Statistics and Evolution of Functional Genomic Sequence

In this thesis, three separate problems of genomics are addressed, utilizing methods related to the field of statistical mechanics. The goal of the project discussed in the first chapter is the elucidation of post-transcriptional gene regulation imposed by microRNAs, a recently discovered class of tiny non-coding RNAs. A probabilistic algorithm for the computational identification of genes regulated by microRNAs is introduced, which was developed based on experimental data and statistical analysis of whole genome data. In particular, the application of this algorithm to multiple-alignments of groups of related species allows for the specific and sensitive detection of genes targeted by microRNAs on a genome-wide level. Examination of clade-specific predictions and cross-clade comparison yields deeper insights into microRNA biology and first clues about long-term evolution of microRNA regulation, which are discussed in detail. Modeling evolutionary dynamics of microsatellites, an abundant class of repetitive sequence in eukaryotic genomes, was the objective of the second project and is discussed in chapter two. Inspired by the putative functionality of some of these elements and the difficulty of constructing correct sequence alignments that reflect the evolutionary relationships between microsatellites, a neutral model for microsatellite evolution is developed and tested in the fruit fly Drosophila melanogaster by comparing evolutionary rates predicted by the model to independent measurements of these rates from multiple alignments of three closely relates Drosophila species. The model is applied separately to genomic sequence categories of different functional annotations in order to assess the varying influence of selective constraint among these categories. In the last chapter, a general population genetic model is introduced that allows for the determination of transcription factor binding site stability as a function of selection strength, mutation rate and effective population size at arbitrary values of these parameters. The analytical solution of this model indicates the probability of a binding site to be functional. The model is used to compute the population fraction of functional binding sites at fixed selection pressure across a variety of different taxa. The results lead to the conclusion that a decreasing effective population size, such as observed at the evolutionary transition from prokaryotes to eukaryotes, could result in loss of binding site stability. An extension to our model serves us to assess the compensatory effect of the emergence of multiple binding sites for the same transcription factor in order to maintain the existing regulatory relationship

microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu

A Genome-Wide Map of Conserved MicroRNA Targets in C. elegans

SummaryBackgroundMetazoan miRNAs regulate protein-coding genes by binding the 3′ UTR of cognate mRNAs. Identifying targets for the 115 known C. elegans miRNAs is essential for understanding their function.ResultsBy using a new version of PicTar and sequence alignments of three nematodes, we predict that miRNAs regulate at least 10% of C. elegans genes through conserved interactions. We have developed a new experimental pipeline to assay 3′ UTR-mediated posttranscriptional gene regulation via an endogenous reporter expression system amenable to high-throughput cloning, demonstrating the utility of this system using one of the most intensely studied miRNAs, let-7. Our expression analyses uncover several new potential let-7 targets and suggest a new let-7 activity in head muscle and neurons. To explore genome-wide trends in miRNA function, we analyzed functional categories of predicted target genes, finding that one-third of C. elegans miRNAs target gene sets are enriched for specific functional annotations. We have also integrated miRNA target predictions with other functional genomic data from C. elegans.ConclusionsAt least 10% of C. elegans genes are predicted miRNA targets, and a number of nematode miRNAs seem to regulate biological processes by targeting functionally related genes. We have also developed and successfully utilized an in vivo system for testing miRNA target predictions in likely endogenous expression domains. The thousands of genome-wide miRNA target predictions for nematodes, humans, and flies are available from the PicTar website and are linked to an accessible graphical network-browsing tool allowing exploration of miRNA target predictions in the context of various functional genomic data resources

Patentability, R&D direction, and cumulative innovation

We present a model of cumulative innovation where firms can conduct R&D in both a safe and a risky direction. Innovations in the risky direction produce quality improvements with higher expected sizes and variances. As patentability standards rise, an innovation in the risky direction is less likely to receive a patent that replaces the current technology, which decreases the static incentive for new entrants to conduct risky R&D, but increases their dynamic incentive because of the longer duration---and hence higher reward---for incumbency. These, together with a strategic substitution and a market structure effect, result in an inverted-U shape in the risky direction but a U shape in the safe direction for the relationship between R&D intensity and patentability standards. There exists a patentability standard that induces the efficient innovation direction, whereas R&D is biased towards (against) the risky direction under lower (higher) standards. The optimal patentability standard may distort the R&D direction to increase the industry innovation rate that is socially deficient

Chemotherapy-induced transposable elements activate MDA5 to enhance haematopoietic regeneration.

Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/10.13039/501100000265Funder: Max-Planck-Gesellschaft (Max Planck Society); doi: https://doi.org/10.13039/501100004189Haematopoietic stem cells (HSCs) are normally quiescent, but have evolved mechanisms to respond to stress. Here, we evaluate haematopoietic regeneration induced by chemotherapy. We detect robust chromatin reorganization followed by increased transcription of transposable elements (TEs) during early recovery. TE transcripts bind to and activate the innate immune receptor melanoma differentiation-associated protein 5 (MDA5) that generates an inflammatory response that is necessary for HSCs to exit quiescence. HSCs that lack MDA5 exhibit an impaired inflammatory response after chemotherapy and retain their quiescence, with consequent better long-term repopulation capacity. We show that the overexpression of ERV and LINE superfamily TE copies in wild-type HSCs, but not in Mda5-/- HSCs, results in their cycling. By contrast, after knockdown of LINE1 family copies, HSCs retain their quiescence. Our results show that TE transcripts act as ligands that activate MDA5 during haematopoietic regeneration, thereby enabling HSCs to mount an inflammatory response necessary for their exit from quiescence

Publisher Correction: Chemotherapy-induced transposable elements activate MDA5 to enhance haematopoietic regeneration.

Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/10.13039/501100000265Funder: Max-Planck-Gesellschaft (Max Planck Society); doi: https://doi.org/10.13039/50110000418

Design and Analysis of Single-Cell Sequencing Experiments

Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution. Sequencing the genome of individual cells can reveal somatic mutations and allows the investigation of clonal dynamics. Single-cell transcriptome sequencing can elucidate the cell type composition of a sample. However, single-cell sequencing comes with major technical challenges and yields complex data output. In this Primer, we provide an overview of available methods and discuss experimental design and single-cell data analysis. We hope that these guidelines will enable a growing number of researchers to leverage the power of single-cell sequencing

Validation of noise models for single-cell transcriptomics

Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition

Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration

Abstract Microglia are brain immune cells that constantly survey their environment to maintain homeostasis. Enhanced microglial reactivity and proliferation are typical hallmarks of neurodegenerative diseases. Whether specific disease-linked microglial subsets exist during the entire course of neurodegeneration, including the recovery phase, is currently unclear. Taking a single-cell RNA-sequencing approach in a susceptibility gene-free model of nerve injury, we identified a microglial subpopulation that upon acute neurodegeneration shares a conserved gene regulatory profile compared to previously reported chronic and destructive neurodegeneration transgenic mouse models. Our data also revealed rapid shifts in gene regulation that defined microglial subsets at peak and resolution of neurodegeneration. Finally, our discovery of a unique transient microglial subpopulation at the onset of recovery may provide novel targets for modulating microglia-mediated restoration of brain health

Mapping the physical network of cellular interactions

A cell's function is influenced by the environment, or niche, in which it resides. Studies of niches usually require assumptions about the cell types present, which impedes the discovery of new cell types or interactions. Here we describe ProximID, an approach for building a cellular network based on physical cell interaction and single-cell mRNA sequencing, and show that it can be used to discover new preferential cellular interactions without prior knowledge of component cell types. ProximID found specific interactions between megakaryocytes and mature neutrophils and between plasma cells and myeloblasts and/or promyelocytes (precursors of neutrophils) in mouse bone marrow, and it identified a Tac1+ enteroendocrine cell-Lgr5+ stem cell interaction in small intestine crypts. This strategy can be used to discover new niches or preferential interactions in a variety of organs