Prioritizing bona fide bacterial small RNAs with machine learning classifiers

CA, Harmon B, Espinola JA, Reck Cechinel L, Batabyal R, Freishtat RJ, Hahn A. Bacterial small RNAs may mediate immune response differences seen in respiratory syncytial virus versus rhinovirus bronchiolitis

Bronchiolitis, a viral lower respiratory infection, is the leading cause of infant hospitalization, which is associated with an increased risk for developing asthma later in life. Bronchiolitis can be caused by several respiratory viruses, such as respiratory syncytial virus (RSV), rhinovirus (RV), and others. It can also be caused by a solo infection (e.g., RSV- or RV-only bronchiolitis) or co-infection with two or more viruses. Studies have shown viral etiology-related differences between RSV- and RV-only bronchiolitis in the immune response, human microRNA (miRNA) profiles, and dominance of certain airway microbiome constituents. Here, we identified bacterial small RNAs (sRNAs), the prokaryotic equivalent to eukaryotic miRNAs, that differ between infants of the 35th Multicenter Airway Research Collaboration (MARC-35) cohort with RSV- versus RV-only bronchiolitis. We first derived reference sRNA datasets from cultures of four bacteria known to be associated with bronchiolitis (i.e., Haemophilus influenzae, Moraxella catarrhalis, Moraxella nonliquefaciens, and Streptococcus pneumoniae). Using these reference sRNA datasets, we found several sRNAs associated with RSV- and RV-only bronchiolitis in our human nasal RNA-Seq MARC-35 data. We also determined potential human transcript targets of the bacterial sRNAs and compared expression of the sRNAs between RSV- and RV-only cases. sRNAs are known to downregulate their mRNA target, we found that, compared to those associated with RV-only bronchiolitis, sRNAs associated with RSV-only bronchiolitis may relatively activate the IL-6 and IL-8 pathways and relatively inhibit the IL-17A pathway. These data support that bacteria may be contributing to inflammation differences seen in RSV- and RV-only bronchiolitis, and for the first time indicate that the potential mechanism in doing so may be through bacterial sRNAs.

Methods for Bioinformatic Prediction of Genuine sRNAs from Outer Membrane Vesicles

The molecular pathogenesis of Gram-negative bacteria remains a complex and incompletely understood phenomenon. Various factors are believed to contribute to the pathogenicity of these bacteria. One key mechanism utilized by Gram-negative bacteria is the production of outer membrane vesicles (OMVs), which are small spherical particles derived from the bacterial outer membrane. These OMVs are crucial in delivering virulence factors to the host, facilitating host-pathogen interactions. Within these OMVs, small regulatory RNAs (sRNAs) have been identified as important players in modulating the host immune response. One of the main challenges in studying OMVs and their cargo of sRNAs is the difficulty in isolating and purifying sufficient quantities of OMVs, as well as accurately predicting genuine sRNAs computationally. In this chapter, we present protocols aimed at overcoming these obstacles.

Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning

Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 (E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance.

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g. Cancer and coronavirus disease 2019, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine and precision medicine. These advances benefit our society and economy, directly impacting people’s lives in various areas, such as health care, drug discovery, forensic analysis and food processing. Nevertheless, ML-based approaches to biological data require representative, quantitative and informative features. Many ML algorithms can handle only numerical data, and therefore sequences need to be translated into a numerical feature vector. This process, known as feature extraction, is a fundamental step for developing high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with design and selection of suitable features. Feature engineering, ML algorithm selection and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge. To deal with this problem, we present a new package: BioAutoML. BioAutoML automatically runs an end-to-end ML pipeline, extracting numerical and informative features from biological sequence databases, using the MathFeature package, and automating the feature selection, ML algorithm(s) recommendation and tuning of the selected algorithm(s) hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules: (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyper-parameter tuning modules). We experimentally evaluate BioAutoML in two different scenarios: (i) prediction of the three main classes of noncoding RNAs (ncRNAs) and (ii) prediction of the eight categories of ncRNAs in bacteria, including housekeeping and regulatory types. To assess BioAutoML predictive performance, it is experimentally compared with two other AutoML tools (RECIPE and TPOT). According to the experimental results, BioAutoML can accelerate new studies, reducing the cost of feature engineering processing and either keeping or improving predictive performance. BioAutoML is freely available at https://github.com/Bonidia/BioAutoML.

Salmonella Typhimurium reprograms macrophage metabolism via T3SS effector SopE2 to promote intracellular replication and virulence

Salmonella Typhimurium establishes systemic infection by replicating in host macrophages. Here we show that macrophages infected with S. Typhimurium exhibit upregulated glycolysis and decreased serine synthesis, leading to accumulation of glycolytic intermediates. The effects on serine synthesis are mediated by bacterial protein SopE2, a type III secretion system (T3SS) effector encoded in pathogenicity island SPI-1. The changes in host metabolism promote intracellular replication of S. Typhimurium via two mechanisms: decreased glucose levels lead to upregulated bacterial uptake of 2- and 3-phosphoglycerate and phosphoenolpyruvate (carbon sources), while increased pyruvate and lactate levels induce upregulation of another pathogenicity island, SPI-2, known to encode virulence factors. Pharmacological or genetic inhibition of host glycolysis, activation of host serine synthesis, or deletion of either the bacterial transport or signal sensor systems for those host glycolytic intermediates impairs S. Typhimurium replication or virulence.

Salmonella Typhimurium establishes systemic infection by replicating in host macrophages. Here, Jiang et al. show that infected macrophages exhibit upregulated glycolysis and decreased serine synthesis, leading to accumulation of glycolytic intermediates that promote intracellular replication and virulence of S. Typhimurium.

Applying a New REFINE Approach in Zymomonas mobilis Identifies Novel sRNAs That Confer Improved Stress Tolerance Phenotypes

As global controllers of gene expression, small RNAs represent powerful tools for engineering complex phenotypes. However, a general challenge prevents the more widespread use of sRNA engineering strategies: mechanistic analysis of these regulators in bacteria lags far behind their high-throughput search and discovery. This makes it difficult to understand how to efficiently identify useful sRNAs to engineer a phenotype of interest. To help address this, we developed a forward systems approach to identify naturally occurring sRNAs relevant to a desired phenotype: RNA-seq Examiner for Phenotype-Informed Network Engineering (REFINE). This pipeline uses existing RNA-seq datasets under different growth conditions. It filters the total transcriptome to locate and rank regulatory-RNA-containing regions that can influence a metabolic phenotype of interest, without the need for previous mechanistic characterization. Application of this approach led to the uncovering of six novel sRNAs related to ethanol tolerance in non-model ethanol-producing bacterium Zymomonas mobilis. Furthermore, upon overexpressing multiple sRNA candidates predicted by REFINE, we demonstrate improved ethanol tolerance reflected by up to an approximately twofold increase in relative growth rate compared to controls not expressing these sRNAs in 7% ethanol (v/v) RMG-supplemented media. In this way, the REFINE approach informs strain-engineering strategies that we expect are applicable for general strain engineering.