SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through CRISPR screening, a knowledge graph about human SLs, and new web interface, etc. Over 16,000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14,100 genes, 53 cancers, and 1,898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful APIs. The database is accessible online at http://synlethdb.sist.shanghaitech.edu.cn/v2.

mslp: a comprehensive pipeline in predicting cancer mutations specific synthetic lethal partners

Background: Mutation specific synthetic lethal partners (SLPs) offer significant insights in identifying novel targets and designing personalized treatments in cancer studies. Large scale genetic screens in cell lines and model organisms provide crucial resources for mining SLPs, yet those experiments are expensive and might be difficult to set up. Various computational methods have been proposed to predict the potential SLPs from different perspectives. However, those efforts are hampered by the low signal-to-noise ratio in simple correlation based approaches, or incomplete reliable training sets in supervised approaches. Results: Here we present mslp, a comprehensive pipeline to identify potential SLPs via integrating genomic and transcriptomic datasets from both patient tumours and cancer cell lines. Leveraging cutting-edges algorithms, we identify a broad spectrum of primary SLPs for mutations presented in patient tumours. Further, for mutations detected in cell lines, we develop the idea of consensus SLPs which are also identified as screen hits, and show consistency impact on cell viability. Applied in real datasets, we successfully identified known synthetic lethal gene pairs. Remarkably, genetic screen results suggested that consensus SLPs have a significant impact on cell viability compared to common hits. Conclusions: Mslp is a powerful and flexible pipeline to identify potential SLPs in a cancer context-specific manner, which might aid in drug developments and precise medicines in cancer treatments. The pipeline is implemented in R and freely available in github.

Broad spectrum of CRISPR-induced edits in an embryonic lethal gene

Mendelian genetics poses practical limitations on the number of mutant genes that can be investigated simultaneously for their roles in embryonic development in the mouse. While CRISPR-based gene editing of multiple genes at once offers an attractive alternative strategy, subsequent breeding or establishment of permanent mouse lines will rapidly segregate the different mutant loci again. Direct phenotypic analysis of genomic edits in an embryonic lethal gene in F0 generation mice, or F0 mouse embryos, circumvents the need for breeding or establishment of mutant mouse lines. In the course of genotyping a large cohort of F0 CRISPants, where the embryonic lethal gene T/brachyury was targeted, we noted the presence of multiple CRISPR-induced modifications in individual embryos. Using long-read single-molecule Nanopore sequencing, we identified a wide variety of deletions, ranging up to 3 kb, that would not have been detected or scored as wildtype with commonly used genotyping methods that rely on subcloning and short-read or Sanger sequencing. Long-read sequencing results were crucial for accurate genotype–phenotype correlation in our F0 CRISPants. We thus demonstrate feasibility of screening manipulated F0 embryos for mid-gestation phenotypic consequences of CRISPR-induced mutations without requiring derivation of permanent mouse lines.

Development of an Autoinducible Plasmid for Recombinant Protein Production

Background: The production of recombinant proteins in E. coli involves such factors as host strains, expression vectors, culture media, and induction methods. The typical procedure to produce heterologous proteins consists of the following: (1) insertion of the target gene into a suitable vector to construct an overexpression plasmid, (2) transformation of a strain specialized for protein production with the constructed plasmid DNA, (3) growth of the host in a suitable medium and induction of the protein production at a right moment, and (4) further growth to get the maximum yield. There are hurdles involved in each of these steps, and researchers have developed many materials or methods, which often require special recipes or procedures. Objective: To eliminate the special requirements for the recombinant protein production by using readily available materials. Also to save time and effort in the routine protein production work. Method: We started with a vector capable of producing a target protein fused to the C-terminus of the maltose binding protein (MBP). The mCherry (red fluorescent protein) gene was fused to MBP. It acted as a reporter in the initial screening procedure. The original lethal gene (barnase) was replaced with sacB. We chose 3 stationary phase promoters, and made hybrids of them by mixing halves from each one. The T5 promoter was replaced with these stationary phase promoters or their hybrids. The best plasmid was selected by the color intensity of the cell pellet. MBP and GST genes were inserted in place of sacB, and their production yields were compared with the original plasmid in the conventional way of expression. Results: We constructed an expression plasmid with an autoinducible promoter working in a host that was not specially designed for protein production and in a TB medium which did not contain any secret ingredient, nor was difficult to prepare unlike Studier’s defined medium. This plasmid also contains a color indicator which turns red when protein production is successful. We tested our system with the maltose binding protein (MBP) and the glutathione S-transferase (GST), and showed that both proteins were produced to a level comparable to what the commercial medium and/or the specialized strain yielded. Conclusion: We developed a plasmid equipped with an autoinducible promoter, a hybrid of the two promoters which were activated at the stationary phase. This plasmid does not need a special E. coli strain nor a sophisticated nor an expensive medium. It produces an intense red (or pink) color, which can be used as an indicator of a successful production of the target protein and as a predictive measure of the amount of the produced target protein. We speculate that this plasmid will have its greatest advantage when growing cells at low temperatures which would inevitably take a long time.

Genome-wide identification of the tobacco GDSL family and apical meristem-specific expression conferred by the GDSL promoter

Background GDSL esterases/lipases are a large protein subfamily defined by the distinct GDSL motif, and play important roles in plant development and stress responses. However, few studies have reported on the role of GDSLs in the growth and development of axillary buds. This work aims to identify the GDSL family members in tobacco and explore whether the NtGDSL gene contributes to development of the axillary bud in tobacco. Results One hundred fifty-nine GDSL esterase/lipase genes from cultivated tobacco (Nicotiana tabacum) were identified, and the dynamic changes in the expression levels of 93 of these genes in response to topping, as assessed using transcriptome data of topping-induced axillary shoots, were analysed. In total, 13 GDSL esterase/lipase genes responded with changes in expression level. To identify genes and promoters that drive the tissue-specific expression in tobacco apical and axillary buds, the expression patterns of these 13 genes were verified using qRT-PCR. GUS activity and a lethal gene expression pattern driven by the NtGDSL127 promoter in transgenic tobacco demonstrated that NtGDSL127 is specifically expressed in apical buds, axillary buds, and flowers. Three separate deletions in the NtGDSL127 promoter demonstrated that a minimum upstream segment of 235 bp from the translation start site can drive the tissue-specific expression in the apical meristem. Additionally, NtGDSL127 responded to phytohormones, providing strategies for improving tobacco breeding and growth. Conclusion We propose that in tobacco, the NtGDSL127 promoter directs expression specifically in the apical meristem and that expression is closely correlated with axillary bud development.

A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy

Background Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. Results We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. Conclusions CanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available at https://github.com/GilbertLabUCSF/CanDI.

Resistance to a CRISPR-based gene drive at an evolutionarily conserved site is revealed by mimicking genotype fixation

CRISPR-based homing gene drives can be designed to disrupt essential genes whilst biasing their own inheritance, leading to suppression of mosquito populations in the laboratory. This class of gene drives relies on CRISPR-Cas9 cleavage of a target sequence and copying (‘homing’) therein of the gene drive element from the homologous chromosome. However, target site mutations that are resistant to cleavage yet maintain the function of the essential gene are expected to be strongly selected for. Targeting functionally constrained regions where mutations are not easily tolerated should lower the probability of resistance. Evolutionary conservation at the sequence level is often a reliable indicator of functional constraint, though the actual level of underlying constraint between one conserved sequence and another can vary widely. Here we generated a novel adult lethal gene drive (ALGD) in the malaria vector Anopheles gambiae, targeting an ultra-conserved target site in a haplosufficient essential gene (AGAP029113) required during mosquito development, which fulfils many of the criteria for the target of a population suppression gene drive. We then designed a selection regime to experimentally assess the likelihood of generation and subsequent selection of gene drive resistant mutations at its target site. We simulated, in a caged population, a scenario where the gene drive was approaching fixation, where selection for resistance is expected to be strongest. Continuous sampling of the target locus revealed that a single, restorative, in-frame nucleotide substitution was selected. Our findings show that ultra-conservation alone need not be predictive of a site that is refractory to target site resistance. Our strategy to evaluate resistance in vivo could help to validate candidate gene drive targets for their resilience to resistance and help to improve predictions of the invasion dynamics of gene drives in field populations.

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.

Learning epistatic gene interactions from perturbation screens

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.