive a wide range of higher-level information needed for systems biology studies to tackle complex and fundamental biology questions.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.

Perspectives of CRISPR/Cas-mediated cis-engineering in horticulture: unlocking the neglected potential for crop improvement

Directed breeding of horticultural crops is essential for increasing yield, nutritional content, and consumer-valued characteristics such as shape and color of the produce. However, limited genetic diversity restricts the amount of crop improvement that can be achieved through conventional breeding approaches. Natural genetic changes in cis-regulatory regions of genes play important roles in shaping phenotypic diversity by altering their expression. Utilization of CRISPR/Cas editing in crop species can accelerate crop improvement through the introduction of genetic variation in a targeted manner. The advent of CRISPR/Cas-mediated cis-regulatory region engineering (cis-engineering) provides a more refined method for modulating gene expression and creating phenotypic diversity to benefit crop improvement. Here, we focus on the current applications of CRISPR/Cas-mediated cis-engineering in horticultural crops. We describe strategies and limitations for its use in crop improvement, including de novo cis-regulatory element (CRE) discovery, precise genome editing, and transgene-free genome editing. In addition, we discuss the challenges and prospects regarding current technologies and achievements. CRISPR/Cas-mediated cis-engineering is a critical tool for generating horticultural crops that are better able to adapt to climate change and providing food for an increasing world population.

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

MOTIVATION

As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modification systems. For better understanding of their functional mechanisms, it is fundamentally important to identify 4mC modification. Machine learning methods have recently emerged as an effective and efficient approach for the high-throughput identification of 4mC sites, although high predictive error rates are still challenging for existing methods. Therefore, it is highly desirable to develop a computational method to more accurately identify m4C sites.

RESULTS

In this study, we propose a machine learning based predictor, namely 4mcPred-SVM, for the genome-wide detection of DNA 4mC sites. In this predictor, we present a new feature representation algorithm that sufficiently exploits sequence-based information. To improve the feature representation ability, we use a two-step feature optimization strategy, thereby obtaining the most representative features. Using the resulting features and Support Vector Machine (SVM), we adaptively train the optimal models for different species. Comparative results on benchmark datasets from six species indicate that our predictor is able to achieve generally better performance in predicting 4mC sites as compared to the state-of-the-art predictors. Importantly, the sequence-based features can reliably and robust predict 4mC sites, facilitating the discovery of potentially important sequence characteristics for the prediction of 4mC sites.

AVAILABILITY AND IMPLEMENTATION

The user-friendly webserver that implements the proposed 4mcPred-SVM is well established, and is freely accessible at http://server.malab.cn/4mcPred-SVM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Design powerful predictor for mRNA subcellular location prediction in Homo sapiens

Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive. It is highly desired to develop computational tools for timely and effectively predicting mRNA subcellular location. In this work, by using binomial distribution and one-way analysis of variance, the optimal nonamer composition was obtained to represent mRNA sequences. Subsequently, a predictor based on support vector machine was developed to identify the mRNA subcellular localization. In 5-fold cross-validation, results showed that the accuracy is 90.12% for Homo sapiens (H. sapiens). The predictor may provide a reference for the study of mRNA localization mechanisms and mRNA translocation strategies. An online web server was established based on our models, which is available at http://lin-group.cn/server/iLoc-mRNA/.

Regulatory Role of PlaR (YiaJ) for Plant Utilization in Escherichia coli K-12

Outside a warm-blooded animal host, the enterobacterium Escherichia coli K-12 is also able to grow and survive in stressful nature. The major organic substance in nature is plant, but the genetic system of E. coli how to utilize plant-derived materials as nutrients is poorly understood. Here we describe the set of regulatory targets for uncharacterized IclR-family transcription factor YiaJ on the E. coli genome, using gSELEX screening system. Among a total of 18 high-affinity binding targets of YiaJ, the major regulatory target was identified to be the yiaLMNOPQRS operon for utilization of ascorbate from fruits and galacturonate from plant pectin. The targets of YiaJ also include the genes involved in the utilization for other plant-derived materials as nutrients such as fructose, sorbitol, glycerol and fructoselysine. Detailed in vitro and in vivo analyses suggest that L-ascorbate and α-D-galacturonate are the effector ligands for regulation of YiaJ function. These findings altogether indicate that YiaJ plays a major regulatory role in expression of a set of the genes for the utilization of plant-derived materials as nutrients for survival. PlaR was also suggested to play protecting roles of E. coli under stressful environments in nature, including the formation of biofilm. We then propose renaming YiaJ to PlaR (regulator of plant utilization). The natural hosts of enterobacterium Escherichia coli are warm-blooded animals, but even outside hosts, E. coli can survive even under stressful environments. On earth, the most common organic materials to be used as nutrients by E. coli are plant-derived components, but up to the present time, the genetic system of E. coli for plant utilization is poorly understand. In the course of gSELEX screening of the regulatory targets for hitherto uncharacterized TFs, we identified in this study the involvement of the IclR-family YiaJ in the regulation of about 20 genes or operons, of which the majority are related to the catabolism of plant-derived materials such as ascorbate, galacturonate, sorbitol, fructose and fructoselysine. Therefore, we propose to rename YiaJ to PlaR (regulator of plant utilization).

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein–DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein–protein–DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF–DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework.

2CS-CHXT Operon Signature of Chlorhexidine Tolerance among Enterococcus faecium Isolates

Chlorhexidine (CHX) is a broad-spectrum antiseptic widely used in community and clinical contexts for many years that has recently acquired higher relevance in nosocomial infection control worldwide. Despite this, CHX tolerance among Enterococcus faecium bacteria, representing one of the leading agents causing nosocomial infections, has been poorly understood. This study provides new phenotypic and molecular data for better identification of CHX-tolerant E. faecium subpopulations in community and clinical contexts. The chlorhexidine MIC (MIC_CHX) distribution of 106 E. faecium isolates suggested the occurrence of tolerant subpopulations in diverse sources (human, animal, food, environment) and phylogenomic backgrounds (clades A1/A2/B), with predominance in clade A1. They carried a specific variant of the 2CS-CHX^T operon, identified here. It encodes glucose and amino acid-polyamine-organocation family transporters, besides the DNA-binding response regulator ChtR, with a P102H mutation previously described only in CHX-tolerant clade A1 E. faecium, and the ChtS sensor. 2CS-CHX^T seems to be associated with three regulons modulating diverse bacterial biological functions. Combined data from normal MIC distribution and 2CS-CHX^T operon characterization support a tentative epidemiological cutoff (ECOFF) of 8 mg/liter to CHX, which is useful to detect tolerant E. faecium populations in future surveillance studies. The spread of tolerant E. faecium in diverse epidemiological backgrounds calls for the prudent use of CHX in multiple contexts.IMPORTANCE Chlorhexidine is one of the substances included in the World Health Organization's list of essential medicines, which comprises the safest and most effective medicines needed in global health systems. Although it has been widely applied as a disinfectant and antiseptic in health care (skin, hands, mouthwashes, eye drops) since the 1950s, its use in hospitals to prevent nosocomial infections has increased worldwide in recent years. Here, we provide a comprehensive study on chlorhexidine tolerance among strains of Enterococcus faecium, one of the leading nosocomial agents worldwide, and identify a novel 2CS-CHX^T operon as a signature of tolerant strains occurring in diverse phylogenomic groups. Our data allowed for the proposal of a tentative epidemiological cutoff of 8 mg/liter, which is useful to detect tolerant E. faecium populations in surveillance studies in community and clinical contexts. The prediction of 2CS-CHX^T regulons will also facilitate the design of future experimental studies to better uncover chlorhexidine tolerance among E. faecium bacteria.

Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods

Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.

iRNA-m2G: Identifying N2-methylguanosine Sites Based on Sequence-Derived Information

RNA N²-methylguanosine (m2G) is one kind of posttranscriptional modification and plays crucial roles in the control and stabilization of tRNA. However, our knowledge about the biological functions of m2G is still limited. The key step of revealing its new function is to recognize the m2G sites in the transcriptome. Since there is no effective method for detecting m2G sites, it is desirable to develop new methods to identify m2G sites. In this study, a computational predictor called iRNA-m2G was proposed to identify m2G sites in eukaryotic transcriptomes. In iRNA-m2G, the RNA sequences were encoded by using nucleotide chemical property and accumulated nucleotide frequency. iRNA-m2G was not only validated by the rigorous jackknife test on the benchmark dataset but also examined by performing cross-species validations. In addition, iRNA-m2G was also tested on an independent dataset. It was found that the accuracies obtained by iRNA-m2G were all quite promising in these tests, indicating that the proposed method could become a powerful tool for identifying m2G sites. Finally, a user-friendly web server for iRNA-m2G is freely accessible at http://lin-group.cn/server/iRNA-m2G.php.

AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees

Mycobacterium tuberculosis is one of the most dangerous pathogens in humans. It acts as an etiological agent of tuberculosis (TB), infecting almost one-third of the world's population. Owing to the high incidence of multidrug-resistant TB and extensively drug-resistant TB, there is an urgent need for novel and effective alternative therapies. Peptide-based therapy has several advantages, such as diverse mechanisms of action, low immunogenicity, and selective affinity to bacterial cell envelopes. However, the identification of anti-tubercular peptides (AtbPs) via experimentation is laborious and expensive; hence, the development of an efficient computational method is necessary for the prediction of AtbPs prior to both in vitro and in vivo experiments. To this end, we developed a two-layer machine learning (ML)-based predictor called AtbPpred for the identification of AtbPs. In the first layer, we applied a two-step feature selection procedure and identified the optimal feature set individually for nine different feature encodings, whose corresponding models were developed using extremely randomized tree (ERT). In the second-layer, the predicted probability of AtbPs from the above nine models were considered as input features to ERT and developed the final predictor. AtbPpred respectively achieved average accuracies of 88.3% and 87.3% during cross-validation and an independent evaluation, which were ~8.7% and 10.0% higher than the state-of-the-art method. Furthermore, we established a user-friendly webserver which is currently available at http://thegleelab.org/AtbPpred. We anticipate that this predictor could be useful in the high-throughput prediction of AtbPs and also provide mechanistic insights into its functions.

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We developed a novel computational framework for the identification of anti-tubercular peptides using Extremely randomized tree.

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AtbPpred displayed superior performance compared to the existing method on both benchmark and independent datasets.

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We constructed a user-friendly web server that implements the proposed AtbPpred method.

Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies

Sigma factor, as a unit of RNA polymerase holoenzyme, is a critical factor in the process of gene transcriptional regulation. It recognizes the specific DNA sites and brings the core enzyme of RNA polymerase to the upstream regions of target genes. Therefore, the prediction of the promoters for a particular sigma factor is essential for interpreting functional genomic data and observation. This paper develops a new method to predict sigma-54 promoters in bacterial genomes. The new method organically integrates motif finding and machine learning strategies to capture the intrinsic features of sigma-54 promoters. The experiments on E. coli benchmark test set show that our method has good capability to distinguish sigma-54 promoters from surrounding or randomly selected DNA sequences. The applications of the other three bacterial genomes indicate the potential robustness and applicative power of our method on a large number of bacterial genomes. The source code of our method can be freely downloaded at https://github.com/maqin2001/PromotePredictor.

iProEP: A Computational Predictor for Predicting Promoter

Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).

Evaluation of different computational methods on 5-methylcytosine sites identification

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

The LiaFSR and BsrXRS Systems Contribute to Bile Salt Resistance in Enterococcus faecium Isolates

Two-component systems (TCSs) are dominant regulating components in bacteria for responding to environmental stimuli. However, little information is available on how TCSs in Enterococcus faecium respond to bile salts - an important environmental stimulus for intestinal bacteria. In this study, the gene expression of 2 TCSs, BsrXRS and LiaFSR, was positively correlated with survival rates of different E. faecium isolates during exposure to ox gall. Moreover, gene disruptions of bsrR, bsrS, liaS, and liaR significantly reduced the survival rates of E. faecium in the presence of ox gall. Finally, EMSA results indicated that BsrR functioned as a transcription regulator for expression of its own gene as well as lipoate-protein ligase A (lplA). Additional 27 potential target genes by BsrR were revealed through in silico analyses. These findings suggest that BsrXRS and LiaFSR systems play important roles in bile salt resistance in E. faecium.

IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI's Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.

Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods

Motivation: Pentatricopeptide repeat (PPR) is a triangular pentapeptide repeat domain that plays a vital role in plant growth. In this study, we seek to identify PPR coding genes and proteins using a mixture of feature extraction methods. We use four single feature extraction methods focusing on the sequence, physical, and chemical properties as well as the amino acid composition, and mix the features. The Max-Relevant-Max-Distance (MRMD) technique is applied to reduce the feature dimension. Classification uses the random forest, J48, and naïve Bayes with 10-fold cross-validation. Results: Combining two of the feature extraction methods with the random forest classifier produces the highest area under the curve of 0.9848. Using MRMD to reduce the dimension improves this metric for J48 and naïve Bayes, but has little effect on the random forest results. Availability and Implementation: The webserver is available at: http://server.malab.cn/MixedPPR/index.jsp.

In vivo promoter engineering in plants: Are we ready?

Engineering plant promoter sequence for optimal expression of a gene has been a long standing goal for plant scientists. In recent times, Sequence Specific Nucleases (SSNs) like CRISPR/Cas9 are enabling researchers to achieve this goal, in vivo in the genome. It is well known that SSNs have met with unprecedented success in rapid transgene free crop improvement largely by targeting the coding sequence. Here, we discuss the strategies being employed by plant scientists in targeting SSNs to non-coding promoter regions/Cis Regulatory Elements (CRE). We collectively refer all such endeavors as in vivo promoter engineering (IPE). We further classify the IPE efforts into CRE addition, CRE deletion/disruption, promoter swap/insertion and targeted promoter polymorphism. Till date, IPE has proven useful in altering plant architecture in tomato, developing resistance against Xanthomonas sp in rice and citrus, and engineering drought tolerance in maize. However it is quite challenging to achieve predictable changes in gene expression using IPE at this point. In future years, data generated from high throughput techniques to investigate non coding genome may immensely augment the efforts in this direction. As IPE does not involve addition of the transgene for modifying crop traits, it will be relatively more conducive to public acceptance in crop improvement programs.

Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell-cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs.

RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis

Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed.

Control of mitotic chromosome condensation by the fission yeast transcription factor Zas1

How chromosomes compact into rod-shaped structures is a longstanding unresolved question of cell biology. Schiklenk et al. identify the transcription factor Zas1 as a central regulator of mitotic chromosome condensation in fission yeast and show that it uses a conserved transactivation domain–based mechanism to control gene expression.

Although the formation of rod-shaped chromosomes is vital for the correct segregation of eukaryotic genomes during cell divisions, the molecular mechanisms that control the chromosome condensation process have remained largely unknown. Here, we identify the C₂H₂ zinc-finger transcription factor Zas1 as a key regulator of mitotic condensation dynamics in a quantitative live-cell microscopy screen of the fission yeast Schizosaccharomyces pombe. By binding to specific DNA target sequences in their promoter regions, Zas1 controls expression of the Cnd1 subunit of the condensin protein complex and several other target genes, whose combined misregulation in zas1 mutants results in defects in chromosome condensation and segregation. Genetic and biochemical analysis reveals an evolutionarily conserved transactivation domain motif in Zas1 that is pivotal to its function in gene regulation. Our results suggest that this motif, together with the Zas1 C-terminal helical domain to which it binds, creates a cis/trans switch module for transcriptional regulation of genes that control chromosome condensation.

A systematic approach to RNA-associated motif discovery

BACKGROUND

Sequencing-based large screening of RNA-protein and RNA-RNA interactions has enabled the mechanistic study of post-transcriptional RNA processing and sorting, including exosome-mediated RNA secretion. The downstream analysis of RNA binding sites has encouraged the investigation of novel sequence motifs, which resulted in exceptional new challenges for identifying motifs from very short sequences (e.g., small non-coding RNAs or truncated messenger RNAs), where conventional methods tend to be ineffective. To address these challenges, we propose a novel motif-finding method and validate it on a wide range of RNA applications.

RESULTS

We first perform motif analysis on microRNAs and longer RNA fragments from various cellular and exosomal sources, and then validate our prediction through literature search and experimental test. For example, a 4 bp-long motif, GUUG, was detected to be responsible for microRNA loading in exosomes involved in human colon cancer (SW620). Additional performance comparisons in various case studies have shown that this new approach outperforms several existing state-of-the-art methods in detecting motifs with exceptional high coverage and explicitness.

CONCLUSIONS

In this work, we have demonstrated the promising performance of a new motif discovery approach that is particularly effective in current RNA applications. Important discoveries resulting from this work include the identification of possible RNA-loading motifs in a variety of exosomes, as well as novel insights in sequence features of RNA cargos, i.e., short non-coding RNAs and messenger RNAs may share similar loading mechanism into exosomes. This method has been implemented and deployed as a new webserver named MDS2 which is accessible at http://sbbi-panda.unl.edu/MDS2/ , along with a standalone package available for download at https://github.com/sbbi/MDS2 .

Comparison of expression of key sporulation, solventogenic and acetogenic genes in C. beijerinckii NRRL B-598 and its mutant strain overexpressing spo0A

The production of acetone, butanol and ethanol by fermentation of renewable biomass has potential to become a valuable industrial process. Mechanisms of solvent production and sporulation involve some common regulators in some ABE-producing clostridia, although details of the links between the pathways are not clear. In this study, we compare a wild-type (WT) Clostridium beijerinckii NRRL B-598 with its mutant strain OESpo0A, in which the gene encoding Spo0A, an important regulator of both sporulation and solventogenesis, is overexpressed in terms of solvent and acid production. We also compare morphologies during growth on two different media: TYA broth, where the WT culture sporulates, and RCM, where the WT culture does not. In addition, RT-qPCR-based analysis of expression profiles of spo0A, spoIIE, sigG, spoVD, ald and buk1 genes involved in sporulation or solvent production in these strains, were compared. The OESpo0A mutant did not produce spores and butanol titre was lower compared to the WT, but increased amounts of butyric acid and ethanol were produced. The gene spo0A had high levels of expression in the WT under non-sporulating culture conditions while other selected genes for sporulation factors were downregulated significantly. Similar observations were obtained for OESpo0A where spo0A overexpression and downregulation of other sporulation genes were demonstrated. Higher expression of spo0A led to higher expression of buk1 and ald, which could confirm the role of spo0A in activation of the solventogenic pathway, although solvent production was not affected significantly in the WT and was weakened in the OESpo0A mutant.