Systems biology-based investigation of cooperating microRNAs as monotherapy or adjuvant therapy in cancer

MicroRNAs (miRNAs) are short, noncoding RNAs that regulate gene expression by suppressing mRNA translation and reducing mRNA stability. A miRNA can potentially bind many mRNAs, thereby affecting the expression of oncogenes and tumor suppressor genes as well as the activity of whole pathways. The promise of miRNA therapeutics in cancer is to harness this evolutionarily conserved mechanism for the coordinated regulation of gene expression, and thus restoring a normal cell phenotype. However, the promiscuous binding of miRNAs can provoke unwanted off-target effects, which are usually caused by high-dose single-miRNA treatments. Thus, it is desirable to develop miRNA therapeutics with increased specificity and efficacy. To achieve that, we propose the concept of miRNA cooperativity in order to exert synergistic repression on target genes, thus lowering the required total amount of miRNAs. We first review miRNA therapies in clinical application. Next, we summarize the knowledge on the molecular mechanism and biological function of miRNA cooperativity and discuss its application in cancer therapies. We then propose and discuss a systems biology approach to investigate miRNA cooperativity for the clinical setting. Altogether, we point out the potential of miRNA cooperativity to reduce off-target effects and to complement conventional, targeted, or immune-based therapies for cancer

CTCF as a regulator of alternative splicing: new tricks for an old player

Three decades of research have established the CCCTC-binding factor (CTCF) as a ubiquitously expressed chromatin organizing factor and master regulator of gene expression. A new role for CTCF as a regulator of alternative splicing (AS) has now emerged. CTCF has been directly and indirectly linked to the modulation of AS at the individual transcript and at the transcriptome-wide level. The emerging role of CTCF-mediated regulation of AS involves diverse mechanisms; including transcriptional elongation, DNA methylation, chromatin architecture, histone modifications, and regulation of splicing factor expression and assembly. CTCF thereby appears to not only co-ordinate gene expression regulation but contributes to the modulation of transcriptomic complexity. In this review, we highlight previous discoveries regarding the role of CTCF in AS. In addition, we summarize detailed mechanisms by which CTCF mediates AS regulation. We propose opportunities for further research designed to examine the possible fate of CTCF-mediated alternatively spliced genes and associated biological consequences. CTCF has been widely acknowledged as the ‘master weaver of the genome’. Given its multiple connections, further characterization of CTCF’s emerging role in splicing regulation might extend its functional repertoire towards a ‘conductor of the splicing orchestra’

Application and comparative performance of network modularity algorithms to ecological communities classification

Network modularity is a well-studied large-scale connectivity pattern in networks. The detection of modules in real networks constitutes a crucial step towards a description of the network building blocks and their evolutionary dynamics. The performance of modularity detection algorithms is commonly quantified using simulated networks data. However, a comparison of the modularity algorithms utility for real biological data is scarce. Here we investigate the utility of network modularity algorithms for the classification of ecological plant communities. Plant community classification by the traditional approaches requires prior knowledge about the characteristic and differential species, which are derived from a manual inspection of vegetation tables. Using the raw species abundance data we constructed six different networks that vary in their edge definitions. Four network modularity algorithms were examined for their ability to detect the traditionally recognized plant communities. The use of more restrictive edge definitions significantly increased the accuracy of community detection, that is, the correspondence between network-based and traditional community classification. Random-walk based modularity methods yielded slightly better results than approaches based on the modularity function. For the whole network, the average agreement between the manual classification and the network-based modules is 76% with varying congruence levels for different communities ranging between 11% and 100%. The network-based approach recovered the known ecological gradient from riverside – sand and gravel bank vegetation – to dryer habitats like semidry grassland on dykes. Our results show that networks modularity algorithms offer new avenues of pursuit for the computational analysis of species communities

The application of long‑read sequencing in clinical settings

Long-read DNA sequencing technologies have been rapidly evolving in recent years, and their ability to assess large and complex regions of the genome makes them ideal for clinical applications in molecular diagnosis and therapy selection, thereby providing a valuable tool for precision medicine. In the third-generation sequencing duopoly, Oxford Nanopore Technologies and Pacific Biosciences work towards increasing the accuracy, throughput, and portability of long-read sequencing methods while trying to keep costs low. These trades have made long-read sequencing an attractive tool for use in research and clinical settings. This article provides an overview of current clinical applications and limitations of long-read sequencing and explores its potential for point-of-care testing and health care in remote settings

Challenges in defining the role of intron retention in normal biology and disease

RNA sequencing has revealed a striking diversity in transcriptomic complexity, to which alternative splicing is a major contributor. Intron retention (IR) is a conserved form of alternative splicing that was originally overlooked in normal mammalian physiology and development, due mostly to difficulties in its detection. IR has recently been revealed as an independent mechanism of controlling and enhancing the complexity of gene expression. IR facilitates rapid responses to biological stimuli, is involved in disease pathogenesis, and can generate novel protein isoforms. Many challenges, however, remain in detecting and quantifying retained introns and in determining their effects on cellular phenotype. In this review, we provide an overview of these challenges, and highlight approaches that can be used to address them

Recent Advances in Cancer Fusion Transcript Detection

Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts

The changing paradigm of intron retention: regulation, ramifications and recipes

Intron retention (IR) is a form of alternative splicing that has long been neglected in mammalian systems although it has been studied for decades in non-mammalian species such as plants, fungi, insects and viruses. It was generally assumed that mis-splicing, leading to the retention of introns, would have no physiological consequence other than reducing gene expression by nonsense-mediated decay. Relatively recent landmark discoveries have highlighted the pivotal role that IR serves in normal and disease-related human biology. Significant technical hurdles have been overcome, thereby enabling the robust detection and quantification of IR. Still, relatively little is known about the cis- and trans-acting modulators controlling this phenomenon. The fate of an intron to be, or not to be, retained in the mature transcript is the direct result of the influence exerted by numerous intrinsic and extrinsic factors at multiple levels of regulation. These factors have altered current biological paradigms and provided unexpected insights into the transcriptional landscape. In this review, we discuss the regulators of IR and methods to identify them. Our focus is primarily on mammals, however, we broaden the scope to non-mammalian organisms in which IR has been shown to be biologically relevant

The intricate interplay between epigenetic events, alternative splicing and noncoding RNA deregulation in colorectal cancer

Colorectal cancer (CRC) results from a transformation of colonic epithelial cells into adenocarcinoma cells due to genetic and epigenetic instabilities, alongside remodelling of the surrounding stromal tumour microenvironment. Epithelial-specific epigenetic variations escorting this process include chromatin remodelling, histone modifications and aberrant DNA methylation, which influence gene expression, alternative splicing and function of non-coding RNA. In this review, we first highlight epigenetic modulators, modifiers and mediators in CRC, then we elaborate on causes and consequences of epigenetic alterations in CRC pathogenesis alongside an appraisal of the complex feedback mechanisms realized through alternative splicing and non-coding RNA regulation. An emphasis in our review is put on how this intricate network of epigenetic and post-transcriptional gene regulation evolves during the initiation, progression and metastasis formation in CRC

Genome scale model reconstruction of the methylotrophic yeast Ogataea polymorpha

Ogataea polymorpha is a thermotolerant, methylotrophic yeast with significant industrial applications. It is a promising host to generate platform chemicals from methanol, derived e.g. from carbon capture and utilization streams. Full development of the organism into a production strain requires additional strain design, supported by metabolic modeling on the basis of a genome-scale metabolic model. However, to date, no genome-scale metabolic model is available for O. polymorpha. To overcome this limitation, we used a published reconstruction of the closely related yeast Pichia pastoris as reference and corrected reactions based on KEGG annotations. Additionally, we conducted phenotype microarray experiments to test O. polymorpha’s metabolic capabilities to grown on or respire 192 different carbon sources. Over three-quarter of the substrate usage was correctly reproduced by the model. However, O. polymorpha failed to metabolize eight substrates and gained 38 new substrates compared to the P. pastoris reference model. To enable the usage of these compounds, metabolic pathways were inferred from literature and database searches and potential enzymes and genes assigned by conducting BLAST searches. To facilitate strain engineering and identify beneficial mutants, gene-protein-reaction relationships need to be included in the model. Again, we used the P. pastoris model as reference to extend the O. polymorpha model with this relevant information. The final metabolic model of O. polymorpha supports the engineering of synthetic metabolic capabilities and enabling the optimization of production processes, thereby supporting a sustainable future methanol econom