Michael H. Baym

IsoRankN: spectral methods for global alignment of multiple protein networks

Motivation: With the increasing availability of large protein–protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species. Results: We introduce IsoRankN (IsoRank-Nibble) a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error tolerant and computationally efficient. Availability: Our software is available freely for non-commercial purposes on request from: http://isorank.csail.mit.edu/ Contact: bab@mit.edu

Large-scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi‐level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi‐level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low‐complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.

Multidrug evolutionary strategies to reverse antibiotic resistance

Evolving antibiotic rescue stratagems Antibiotic resistance threatens to put modern medicine into reverse. But we are not at the end of our options for currently available drugs. Baym et al. review what can be done by using combinations of antibiotics to circumvent bacterias evolutionary strategies. For instance, resistance to one drug may cause sensitivity to another, the effectiveness of two drugs can be synergized by a resistance mutation, and some negative drug interactions may even be beneficial in selecting against resistance. Although not simple to assess, drug combinations still have something to offer for the development of sorely needed anti-infectives. Science, this issue p. 10.1126/science.aad3292 BACKGROUND Antibiotics are among the most important tools in medicine, but their efficacy is threatened by the evolution of resistance. Since the earliest days of antibiotics, resistance has been observed and recognized as a threat; today, many first-generation drugs are all but ineffective. The paradox of antibiotics is that through their use, they not only inhibit an infection but also select for the emergence and spread of resistance, directly counteracting their long-term efficacy. We have thus far avoided a crisis through the continued modification of existing compounds and the discovery of new antibiotic classes. It has been hoped that restricting the use of particular antibiotics would neutralize the selective advantage of resistance and restore widespread sensitivity over time; however, decades of experience have shown that resistance does not disappear so easily. The same is true for combining antibiotics with compounds that inhibit their specific resistance mechanisms; this approach is effective in potentiating and broadening the spectrum of antibiotics, but it only neutralizes the advantage of resistant bacteria and does not actively select against resistance over time. To prevent the evolution of resistance or turn a resistant population susceptible again, we need ways to fully invert the selective advantage of resistance. ADVANCES Recent discoveries have shown that it is possible to invert the selective advantage of resistant bacteria and reverse the evolution of antibiotic resistance. Whereas with single-drug therapy, there is always a selective advantage to resistance, specific combinations of drugs can inhibit bacterial growth while disfavoring resistance to the individual components. To confer a direct disadvantage to resistant mutants, techniques have been developed that exploit the specific physiological and evolutionary interactions between drugs. First, if one drug partially suppresses the effect of another, becoming resistant to the first drug will remove its protection against the second, giving a disadvantage to the resistant mutants. Second, mutations that confer resistance to a drug can be counteracted if they induce synergy between the drug and another compound. Finally, there can be trade-offs between resistances to different compounds such that resistance to one antibiotic causes collateral sensitivity to another antibiotic or to a compound whose toxicity is mediated by the resistance mechanism. These approaches can be used to invert the selective advantage of resistant bacteria competing with their sensitive cousins and can potentially decrease the rate at which resistance evolves, or even drive a resistant bacterial population back toward drug sensitivity. OUTLOOK Substantial barriers remain for the clinical application of selection-inverting treatment strategies. Antibiotic treatment decisions must typically be made within minutes, whereas the isolation and analysis of an infection take between hours and days, even with state-of-the art technology. Further, the optimal choice of these strategies depends on the specific genetics of the pathogen and the resistance mechanism. Thus, practical deployment of selection inversion approaches will require the development of fast, genomic diagnostics that can identify not only the pathogen’s current resistance profile but also its future potential for evolution of resistance. Such genomic diagnostics could further be used to inform treatment, channel pathogens toward less resistance-prone genotypes, monitor population-wide and environmental resistance levels, and identify new resistance mechanisms before they enter the clinic. Additionally, most of the studies on selection inversion have been performed in vitro and need to be validated in animal models and clinical isolates. Strategies relying on coadministration are further complicated by pharmacokinetics, which may vary across compounds. Moreover, the unique drug interactions underlying these approaches may change across different environments and genetic backgrounds or over time as the pathogens evolve. Finally, the deployment of these strategies requires a careful ethical balance between curing the individual and reducing resistance in the community. Ultimately, combating resistance will necessitate a portfolio of strategies that anticipate the evolution of the infection and adapt to both treat and avoid resistance. Countering antibiotic resistance through selection inversion. Resistance to antibiotics evolves as a direct consequence of their use to suppress bacterial growth. The present strategy of discovering new antibiotics and waiting for new resistance to evolve is untenable in the long term. However, promising new strategies to manipulate evolution and invert selection against resistance may prolong the utility of existing antibiotics or even restore the activity of old drugs. Antibiotic treatment has two conflicting effects: the desired, immediate effect of inhibiting bacterial growth and the undesired, long-term effect of promoting the evolution of resistance. Although these contrasting outcomes seem inextricably linked, recent work has revealed several ways by which antibiotics can be combined to inhibit bacterial growth while, counterintuitively, selecting against resistant mutants. Decoupling treatment efficacy from the risk of resistance can be achieved by exploiting specific interactions between drugs, and the ways in which resistance mutations to a given drug can modulate these interactions or increase the sensitivity of the bacteria to other compounds. Although their practical application requires much further development and validation, and relies on advances in genomic diagnostics, these discoveries suggest novel paradigms that may restrict or even reverse the evolution of resistance.

Inexpensive Multiplexed Library Preparation for Megabase-Sized Genomes

Whole-genome sequencing has become an indispensible tool of modern biology. However, the cost of sample preparation relative to the cost of sequencing remains high, especially for small genomes where the former is dominant. Here we present a protocol for rapid and inexpensive preparation of hundreds of multiplexed genomic libraries for Illumina sequencing. By carrying out the Nextera tagmentation reaction in small volumes, replacing costly reagents with cheaper equivalents, and omitting unnecessary steps, we achieve a cost of library preparation of

IsoBase: a database of functionally related proteins across PPI networks

8 per sample, approximately 6 times cheaper than the standard Nextera XT protocol. Furthermore, our procedure takes less than 5 hours for 96 samples. Several hundred samples can then be pooled on the same HiSeq lane via custom barcodes. Our method will be useful for re-sequencing of microbial or viral genomes, including those from evolution experiments, genetic screens, and environmental samples, as well as for other sequencing applications including large amplicon, open chromosome, artificial chromosomes, and RNA sequencing.

Spatiotemporal microbial evolution on antibiotic landscapes

We describe IsoBase, a database identifying functionally related proteins, across five major eukaryotic model organisms: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus and Homo Sapiens. Nearly all existing algorithms for orthology detection are based on sequence comparison. Although these have been successful in orthology prediction to some extent, we seek to go beyond these methods by the integration of sequence data and protein–protein interaction (PPI) networks to help in identifying true functionally related proteins. With that motivation, we introduce IsoBase, the first publicly available ortholog database that focuses on functionally related proteins. The groupings were computed using the IsoRankN algorithm that uses spectral methods to combine sequence and PPI data and produce clusters of functionally related proteins. These clusters compare favorably with those from existing approaches: proteins within an IsoBase cluster are more likely to share similar Gene Ontology (GO) annotation. A total of 48 120 proteins were clustered into 12 693 functionally related groups. The IsoBase database may be browsed for functionally related proteins across two or more species and may also be queried by accession numbers, species-specific identifiers, gene name or keyword. The database is freely available for download at http://isobase.csail.mit.edu/.

Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes.

A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)–plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front. While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front, we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behind more sensitive lineages. The MEGA-plate provides a versatile platform for studying microbial adaption and directly visualizing evolutionary dynamics.

Engineered bacteria can function in the mammalian gut long-term as live diagnostics of inflammation

Predicting evolutionary paths to antibiotic resistance is key for understanding and controlling drug resistance. When considering a single final resistant genotype, epistatic contingencies among mutations restrict evolution to a small number of adaptive paths. Less attention has been given to multi-peak landscapes, and while specific peaks can be favoured, it is unknown whether and how early a commitment to final fate is made. Here we characterize a multi-peaked adaptive landscape for trimethoprim resistance by constructing all combinatorial alleles of seven resistance-conferring mutations in dihydrofolate reductase. We observe that epistatic interactions increase rather than decrease the accessibility of each peak; while they restrict the number of direct paths, they generate more indirect paths, where mutations are adaptively gained and later adaptively lost or changed. This enhanced accessibility allows evolution to proceed through many adaptive steps while delaying commitment to genotypic fate, hindering our ability to predict or control evolutionary outcomes.

Compounds that select against the tetracycline-resistance efflux pump

Bacteria can be engineered to function as diagnostics or therapeutics in the mammalian gut but commercial translation of technologies to accomplish this has been hindered by the susceptibility of synthetic genetic circuits to mutation and unpredictable function during extended gut colonization. Here, we report stable, engineered bacterial strains that maintain their function for 6 months in the mouse gut. We engineered a commensal murine Escherichia coli strain to detect tetrathionate, which is produced during inflammation. Using our engineered diagnostic strain, which retains memory of exposure in the gut for analysis by fecal testing, we detected tetrathionate in both infection-induced and genetic mouse models of inflammation over 6 months. The synthetic genetic circuits in the engineered strain were genetically stable and functioned as intended over time. The durable performance of these strains confirms the potential of engineered bacteria as living diagnostics.

Barcode extension for analysis and reconstruction of structures

We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline resistance efflux pump in E. coli and identified two hits: β-thujaplicin and disulfiram. Treating a tetracycline resistant population with β-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline.