Databases and resources for human small non-coding RNAs

The Acinetobacter baumannii model can explain the role of small non-coding RNAs as potential mediators of host-pathogen interactions

Bacterial small RNAs (sRNAs) research has accelerated over the past decade, boosted by advances in RNA-seq technologies and methodologies for capturing both protein-RNA and RNA-RNA interactions. The emerging picture is that these regulatory sRNAs play important roles in controlling complex physiological processes and are required to survive the antimicrobial challenge. In recent years, the RNA content of OMVs/EVs has also gained increasing attention, particularly in the context of infection. Secreted RNAs from several bacterial pathogens have been characterized but the exact mechanisms promoting pathogenicity remain elusive. In this review, we briefly discuss how secreted sRNAs interact with targets in infected cells, thus representing a novel perspective of host cell manipulation during bacterial infection. During the last decade, Acinetobacter baumannii became clinically relevant emerging pathogens responsible for nosocomial and community-acquired infections. Therefore, we also summarize recent findings of regulation by sRNAs in A. baumannii and discuss how this emerging bacterium utilizes many of these sRNAs to adapt to its niche and become successful human pathogen.

The Underlying Mechanisms of Noncoding RNAs in the Chemoresistance of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal human malignancies. Chemotherapeutic agents, such as sorafenib and lenvatinib, can improve the outcomes of HCC patients. Nevertheless, chemoresistance has become a major hurdle in the effective treatment of HCC. Noncoding RNAs (ncRNAs), including mircoRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have been demonstrated to participate in the onset and progression of HCC. Moreover, multiple lines of evidence have indicated that ncRNAs also play a pivotal role in HCC drug resistance. ncRNAs can regulate drug efflux and metabolism, glucose metabolism, cellular death pathways, and malignant characteristics in HCC. A deeper understanding of the molecular mechanisms responsible for ncRNA-mediated drug resistance in HCC will provide new opportunities for improving the treatment of HCC. In this review, we summarize recent findings on the molecular mechanisms by which ncRNAs regulate HCC chemoresistance, as well as their potential clinical implications in overcoming HCC chemoresistance.

Bioinformatic analysis of endogenous and exogenous small RNAs on lipoproteins

To comprehensively study extracellular small RNAs (sRNA) by sequencing (sRNA-seq), we developed a novel pipeline to overcome current limitations in analysis entitled, "Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER)". To demonstrate the power of this tool, sRNA-seq was performed on mouse lipoproteins, bile, urine and livers. A key advance for the TIGER pipeline is the ability to analyse both host and non-host sRNAs at genomic, parent RNA and individual fragment levels. TIGER was able to identify approximately 60% of sRNAs on lipoproteins and >85% of sRNAs in liver, bile and urine, a significant advance compared to existing software. Moreover, TIGER facilitated the comparison of lipoprotein sRNA signatures to disparate sample types at each level using hierarchical clustering, correlations, beta-dispersions, principal coordinate analysis and permutational multivariate analysis of variance. TIGER analysis was also used to quantify distinct features of exRNAs, including 5' miRNA variants, 3' miRNA non-templated additions and parent RNA positional coverage. Results suggest that the majority of sRNAs on lipoproteins are non-host sRNAs derived from bacterial sources in the microbiome and environment, specifically rRNA-derived sRNAs from Proteobacteria. Collectively, TIGER facilitated novel discoveries of lipoprotein and biofluid sRNAs and has tremendous applicability for the field of extracellular RNA.

The discovery potential of RNA processing profiles

Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, a new computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare sncRNAs by harnessing the power of read profiles. We applied SeRPeNT to: (i) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, (ii) show pervasive differential processing of sncRNAs between cell compartments and (iii) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of sncRNAs at an unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.

An Introduction to Programming for Bioscientists: A Python-Based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language’s usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a “variable,” the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.

Contemporary biology has largely become computational biology, whether it involves applying physical principles to simulate the motion of each atom in a piece of DNA, or using machine learning algorithms to integrate and mine “omics” data across whole cells (or even entire ecosystems). The ability to design algorithms and program computers, even at a novice level, may be the most indispensable skill that a modern researcher can cultivate. As with human languages, computational fluency is developed actively, not passively. This self-contained text, structured as a hybrid primer/tutorial, introduces any biologist—from college freshman to established senior scientist—to basic computing principles (control-flow, recursion, regular expressions, etc.) and the practicalities of programming and software design. We use the Python language because it now pervades virtually every domain of the biosciences, from sequence-based bioinformatics and molecular evolution to phylogenomics, systems biology, structural biology, and beyond. To introduce both coding (in general) and Python (in particular), we guide the reader via concrete examples and exercises. We also supply, as Supplemental Chapters, a few thousand lines of heavily-annotated, freely distributed source code for personal study.