CRISPRi screen for enhancing heterologous α-amylase yield in Bacillus subtilis.

Yield improvements in cell factories can potentially be obtained by fine-tuning the regulatory mechanisms for gene candidates. In pursuit of such candidates, we performed RNA-sequencing of two α-amylase producing Bacillus strains and predict hundreds of putative novel non-coding transcribed regions. Surprisingly, we found among hundreds of non-coding and structured RNA candidates that non-coding genomic regions are proportionally undergoing the highest changes in expression during fermentation. Since these classes of RNA are also understudied, we targeted the corresponding genomic regions with CRIPSRi knockdown to test for any potential impact on the yield. From differentially expression analysis, we selected 53 non-coding candidates. Although CRISPRi knockdowns target both the sense and the antisense strand, the CRISPRi experiment cannot link causes for yield changes to the sense or antisense disruption. Nevertheless, we observed on several instances with strong changes in enzyme yield. The knockdown targeting the genomic region for a putative antisense RNA of the 3' UTR of the skfA-skfH operon led to a 21% increase in yield. In contrast, the knockdown targeting the genomic regions of putative antisense RNAs of the cytochrome c oxidase subunit 1 (ctaD), the sigma factor sigH, and the uncharacterized gene yhfT decreased yields by 31 to 43%.

The transcriptomic landscape of neurons carrying PSEN1 mutations reveals changes in extracellular matrix components and non-coding gene expression.

Alzheimer's disease (AD) is a progressive and irreversible brain disorder, which can occur either sporadically, due to a complex combination of environmental, genetic, and epigenetic factors, or because of rare genetic variants in specific genes (familial AD, or fAD). A key hallmark of AD is the accumulation of amyloid beta (Aß) and Tau hyperphosphorylated tangles in the brain, but the underlying pathomechanisms and interdependencies remain poorly understood. Here, we identify and characterise gene expression changes related to two fAD mutations (A79V and L150P) in the Presenilin-1 (PSEN1) gene. We do this by comparing the transcriptomes of glutamatergic forebrain neurons derived from fAD-mutant human induced pluripotent stem cells (hiPSCs) and their individual isogenic controls generated via precision CRISPR/Cas9 genome editing. Our analysis of Poly(A) RNA-seq data detects 1111 differentially expressed coding and non-coding genes significantly altered in fAD. Functional characterisation and pathway analysis of these genes reveal profound expression changes in constituents of the extracellular matrix, important to maintain the morphology, structural integrity, and plasticity of neurons, and in genes involved in calcium homeostasis and mitochondrial oxidative stress. Furthermore, by analysing total RNA-seq data we reveal that 30 out of 31 differentially expressed circular RNA genes are significantly upregulated in the fAD lines, and that these may contribute to the observed protein-coding gene expression changes. The results presented in this study contribute to a better understanding of the cellular mechanisms impacted in AD neurons, ultimately leading to neuronal damage and death.

CRISPRon/off: CRISPR/Cas9 on- and off-target gRNA design.

SUMMARY: The effectiveness of CRISPR/Cas9-mediated genome editing experiments largely depends on the guide RNA (gRNA) used by the CRISPR/Cas9 system for target recognition and cleavage activation. Careful design is necessary to select a gRNA with high editing efficiency at the on-target site and with minimum off-target potential. Here, we present our webserver for gRNA design with a user-friendly graphical interface, which provides interoperability between our on- and off-target prediction tools, CRISPRon and CRISPRoff, for a complete and streamlined gRNA selection. AVAILABILITY AND IMPLEMENTATION: The graphical interface uses the Integrative Genomic Viewer (IGV) JavaScript plugin. The backend tools are implemented in Python and C. The CRISPRon and CRISPRoff webservers and command-line tools are freely available at https://rth.dk/resources/crispr.

The impact of PrsA over-expression on the Bacillus subtilis transcriptome during fed-batch fermentation of alpha-amylase production.

The production of the alpha-amylase (AMY) enzyme in Bacillus subtilis at a high rate leads to the accumulation of unfolded AMY, which causes secretion stress. The over-expression of the PrsA chaperone aids enzyme folding and reduces stress. To identify affected pathways and potential mechanisms involved in the reduced growth, we analyzed the transcriptomic differences during fed-batch fermentation between a PrsA over-expressing strain and control in a time-series RNA-seq experiment. We observe transcription in 542 unannotated regions, of which 234 had significant changes in expression levels between the samples. Moreover, 1,791 protein-coding sequences, 80 non-coding genes, and 20 riboswitches overlapping UTR regions of coding genes had significant changes in expression. We identified putatively regulated biological processes via gene-set over-representation analysis of the differentially expressed genes; overall, the analysis suggests that the PrsA over-expression affects ATP biosynthesis activity, amino acid metabolism, and cell wall stability. The investigation of the protein interaction network points to a potential impact on cell motility signaling. We discuss the impact of these highlighted mechanisms for reducing secretion stress or detrimental aspects of PrsA over-expression during AMY production.

The Bacillaceae-1 RNA motif comprises two distinct classes.

Non-coding RNAs are key regulatory players in bacteria. Many computationally predicted non-coding RNAs, however, lack functional associations. An example is the Bacillaceae-1 RNA motif, whose Rfam model consists of two hairpin loops. We find the motif conserved in nine of 13 non-pathogenic strains of the genus Bacillus but only in one pathogenic strain. To elucidate functional characteristics, we studied 118 hits of the Rfam model in 11 Bacillus spp. and found two distinct classes based on the ensemble diversity of their RNA secondary structure and the genomic context concerning the ribosomal RNA (rRNA) cluster. Forty hits are associated with the rRNA cluster, of which all 19 hits upstream flanking of 16S rRNA have a reverse complementary structure of low structural diversity. Fifty-two hits have large ensemble diversity, of which 38 are located between two coding genes. For eight hits in Bacillus subtilis, we investigated public expression data under various conditions and observed either the forward or the reverse complementary motif expressed. Five hits are associated with the rRNA cluster. Four of them are located upstream of the 16S rRNA and are not transcriptionally active, but instead, their reverse complements with low structural diversity are expressed together with the rRNA cluster. The three other hits are located between two coding genes in non-conserved genomic loci. Two of them are independently expressed from their surrounding genes and are structurally diverse. In summary, we found that Bacillaceae-1 RNA motifs upstream flanking of ribosomal RNA clusters tend to have one stable structure with the reverse complementary motif expressed in B. subtilis. In contrast, a subgroup of intergenic motifs has the thermodynamic potential for structural switches.

Massively targeted evaluation of therapeutic CRISPR off-targets in cells.

Methods for sensitive and high-throughput evaluation of CRISPR RNA-guided nucleases (RGNs) off-targets (OTs) are essential for advancing RGN-based gene therapies. Here we report SURRO-seq for simultaneously evaluating thousands of therapeutic RGN OTs in cells. SURRO-seq captures RGN-induced indels in cells by pooled lentiviral OTs libraries and deep sequencing, an approach comparable and complementary to OTs detection by T7 endonuclease 1, GUIDE-seq, and CIRCLE-seq. Application of SURRO-seq to 8150 OTs from 110 therapeutic RGNs identifies significantly detectable indels in 783 OTs, of which 37 OTs are found in cancer genes and 23 OTs are further validated in five human cell lines by targeted amplicon sequencing. Finally, SURRO-seq reveals that thermodynamically stable wobble base pair (rG•dT) and free binding energy strongly affect RGN specificity. Our study emphasizes the necessity of thoroughly evaluating therapeutic RGN OTs to minimize inevitable off-target effects.

Cross-species high-resolution transcriptome profiling suggests biomarkers and therapeutic targets for ulcerative colitis.

Background: Ulcerative colitis (UC) is a disorder with unknown etiology, and animal models play an essential role in studying its molecular pathophysiology. Here, we aim to identify common conserved pathological UC-related gene expression signatures between humans and mice that can be used as treatment targets and/or biomarker candidates. Methods: To identify differentially regulated protein-coding genes and non-coding RNAs, we sequenced total RNA from the colon and blood of the most widely used dextran sodium sulfate Ulcerative colitis mouse. By combining this with public human Ulcerative colitis data, we investigated conserved gene expression signatures and pathways/biological processes through which these genes may contribute to disease development/progression. Results: Cross-species integration of human and mouse Ulcerative colitis data resulted in the identification of 1442 genes that were significantly differentially regulated in the same direction in the colon and 157 in blood. Of these, 51 genes showed consistent differential regulation in the colon and blood. Less known genes with importance in disease pathogenesis, including SPI1, FPR2, TYROBP, CKAP4, MCEMP1, ADGRG3, SLC11A1, and SELPLG, were identified through network centrality ranking and validated in independent human and mouse cohorts. Conclusion: The identified Ulcerative colitis conserved transcriptional signatures aid in the disease phenotyping and future treatment decisions, drug discovery, and clinical trial design.

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.

The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/ . CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.

BSGatlas: a unified Bacillus subtilis genome and transcriptome annotation atlas with enhanced information access.

A large part of our current understanding of gene regulation in Gram-positive bacteria is based on Bacillus subtilis, as it is one of the most well studied bacterial model systems. The rapid growth in data concerning its molecular and genomic biology is distributed across multiple annotation resources. Consequently, the interpretation of data from further B. subtilis experiments becomes increasingly challenging in both low- and large-scale analyses. Additionally, B. subtilis annotation of structured RNA and non-coding RNA (ncRNA), as well as the operon structure, is still lagging behind the annotation of the coding sequences. To address these challenges, we created the B. subtilis genome atlas, BSGatlas, which integrates and unifies multiple existing annotation resources. Compared to any of the individual resources, the BSGatlas contains twice as many ncRNAs, while improving the positional annotation for 70 % of the ncRNAs. Furthermore, we combined known transcription start and termination sites with lists of known co-transcribed gene sets to create a comprehensive transcript map. The combination with transcription start/termination site annotations resulted in 717 new sets of co-transcribed genes and 5335 untranslated regions (UTRs). In comparison to existing resources, the number of 5' and 3' UTRs increased nearly fivefold, and the number of internal UTRs doubled. The transcript map is organized in 2266 operons, which provides transcriptional annotation for 92 % of all genes in the genome compared to the at most 82 % by previous resources. We predicted an off-target-aware genome-wide library of CRISPR-Cas9 guide RNAs, which we also linked to polycistronic operons. We provide the BSGatlas in multiple forms: as a website (https://rth.dk/resources/bsgatlas/), an annotation hub for display in the UCSC genome browser, supplementary tables and standardized GFF3 format, which can be used in large scale -omics studies. By complementing existing resources, the BSGatlas supports analyses of the B. subtilis genome and its molecular biology with respect to not only non-coding genes but also genome-wide transcriptional relationships of all genes.

Human pathways in animal models: possibilities and limitations.

Animal models are crucial for advancing our knowledge about the molecular pathways involved in human diseases. However, it remains unclear to what extent tissue expression of pathways in healthy individuals is conserved between species. In addition, organism-specific information on pathways in animal models is often lacking. Within these limitations, we explore the possibilities that arise from publicly available data for the animal models mouse, rat, and pig. We approximate the animal pathways activity by integrating the human counterparts of curated pathways with tissue expression data from the models. Specifically, we compare whether the animal orthologs of the human genes are expressed in the same tissue. This is complicated by the lower coverage and worse quality of data in rat and pig as compared to mouse. Despite that, from 203 human KEGG pathways and the seven tissues with best experimental coverage, we identify 95 distinct pathways, for which the tissue expression in one animal model agrees better with human than the others. Our systematic pathway-tissue comparison between human and three animal modes points to specific similarities with human and to distinct differences among the animal models, thereby suggesting the most suitable organism for modeling a human pathway or tissue.

Novel circRNA discovery in sheep shows evidence of high backsplice junction conservation.

Circular RNAs (circRNAs) are covalently closed circular non-coding RNAs. Due to their structure, circRNAs are more stable and have longer half-lives than linear RNAs making them good candidates for disease biomarkers. Despite the scientific relevance of these molecules, the study of circRNAs in non-model organisms is still in its infancy. Here, we analyse total RNA-seq data to identify circRNAs in sheep from peripheral blood mononuclear cells (PBMCs) and parietal lobe cortex. Out of 2510 and 3403 circRNAs detected in parietal lobe cortex and in PBMCs, a total of 1379 novel circRNAs were discovered. Remarkably, around 63% of all detected circRNAs were found to be completely homologous to a circRNA annotated in human. Functional enrichment analysis was conducted for both tissues based on GO terms and KEGG pathways. The enriched terms suggest an important role of circRNAs from encephalon in synaptic functions and the involvement of circRNAs from PBMCs in basic immune system functions. In addition to this, we investigated the role of circRNAs in repetitive vaccination experiments via differential expression analysis and did not detect any significant relationship. At last, our results support both the miRNA sponge and the miRNA shuttle functions of CDR1-AS in sheep brain. To our knowledge, this is the first study on circRNA annotation in sheep PBMCs or parietal lobe cortex samples.

Epigenetic and Transcriptomic Characterization of Pure Adipocyte Fractions From Obese Pigs Identifies Candidate Pathways Controlling Metabolism.

Reprogramming of adipocyte function in obesity is implicated in metabolic disorders like type 2 diabetes. Here, we used the pig, an animal model sharing many physiological and pathophysiological similarities with humans, to perform in-depth epigenomic and transcriptomic characterization of pure adipocyte fractions. Using a combined DNA methylation capture sequencing and Reduced Representation bisulfite sequencing (RRBS) strategy in 11 lean and 12 obese pigs, we identified in 3529 differentially methylated regions (DMRs) located at close proximity to-, or within genes in the adipocytes. By sequencing of the transcriptome from the same fraction of isolated adipocytes, we identified 276 differentially expressed transcripts with at least one or more DMR. These transcripts were over-represented in gene pathways related to MAPK, metabolic and insulin signaling. Using a candidate gene approach, we further characterized 13 genes potentially regulated by DNA methylation and identified putative transcription factor binding sites that could be affected by the differential methylation in obesity. Our data constitute a valuable resource for further investigations aiming to delineate the epigenetic etiology of metabolic disorders.

Multiple Sequence Alignments Enhance Boundary Definition of RNA Structures.

Self-contained structured domains of RNA sequences have often distinct molecular functions. Determining the boundaries of structured domains of a non-coding RNA (ncRNA) is needed for many ncRNA gene finder programs that predict RNA secondary structures in aligned genomes because these methods do not necessarily provide precise information about the boundaries or the location of the RNA structure inside the predicted ncRNA. Even without having a structure prediction, it is of interest to search for structured domains, such as for finding common RNA motifs in RNA-protein binding assays. The precise definition of the boundaries are essential for downstream analyses such as RNA structure modelling, e.g., through covariance models, and RNA structure clustering for the search of common motifs. Such efforts have so far been focused on single sequences, thus here we present a comparison for boundary definition between single sequence and multiple sequence alignments. We also present a novel approach, named RNAbound, for finding the boundaries that are based on probabilities of evolutionarily conserved base pairings. We tested the performance of two different methods on a limited number of Rfam families using the annotated structured RNA regions in the human genome and their multiple sequence alignments created from 14 species. The results show that multiple sequence alignments improve the boundary prediction for branched structures compared to single sequences independent of the chosen method. The actual performance of the two methods differs on single hairpin structures and branched structures. For the RNA families with branched structures, including transfer RNA (tRNA) and small nucleolar RNAs (snoRNAs), RNAbound improves the boundary predictions using multiple sequence alignments to median differences of -6 and -11.5 nucleotides (nts) for left and right boundary, respectively (window size of 200 nts).

WebCircRNA: Classifying the Circular RNA Potential of Coding and Noncoding RNA.

Circular RNAs (circRNAs) are increasingly recognized to play crucial roles in post-transcriptional gene regulation including functioning as microRNA (miRNA) sponges or as wide-spread regulators, for example in stem cell differentiation. It is therefore highly relevant to identify if a transcript of interest can also function as a circRNA. Here, we present a user-friendly web server that predicts if coding and noncoding RNAs have circRNA isoforms and whether circRNAs are expressed in stem cells. The predictions are made by random forest models using sequence-derived features as input. The output scores are converted to fractiles, which are used to assess the circRNA and stem cell potential. The performances of the three models are reported as the area under the receiver operating characteristic (ROC) curve and are 0.82 for coding genes, 0.89 for long noncoding RNAs (lncRNAs) and 0.72 for stem cell expression. We present WebCircRNA for quick evaluation of human genes and transcripts for their circRNA potential, which can be essential in several contexts.

CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters.

BACKGROUND: Recent experimental efforts of CRISPR-Cas9 systems have shown that off-target binding and cleavage are a concern for the system and that this is highly dependent on the selected guide RNA (gRNA) design. Computational predictions of off-targets have been proposed as an attractive and more feasible alternative to tedious experimental efforts. However, accurate scoring of the high number of putative off-targets plays a key role for the success of computational off-targeting assessment. RESULTS: We present an approximate binding energy model for the Cas9-gRNA-DNA complex, which systematically combines the energy parameters obtained for RNA-RNA, DNA-DNA, and RNA-DNA duplexes. Based on this model, two novel off-target assessment methods for gRNA selection in CRISPR-Cas9 applications are introduced: CRISPRoff to assign confidence scores to predicted off-targets and CRISPRspec to measure the specificity of the gRNA. We benchmark the methods against current state-of-the-art methods and show that both are in better agreement with experimental results. Furthermore, we show significant evidence supporting the inverse relationship between the on-target cleavage efficiency and specificity of the system, in which introduced binding energies are key components. CONCLUSIONS: The impact of the binding energies provides a direction for further studies of off-targeting mechanisms. The performance of CRISPRoff and CRISPRspec enables more accurate off-target evaluation for gRNA selections, prior to any CRISPR-Cas9 genome-editing application. For given gRNA sequences or all potential gRNAs in a given target region, CRISPRoff-based off-target predictions and CRISPRspec-based specificity evaluations can be carried out through our webserver at https://rth.dk/resources/crispr/ .

IFN-λ and microRNAs are important modulators of the pulmonary innate immune response against influenza A (H1N2) infection in pigs.

The innate immune system is paramount in the response to and clearance of influenza A virus (IAV) infection in non-immune individuals. Known factors include type I and III interferons and antiviral pathogen recognition receptors, and the cascades of antiviral and pro- and anti-inflammatory gene expression they induce. MicroRNAs (miRNAs) are increasingly recognized to participate in post-transcriptional modulation of these responses, but the temporal dynamics of how these players of the antiviral innate immune response collaborate to combat infection remain poorly characterized. We quantified the expression of miRNAs and protein coding genes in the lungs of pigs 1, 3, and 14 days after challenge with swine IAV (H1N2). Through RT-qPCR we observed a 400-fold relative increase in IFN-λ3 gene expression on day 1 after challenge, and a strong interferon-mediated antiviral response was observed on days 1 and 3 accompanied by up-regulation of genes related to the pro-inflammatory response and apoptosis. Using small RNA sequencing and qPCR validation we found 27 miRNAs that were differentially expressed after challenge, with the highest number of regulated miRNAs observed on day 3. In contrast, the number of protein coding genes found to be regulated due to IAV infection peaked on day 1. Pulmonary miRNAs may thus be aimed at fine-tuning the initial rapid inflammatory response after IAV infection. Specifically, we found five miRNAs (ssc-miR-15a, ssc-miR-18a, ssc-miR-21, ssc-miR-29b, and hsa-miR-590-3p)-four known porcine miRNAs and one novel porcine miRNA candidate-to be potential modulators of viral pathogen recognition and apoptosis. A total of 11 miRNAs remained differentially expressed 14 days after challenge, at which point the infection had cleared. In conclusion, the results suggested a role for miRNAs both during acute infection as well as later, with the potential to influence lung homeostasis and susceptibility to secondary infections in the lungs of pigs after IAV infection.

RAIN: RNA-protein Association and Interaction Networks.

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded.Database URL: http://rth.dk/resources/rain.

Tools and data services registry: a community effort to document bioinformatics resources.

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure.

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence.