E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype–phenotype map for this experimental system.Changes in an organism’s environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism’s metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the long-term evolution experiment (LTEE) with  Drosophila found that these differences can affect lifespan and are sex specific  are propagated in a carbon-limited medium with a 24-hr serial transfer regime (Laboratory evolution experiments provide ample opportunities to study metabolic evolution. The long-term evolution experiment (LTEE) is an experimental evolution system where 12 replicate populations of r regime . Begun ir regime . Cellular regime . Paraller regime . Despiter regime . In prinr regime  and A-2 r regime . These er regime . Reducedr regime . Despiter regime . In partr regime .While specific examples of how genetic changes result in metabolic changes in the LTEE  exist, a more general survey of the metabolome is lacking. Such a survey would allow a better understanding of the effects of genetic and expression changes on metabolism and provide the ability to test hypotheses from previous data. By integrating genomic , expressWe used liquid chromatography coupled mass spectrometry (LC/MS) to scan a broad mass range using both positive and negative ionization modes to survey the metabolomes of both ancestral strains and clones from each of the 12 evolved lines of the LTEE at 50,000 generations. We sampled two time points, 2 and 24 hr, to represent exponential and stationary phase samples, each with two biological replicates. It is important to note that the 2 hr time point captures the evolved lines, including A-3, during their growth on glucose. Additionally, the evolved lines reach stationary phase after about 4 hr of growth, with A-3 experiencing growth that levels off slowly over around 14 hr . At our E. coli exhibits large physiological differences across growth phases  of metabolite abundances in a phase-specific manner. In the exponential phase, the PCA strongly separates A-3 from the other samples . This isSimilar to the exponential phase, A-3 separates from the rest of the evolved lines in a PCA of metabolites in the stationary phase . The prit-test). In the exponential phase, the lowest correlations were mainly between a pair involving one of A-2, A-4, or A-6, and another evolved line  method , essentiIn the LTEE, mutations to the nadR gene have occurred in all of the evolved lines and appear to have been under strong selection to improve fitness . NadR isNAD and its phosphorylated derivative NADP function as electron carriers and are necessary for many catabolic and anabolic pathways in the cell. While untested, hypotheses for how nadR mutations might increase fitness involve derepression of its target genes and increases to intracellular NAD; this may aid in shortening the lag phase and modulating redox chemistry . The abuThe genes regulated by NadR participate in specific reactions along NAD synthesis pathways. We detected some of the compounds in these pathways as well as both oxidized and reduced forms of NAD and NADP . CompareSimilar to nadR, another consistent target of mutations in the LTEE is a transcriptional repressor of genes involved in arginine biosynthesis argR . ArgR, aMutations in argR are localized to specific regions, potentially disrupting the DNA-binding domain, the arginine sensing domain, or interaction domain, which is required for hexermerization of the protein . These mWe were able to detect 11 compounds involved in the superpathway of arginine and polyamine biosynthesis . CompareLow carbon availability is the key feature of the minimal medium used in the LTEE, and selection for better use of carbon is a driving force of adaptation in the system. Hence, how mutations and expression changes might affect fitness by altering central carbon metabolism pathways is of interest. The evolved lines have seen significant positive and negative changes in their ability to grow on different carbon substrates. Rather than metabolic specialization, this was found to be due to the accumulation of deleterious mutations that affect the ability to grow on other substrates . Unique We again sought instances of parallel sets of mutations and expression changes that may exert their effects at the metabolic level. Three genes that are key components in the glyoxylate cycle, aceB, aceA, and aceK were commonly upregulated in the evolved lines . Their tOverall, 18 compounds from these pathways are detected in our LC/MS assay. Regarding the glyoxylate shunt, malate, aconitate, and succinate were generally elevated in the evolved lines . IntereAn increase in PEP is also consistent with a hypothesis related to increased glucose uptake. It was previously noted that all of the evolved lines contain what are likely inactivating mutations in the gene pykF. pykF encodes pyruvate kinase I, one of two isozymes that generate ATP by converting PEP to pyruvate . It was The citric acid cycle is a major metabolic pathway that extracts energy via the reduction of electron carriers like NAD. These electrons are shuttled to the electron transport chain by NAD, where they are used to generate ATP. NAD can be reduced at two points during the citric acid cycle, the conversion of alpha-ketoglutarate to succinate and the conversion of malate to oxaloacetate. If the citric acid cycle is running faster in the evolved lines due to their higher growth rates and hence higher energy demands, then more NAD may be required to efficiently shuttle electrons from the citric acid cycle to the electron transport chain. Interestingly, we measured a median fold-change of 3.13 for alpha-ketoglutarate and 5.38 for malate . AdditioE. coli  showed that mutations continue to accumulate during this time and that patterns suggestive of adaptive evolution had occurred . Sterile plastic Petri dishes were placed onto a metal tray on dry ice, and 1.2 ml of extraction solvent (40:40:20 acetonitrile:methanol:water +0.5% formic acid) was added to the Petri dish to chill. When filtration was complete, the filter was placed cell-side down into the Petri dish. The metal tray was moved to wet ice to extract for 20 min. After 20 min, the acid was neutralized by the addition of 1.07%  ammonium bicarbonate, then cell debris was spun down at 14,000 rpm at 4°C for 10 min, and the clarified extract was transferred to a chilled 2 ml tube and stored at −20°C. The filter was extracted again by the addition of 0.4 ml extraction solvent and sat for 15 min. The extract was neutralized, clarified, and consolidated with the first extract and incubated overnight at −20°C.The next day, precipitated protein was spun down at 14,000 rpm at 4°C for 10 min, and solvents were removed by speed vac for 2 hr at room temperature. Lastly, the sample was concentrated by removing the remaining water by lyophilization. Dried samples were reconstituted in 40 µL of extraction solvent and submitted for mass spectrometry.Mass spectrometry was performed as described previously in https://github.com/shahlab/ltee_massspec,  to broadly compare how the metabolomes of these strains are similar and different. Then, they describe several instances where the changes in metabolite abundance they see in specific pathways correlate with mutations that lead to changes in the expression of genes that encode enzymes in those pathways.Favate et al. measure the relative levels of metabolites in 12 *Strengths:The statistical analyses and presentation of the high-throughput data are excellent. The most compelling results are communicated in wonderful figures that integrate their measurements of metabolite levels in this study with results from a prior study they conducted looking at changes in gene expression levels in the same bacterial strains. These sections include the ones describing large increases in NAD(P) pools due to mutations in nadR, changes in the levels of arginine and related compounds due to mutations in argR, and changes in metabolites from glycolysis and the TCA cycle related to iclR and arcB.Weaknesses:After addressing prior reviews, the main remaining weaknesses of the study are limitations inherent to the metabolomics approach that are noted by the authors. Namely, that it gives a static and incomplete picture of cellular metabolism, lacking any information about flux and missing measurements for many metabolites. Additional biochemical and genetic experiments will be necessary to fully test the hypotheses suggested by the metabolomics data.Impact and Significance:While there has been past speculation about the effects of LTEE mutations on metabolism, this study measures changes in the levels of metabolites in related metabolic pathways for the first time. Therefore, it provides useful information about how metabolism evolves, in general, and will also be a useful resource those studying other aspects of the LTEE related to metabolism, such as contingency in the evolution of citrate utilization. This preprint presents a compelling study examining the relationship between genotypic changes and phenotypic traits in bacteria over an extended period using the Long-Term Evolution Experiment (LTEE) as a model. The primary advances in methodology include employing high-resolution mass spectrometry for comprehensive metabolic profiling and combining it with previous gene expression and DNA sequencing datasets. This approach provides insight into how specific genetic mutations can alter metabolic pathways over 50,000 generations, enabling a deeper understanding of how genetic changes lead to observed differences in evolved bacterial strains. The findings reveal that evolved bacteria possess more diverse metabolic profiles compared to their ancestors, suggesting that these populations have uniquely adapted to their environment. The work also attempts to uncover the molecular basis for this adaptive evolution, demonstrating how specific genetic changes have influenced the bacteria's metabolic pathways.Overall, this is a significant and well-executed research study. It offers new insights into the complex relationship between genetic changes and observable traits in evolving populations and utilizes metabolomics in the LTEE, a novel approach in combination with RNA-seq and mutation datasets. The following is the authors’ response to the original reviews.Reviewer #1 (Public Review):[...] WeaknessesShowing that A-2 and especially A-3 are outliers in the PCA analysis is useful, but it may be hiding other interesting signals in the data. The other strains are remarkably colinear on these plots, hinting that if the outliers were removed, one main component would emerge along which they are situated. It also seems possible that this additional analysis step would allow the second dimension to better differentiate them in a way that is interesting with respect to their mutator status or mutations in key metabolic or regulatory genes.We thank the reviewer for their positive comments and their constructive feedback on the manuscript. Following reviewer’s recommendation, we performed the PCA analysis on metabolism data after removing A-2 and A-3 data. We have detailed those results below. Consistent with a similar analysis performed on RNA-seq datasets in our previous publication, we find that removing these outliers has only a modest effect on separating mutators from non-mutators. We find that, while the new PC2 separates most mutators from the non-mutators, the separation is rather weak. Moreover, we do not see a similar distinction when looking at metabolic data in the Stationary phase. In the interest of improving the readability of the manuscript, we recommend not including these analysis in the final manuscript. We have presented the data for the reviewer’s benefit in Author response image 1, 2 and 3.E. coli encodes for this reaction. There has been speculation that inactivating pykF increases the concentration of phosphoenolpyruvate (PEP) in cells and that this can lead to increased rates of glucose import because PEP is used by the phosphotransferase system of E. coli to import glucose (see https://doi.org/10.1002/bies.20629). The current study has confirmed the higher PEP levels, which is consistent with this model.There is a missed opportunity to connect some key results to what is known about LTEE mutations that reduce the activity of pykF (pyruvate kinase I). This gene is mutated in all 12 LTEE populations, and often these mutations are frameshifts or transposon insertions that should completely knock out its activity. At first glance, inactivating an enzyme for a step in glycolysis does not make sense when the nutrient source in the growth medium is glucose, even though PykF is only one of two isozymes We thank the reviewer for pointing out this missed opportunity. We have expanded the discussion around the role of pykF mutations and the elevated concentrations of PEP observed in our data in section 3.4.https://doi.org/10.1038/nature01149), and (2) discussions of how cross-feeding, metabolic niche specialization, and metabolic interdependence evolve in microbial communities, including in other evolution experiments .In the introduction, the papers cited to show the importance of changes in metabolism for adaptation do not seem to fit the focus of this study very well. They stress production of toxins and secondary metabolites, which do not seem to be mechanisms that are at work in the LTEE. I can think of two areas of background that would be more relevant: (1) studies of how bacterial metabolism evolves in adaptive laboratory evolution (ALE) experiments to optimize metabolic fluxes toward biomass production :[...] Overall, this is a significant and well-executed research study. It offers new insights into the complex relationship between genetic changes and observable traits in evolving populations and utilizes metabolomics in the LTEE, a novel approach in combination with RNA-seq and mutation datasets.However, the paper's overall clarity is lacking. It is spread too thin and covers many topics without a clear focus. I strongly recommend a substantial rewrite of the manuscript, emphasizing structure and readability. The science is well executed, but the current writing does not do it justice.We thank the reviewer for their positive comments and their constructive feedback on the lack of clarity in writing. Following the reviewer’s suggestions, we have rewritten parts of the manuscript and reorganizd a few sections to improve readability. We hope the revised manuscript is significantly improved.Recommendations for the authorsReviewer #1 (Recommendations For The Authors):E. coli is not mentioned in either! I'm also not sure that the LTEE is a sufficiently well-known acronym to abbreviate this in the title.1. Title and Abstract: Add the study organism to the abstract, and probably also the title. Currently, E. coli in the title and the abstract.We have revised the title of the manuscript and now spell out LTEE and included metabolism", "networked and convoluted nature of metabolism". The metabolome, the concentrations of all metabolites, is what is being measured, but I think of this as a phenotypic readout of how metabolism evolving.1. Abstract: I would switch the usage of metabolome to metabolism in a few more places. For example, "changes in its We have changed “metabolome” to “metabolism” in cases where we refer to what is evolving and use “metabolome” when we refer to what is being measured.E. coli are propagated" or something similar so that it can be correct without needing to describe this unnecessary detail.1. Line 16: Technically, the 12 LTEE populations were not initially identical. The Ara- differed from the Ara+ ancestors by one intentional mutation and one unintentional mutation that was not discovered until whole genomes were sequenced. I would rephrase this to "where 12 replicate populations of The line has been rephrased as suggested.1. General Note: The text refers to populations as Ara-3 but the figures use A-3. I'd suggest going with A-3 and similar throughout for consistency.Instances of Ara have been changed to A+/-, and a sentence specifying as such has been added to the intro to make mention of this.1. Lines 43-44, 97-98. My understanding is that both S and L ecotypes in A-2 can use both glucose and acetate, but that the differentiation is related to their specialization that leads to each one being better on one or the other nutrient. The descriptions make it sound like each grows at a different time. Also, by definition, cells are not growing during "stationary phase". The change from glucose utilization (and acetate secretion) to acetate utilization during one cycle of growth is better described as a diauxic shift.We have reworded this part to remove mention of “growth” during stationary phase and changed the wording such that it no longer sounds like they grow at different times.1. Line 54: The statement "provide the ability to test hypotheses from previous data" is vague. Either provide an example or delete.We have removed this sentence as suggested.1. Lines 71-72: The terms "interphase" and "intraphase" sound too much like parts of the cell cycle. I'd suggest describing the comparisons as between and within growth phases.The use of intra and interphase have been changed as suggested.1. Line 79: The citrate is presumably still a chelating agent, so change phrasing to "Citrate is present in the medium because it was originally added as a chelating agent" or something similar.This sentence has been rewritten as suggested.1. Line 83: Write out "mutation accumulations" so it is easier to understand as "the number of mutations that have accumulated".The phrase has been changed as suggested.1. Line 116: It's unclear whether the abundances of metabolites are "strategies of survival" in stationary phase. An equally valid explanation is that there is less selection on the metabolome to have a specific composition during stationary phase to have high fitness.We have added a line about the possibility for alternative hypotheses.1. Figure 1: There seems to be some information missing from the legend. What are R06 and R07 in Panels A and B? Is panel D exponential phase and panel E stationary phase?This information was inadvertently missing from the caption and has been added.1. Figures 2 and 3: Gene names should be in italics. To me, the gray for deleted genes is hard to tell apart from the blue/red. Perhaps you could put a little X in these boxes instead? I think that having a little triangle pointing from each gene or metabolite name its corresponding abundance panel would help the reader track which information goes with which features. In Fig. 3 the placement of L-aspartate is a bit awkward. I'd suggest moving it down so the dashed line does not have to go through the abundance panel.These figures have been edited to include small triangles that link a gene or metabolite and its heatmap. Additionally, an X has been added where genes have suffered inactivating mutations and the placement of some elements has been moved to improve overall clarity.1. Lines 183-185: It would be easier to see and judge the consistency of these argR related relationships if a correlation graph of some kind was shown, probably as a supplemental figure. This plot could, for example, have genes/metabolites across the x-axis and fold-change on the y-axis with lines connecting points corresponding to each of the twelve populations across these categories . Alternatively, it could be a heat map with the populations across one axis and the genes/metabolites across the other axis .We have added a supplementary figure consisting of heatmaps showing the consistency of these changes within an evolved line. It is now figure S9.1. Line 195: I think adding a sentence elaborating on what exactly mutation accumulation means in this context would be helpful to readers.We have attempted to clarify the meaning of this by specifically stating that it is due to the accumulation of deleterious mutations.1. Line 293: Is standard LTEE medium DM25? These omics experiments with the LTEE sometimes use similar media with different glucose concentrations, and this is a very important detail to precisely specify.We reference “standard” LTEE medium in the methods section and have additionally specified the amount of sugar to make it clear that we are not supplementing the media with additional sugar.1. Figure S8B. Is "cystine" used instead of "cysteine" on purpose here since the compound is oxidized in the metabolomics treatment?The use of cystine is intentional, we detect the oxidized compound.Reviewer #2 (Recommendations For The Authors):Title:E. coli," should be used, or the title should be rephrased to "Linking genotypic and phenotypic changes during a long-term evolution experiment using metabolomics."The abbreviation "LTEE" should not be in the title. Most readers will not recognize what it means. Instead, either the full name of the experiment, "Long-Term Evolution Experiment with E. coli in the title.We have spelled out LTEE and included Abstract:Sentence 1: Consider softening the statement: "Do changes in an organism's environment, genome, or gene expression patterns often lead to changes in its metabolome?"We have rephrased this sentence to “Changes in an organism's environment, genome, or gene expression patterns can lead to changes in its metabolism”.Sentence 4: Use a hyphen for "Long-Term."This addition has been made.Sentence 4: Replace "transduce" with a more appropriate term: "...how the effects of mutations can be distributed through a cellular network to eventually affect metabolism and fitness."We have rewritten this sentence as “to understand how mutations can eventually affect metabolism and perhaps fitness”.Sentence 5: Clarify the use of "both" to refer to the ancestor of the LTEE and its descendant populations as two classes.We have reworded this sentence so it’s clear that the ancestors and evolved lines are two separate classes “We used mass-spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines…”.Sentence 6: Reverse the order for better emphasis: "Our work provides a better understanding of how mutations might affect fitness through the metabolome in the LTEE, and thus provides a major step in developing a complete genotype-phenotype map for this experimental system."We have rearranged this sentence per the reviewers suggestion.Introduction:Revise the introduction for clarity, readability, and logical narrative progression. Start with the second paragraph to set up the basic scientific principles being studied and then transition to describing the LTEE as a model system to examine those principles.The introduction has been rearranged and reworded in parts to increase clarity.Escherichia coli as they have evolved in a carbon-limited, minimal glucose medium under a daily serial transfer regime."Sentence 1: Revise for clarity: "The Long-Term Evolution Experiment (LTEE) has studied 12 initially identical populations of Sentence 2: Suggestion: "Begun in 1988, the LTEE populations have evolved for more than 75,000 generations, making it the longest-running experiment of its kind."Drosophila."Paragraph 2, sentence 2: Italicize "Paragraph 3, sentence 2: Make an important distinction: "Ara-3 is unique in that it evolved the ability to grow aerobically on citrate."Paragraph 3, sentence 4: Introduce the IS-mediated loss of the rbs operon in the LTEE as if it has not been described elsewhere.These suggestions have been incorporated into the manuscript.Results:Section 3.1: The use of samples from hours 2 and 24 to represent exponential and stationary phase may present some issues. For instance, capturing Ara-3 during its exponential growth on glucose, but not citrate, at hour 2. Furthermore, except for Ara-3, the LTEE populations reach stationary phase after approximately 4 hours, and there could be significant differences between early, mid, and late stationary phase. This possibility should be acknowledged, and future follow-up work should consider exploring these differences.E. coli during long term stationary phase.We have added sentences in the first paragraph of the results section to include these details. We have also added a short paragraph to the conclusions suggesting additional studies of stationary phase, citing work on evolution of Paragraph 3: While Turner et al. 2017 is an essential reference regarding resource use differences between Ara-3 and other LTEE populations, it would be more suitable to reference Blount et al. 2012 for the mutations that enabled access to citrate. Also, it is important to note that the difference lies in the ability to grow aerobically on citrate, rather than the ability to metabolize it.This citation has been added.Paragraph 4: As mentioned elsewhere, most LTEE populations exhibit balanced polymorphisms. Therefore, it is more appropriate to state that Ara-2 is the best-understood example of long-term diversity. It is likely that there are important metabolic differences between co-existing lineages in other LTEE populations.We now refer to Ara-2 as being the best-understood example of long term diversity..Paragraph 5: The first sentence of this paragraph should likely end with "levels."The word “levels” was added to the end of this sentence.Figure 3: It is preferable to refer to the "Superpathway of arginine and polyamine biosynthesis," citing EcoCyc as a reference, rather than a descriptor.This has been changed to a reference.Section 3.3, Paragraph 3: While higher intracellular amino acid abundances may facilitate higher translation rates and faster growth, the higher abundances themselves do not evaluate the hypothesis. To evaluate the hypothesis, it is necessary to demonstrate that higher abundances are associated with higher translation or growth rates. Therefore, the final sentence of this paragraph is not meaningful.We have reworded this sentence to say that it’s not possible to tell what the additional amino acids are being used for given only this data and that additional experiments are needed to confirm this hypothesis.Section 3.4: The first paragraph of this section misstates how evolution works. The low level of glucose in the LTEE does not drive innovation; instead, innovation occurs at random through the introduction of variation by mutation. Although the existence of the citrate resource acts as a reward that selects for variation that provides access to it, it is essential to remember that evolution is blind to such a reward. Moreover, regarding the evolution of the Cit+ trait, it is incorrect to assert that low glucose contributed to its evolution. As shown by Quandt et al. (2015), it seems probable that Cit+ evolution was potentiated by adaptation to specialization on acetate, which is produced by overflow metabolism resulting from rapid growth on glucose. This rapid growth only occurs when glucose is relatively abundant. The level of glucose seems low to us because it is low relative to traditional levels in bacteriological media, but not to the bacteria.We agree that this is a semantical, but important distinction. We have reworded this part as to not suggest that evolution has any forward thinking properties and is indeed blind to any rewards that might occur as the result of adaptation.In general, all instances of "utilize" and its cognates should be replaced with "use" and its cognates.Instances of “utilize” have been changed to use and its cognates.There is some uncertainty about the expectation of ramping up the TCA cycle in the LTEE. Overflow metabolism and acetate production appear to be prevalent in the LTEE, suggesting that many lineages only partially oxidize carbon derived from glucose, thereby bypassing the TCA cycle. While it is possible that this interpretation is incorrect, it would be helpful to see it addressed in the manuscript.We agree that this is a plausible hypothesis, we have added a paragraph at the end of this section that discusses the implications of overflow metabolism as an alternative hypothesis.


In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco’s modified eagle medium (DMEM) and Ham’s medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham’s medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.The online version contains supplementary material available at 10.1186/s12859-023-05487-7. Respiratory failure is the primary cause of death among patients with COVID-19; however, cardiac problems might contribute to the overall mortality and even be the primary cause of death for these patients [Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID-19. At the beginning of the COVID-19 pandemic, COVID-19 was considered a respiratory disease that primarily affects human airways and lungs; however, later, COVID-19 was found to affect not only the respiratory system but also heart tissue, thereby causing serious heart problems . Accordipatients –8.Originally classified as a respiratory syndrome, COVID-19 has been shown to have a significant impact on cardiac and cardiovascular function –8. In paA few antiviral drugs have been approved by the US Food and Drug Administration (FDA) for the treatment of COVID-19. Remdesivir was the first drug approved by the FDA for treating COVID-19. Subsequently, ritonavir-boosted nirmatrelvir (Paxlovid), molnupiravir, and certain anti-SARS-CoV-2 monoclonal antibodies received emergency-use authorizations from the FDA for the treatment of COVID-19 . Many emViruses infect host cells and seize control of the metabolism of these cells to enable viral replication –27. Conshttps://www.ncbi.nlm.nih.gov/nuccore/) and used to design protein and RNA nucleotide polymerization reactions for the VBR. Subsequently, the ratio of the mass of lipids in the viral biomass to that in its host cell was used to estimate the stoichiometric coefficients of viral lipids for establishing a generic VBR. The generic VBR was integrated with a generic human Recon3D model to create an HV model for analysis. However, this HV model is tissue-unspecific; that is, the model was unable to identify infected tissue. The RNA-seq expression of heart cells infected with SARS-CoV-2 was used to reconstruct cell-specific genome-scale metabolic models (GSMMs) of infected and healthy host cells . These models were then used in an antiviral target discovery (AVTD) platform [In the present study, the protein and gene sequences of SARS-CoV-2 were retrieved from the National Center for Biotechnology Information (NCBI) database (platform  to identhttps://www.ncbi.nlm.nih.gov/nuccore/) to establish a generic VBR. In Step B, integration of this VBR with the generic human genome-scale metabolic network Recon3D [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169241) to reconstruct models for HV and HT cells. These expression data have been collected from both non-COVID-19 donors and three patients infected with COVID-19 in the heart. Lastly, Step D uses statistical methods to analyze RNA-seq expressions of healthy and infected samples, facilitating the reconstruction of cell-specific GSMMs and gene–protein reaction models for HV cells and HT cells, respectively.A computer-aided platform was developed in this study for screening potential therapeutic antiviral targets for treating heart cells infected with SARS-CoV2. This platform . In Step L, the fitness value for each antiviral candidate is used to determine which candidates have achieved a satisfactory level. Proceeding to Step M, if the decision criterion is unsatisfactory, a subsequent set of antiviral candidates is generated using a nested hybrid differential evolution algorithm.The second framework comprises a series of iterative procedures within the AVTD algorithm. These procedures aim to identify potential antiviral targets, as depicted in Fig. A viral biomass reaction (VBR) is a pseudo-reaction that mimics the growth rate of virus particles by utilizing nucleotides, amino acids, and lipids. However, due to incomplete understanding, the stoichiometric details of lipids are often omitted in VBRs , 34–36. https://www.ncbi.nlm.nih.gov/nuccore/), can be used to generate the stoichiometric coefficients of protein and nucleotide polymerization in the VBR of this variant. Following procedures discussed in [α is a mass ratio of lipids in the VBR relative to that of the host cells as follows:Ni), amino acids (Aj), water (H2O), adenosine triphosphate (ATP), adenosine diphosphate (ADP), orthophosphate (Pi), diphosphate (PPi) and proton (H+) in the VBR of HV cells and the biomass reaction of a generic human GSMM of HT cells, respectively. The stoichiometric coefficients for each metabolite in the VBR can be calculated as follows:The protein sequence of the SARS-CoV-2 alpha (NC_045512) variant, which can be downloaded from the NCBI database ; CG, kth structural protein and the kth nonstructural protein, respectively. The frequency Aj in the kth structural protein and the frequency Aj in the kth non-structural protein are calculated using the viral protein sequence obtained from the NCBI database. The stoichiometric coefficient of water molecules is considered in protein polymerization [The total numbers of moles of the rization , 33 is trization  revised The biomass reaction of HT cells (Eq. ) is usedNi, Aj,…, and H+ denote as their corresponding molecular weights. Similarly, the mass ratio between the lipids and the viral biomass of HV cells is computed, and this ratio is assumed to be identical for HT and HV cells. Therefore, α is calculated as follows:Here, In this study, the generic VBR in Eq.  was inteWe extended the AVTD framework developed in  to consiThe second goal is to achieve the fuzzy maximization (vj) and metabolite flow rates (rm) of TR and PH cells and those of the HT template; this goal is expressed as follows:The third goal is to evaluate the fuzzy similarity  algorithm , th, th10), mth metabolite of the TR and PH cells  association are reconstructed from Step A to Step D in Fig. jth forward flux and jth backward flux, respectively. The regulated LB and UB, namely, ith forward and backward fluxes, respectively, obtained from the HV and HT templates; ΩIZ denotes the set of reactions regulated by isozymes identified using the GPR associations, and δ is the modulation parameter determined by the NHDE algorithm [ε) to 0.03 in the present study to restrict the flux value. The flux distributions and metabolite flow rates for the HV and HT cells can be used as HV and HT templates, respectively. These templates can be obtained by solving Eq. (The flow rate of the ls (Eqs.  and 10)mth metablgorithm , 43. Thelgorithm , 43. Thementclasspt{minimaIn our previous study , 43, ideFor a high confidence reaction, the smallest weighting factor is set to obtain a higher flux value in the UFD problem.FV represents the fluxes or metabolite flow rates computed using the TR or PH model. The LB and UB are obtained using the corresponding HV or HT template; that is, LB = ST/4 and UB = 4ST. The term ST denotes the standard value for the HV or HT template used in the present study.The AVTD problem expressed in Eqs. –12) is  is 12) iTwo-sided linear membership functions are used to attribute fuzzy similarity 19) indicηD is a hierarchical criterion that the cell viability grade The AVTD problem is therefore transformed into an MDM problem by applying the membership functions as follows:The membership grades  in Eqs.  and 22)\document in Eqs. . The memons Fig.  and 17)\documentons Fig.  and 19)\documentηD  of the R in Eq.  is the fprograms , 45. We programs . The impmentclass2pt{minimhttp://www.vmh.life) and integrated with the established VBR to form a universal network. The integrated network consisted of 5835 metabolites, 10,601 reactions, 2248 genes and 2426 gene-encoding enzymes. Some of the enzymes in the network regulated the same reactions. The pruning procedures discussed in our previous study [The human metabolic network Recon3D was downloaded from the Virtual Metabolic Human (us study  were usehttps://string-db.org/) and GeneCards (https://www.genecards.org/) databases were used to classify the protein–protein interaction (PPI) networks encoded by these 26 genes into six classes   and Ham’s medium in which a cholesterol uptake reaction did not occur (denoted as Ham − cholesterol). These media were used in the AVTD platform to identify antiviral targets to investigate the relationships between viral biomass growth and different nutrient components. The computational results are presented in Table The data in Tables Drug target combinations can be used to increase therapeutic efficacy and reduce toxicity . HoweverIn the aforementioned results, the enzymes in the GSMM used as the decision variables of the MDM problem and then identified antiviral enzymes according to the NHDE algorithm. We extended this algorithm to combine exchange reactions in the GSMM with candidate enzymes as decision variables to investigate whether the occurrence of an additional uptake reaction in DMEM and Ham’s medium can enhance treatment. The computational results -Acetyl-L-Ornithine could achieve a cell viability grade of 1 and a metabolic deviation grade of 0.641. Notably, the cell viability and metabolic deviation grades for most of the identified two-target combinations were higher than those for the corresponding one-target enzymes, and each combination contained at least one identified target.Additional file 1. Computational procedures of the nested hybrid differential evolution algorithmAdditional file 2. Cell-specific genome-scale metabolic network for heart muscle cellsAdditional file 3. Cell-specific genome-scale metabolic network for SARS-CoV-2 infection in the heartAdditional file 4. A list of uptake reactions in Dulbecco’s Modified Eagle Medium (DMEM) and Ham’s mediumAdditional file 5. A compilation of one-target enzymes identified through the AVTD platform with RPMI medium, and HPLM mediumAdditional file 6. A compilation of one-target enzymes identified through the AVTD platform with RPMI medium complemented with cholesterol, and HPLM medium complemented with cholesterolAdditional file 7. A list of two-target combinations identified by the developed AVTD platform


Genome editing through the development of CRISPR –Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes. Streptococcus pyogenes SpyCas9 (thereafter Cas9), is comprised of several functional domains, which each have specific functional roles in CRISPR interference [CRISPR –Cas technology is arguably now widely used for the generation of genetically modified organisms, synthetic biology and biotechnology applications . A classrference –6.Recognition and binding in Cas9 endonuclease are enabled by two RNA molecules: CRISPR RNA (crRNA) and trans-activating crRNA (tracrRNA) ,4. TogetUnintended targets that are cleaved, known as off-targets, as well as unintended on-target effects such as structural variants, can lead to cell lethality due to gene knockouts or loss of heterozygosity due to chromosomal translocations or rearrangements –13. BecaAcidaminococcus Sp. Cas12a (AsCas12a) is less efficient than Cas9 [Regarding specificity, previous reports that have compared both nucleases suggest that Cas12a is either comparable to, or more specific than Cas9 ,24. Convhan Cas9 ,26 and Chan Cas9 . Despitehan Cas9 –30. Somehan Cas9  and sgRNhan Cas9 , have behan Cas9 –35, wherhan Cas9 ,37.This information makes apparent the large gap between Cas9 and Cas12a prediction tools. This is likely because of a lack of data. For example, Cas9 datasets exist with up to 40,000 gRNAs . No publIn this study we confirmed previous evidence that nucleotide bias and mismatches at the target site relative to the PAM sequence drive the cleavage activity and efficiency at the target and non-target sites. We noticed that Deepcpf1 predicting tool has a different mutational landscape in its training dataset to that observed in cells, potentially affecting its accuracy for guide RNA efficiency prediction. Our alternative prediction model based on a small number of endogenous targets could improve for over 15% guide RNA prediction efficiency for Cas12a. However, despite the improvement, the lack of available datasets with large scale phenotypic measures of guide RNA efficiency of endogenous targets adversely affects the ability to correctly predict guide RNA efficiency. Overall, our results highlight the need for more representative datasets and further benchmarking to accurately predict guide RNA efficiency and off-target effects for Cas12a effectors.Zinderia) to 73% (Nocardioides) and genome size varying from 2kb to 23 Mb (S1 Table). A combination of a custom script and gRNA search using Flashfry [Streptococcus pyogenes SpCas9 5’-NGG-3’ and the common Cas12a orthologs Francicella novicida Cas12a (FnCas12a), Acidaminociccus. Sp (AsCas12a) and Lachnospiraceae bacterium (LbCas12a) harboring 5’-TTTN-3’ PAM sequences. To identify potential off-targets, a combination of Flashfry off-target prediction and Cas-OFFinder [https://www.R-project.org.To compare Cas9 and Cas12 off-target predictions at genome wide levels, based on GC content and genome size, we selected 175 prokaryotic and eukaryotic genome assemblies from NCBI RefSeq and Genebank databases with GC contents varying from 13.5% (Flashfry  was utilOFFinder  was emplS2 Table). We manually curated read counts for cleaved genomic sites from these reports. The read count was grouped by “target” and “number of mismatches” for plotting and analysis. Cas-OFFinder was utilized with default settings to identify potential off-targets with up to five mismatches for each target. The off-target guide prediction was compared to the experimental off-target rates to ensure that targets from the two publications were comparable.To compare Cas9 and Cas12a off-targets using results from two previous GUIDE-Seq experiments, we acquired Cas9 and Cas12a data from Kleinstiver et al. ,39  and paired-end alignments (HEK293T-plasmid), respectively. To quantify the mutational landscape, reads were aligned to a reference genome. For the “HT 1–1” reads, reads barcode and adaptor sequences were trimmed using a custom script. We aligned both sets of reads to the human GRCh38 genome using Bowtie2 [https://www.htslib.org) was utilized to convert the resulting output from bowtie2 (https://bowtie-bio.sourceforge.net/bowtie2/index.shtml) into a BAM file, sort the BAM file and index it. GOANA [https://gt-scan.csiro.au/goana/src/) was used to quantify the mutational landscape of the newly aligned reads. As GOANA excludes alleles with a low read coverage by default, common mutations will be filtered out if they occur on the same allele as rare mutations. To mitigate this, the -mr 0 argument instructs GOANA to include all mutant alleles, regardless of read coverage with the following instruction: python3 GOANA.py regions.bed control.bam treated.bam -o output.file -mr 0. A custom python script was used to parse the output and read it into a DataFrame. For each target, sgRNA sequence and target-adjacent nucleotides, target coordinates, cleavage efficiency and mutations were recorded.We extracted raw read data from Kim et al.  from the Bowtie2  with the Bowtie2   Defining a model involves specifying the algorithm and the hyperparameters. Each algorithm has default hyperparameter values and tuning them may result in an improved model. However, after trialing different hyperparameters, we only ended up adjusting the number of decision trees in our Random Forest model (from 100 trees to 450). We selected the Random Forest algorithm due to its generalizability and resistance to overfitting .Fig 1). This process simply requires two lists, one of the labels from each sample and one of the features. This can be performed on the entire dataset or a slice of the dataset, with the latter being useful for model validation [2) The “fit” step involves training the model on data . The output from this step can be compared to known values for validation.3) The “predict” step takes the trained model and predicts the label for unlabeled samples (mini-human in S3 Table.To train our models we used a monocistronic CRISPR/Cas9 library from Liu and colleagues . This isS3 Table). We also used the “HEK-lentivirus” and “HCT116-plasmid” datasets.To validate our Cas12a efficiency models, we used the “HEK293T-plasmid” dataset , we created a custom version of the GRCh38 reference genome. We used a script to generate a FASTA file with an entry for each of the 2061 targets listed in lleagues . The codWe aligned the HEK293T-plasmid, HEK-lentivirus and HCT116-plasmid to the GRCh38 genome. We subsequently generated a BAM file and index, and quantified the HEK293T-lentivirus and HCT116-plasmid reads using GOANA.As a pooled-library screen, efficiency can be inferred from the log-fold change in read-count, for each CRISPR target, over time. Our hypothesis is that editing “essential genes”, i.e., genes that are required for cell-viability, will result in non-viable cells. This in turn will result in lower cell counts at later time points. Because of this, reads from targets with a low efficiency will be relatively high (with few edits to disrupt cell viability) compared to reads from targets with a high efficiency (with many edits to disrupt cell viability). There should be an inverse correlation between sgRNA efficiency and change in read count over time. A confounding factor here is the “essentiality” of a gene. Because if edits do not reduce a cell’s viability, despite disrupting a gene, then reads from that target will be remain high, regardless of sgRNA efficiency. To minimize the effects of this confounding variable, we only included genes with a high Bayes Factor (BF), as calculated with BAGEL  . We downAn additional feature that has been observed to modulate sgRNA efficiency is chromatin accessibility . QuantifENCFF127KSH (HEK293T)ENCFF912FSU (HCT116)These datasets specify regions of DNA that are DNase hypersensitive for the respective cell types (HEK293T and HCT116). To integrate this data with CRISPR targets, we use a custom script to identify whether CRISPR targets lie within DNase hypersensitive regions. The script assigns a 1 to targets that are in hypersensitive regions and a 0 to targets that are not.We tokenized the sequence for each sample using a custom function that is applied to each row in the dataset. The input is a DNA sequence, and the output is an array of numerical values. This array represents the DNA sequence string using, for example, “global nucleotide” counts and “positional nucleotide” counts. Global nucleotides represent the count of each nucleotide in the string whereas positional nucleotides represent the nucleotide at each position. As well as single nucleotides, we also include global/positional dinucleotides  and GC content.We processed individual sequences separately. So, for example, to model the sgRNA, ssODN and nucleotides adjacent to the target we would apply the above function to each of these components separately. This minimizes the sequence of relevant components being diluted by irrelevant components.To validate a model, or quantify its performance, prediction values are compared to truth values. For example, the predicted sgRNA efficiency to experimentally measured sgRNA efficiency. Every trained model was validated to quantify its performance and compare against other models. This is achieved through an accuracy or error score for which there are various measures that we used. For regression, an option is the mean squared error (MSE). This is the average of the squared differences between the prediction and truth values for each sample, where values closer to zero indicate predictions closer to the truth. MSE is a function included in the scikit-learn Python library which we called on the two lists of values, i.e., mean_squared_error . However, to be able to calculate a prediction measure for a model, samples which were not included when training said model were required. One option was to divide the data into two discrete sets, a train set and a test set. However, another option was cross-validation.Rather than dividing samples into two discrete sets for validation, we used k-fold cross-validation to quantify the performance of models . Using 5An additional use of some ML algorithms is the ability to identify which features are correlated with the label. For Random Forest models, after training a model, feature importance can be retrieved using the feature_importances_ parameter. This returns a list of features (be that nucleotides or reagent quantities) with an assigned weight value for each.To visualize classification predictions, we used a confusion matrix. For two-class predictions (i.e. high/low) this is simply a 2x2 matrix where rows indicate prediction values and columns indicate truth values. In effect, this presents the number of true positives, false positives, true negatives and false negatives. Here we used confusion_matrix from scikit-learn which takes a list of truth values and a list of prediction values.The percentile rank presents how ranked prediction values compare to ranked truth values. The percentile rank illustrates whether the ordering of predictions is correct. Here we used the percentileofscore function from the scipy.stats module to calculate the percentile ranks prediction and truth values and subsequently plot the results with Matplotlib.Receiver operator characteristic (ROC) curves plot the true positive rate against the false positive rate. It represents the discrimination ability of a model, i.e. a model’s ability to distinguish between high and low efficiency samples . The areZinderia) to 73% (Nocardioides sambongensis) (S4 Table). Indeed, we found the ratio of Cas9 to Cas12a targets was correlated with GC content . Additionally, we found a better correlation between GC content and sgRNA availability for Cas9  versus Cas12a . It suggests a disproportionate nature of Cas9 and Cas12a targets. Next, we investigated whether the predicted off-target effects would be a function of the GC content and genome size. We calculated the predicted Cas12a and Cas9 off-target effects, normalized by genome size from 0 to 4 mismatches and calculated the ratio between Cas12a and Cas9 predicted off-targets. A ratio > 1 indicated a higher predicted off-target for Cas9 enzyme. For similar number of predicted sgRNA for Cas9 and Cas12a (GC content = 38.4%), we found a significant difference between Cas9 and Cas12a from none (median ratio (IQR) = 1.06 (1.01 to 1.09)) to three (median ratio (IQR) = 0.83 (0.78 to 1.07)) and four predicted off-targets (median ratio (IQR) = 0.79 (0.74 to 0.95)) suggesting higher off-target rate for Cas12a in our genome sizes varying from 2 kb to 7 Mb . To confirm this finding, we performed a similar analysis on low and high GC content (respectively 30.8% and 66%) were respectively the number of Cas12a target sites are higher and lower to Cas9. Interestingly, while we found a similar trend on low GC content from none (median ratio (IQR) = 1.006 (1.002 to 1.07)) to four predicted off-targets (median ratio (IQR) = 0.48 (0.32 to 0.64))  whereas we found a higher number Cas9 off-targets at high-GC content from none (median (IQR) = 1 (0.97 to 1.006)) to four predicted off-targets (median (IQR) = 29.94 (20.83 to 32.94)) . Together these analyses suggest that nucleotide bias and GC-composition is a major driver for off-target prediction of Cas9 and Cas12a nucleases double stranded break cleavage. As these analyses regard computationally predicted off-targets, we wondered whether this hypothesis would still be valid in an empirical setting.Nucleotide bias, which is a preference towards A/T or C/G nucleotides, varies between organisms or even within individual genomes . We postFig 3A), for Cas9 but not for Cas12a , for similar on-target efficiencies [Fig 3C) thanCas12a . This was likely due to there being more potential predicted off-targets . For example, although only 30% or less of potential detected off-targets with three mismatches were cleaved respectively with Cas9 and cas12a , there were more potential off-targets with three mismatches than with two mismatches or one mismatch . Together, despite the low sample size analyzed in this meta-analysis, these observations suggest firstly that there are lower off-target effects from Cas12a, and secondly, off-target sites are cleaved from Cas9 and Cas12a enzymes at low efficiencies, confirming previous observations [We next assessed the off-target cleavage behaviors for Cas9 and Cas12a on empirical data. we compared side by side Cas12a and Cas9 enzyme specificities by performing a meta-analysis on two publicly available datasets. These datasets compared Cas9 to Cas12a on identical target sites and similar high-throughput off-target detection techniques (GUIDE-seq ). We ideciencies . AlthougOFFinder ) with hirvations –59.Previous works described that the outcomes of NHEJ and microhomology-mediated end joining (MMEJ) are non-random and target site sequence dependent ,60,61 met-test, t = 70.07, p < 10−5 and Cohen’s d of 0.776) . For insertions, the most frequently occurring distribution, had a length of one nucleotide. Insertions of two or more nucleotides occurred less frequently, with the observed number of insertions decreasing as insertion length increased. The longest detectable insertion was eight base-pairs in length. The difference between every distribution of insertions was significant (p < 0.05) except for between seven and eight nucleotide insertions .In the HT 1–1 dataset, we found that deletions occurred more frequently than insertions, with 1.91 deletions for every insertion , These accounted for 25% of all indels. We observed deletions of up to five nucleotides, as there were zero reads capturing deletions of six or more nucleotides in length.Fig 4A). Deletions again had a higher frequency than insertions , however, the difference between insertion frequency and deletion frequency was greater in this dataset (Cohen’s d = 0.958). Perhaps contributing to the increased rate of deletions in this dataset was that the full range of deletions were captured, unlike in the HT 1–1 dataset. This is supported by more than half (53%) of all deletions being greater than five bases in length, which was the maximum deletion length in the HT 1–1 dataset. These observations suggest that the HT 1–1 dataset, whilst large, is not a true representation of the mutational outcomes. This may limit its use in modelling editing outcome.This is surprising as deletions of ten bases and longer were observed for Cas12a editing outcomes in a previous study . We postFig 4B). However, for the “synthetic” treated samples, SNVs existed in a bimodal distribution with the global maximum at nucleotide position 17, downstream from the PAM . This was three nucleotides upstream from the end of the 20nt target. Where the expected proportion of SNVs at each position in the sequenced region being 2.56%, the global maximum is more than three folds this value, at 7.82%. In fact, nearly half (47.30%) of all SNV-containing reads have an SNV at position 17 and its three adjacent positions [We also noticed in the HEK293T-plasmid dataset, that single nucleotide variants (SNVs) contributed to just 6.89% of all short variants whereas it was 63.4% in the HT 1–1 dataset. We plotted the SNV outcome counts at each position in the gRNA targets. For control (no Cas12a editing), SNVs existed in a uniform distribution across the length of the reads . With three possible outcomes for each nucleotide, this is more than three folds greater than the chance. Predicting a nucleotide A outcome was the least accurate, with an accuracy of 49%, but this is still more a 200% improvement over chance. We plotted the model as one versus all receiver operating curves (ROCs) , with the average area under the ROC of 0.84 further supporting the model.We next investigated whether features were present in the dataset that were modulating SNV outcome. We trained a Random Forest multiclass classifier on targets with an SNV at position 17. We labelled this set of targets with the SNV outcome . To filter out noise, we only included SNVs that were present in greater than 1% of target reads. we created features from the nucleotide sequence of the target and surrounding region, 39 bases in total based on the premise that prior nucleotides modulate Cas9 mediated double stranded break outcomes ,65. FromAlthough this prediction model performs well on unseen HT 1.1, it was no better than chance at predicting the SNV outcome on the HEK293T-plasmid dataset. Furthermore, we observed that a single nucleotide 26 nucleotides upstream from the PAM was correlated with the SNV outcome at position 17. This observation was not present in the HEK293T-plasmid dataset, suggesting it is either a result of the non-endogenous targets, or another sequencing artefact. In addition to the dataset lacking reads with deletions longer than five bases, we found this dataset to not be a suitable candidate for modelling editing outcome.Fig 6).DeepCpf1, is trained on the HT 1–1 dataset. This model is reported to perform well, with a Spearman’s coefficient of 0.87 on HEK293T-plasmid dataset and 0.77 on the HCT116-plasmid dataset . We nextFig 6A). However, cleavage efficiency also had a high degree of variability within groups, with variances ranging from 21 to 1045 . Cas12a cleavage was significantly more efficient in cleaving “synthetic” targets (average = 41.39% ± 32.31) than in endogenous targets (average = 6.98% ± 12.37); Welsh t statistics = 43.06, p < 1x10-10. Two factors that appeared to be correlated with cleavage efficiency were cell type and CRISPR delivery method. Comparing different cell types for accessible targets with a plasmid delivery method, cleavage efficiency of targets in HEK293T cells (average = 23.85% ± 18.91) was significantly higher than the cleavage efficiency of targets in HCT116 cells (average = 8.84% ± 8.13); Welsh t = 3.28, p = 0.003. Comparing different delivery methods for accessible targets in HEK293 cells, a plasmid delivery method resulted in a higher cleavage efficiency (average = 23.85% ± 18.91) than a lentiviral delivery method (average = 15.09% ± 17.35), however this was not significant; Welsh t = 1.79, p = 0.08. More importantly, a potential confounding variable was chromatin accessibility, as this has been observed to modulate cleavage efficiency [The mean cleavage efficiency had a high degree of variability between groups, with means ranging from 2% to 45% , we identified a pooled-library screen (Mini-human) with 2,061 samples across 687 human genes . In thisTable 1 along with Pearson’s coefficients from the DeepCpf1 model [P < 0.05).After pre-processing, the sample size was reduced to 306 target sites. Because of the relatively small sample size, we trained models using Random Forest algorithm. We included global nucleotide counts and local nucleotide counts as features and sampled discrete regions of the guide in a sliding window. We used five-fold cross validation on a customized mini-human dataset for training and testing, and validated on each of the HEK293T-plasmid, HCT116-plasmid and HEK293T-lentivirus deliveries datasets from Kim and colleagues . We idenf1 model . For eacThe improvement on the independent datasets achieved by Random Forest over DeepCpf1 was despite the training set for Random Forest being 50x smaller than the training set for DeepCpf1. Despite the improvements, the models do share similarities regarding different datasets, which suggests that both models are lacking in features modelled. This is because the performance of both models varies between cell type and CRISPR delivery method. Furthermore, both models tend to over- or under-estimate the efficiencies of different datasets to different degrees . These observations on the endogenous targets are consistent to previously published work, which has demonstrated a higher indel frequency and length [. Further suggesting these are artefactual, a previous genome-wide sequencing effort on Cas12a edited cells revealed that observed SNVs were largely from background rather than Cas12a mediated [DeepCpf1, the deep learning model for predicting the guide RNA efficiency for Cas12a enzymes, is trained on the largest available empirical dataset (HT 1–1), to date . Howeverd length ,63,75. Tmediated . These c. To test this hypothesis, we trained a machine learning model on an endogenous dataset and validated on the same datasets used to validate DeepCpf1. The results supported our hypothesis that a model trained on more representative data will be more generalizable . On accessible targets, the correlation between predicted cleavage efficiency and observed cleavage efficiency improved from between 14% to 50%. This was despite our Random Forest modelling facing several disadvantages. Firstly, the sample size was low. At just 306 sgRNAs, the size of this dataset used in training was relatively small. We propose that a dataset the size of HT 1–1, with 15,000 sgRNAs at endogenous targets would result in an improved performance over both our model and DeepCpf1. The second was the type of dataset being a pooled-library screen. As although it enabled training an accurate efficiency prediction model, as a knock-out library, the scope of modelling was limited. For example, it was not possible to model editing outcome as the sequence reads represented changes in cell counts, rather than editing outcomes. Finally, model validation suggested that the influence of other variables on cleavage efficiency modulate Cas12a cleavage efficiency. With both models separating the three experiments into different distributions by cell type and CRISPR delivery method, this indicates that information is still lacking from the model. To enable modelling of these properties, the hypothetical 15,000 endogenous targets would be required to exist across different cell types through different delivery methods.We therefore hypothesized that a model trained on endogenous targets would outperform the DeepCpf1 model. We postulate that this is due to the discrepancy in the “synthetic” HT 1–1 dataset and the observed editing outcomes from endogenous targets In conclusion, in accordance with previous work, we demonstrated that Cas12a enzymes cleave more unique targets than Cas9 and exhibit in general lower cleavage efficiency at the target and off-target sites. Importantly our results highlight that despite the recent progresses in the field, it underscores the need to gain more information from experimental data to enable an efficient predictive model of target cleavage efficiency from guide RNA design.S1 Figa) low GC content (30.8%) and (b) high GC content (66%) with zero to five mismatches to the original target and aggregated per genome. A ratio > 1 signifies Cas9 off-targets are higher than Cas12a. Statistical tests were performed using a non-parametric test  and multiple comparisons were corrected with a Bonferroni test. ** indicates corrected p < 0.01 and *** indicates corrected p < 0.001 n = 10 (low GC content) and n = 18 (high GC content) per group.The ratio of Cas9 to Cas12a predicted off-targets across the genomes for each gRNA designed for ((PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.