Simultaneous Viral Exposure and Protection from Neanderthal Introgression.

In this issue, Enard and Petrov present intriguing results on the possibility of genetic traces left behind in our genomes from adaptation to past viral epidemics that may have been initiated by interaction with Neanderthal archaic hominins. The work highlights how powerful infectious agents can act as a selective force to shape our genetic makeup.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

BACKGROUND: Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS: Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS: We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

Quickomics: exploring omics data in an intuitive, interactive and informative manner.

SUMMARY: We developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics statistical analysis results and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customizable options and generates both interactive and publication-ready plots to uncover biological insights from data. The modular design makes the tool extensible with ease. AVAILABILITY AND IMPLEMENTATION: Researchers can experience the functionalities with their own data or demo RNA-Seq and proteomics datasets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/Quickomics for local installation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Two Distinct Mechanisms of Inhibition of LpxA Acyltransferase Essential for Lipopolysaccharide Biosynthesis.

The lipopolysaccharide biosynthesis pathway is considered an attractive drug target against the rising threat of multi-drug-resistant Gram-negative bacteria. Here, we report two novel small-molecule inhibitors (compounds 1 and 2) of the acyltransferase LpxA, the first enzyme in the lipopolysaccharide biosynthesis pathway. We show genetically that the antibacterial activities of the compounds against efflux-deficient Escherichia coli are mediated by LpxA inhibition. Consistently, the compounds inhibited the LpxA enzymatic reaction in vitro. Intriguingly, using biochemical, biophysical, and structural characterization, we reveal two distinct mechanisms of LpxA inhibition; compound 1 is a substrate-competitive inhibitor targeting apo LpxA, and compound 2 is an uncompetitive inhibitor targeting the LpxA/product complex. Compound 2 exhibited more favorable biological and physicochemical properties than compound 1 and was optimized using structural information to achieve improved antibacterial activity against wild-type E. coli. These results show that LpxA is a promising antibacterial target and imply the advantages of targeting enzyme/product complexes in drug discovery.

Mode of Action of the Monobactam LYS228 and Mechanisms Decreasing In Vitro Susceptibility in Escherichia coli and Klebsiella pneumoniae.

The monobactam scaffold is attractive for the development of new agents to treat infections caused by drug-resistant Gram-negative bacteria because it is stable to metallo-ß-lactamases (MBLs). However, the clinically used monobactam aztreonam lacks stability to serine ß-lactamases (SBLs) that are often coexpressed with MBLs. LYS228 is stable to MBLs and most SBLs. LYS228 bound purified Escherichia coli penicillin binding protein 3 (PBP3) similarly to aztreonam (derived acylation rate/equilibrium dissociation constant [k2/Kd ] of 367,504 s-1 M-1 and 409,229 s-1 M-1, respectively) according to stopped-flow fluorimetry. A gel-based assay showed that LYS228 bound mainly to E. coli PBP3, with weaker binding to PBP1a and PBP1b. Exposing E. coli cells to LYS228 caused filamentation consistent with impaired cell division. No single-step mutants were selected from 12 Enterobacteriaceae strains expressing different classes of ß-lactamases at 8× the MIC of LYS228 (frequency, <2.5 × 10-9). At 4× the MIC, mutants were selected from 2 of 12 strains at frequencies of 1.8 × 10-7 and 4.2 × 10-9 LYS228 MICs were ≤2 µg/ml against all mutants. These frequencies compared favorably to those for meropenem and tigecycline. Mutations decreasing LYS228 susceptibility occurred in ramR and cpxA (Klebsiella pneumoniae) and baeS (E. coli and K. pneumoniae). Susceptibility of E. coli ATCC 25922 to LYS228 decreased 256-fold (MIC, 0.125 to 32 µg/ml) after 20 serial passages. Mutants accumulated mutations in ftsI (encoding the target, PBP3), baeR, acrD, envZ, sucB, and rfaI These results support the continued development of LYS228, which is currently undergoing phase II clinical trials for complicated intraabdominal infection and complicated urinary tract infection (registered at ClinicalTrials.gov under identifiers NCT03377426 and NCT03354754).

Leveraging Multiple Populations across Time Helps Define Accurate Models of Human Evolution: A Reanalysis of the Lactase Persistence Adaptation.

Access to a geographically diverse set of modern human samples from the present time and from ancient remains, combined with archaic hominin samples, provides an unprecedented level of resolution to study both human history and adaptation. The amount and quality of ancient human data continue to improve and enable tracking the trajectory of genetic variation over time. These data have the potential to help us redefine or generate new hypotheses of how human evolution occurred and to revise previous conjectures. In this article, we argue that leveraging all these data will help us better detail adaptive histories in humans. As a case in point, we focus on one of the most celebrated examples of human adaptation: the evolution of lactase persistence. We briefly review this dietary adaptation and argue that, effectively, the evolutionary history of lactase persistence is still not fully resolved. We propose that, by leveraging data from multiple populations across time and space, we will find evidence of a more nuanced history than just a simple selective sweep. We support our hypotheses with simulation results and make some cautionary notes regarding the use of haplotype-based summary statistics to estimate evolutionary parameters.

Discovering anti-platelet drug combinations with an integrated model of activator-inhibitor relationships, activator-activator synergies and inhibitor-inhibitor synergies.

Identifying effective therapeutic drug combinations that modulate complex signaling pathways in platelets is central to the advancement of effective anti-thrombotic therapies. However, there is no systems model of the platelet that predicts responses to different inhibitor combinations. We developed an approach which goes beyond current inhibitor-inhibitor combination screening to efficiently consider other signaling aspects that may give insights into the behaviour of the platelet as a system. We investigated combinations of platelet inhibitors and activators. We evaluated three distinct strands of information, namely: activator-inhibitor combination screens (testing a panel of inhibitors against a panel of activators); inhibitor-inhibitor synergy screens; and activator-activator synergy screens. We demonstrated how these analyses may be efficiently performed, both experimentally and computationally, to identify particular combinations of most interest. Robust tests of activator-activator synergy and of inhibitor-inhibitor synergy required combinations to show significant excesses over the double doses of each component. Modeling identified multiple effects of an inhibitor of the P2Y12 ADP receptor, and complementarity between inhibitor-inhibitor synergy effects and activator-inhibitor combination effects. This approach accelerates the mapping of combination effects of compounds to develop combinations that may be therapeutically beneficial. We integrated the three information sources into a unified model that predicted the benefits of a triple drug combination targeting ADP, thromboxane and thrombin signaling.

Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis.

BACKGROUND: Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion. RESULTS: Virtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon ß (IFNß) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFNß that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFNß on proliferation of fibroblast-like synoviocytes and interleukin-10 synthesis in macrophages each partially counteract reductions in synovial inflammation imparted by rituximab. A multianalyte biomarker panel predictive for virtual population therapeutic responses suggested population dependencies on B cell-dependent mediators as well as additional markers implicating fibroblast-like synoviocytes. CONCLUSIONS: The results illustrate how the MAPEL algorithm can leverage knowledge of cellular and molecular function through biosimulation to propose clear mechanistic hypotheses for differences in clinical populations. Furthermore, MAPEL facilitates the development of multianalyte biomarkers prognostic of patient responses in silico.

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework.

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place. Researchers can experience the functionalities by exploring demo data sets hosted at http://shinyone.bxgenomics.com or following the tutorial, https://interactivereport.github.io/RNASequest/tutorial/docs/introduction.html to set up the framework locally to process private RNAseq datasets. The source code released under MIT open-source license is provided at https://github.com/interactivereport/RNASequest.

Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer.

Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85−5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers.

OmicsView: Omics data analysis through interactive visual analytics.

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000 s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohn's disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap.

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

Benchmarking and optimization of a high-throughput sequencing based method for transgene sequence variant analysis in biotherapeutic cell line development.

In recent years, High-Throughput Sequencing (HTS) based methods to detect mutations in biotherapeutic transgene products have become a key quality step deployed during the development of manufacturing cell line clones. Previously we reported on a higher throughput, rapid mutation detection method based on amplicon sequencing (targeting transgene RNA) and detailed its implementation to facilitate cell line clone selection. By gaining experience with our assay in a diverse set of cell line development programs, we improved the computational analysis as well as experimental protocols. Here we report on these improvements as well as on a comprehensive benchmarking of our assay. We evaluated assay performance by mixing amplicon samples of a verified mutated antibody clone with a non-mutated antibody clone to generate spike-in mutations from ∼60% down to ∼0.3% frequencies. We subsequently tested the effect of 16 different sample and HTS library preparation protocols on the assay's ability to quantify mutations and on the occurrence of false-positive background error mutations (artifacts). Our evaluation confirmed assay robustness, established a high confidence limit of detection of ∼0.6%, and identified protocols that reduce error levels thereby significantly reducing a source of false positives that bottlenecked the identification of low-level true mutations.

Next-generation Bruton's tyrosine kinase inhibitor BIIB091 selectively and potently inhibits B cell and Fc receptor signaling and downstream functions in B cells and myeloid cells.

OBJECTIVES: Bruton's tyrosine kinase (BTK) plays a non-redundant signaling role downstream of the B-cell receptor (BCR) in B cells and the receptors for the Fc region of immunoglobulins (FcR) in myeloid cells. Here, we characterise BIIB091, a novel, potent, selective and reversible small-molecule inhibitor of BTK. METHODS: BIIB091 was evaluated in vitro and in vivo in preclinical models and in phase 1 clinical trial. RESULTS: In vitro, BIIB091 potently inhibited BTK-dependent proximal signaling and distal functional responses in both B cells and myeloid cells with IC50s ranging from 3 to 106 nm, including antigen presentation to T cells, a key mechanism of action thought to be underlying the efficacy of B cell-targeted therapeutics in multiple sclerosis. BIIB091 effectively sequestered tyrosine 551 in the kinase pocket by forming long-lived complexes with BTK with t 1/2 of more than 40 min, thereby preventing its phosphorylation by upstream kinases. As a key differentiating feature of BIIB091, this property explains the very potent whole blood IC50s of 87 and 106 nm observed with stimulated B cells and myeloid cells, respectively. In vivo, BIIB091 blocked B-cell activation, antibody production and germinal center differentiation. In phase 1 healthy volunteer trial, BIIB091 inhibited naïve and unswitched memory B-cell activation, with an in vivo IC50 of 55 nm and without significant impact on lymphoid or myeloid cell survival after 14 days of dosing. CONCLUSION: Pharmacodynamic results obtained in preclinical and early clinical settings support the advancement of BIIB091 in phase 2 clinical trials.

Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology.

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.

Optimal stepwise experimental design for pairwise functional interaction studies.

MOTIVATION: Pairwise experimental perturbation is increasingly used to probe gene and protein function because these studies offer powerful insight into the activity and regulation of biological systems. Symmetric two-dimensional datasets, such as pairwise genetic interactions are amenable to an optimally designed measurement procedure because of the equivalence of cases and conditions where fewer experimental measurements may be required to extract the underlying structure. RESULTS: We show that optimal experimental design can provide improvements in efficiency when collecting data in an iterative manner. We develop a method built on a statistical clustering model for symmetric data and the Fisher information uncertainty estimates, and we also provide simple heuristic approaches that have comparable performance. Using yeast epistatic miniarrays as an example, we show that correct assignment of the major subnetworks could be achieved with <50% of the measurements in the complete dataset. Optimization is likely to become critical as pairwise functional studies extend to more complex mammalian systems where all by all experiments are currently intractable.

Discovery of small molecule inhibitors of protein-protein interactions using combined ligand and target score normalization.

Docking experiments of multiple compounds typically focus on a single protein. However, other targets provide information about relative binding efficiencies that is otherwise lacking. We developed a docking strategy that normalized results in both the ligand and target dimensions. This was applied to dock 287 approved small drugs with 35 peptide-binding proteins, including 15 true positives. The combined docking score was normalized by drug and protein and by incorporating information on contact similarity to the template protein-peptide contacts. The 20 top ranking hits included 6 true positives, and three matches with suggestive evidence in the literature: the cardiac glycoside digitoxin may inhibit WW domain interactions, the 14-3-3 zeta protein may bind negatively charged ligands, and the nuclear receptor coactivator site may bind nuclear receptor agonists. Additionally, the Bcl-2 antiapoptotic protein is predicted to bind pargyline, and the antiapoptic p53 interacting protein MDM2 is suggested to bind clofazimine. These predictions represent starting points for the experimental development of PPI inhibitors based on an existing database of approved drugs and demonstrate that two-dimensional normalization improves docking efficiency.

Universally sloppy parameter sensitivities in systems biology models.

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Variational method for estimating the rate of convergence of Markov-chain Monte Carlo algorithms.

We demonstrate the use of a variational method to determine a quantitative lower bound on the rate of convergence of Markov chain Monte Carlo (MCMC) algorithms as a function of the target density and proposal density. The bound relies on approximating the second largest eigenvalue in the spectrum of the MCMC operator using a variational principle and the approach is applicable to problems with continuous state spaces. We apply the method to one dimensional examples with Gaussian and quartic target densities, and we contrast the performance of the random walk Metropolis-Hastings algorithm with a "smart" variant that incorporates gradient information into the trial moves, a generalization of the Metropolis adjusted Langevin algorithm. We find that the variational method agrees quite closely with numerical simulations. We also see that the smart MCMC algorithm often fails to converge geometrically in the tails of the target density except in the simplest case we examine, and even then care must be taken to choose the appropriate scaling of the deterministic and random parts of the proposed moves. Again, this calls into question the utility of smart MCMC in more complex problems. Finally, we apply the same method to approximate the rate of convergence in multidimensional Gaussian problems with and without importance sampling. There we demonstrate the necessity of importance sampling for target densities which depend on variables with a wide range of scales.

Interplay of Klebsiella pneumoniae fabZ and lpxC Mutations Leads to LpxC Inhibitor-Dependent Growth Resulting from Loss of Membrane Homeostasis.

Tight coordination of inner and outer membrane biosynthesis is very important in Gram-negative bacteria. Biosynthesis of the lipid A moiety of lipopolysaccharide, which comprises the outer leaflet of the outer membrane has garnered interest for Gram-negative antibacterial discovery. In particular, several potent inhibitors of LpxC (the first committed step of the lipid A pathway) are described. Here we show that serial passaging of Klebsiella pneumoniae in increasing levels of an LpxC inhibitor yielded mutants that grew only in the presence of the inhibitor. These strains had mutations in fabZ and lpxC occurring together (encoding either FabZR121L/LpxCV37G or FabZF51L/LpxCV37G). K. pneumoniae mutants having only LpxCV37G or LpxCV37A or various FabZ mutations alone were less susceptible to the LpxC inhibitor and did not require LpxC inhibition for growth. Western blotting revealed that LpxCV37G accumulated to high levels, and electron microscopy of cells harboring FabZR121L/LpxCV37G indicated an extreme accumulation of membrane in the periplasm when cells were subcultured without LpxC inhibitor. Significant accumulation of detergent-like lipid A pathway intermediates that occur downstream of LpxC (e.g., lipid X and disaccharide monophosphate [DSMP]) was also seen. Taken together, our results suggest that redirection of lipid A pathway substrate by less active FabZ variants, combined with increased activity from LpxCV37G was overdriving the lipid A pathway, necessitating LpxC chemical inhibition, since native cellular maintenance of membrane homeostasis was no longer functioning.IMPORTANCE Emergence of antibiotic resistance has prompted efforts to identify and optimize novel inhibitors of antibacterial targets such as LpxC. This enzyme catalyzes the first committed step of lipid A synthesis, which is necessary to generate lipopolysaccharide and ultimately the Gram-negative protective outer membrane. Investigation of this pathway and its interrelationship with inner membrane (phospholipid) biosynthesis or other pathways is therefore highly important to the fundamental understanding of Gram-negative bacteria and by extension to antibiotic discovery. Here we exploited the availability of a novel LpxC inhibitor to engender the generation of K. pneumoniae resistant mutants whose growth depends on chemical inhibition of LpxC. Inhibitor dependency resulted from the interaction of different resistance mutations and was based on loss of normal cellular mechanisms required to establish membrane homeostasis. This study provides new insights into the importance of this process in K. pneumoniae and how it may be linked to novel biosynthetic pathway inhibitors.