Systems Biology

Genome-wide CRISPR-based screening is a powerful tool in forward genetics, enabling biologic discovery by linking a desired phenotype to a specific genetic perturbation. However, hits from a genome-wide screen require individual validation to reproduce and accurately quantify their effects outside of a pooled experiment. Here, we describe a step-by-step protocol to rapidly assess the effects of individual sgRNAs from CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) systems. All steps, including cloning, lentivirus generation, cell transduction, and phenotypic readout, can be performed entirely in 96-well plates. The system is highly flexible in both cell type and selection system, requiring only that the phenotype(s) of interest be read out via flow cytometry. We expect that this protocol will provide researchers with a rapid way to sift through potential screening hits, and prioritize them for deeper analysis in more complex in vitro or even in vivo systems.

Graphical abstract

One of the cardinal features of post-traumatic stress disorder (PTSD) is a paradoxical memory alteration including both emotional hypermnesia for salient trauma-related cues and amnesia for the surrounding traumatic context. Interestingly, some clinical studies have suggested that contextual amnesia would causally contribute to the PTSD-related hypermnesia insofar as decontextualized, traumatic memory is prone to be reactivated in contexts that can be very different from the original traumatic context. However, most current animal models of PTSD-related memory focus exclusively on the emotional hypermnesia, i.e., the persistence of a strong fear memory, and do not distinguish normal (adaptive) from pathological (PTSD-like) fear memory, leaving unexplored the hypothetical critical role of contextual amnesia in PTSD-related memory formation, and thus challenging the development of innovative treatments. Having developed the first animal model that precisely recapitulates the two memory components of PTSD in mice (emotional hypermnesia and contextual amnesia), we recently demonstrated that contextual amnesia, induced by optogenetic inhibition of the hippocampus (dorsal CA1), is a causal cognitive process of PTSD-like hypermnesia formation. Moreover, the hippocampus-dependent contextualization of traumatic memory, by optogenetic activation of dCA1 in traumatic condition, prevents PTSD-like hypermnesia formation. Finally, once PTSD-like memory has been formed, the re-contextualization of traumatic memory by its reactivation in the original traumatic context normalizes this pathological fear memory. Revealing the key role of contextual amnesia in PTSD-like memory, this procedure opens a therapeutic perspective based on trauma contextualization and the underlying hippocampal mechanisms.

Genome-wide sequencing of RNA (RNA-seq) has become an inexpensive tool to gain key insights into cellular and disease mechanisms. Sample preparation and sequencing are streamlined and allow the acquisition of hundreds of gene expression profiles in a few days; however, in particular, data processing, curation, and analysis involve numerous steps that can be overwhelming to non-experts. Here, the sample preparation, sequencing, and data processing workflow for RNA-seq expression analysis in yeast is described. While this protocol covers only a small portion of the RNA-seq landscape, the principal workflow common to such experiments is described, allowing the reader to adapt the protocol where necessary.

Graphic abstract:

Basic workflow of RNA-seq expression analysis.

DNA methylation in gene promoters plays a major role in gene expression regulation, and alterations in methylation patterns have been associated with several diseases. In this context, different software suites and statistical methods have been proposed to analyze differentially methylated positions and regions. Among them, the novel statistical method implemented in the mCSEA R package proposed a new framework to detect subtle, but consistent, methylation differences. Here, we provide an easy-to-use pipeline covering all the necessary steps to detect differentially methylated promoters with mCSEA from Illumina 450K and EPIC methylation BeadChips data. This protocol covers the download of data from public repositories, quality control, data filtering and normalization, estimation of cell type proportions, and statistical analysis. In addition, we show the procedure to compare disease vs. normal phenotypes, obtaining differentially methylated regions including promoters or CpG Islands. The entire protocol is based on R programming language, which can be used in any operating system and does not require advanced programming skills.

R-loops are non-canonical nucleic structures composed of an RNA–DNA hybrid and a displaced ssDNA. Originally identified as a source of genomic instability, R-loops have been shown over the last decade to be involved in the targeting of proteins and to be associated with different histone modifications, suggesting a regulatory function. In addition, R-loops have been demonstrated to form differentially during the development of different tissues in plants and to be associated with diseases in mammals. Here, we provide a single-strand DRIP-seq protocol to identify R-loop-forming sequences in Drosophila melanogaster embryos and tissue culture cells. This protocol differs from earlier DRIP protocols in the fragmentation step. Sonication, unlike restriction enzymes, generates a homogeneous and highly reproducible nucleic acid fragment pool. In addition, it allows the use of this protocol in any organism with minimal optimization. This protocol integrates several steps from published protocols to identify R-loop-forming sequences with high stringency, suitable for de novo characterization.

Graphic abstract:

Figure 1. Overview of the strand-specific DRIP-seq protocol

COVID-19, the disease caused by the novel SARS-CoV-2 coronavirus, originated as an isolated outbreak in the Hubei province of China but soon created a global pandemic and is now a major threat to healthcare systems worldwide. Following the rapid human-to-human transmission of the infection, institutes around the world have made efforts to generate genome sequence data for the virus. With thousands of genome sequences for SARS-CoV-2 now available in the public domain, it is possible to analyze the sequences and gain a deeper understanding of the disease, its origin, and its epidemiology. Phylogenetic analysis is a potentially powerful tool for tracking the transmission pattern of the virus with a view to aiding identification of potential interventions. Toward this goal, we have created a comprehensive protocol for the analysis and phylogenetic clustering of SARS-CoV-2 genomes using Nextstrain, a powerful open-source tool for the real-time interactive visualization of genome sequencing data. Approaches to focus the phylogenetic clustering analysis on a particular region of interest are detailed in this protocol.

Association mapping is the process of linking phenotypes with genotypes. In genome wide association studies (GWAS), individuals are first genotyped using microarrays or by aligning sequenced reads to reference genomes. However, both these approaches rely on reference genomes which limits their application to organisms with no or incomplete reference genomes. To address this, reference free association mapping methods have been developed. Here we present the protocol of an alignment free method for association studies which is based on counting k-mers in sequenced reads, testing for associations between k-mers and the phenotype of interest, and local assembly of the k-mers of statistical significance. The method can map associations of categorical phenotypes to sequence and structural variations without requiring prior sequencing of reference genomes.

Gene transcription in bacteria often starts some nucleotides upstream of the start codon. Identifying the specific Transcriptional Start Site (TSS) is essential for genetic manipulation, as in many cases upstream of the start codon there are sequence elements that are involved in gene expression regulation. Taken into account the classical gene structure, we are able to identify two kinds of transcriptional start site: primary and secondary. A primary transcriptional start site is located some nucleotides upstream of the translational start site, while a secondary transcriptional start site is located within the gene encoding sequence.

Here, we present a step by step protocol for genome-wide transcriptional start sites determination by differential RNA-sequencing (dRNA-seq) using the enteric pathogen Shigella flexneri serotype 5a strain M90T as model. However, this method can be employed in any other bacterial species of choice. In the first steps, total RNA is purified from bacterial cultures using the hot phenol method. Ribosomal RNA (rRNA) is specifically depleted via hybridization probes using a commercial kit. A 5′-monophosphate-dependent exonuclease (TEX)-treated RNA library enriched in primary transcripts is then prepared for comparison with a library that has not undergone TEX-treatment, followed by ligation of an RNA linker adaptor of known sequence allowing the determination of TSS with single nucleotide precision. Finally, the RNA is processed for Illumina sequencing library preparation and sequenced as purchased service. TSS are identified by in-house bioinformatic analysis.

Our protocol is cost-effective as it minimizes the use of commercial kits and employs freely available software.

Data generated by metagenomic and metatranscriptomic experiments is both enormous and inherently noisy. When using taxonomy-dependent alignment-based methods to classify and label reads, the first step consists in performing homology searches against sequence databases. To obtain the most information from the samples, nucleotide sequences are usually compared to various databases (nucleotide and protein) using local sequence aligners such as BLASTN and BLASTX. Nevertheless, the analysis and integration of these results can be problematic because the outputs from these searches usually show inconsistencies, which can be notorious when working with RNA-seq. Moreover, and to the best of our knowledge, existing tools do not criss-cross and integrate information from the different homology searches, but provide the results of each analysis separately. We developed the HoSeIn workflow to intersect the information from these homology searches, and then determine the taxonomic and functional profile of the sample using this integrated information. The workflow is based on the assumption that the sequences that correspond to a certain taxon are composed of:
1) sequences that were assigned to the same taxon by both homology searches;
2) sequences that were assigned to that taxon by one of the homology searches but returned no hits in the other one.

Memory systems can hold previously presented information for several seconds, bridging gaps between discontinuous events. It has been previously demonstrated that the hippocampus and the medial entorhinal cortex (mEC) are necessary for memory retention over delay intervals in alternation tasks. Here we describe the delayed alternation task, a spatial working memory (WM) task in which animals need to alternate between left and right sides of a figure-8 maze on a trial-by-trial basis to receive a reward. On each trial of this task, the rat has to remember the last episode and turn in the opposite direction compared to the previous trial. We manipulated the WM load by introducing delays of various lengths (10 s and 60 s) between trials. While other alternation task protocols use short delay intervals between trials, our protocol introduces a longer delay condition that requires animals to use long-term memory resources that are not necessarily supported by sequential neuronal firing patterns (i.e., time cells) as are seen with shorter delay intervals.