Generation of an arrayed CRISPR-Cas9 library targeting epigenetic regulators: from high-content screens to in vivo assays

The CRISPR-Cas9 system has revolutionized genome engineering, allowing precise modification of DNA in various organisms. The most popular method for conducting CRISPR-based functional screens involves the use of pooled lentiviral libraries in selection screens coupled with next-generation sequencing. Screens employing genome-scale pooled small guide RNA (sgRNA) libraries are demanding, particularly when complex assays are used. Furthermore, pooled libraries are not suitable for microscopy-based high-content screens or for systematic interrogation of protein function. To overcome these limitations and exploit CRISPR-based technologies to comprehensively investigate epigenetic mechanisms, we have generated a focused sgRNA library targeting 450 epigenetic regulators with multiple sgRNAs in human cells. The lentiviral library is available both in an arrayed and pooled format and allows temporally-controlled induction of gene knock-out. Characterization of the library showed high editing activity of most sgRNAs and efficient knock-out at the protein level in polyclonal populations. The sgRNA library can be used for both selection and high-content screens, as well as for targeted investigation of selected proteins without requiring isolation of knock-out clones. Using a variety of functional assays we show that the library is suitable for both in vitro and in vivo applications, representing a unique resource to study epigenetic mechanisms in physiological and pathological conditions.     

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.     

Genome-scale analysis of anti-metabolite directed strain engineering

Classic strain engineering methods have previously been limited by the low-throughput of conventional sequencing technology. Here, we applied a new genomics technology, scalar analysis of library enrichments (SCALEs), to measure >3 million Escherichia coli genomic library clone enrichment patterns resulting from growth selections employing three aspartic-acid anti-metabolites. Our objective was to assess the extent to which access to genome-scale enrichment patterns would provide strain-engineering insights not reasonably accessible through the use of conventional sequencing. We determined that the SCALEs method identified a surprisingly large range of anti-metabolite tolerance regions (423, 865, or 909 regions for each of the three anti-metabolites) when compared to the number of regions (1-3 regions) indicated by conventional sequencing. Genome-scale methods uniquely enable the calculation of clone fitness values by providing concentration data for all clones within a genomic library before and after a period of selection. We observed that clone fitness values differ substantially from clone concentration values and that this is due to differences in overall clone fitness distributions for each selection. Finally, we show that many of the clones of highest fitness overlapped across all selections, suggesting that inhibition of aspartate metabolism, as opposed to specific inhibited enzymes, dominated each selection. Our follow up studies confirmed our observed growth phenotypes and showed that intracellular amino-acid levels were also altered in several of the identified clones. These results demonstrate that genome-scale methods, such as SCALEs, can be used to dramatically improve understanding of classic strain engineering approaches.     

Counting absolute numbers of molecules using unique molecular identifiers

Counting individual RNA or DNA molecules is difficult because they are hard to copy quantitatively for detection. To overcome this limitation, we applied unique molecular identifiers (UMIs), which make each molecule in a population distinct, to genome-scale human karyotyping and mRNA sequencing in Drosophila melanogaster. Use of this method can improve accuracy of almost any next-generation sequencing method, including chromatin immunoprecipitation-sequencing, genome assembly, diagnostics and manufacturing-process control and monitoring.     

Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera

Background

Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited.

Results

To investigate the metabolism of N. meningitidis we generated and then selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogen's biology we constructed a genome-scale metabolic network: Nmb_iTM560. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the Nmb_iTM560 model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium, such as a requirement for phosphoenolpyruvate carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids.

Conclusions

This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight into the pathogen's metabolism both in vitro and during infection.     

Genetic Architecture of Intrinsic Antibiotic Susceptibility

Background

Antibiotic exposure rapidly selects for more resistant bacterial strains, and both a drug''s chemical structure and a bacterium''s cellular network affect the types of mutations acquired.

Methodology/Principal Findings

To better characterize the genetic determinants of antibiotic susceptibility, we exposed a transposon-mutagenized library of Escherichia coli to each of 17 antibiotics that encompass a wide range of drug classes and mechanisms of action. Propagating the library for multiple generations with drug concentrations that moderately inhibited the growth of the isogenic parental strain caused the abundance of strains with even minor fitness advantages or disadvantages to change measurably and reproducibly. Using a microarray-based genetic footprinting strategy, we then determined the quantitative contribution of each gene to E. coli''s intrinsic antibiotic susceptibility. We found both loci whose removal increased general antibiotic tolerance as well as pathways whose down-regulation increased tolerance to specific drugs and drug classes. The beneficial mutations identified span multiple pathways, and we identified pairs of mutations that individually provide only minor decreases in antibiotic susceptibility but that combine to provide higher tolerance.

Conclusions/Significance

Our results illustrate that a wide-range of mutations can modulate the activity of many cellular resistance processes and demonstrate that E. coli has a large mutational target size for increasing antibiotic tolerance. Furthermore, the work suggests that clinical levels of antibiotic resistance might develop through the sequential accumulation of chromosomal mutations of small individual effect.     

Museum genomics: low-cost and high-accuracy genetic data from historical specimens

Natural history collections are unparalleled repositories of geographical and temporal variation in faunal conditions. Molecular studies offer an opportunity to uncover much of this variation; however, genetic studies of historical museum specimens typically rely on extracting highly degraded and chemically modified DNA samples from skins, skulls or other dried samples. Despite this limitation, obtaining short fragments of DNA sequences using traditional PCR amplification of DNA has been the primary method for genetic study of historical specimens. Few laboratories have succeeded in obtaining genome-scale sequences from historical specimens and then only with considerable effort and cost. Here, we describe a low-cost approach using high-throughput next-generation sequencing to obtain reliable genome-scale sequence data from a traditionally preserved mammal skin and skull using a simple extraction protocol. We show that single-nucleotide polymorphisms (SNPs) from the genome sequences obtained independently from the skin and from the skull are highly repeatable compared to a reference genome.     

Parallel mapping of genotypes to phenotypes contributing to overall biological fitness

Laboratory selection is a powerful approach for engineering new traits in metabolic engineering applications. This approach is limited because determining the genetic basis of improved strains can be difficult using conventional methods. We have recently reported a new method that enables the measurement of fitness for all clones contained within comprehensive genomic libraries, thus enabling the genome-scale mapping of fitness altering genes. Here, we demonstrate a strategy for relating these measurements to the individual phenotypes selected for in a particular environment. We first provide a mathematical framework for decomposing fitness into selectable phenotypes. We then employed this framework to predict that single-batch selections would enrich primarily for library clones with increased growth rate, serial-batch would enrich for a broad collection of clones enhanced via a combination of increased growth rate and/or reduced lag times, and that overlap among selected clones would be minimal. We used the SCalar Analysis of Library Enrichments (SCALEs) method to test these predictions. We mapped all genomic regions for which increased copy number conferred a selective advantage to Escherichia coli when cultured via single- or serial-batch in the presence of 1-naphthol. We identified a surprisingly large collection (163 total) of tolerance regions, including all previously identified solvent tolerance genes in E. coli. We show that the majority of the identified regions were unique to the different selection strategies examined and that such differences were indeed due to differences among enriched clones in growth rate and lag times over the solvent concentrations examined. The combination of a framework for decomposing overall fitness into selectable phenotypes along with a genome-scale method for mapping genes to such phenotypes lays the groundwork for improving the rational design of laboratory selections.     

Engineering improved ethanol production in Escherichia coli with a genome-wide approach

A key challenge to the commercial production of commodity chemical and fuels is the toxicity of such molecules to the microbial host. While a number of studies have attempted to engineer improved tolerance for such compounds, the majority of these studies have been performed in wild-type strains and culturing conditions that differ considerably from production conditions. Here we applied the multiscalar analysis of library enrichments (SCALEs) method and performed a growth selection in an ethanol production system to quantitatively map in parallel all genes in the genome onto ethanol tolerance and production. In order to perform the selection in an ethanol-producing system, we used a previously engineered Escherichia coli ethanol production strain (LW06; ATCC BAA-2466) (Woodruff et al., in press), as the host strain for the multiscalar genomic library analysis (>10⁶ clones for each library of 1, 2, or 4 kb overlapping genomic fragments). By testing individually selected clones, we confirmed that growth selections enriched for clones with both improved ethanol tolerance and production phenotypes. We performed combinatorial testing of the top genes identified (uspC, otsA, otsB) to investigate their ability to confer improved ethanol tolerance or ethanol production. We determined that overexpression of otsA was required for improved tolerance and productivity phenotypes, with the best performing strains showing up to 75% improvement relative to the parent production strain.     

Genomic Treasure Troves: Complete Genome Sequencing of Herbarium and Insect Museum Specimens

Unlocking the vast genomic diversity stored in natural history collections would create unprecedented opportunities for genome-scale evolutionary, phylogenetic, domestication and population genomic studies. Many researchers have been discouraged from using historical specimens in molecular studies because of both generally limited success of DNA extraction and the challenges associated with PCR-amplifying highly degraded DNA. In today''s next-generation sequencing (NGS) world, opportunities and prospects for historical DNA have changed dramatically, as most NGS methods are actually designed for taking short fragmented DNA molecules as templates. Here we show that using a standard multiplex and paired-end Illumina sequencing approach, genome-scale sequence data can be generated reliably from dry-preserved plant, fungal and insect specimens collected up to 115 years ago, and with minimal destructive sampling. Using a reference-based assembly approach, we were able to produce the entire nuclear genome of a 43-year-old Arabidopsis thaliana (Brassicaceae) herbarium specimen with high and uniform sequence coverage. Nuclear genome sequences of three fungal specimens of 22–82 years of age (Agaricus bisporus, Laccaria bicolor, Pleurotus ostreatus) were generated with 81.4–97.9% exome coverage. Complete organellar genome sequences were assembled for all specimens. Using de novo assembly we retrieved between 16.2–71.0% of coding sequence regions, and hence remain somewhat cautious about prospects for de novo genome assembly from historical specimens. Non-target sequence contaminations were observed in 2 of our insect museum specimens. We anticipate that future museum genomics projects will perhaps not generate entire genome sequences in all cases (our specimens contained relatively small and low-complexity genomes), but at least generating vital comparative genomic data for testing (phylo)genetic, demographic and genetic hypotheses, that become increasingly more horizontal. Furthermore, NGS of historical DNA enables recovering crucial genetic information from old type specimens that to date have remained mostly unutilized and, thus, opens up a new frontier for taxonomic research as well.     

Illuminating Choices for Library Prep: A Comparison of Library Preparation Methods for Whole Genome Sequencing of Cryptococcus neoformans Using Illumina HiSeq

The industry of next-generation sequencing is constantly evolving, with novel library preparation methods and new sequencing machines being released by the major sequencing technology companies annually. The Illumina TruSeq v2 library preparation method was the most widely used kit and the market leader; however, it has now been discontinued, and in 2013 was replaced by the TruSeq Nano and TruSeq PCR-free methods, leaving a gap in knowledge regarding which is the most appropriate library preparation method to use. Here, we used isolates from the pathogenic fungi Cryptococcus neoformans var. grubii and sequenced them using the existing TruSeq DNA v2 kit (Illumina), along with two new kits: the TruSeq Nano DNA kit (Illumina) and the NEBNext Ultra DNA kit (New England Biolabs) to provide a comparison. Compared to the original TruSeq DNA v2 kit, both newer kits gave equivalent or better sequencing data, with increased coverage. When comparing the two newer kits, we found little difference in cost and workflow, with the NEBNext Ultra both slightly cheaper and faster than the TruSeq Nano. However, the quality of data generated using the TruSeq Nano DNA kit was superior due to higher coverage at regions of low GC content, and more SNPs identified. Researchers should therefore evaluate their resources and the type of application (and hence data quality) being considered when ultimately deciding on which library prep method to use.     

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Here we outline a next-generation RNA sequencing protocol that enables de novo assemblies and intra-host variant calls of viral genomes collected from clinical and biological sources. The method is unbiased and universal; it uses random primers for cDNA synthesis and requires no prior knowledge of the viral sequence content. Before library construction, selective RNase H-based digestion is used to deplete unwanted RNA — including poly(rA) carrier and ribosomal RNA — from the viral RNA sample. Selective depletion improves both the data quality and the number of unique reads in viral RNA sequencing libraries. Moreover, a transposase-based ''tagmentation'' step is used in the protocol as it reduces overall library construction time. The protocol has enabled rapid deep sequencing of over 600 Lassa and Ebola virus samples-including collections from both blood and tissue isolates-and is broadly applicable to other microbial genomics studies.     

Ribosome-binding and anti-microbial studies of the mycinamicins, 16-membered macrolide antibiotics from Micromonospora griseorubida

Macrolides have been effective clinical antibiotics for over 70 years. They inhibit protein biosynthesis in bacterial pathogens by narrowing the nascent protein exit tunnel in the ribosome. The macrolide class of natural products consist of a macrolactone ring linked to one or more sugar molecules. Most of the macrolides used currently are semi-synthetic erythromycin derivatives, composed of a 14- or 15-membered macrolactone ring. Rapidly emerging resistance in bacterial pathogens is among the most urgent global health challenges, which render many antibiotics ineffective, including next-generation macrolides. To address this threat and advance a longer-term plan for developing new antibiotics, we demonstrate how 16-membered macrolides overcome erythromycin resistance in clinically isolated Staphylococcus aureus strains. By determining the structures of complexes of the large ribosomal subunit of Deinococcus radiodurans (D50S) with these 16-membered selected macrolides, and performing anti-microbial studies, we identified resistance mechanisms they may overcome. This new information provides important insights toward the rational design of therapeutics that are effective against drug resistant human pathogens.     

Insights into identifying resistance genes for cold and disease stresses through chromosome-level reference genome analyses of Poncirus polyandra

Poncirus polyandra, a plant species with extremely small populations in China, has become extinct in the wild. This study aimed to identify functional genes that improve tolerance to abiotic and biotic stresses. Here, we present a high-quality chromosome-scale reference genome of P. polyandra. The reference genome is 315.78 Mb in size, with an N50 scaffold size of 32.07 Mb, and contains nine chromosomes with 20,815 protein-coding genes, covering 97.82% of the estimated gene space. We identified 17 rapidly evolving nucleotide-binding-site (NBS) genes, three C-repeat-binding factors (CBF) genes, 19 citrus greening disease (Huanglongbing, HLB) tolerance genes, 11 citrus tristeza virus (CTV) genes, and one citrus nematode resistance gene. A divergence time of 1.96 million years ago was estimated between P. polyandra and P. trifoliata. This is the first genome-scale assembly and annotation of P. polyandra, which will be useful for genetic, genomic, and molecular research and provide guidance for the development of conservation strategies.     

A clustering method for next-generation sequences of bacterial genomes through multiomics data mapping

With various ‘omics’ data becoming available recently, new challenges and opportunities are provided for researches on the assembly of next-generation sequences. As an attempt to utilize novel opportunities, we developed a next-generation sequence clustering method focusing on interdependency between genomics and proteomics data. Under the assumption that we can obtain next-generation read sequences and proteomics data of a target species, we mapped the read sequences against protein sequences and found physically adjacent reads based on a machine learning-based read assignment method. We measured the performance of our method by using simulated read sequences and collected protein sequences of Escherichia coli (E. coli). Here, we concentrated on the actual adjacency of the clustered reads in the E. coli genome and found that (i) the proposed method improves the performance of read clustering and (ii) the use of proteomics data does have a potential for enhancing the performance of genome assemblers. These results demonstrate that the integrative approach is effective for the accurate grouping of adjacent reads in a genome, which will result in a better genome assembly.