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Background Evolutionary pressures on bacterial pathogens can result in phenotypic change including increased virulence, drug resistance, and transmissibility. Understanding the evolution of these phenotypes in nature and the multiple genetic changes needed has historically been difficult due to sparse and contemporaneous sampling. A complete picture of the evolutionary routes frequently travelled by pathogens would allow us to better understand bacterial biology and potentially forecast pathogen population shifts. Methods In this work, we develop a phylogeny-based method to assess evolutionary dependency between mutations. We apply our method to a dataset of 31,428Mycobacterium tuberculosiscomplex (MTBC) genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. Results We find evolutionary dependency within simultaneously- and sequentially-acquired variation, and identify that genes with dependent sites are enriched in antibiotic resistance and antigenic function. We discover 20 mutations that potentiate the development of antibiotic resistance and 1,003 dependencies that evolve as a consequence antibiotic resistance. Varying by antibiotic, between 9% and 80% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate that mutational dependence can not only improve prediction of phenotype (e.g. antibiotic resistance), but can also detect sequential environmental pressures on the pathogen (e.g. the pressures imposed by sequential antibiotic exposure during the course of standard multi-antibiotic treatment). Taken together, our results demonstrate the feasibility and utility of detecting dependent events in the evolution of natural populations. Data and code available at:https://github.com/farhat-lab/DependentMutations

Long diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited in part by a lack of interpretability and verifiability, especially in deep learning methods. Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates. The CNN performs with state-of-the-art levels of predictive accuracy. Evaluation of salient sequence features permits biologically meaningful interpretation and validation of the CNN’s predictions, with promising repercussions for functional variant discovery, clinical applicability, and translation to phenotype prediction in other organisms.

Bacteria from the orders Bacillales and Clostridiales differentiate into stress-resistant spores that can remain dormant for years, yet rapidly germinate upon nutrient sensing. How spores monitor nutrients is poorly understood but in most cases requires putative membrane receptors. The prototypical receptor from Bacillus subtilis consists of three proteins (GerAA, GerAB, GerAC) required for germination in response to L-alanine. GerAB belongs to the Amino Acid-Polyamine-Organocation superfamily of transporters. Using evolutionary co-variation analysis, we provide evidence that GerAB adopts a structure similar to an L-alanine transporter from this superfamily. We show that mutations in gerAB predicted to disrupt the ligand-binding pocket impair germination, while mutations predicted to function in L-alanine recognition enable spores to respond to L-leucine or L-serine. Finally, substitutions of bulkier residues at these positions cause constitutive germination. These data suggest that GerAB is the L-alanine sensor and that B subunits in this broadly conserved family function in nutrient detection., Germination of Bacillus subtilis spores in response to L-alanine requires a putative membrane receptor consisting of three proteins. Here, Artzi et al. use evolutionary co-variation analysis and functional assays of mutants to provide evidence that one of the proteins, GerAB, likely acts as the L-alanine sensor.

The majority of protein interactions in most organisms are unknown, and experimental methods for determining protein interactions can yield divergent results. Here we use an orthogonal, purely computational method based on sequence coevolution to discover protein interactions at large scale. In the model organism Escherichia coli, 53% of protein pairs in the proteome are eligible for our method given currently available sequenced genomes. When assaying the entire cell envelope proteome, which is understudied due to experimental challenges, we found 620 likely interactions and their predicted structures, increasing the space of known interactions by 529. Our results show that genomic sequencing data can be used to predict and resolve protein interactions to atomic resolution at large scale. Predictions and code are freely available at https://marks.hms.harvard.edu/ecolicomplex and https://github.com/debbiemarkslab/EVcouplings

SummaryCoevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users.Availabilityhttps://github.com/debbiemarkslab/evcouplingsContactsander.research@gmail.com, debbie@hms.harvard.edu

Protocadherins (Pcdhs) are cell adhesion and signaling proteins used by neurons to develop and maintain neuronal networks, relying on trans homophilic interactions between their extracellular cadherin (EC) repeat domains. We present the structure of the antiparallel EC1-4 homodimer of human PcdhγB3, a member of the γ subfamily of clustered Pcdhs. Structure and sequence comparisons of α, β, and γ clustered Pcdh isoforms illustrate that subfamilies encode specificity in distinct ways through diversification of loop region structure and composition in EC2 and EC3, which contains isoform-specific conservation of primarily polar residues. In contrast, the EC1/EC4 interface comprises hydrophobic interactions that provide non-selective dimerization affinity. Using sequence coevolution analysis, we found evidence for a similar antiparallel EC1-4 interaction in non-clustered Pcdh families. We thus deduce that the EC1-4 antiparallel homodimer is a general interaction strategy that evolved before the divergence of these distinct protocadherin families. DOI: http://dx.doi.org/10.7554/eLife.18449.001, eLife digest As the brain develops, nerve cells or neurons connect with one another to form complex networks. These connections form between branch-like structures, called dendrites, that project from the cell body of each neuron. To prevent unneeded connections from forming, dendrites that belong to the same neuron need a way to recognize and avoid one another. A family of proteins called protocadherins supports this process of self-avoidance. Protocadherins have three main parts or domains: an extracellular domain that faces outwards away from the cell, a transmembrane domain that sits within the cell’s surface membrane and an intracellular domain that faces into the cell’s interior. There are two major groups of protocadherins – clustered and non-clustered – and the former are responsible for the self-avoidance behavior between dendrites. Clustered protocadherins in turn comprise three subfamilies, each of which consists of multiple variants with slightly different structures (known as isoforms). The particular set of protocadherin isoforms that a neuron displays on its surface distinguishes that neuron from all others, a little like a barcode. When two dendrites meet, the protocadherins in their membranes come into contact with one another. If both dendrites come from the same neuron and therefore possess identical sets of protocadherins, then all protocadherins can form two-subunit complexes containing one copy of the same isoform from each dendrite. These complexes are called homodimers and their formation acts as a signal that informs the cell that it has encountered one of its own dendrites and should therefore not establish a connection. By using X-rays to determine the structure of a crystallized protocadherin fragment down to the level of its individual atoms, Nicoludis et al. now reveal exactly how clustered protocadherins form homodimers. The results show that each protocadherin subfamily uses a slightly different type of interaction due to differences in the structure of their extracellular domains. The next challenge is to identify the signaling cascade that is triggered by the formation of clustered protocadherin homodimers, and to work out how activation of this cascade prevents a permanent connection from forming. In addition, the results of Nicoludis et al. predict that some non-clustered protocadherins form dimers with a similar architecture to that of clustered protocadherins. This possibility should also be tested experimentally. DOI: http://dx.doi.org/10.7554/eLife.18449.002

Bacteria can evolve rapidly under positive selection owing to their vast numbers, allowing their genes to diversify by adapting to different environments. We asked whether the same genes that are fast evolving in the long-term evolution experiment with Escherichia coli (LTEE) have also diversified extensively in nature. We identified ~2000 core genes shared among 60 E. coli strains. During the LTEE, core genes accumulated significantly more nonsynonymous mutations than flexible (i.e., noncore) genes. Furthermore, core genes under positive selection in the LTEE are more conserved in nature than the average core gene. In some cases, adaptive mutations appear to fine-tune protein functions, rather than merely knocking them out. The LTEE conditions are novel for E. coli, at least in relation to the long sweep of its evolution in nature. The constancy and simplicity of the environment likely favor the complete loss of some unused functions and the fine-tuning of others.Competing Interests StatementWe, the authors, declare that we have no conflicts of interest.