Biological insights from RIL-seq in bacteria

Bacteria reside in constantly changing environments and require rapid and precise adjustments of gene expression to ensure survival. Small regulatory RNAs (sRNAs) are a crucial element that bacteria utilize to achieve this. sRNAs are short RNA molecules that modulate gene expression usually through base-pairing interactions with target RNAs, primarily mRNAs. These interactions can lead to either negative outcomes such as mRNA degradation or translational repression or positive outcomes such as mRNA stabilization or translation enhancement. In recent years, high-throughput approaches such as RIL-seq (RNA interaction by ligation and sequencing) revolutionized the sRNA field by enabling the identification of sRNA targets on a global scale, unveiling intricate sRNA-RNA networks. In this review, we discuss the insights gained from investigating sRNA-RNA networks in well-studied bacterial species as well as in under-studied bacterial species. Having a complete understanding of sRNA-mediated regulation is critical for the development of new strategies for controlling bacterial growth and combating bacterial infections.Comment: 20 pages, 2 tables, 4 figure

Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification

Understanding genome organization and gene regulation requires insight into RNA transcription, processing and modification. We adapted nanopore direct RNA sequencing to examine RNA from a wild-type accession of the model plant Arabidopsis thaliana and a mutant defective in mRNA methylation (m6A). Here we show that m6A can be mapped in full-length mRNAs transcriptome-wide and reveal the combinatorial diversity of cap-associated transcription start sites, splicing events, poly(A) site choice and poly(A) tail length. Loss of m6A from 3’ untranslated regions is associated with decreased relative transcript abundance and defective RNA 30 end formation. A functional consequence of disrupted m6A is a lengthening of the circadian period. We conclude that nanopore direct RNA sequencing can reveal the complexity of mRNA processing and modification in full-length single molecule reads. These findings can refine Arabidopsis genome annotation. Further, applying this approach to less well-studied species could transform our understanding of what their genomes encode

Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features

MicroRNAs (small ~22 nucleotide long non-coding endogenous RNAs) have recently attracted immense attention as critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Recent studies have proved that viruses also express microRNAs, which are thought to contribute to the intricate mechanisms of host-pathogen interactions. Computational predictions have greatly accelerated the discovery of microRNAs. However, most of these widely used tools are dependent on structural features and sequence conservation which limits their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs. In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors. The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals

A Upf3b-mutant mouse model with behavioral and neurogenesis defects.

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA degradation pathway that acts on RNAs terminating their reading frames in specific contexts. NMD is regulated in a tissue-specific and developmentally controlled manner, raising the possibility that it influences developmental events. Indeed, loss or depletion of NMD factors have been shown to disrupt developmental events in organisms spanning the phylogenetic scale. In humans, mutations in the NMD factor gene, UPF3B, cause intellectual disability (ID) and are strongly associated with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia (SCZ). Here, we report the generation and characterization of mice harboring a null Upf3b allele. These Upf3b-null mice exhibit deficits in fear-conditioned learning, but not spatial learning. Upf3b-null mice also have a profound defect in prepulse inhibition (PPI), a measure of sensorimotor gating commonly deficient in individuals with SCZ and other brain disorders. Consistent with both their PPI and learning defects, cortical pyramidal neurons from Upf3b-null mice display deficient dendritic spine maturation in vivo. In addition, neural stem cells from Upf3b-null mice have impaired ability to undergo differentiation and require prolonged culture to give rise to functional neurons with electrical activity. RNA sequencing (RNAseq) analysis of the frontal cortex identified UPF3B-regulated RNAs, including direct NMD target transcripts encoding proteins with known functions in neural differentiation, maturation and disease. We suggest Upf3b-null mice serve as a novel model system to decipher cellular and molecular defects underlying ID and neurodevelopmental disorders

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

Witnessing the Evolution of RNA

The RNA World theory postulates that at a certain point around 4 billion years ago during an RNA World period, a molecule, possibly RNA, developed the ability to self-replicate via polymerization. The theory also suggests that all extant life evolved from this initial RNA-based predecessor. No geological evidence from this RNA World period remains, and a self-replicating RNA polymerase has yet to be discovered or created. However, there is ample evidence from extant life pointing to an RNA-based predecessor. For example, the ribosome is a ribozyme, RNA functions as precursor signaling molecules, riboswitches regulate transcription and translation, and many more. For billions of years and even today, natural selection and the physical laws of nature direct and select molecules deemed fit for the pressure present in their environment. In this way, RNA has been evolving in darkness, unwitnessed by the very entities that are comprised of and still in part regulated by it. With advancements in next-generation sequencing and nucleic acid selections, scientists now witness novel RNA functions that sometimes are accompanied by large structural changes to the RNA secondary structure. Using techniques like in vitro and in vivo selections, we can now control the selection pressure on these molecules. We can now evaluate selections with next-generation sequencing and machine learning to witness RNA evolution in action.      In the past thirty years, scientists have made major contributions towards the selection of a self-replicating polymerase molecule. In 1995, scientists used in vitro selections to create the class I ligase, an RNA that could join two separate strands of RNA through covalent phosphodiester linking. The ligase was selected from a pool of random RNA sequences, much like there would have been on the early earth during this RNA World period. Through further selection, the class I ligase evolved into the molecule termed R18, which had the ability to polymerize from an RNA primer on an RNA template. From the R18 molecule, multiple research groups developed branching RNA polymerase ribozyme lineages, all with the common goal of selecting for a self-replicating molecule. In addition to these branching lineages, non-enzymatic assembly of polynucleotides has also been developed. Despite the significant effort placed in selecting for a self-replicating RNA, such a molecule remains elusive. To understand the role that RNA evolution has played in the development of extant life, we must first understand how RNA evolved to encompass all the roles it serves in the multitude of functions in life today. My colleagues designed a modified version of the R18 polymerase ribozyme, deemed “WT,” which served as the starting sequence for their selections. To evolve the WT polymerase, they developed selection strategies that utilized functional RNAs such as aptamers and self-cleaving ribozymes. They then carried out 52 rounds of either aptamer or self-cleaver selections on this WT population. Every few rounds, a small number of polymerase variants were cloned out of the evolved population. An even smaller number of polymerase variants were biochemically validated to determine if they had increased in polymerization rate. Rather than validating a few cloned sequences, I developed a bioinformatic pipeline that resulted in the ability to tally, align, and cluster all variant sequences in a given selection population. I used the bioinformatic pipeline on every few rounds of selection within the WT lineage, tallying and tracking the frequency of RNA sequence variations over 52 rounds of selection. I then used this method to validate a novel RNA secondary structure pseudoknot rearrangement, termed P8, in the polymerase population.       I subsequently validated the novel secondary structure rearrangement by using in line probing, an in vitro biochemical technique used to determine an RNA’s secondary structure or interaction with another molecule or ligand. The secondary structure for six variant sequences that were pulled from the 52 rounds of selection allowed us to witness how the novel secondary structure pseudoknot gradually evolved to a greater fitness peak. Ribozymes, in particular the RNA polymerase ribozyme, are thought to occupy high and isolated fitness peaks that are tied to the molecule’s secondary structural elements. Because these secondary structural elements are tightly associated with the ribozyme’s optimized fitness peak, exploring alternative structures generally leads to severe negative consequences for fitness. With the bioinformatics pipeline mentioned above, I clustered highly represented variants by the sequence of their P8 region. The P8 pseudoknot structure spontaneously emerged during the evolution process and was optimized and conserved after 28 rounds of selection. Next, I transplanted the novel P8 pseudoknot from the 52-2 variant into the WT sequence. The results of that experiment show that the P8 was necessary, but not sufficient to improve the WT catalytic activity to the 52-2 variant’s capacity. The results showed that the novel P8 region was indeed a jump to a higher fitness local. To my knowledge and after thorough analysis of the literature of the field, this is the first RNA secondary structure remodeling that has been validated and witnessed mid-evolution in a synthetically evolved RNA. Additionally, no such secondary structure remodeling of a natural RNA has been observed. Witnessing the evolution of RNA either synthetic or from nature provides a powerful means of control and understanding our RNA ancestors, our current RNA components, and any future RNA evolution target we select.       Contained within this document I provide a review of instances where RNA evolution has been witnessed, starting 4 billion years ago following the proposed end of an RNA World transition from RNA- to DNA-protein based life, to the present time. Advances in in vitro/vivo selections and next-generation sequencing reveal RNA evolution in action today. Described are instances where scientists have witnessed natural RNA evolution and synthetic RNA evolution, providing evidence for a prehistoric RNA World and a path forward for future RNA evolution advancements. From this breadth of literature, it would appear that the RNA World continues today.      Following this review, I outline my discovery of an RNA polymerase ribozyme that underwent the first observed structural rearrangement of a synthetic RNA, which resulted in an increase in its activity. Furthermore, the RNA polymerase can now synthesize a full length, active copy of its ancestral molecule the class I ligase. While there are other examples of RNA polymerase lineages from other research groups that are mid-evolution, this lineage that I present is the first to catalog a structural rearrangement. I developed bioinformatic means to track the evolution of the RNA polymerase ribozyme. This bioinformatic pipeline can be developed further to track any synthetic or natural RNA evolution over many generations and it provides the foundation to work toward a self-replicating RNA by enabling scientists to design more informed selections. The inevitable discovery of a self-replicating RNA will serve as incontrovertible evidence that RNA has the capacity to initiate darwinian evolution and may demonstrate a possible route to the discovery of the origin of life as we understand it on earth

Quantitative Approaches to the Genomics of Clonal Evolution

Many problems in the biological sciences reduce to questions of genetic evolution. Entire classes of medical pathology, such as malignant neoplasia or infectious disease, can be viewed in the light of Darwinian competition of genomes. With the benefit of today's maturing sequencing technologies we can observe and quantify genetic evolution with nucleotide resolution. This provides a molecular view of genetic material that has adapted, or is in the process of adapting, to its local selection pressures. A series of problems will be discussed in this thesis, all involving the mathematical modeling of genomic data derived from clonally evolving populations. We use a variety of computational approaches to characterize over-represented features in the data, with the underlying hypothesis that we may be detecting fitness-conferring features of the biology. In Part I we consider the cross-sectional sampling of human tumors via RNA-sequencing, and devise computational pipelines for detecting oncogenic gene fusions and oncovirus infections. Genomic translocation and oncovirus infection can each be a highly penetrant alteration in a tumor's evolutionary history, with famous examples of both populating the cancer biology literature. In order to exert a transforming influence over the host cell, gene fusions and viral genetic programs need to be expressed and thus can be detected via whole transcriptome sequencing of a malignant cell population. We describe our approaches to predicting oncogenic gene fusions (Chapter 2) and quantifying host-viral interactions (Chapter 3) in large panels of human tumor tissue. The alterations that we characterize prompt the larger question of how the genetics of tumors and viruses might vary in time, leading us to the study of serially sampled populations. In Part II we consider longitudinal sampling of a clonally evolving population. Phylogenetic trees are the standard representation of a clonal process, an evolutionary picture as old as Darwin's voyages on the Beagle. Chapter 4 first reviews phylogenetic inference and then introduces a certain phylogenetic tree space that forms the starting point of our work on the topic. Specifically, Chapter 4 describes the construction of our projective tree space along with an explicit implementation for visualizing point clouds of rescaled trees. The Chapter finishes by defining a method for stable dimensionality reduction of large phylogenies, which is useful for analyzing long genomic time series. In Chapter 5 we consider medically relevant instances of clonal evolution and the longitudinal genetic data sets to which they give rise. We analyze data from (i) the sequencing of cancers along their therapeutic course, (ii) the passaging of a xenografted tumor through a mouse model, and (iii) the seasonal surveillance of H3N2 influenza's hemagglutinin segment. A novel approach to predicting influenza vaccine effectiveness is demonstrated using statistics of point clouds in tree spaces. Our investigations into clonal processes may be extended beyond naturally occurring genomes. In Part III we focus on the directed clonal evolution of populations of synthetic RNAs in vitro. Analogous to the selection pressures exerted upon malignant cells or viral particles, these synthetic RNA genomes can be evolved against a desired fitness objective. We investigate fitness objectives related to reprogramming ribosomal translation. Chapter 6 identifies high fitness RNA pseudoknot geometries capable of inducing ribosomal frameshift, while Chapter 7 takes an unbiased approach to evolving sequence and structural elements that promote stop codon readthrough

Allotopic RNA expression strategy to rescue an endogenous mitochondrial ATP6[1] mutation in Drosophila

Mitochondria are essential organelles in the cell. One of their most critical functions is the generation of cellular energy in the form of ATP. The presence of DNA in the mitochondrial matrix makes this organelle semi-autonomous. However, it relies heavily on the nucleus and cytosol to import ~99% of its proteins and some RNA molecules for its normal functioning. Mutations in the mitochondrial DNA (mtDNA) cause several devastating disorders. Due to their complexity and our incomplete understanding of mitochondrial disease pathogenesis, these disorders are difficult to diagnose and currently no pharmacological treatment exists. Further, gene therapy for these devastating disorders is impeded due to lack of mitochondrial genome manipulation techniques. Understanding the mechanism of pathogenesis and developing mtDNA manipulation strategies are key to developing remedial therapies. In my thesis, I investigated an RNA allotopic strategy of targeting RNA into the mitochondria in vivo in flies. In my first aim, I improved an in vivo mitochondrial-targeting tool (mtTRES vector) to manipulate proteins encoded by the mitochondrial DNA. This vector integrates into the nuclear genome and results in the transcription of a chimeric RNA consisting of a mitochondrial targeting signal sequence and a small non-coding antisense RNA. Previous studies have attempted allotopic expression via both protein and RNA import with mixed results. Only a few of them, however, have been tested in vivo and none have been examined for rescue in an animal model of mitochondrial disease. Since our lab has a well characterized mtDNA mutation fly model, ATP6[1], I had a unique opportunity to investigate rescue strategies in these models. In my second aim, I improved a unique set of mtTRESPro vectors for both flies and humans to target long coding RNAs into mitochondria. Once imported these long RNAs are designed to be endogenously translated in mitochondria. By targeting a wild type copy of the mutant ATP6 gene, I explored the rescuing potential of allotopic RNA import in vivo. Our data suggest the mtTRES and mtTRESPro mitochondrial manipulation tools have genuine potential to be developed into a mitochondrial disease gene therapy

Discovering cancer-associated transcripts by RNA sequencing

High-throughput sequencing of poly-adenylated RNA (RNA-Seq) in human cancers shows remarkable potential to identify uncharacterized aspects of tumor biology, including gene fusions with therapeutic significance and disease markers such as long non-coding RNA (lncRNA) species. However, the analysis of RNA-Seq data places unprecedented demands upon computational infrastructures and algorithms, requiring novel bioinformatics approaches. To meet these demands, we present two new open-source software packages - ChimeraScan and AssemblyLine - designed to detect gene fusion events and novel lncRNAs, respectively. RNA-Seq studies utilizing ChimeraScan led to discoveries of new families of recurrent gene fusions in breast cancers and solitary fibrous tumors. Further, ChimeraScan was one of the key components of the repertoire of computational tools utilized in data analysis for MI-ONCOSEQ, a clinical sequencing initiative to identify potentially informative and actionable mutations in cancer patients’ tumors. AssemblyLine, by contrast, reassembles RNA sequencing data into full-length transcripts ab initio. In head-to-head analyses AssemblyLine compared favorably to existing ab initio approaches and unveiled abundant novel lncRNAs, including antisense and intronic lncRNAs disregarded by previous studies. Moreover, we used AssemblyLine to define the prostate cancer transcriptome from a large patient cohort and discovered myriad lncRNAs, including 121 prostate cancer-associated transcripts (PCATs) that could potentially serve as novel disease markers. Functional studies of two PCATs - PCAT-1 and SChLAP1 - revealed cancer-promoting roles for these lncRNAs. PCAT1, a lncRNA expressed from chromosome 8q24, promotes cell proliferation and represses the tumor suppressor BRCA2. SChLAP1, located in a chromosome 2q31 ‘gene desert’, independently predicts poor patient outcomes, including metastasis and cancer-specific mortality. Mechanistically, SChLAP1 antagonizes the genome-wide localization and regulatory functions of the SWI/SNF chromatin-modifying complex. Collectively, this work demonstrates the utility of ChimeraScan and AssemblyLine as open-source bioinformatics tools. Our applications of ChimeraScan and AssemblyLine led to the discovery of new classes of recurrent and clinically informative gene fusions, and established a prominent role for lncRNAs in coordinating aggressive prostate cancer, respectively. We expect that the methods and findings described herein will establish a precedent for RNA-Seq-based studies in cancer biology and assist the research community at large in making similar discoveries.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120814/1/mkiyer_1.pd