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Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures.

Affinity maturation is a complex dynamical process allowing the immune system to generate antibodies capable of recognizing antigens. We introduce a model for the evolution of the distribution of affinities across the antibody population in germinal centers. The model is amenable to detailed mathematical analysis and gives insight on the mechanisms through which antigen availability controls the rate of maturation and the expansion of the antibody population. It is also capable, upon maximum-likelihood inference of the parameters, to reproduce accurately the distributions of affinities of IgG-secreting cells we measure in mice immunized against Tetanus Toxoid under largely varying conditions (antigen dosage, delay between injections). Both model and experiments show that the average population affinity depends non-monotonically on the antigen dosage. We show that combining quantitative modeling and statistical inference is a concrete way to investigate biological processes underlying affinity maturation (such as selection permissiveness), hardly accessible through measurements.

Abstract Bacterial genomes primarily diversify via gain, loss, and rearrangement of genetic material in their flexible accessory genome. Yet the dynamics of accessory genome evolution are very poorly understood, in contrast to the core genome where diversification is readily described by mutations and homologous recombination. Here, we tackle this problem for the case of very closely related genomes. We comprehensively describe genome evolution within n=222 genomes of Escherichia coli ST131, which likely shared a common ancestor around 100 years ago. After removing putative recombinant diversity, the total length of the phylogeny is 6,000 core genome substitutions. Within this diversity, we find 22 modifications to core genome synteny and estimate around 2,000 structural changes within the accessory genome, i.e. one structural change for every three core genome substitutions. Sixty-three percent of loci with structural diversity could be resolved into individual gain and loss events with 10-fold more gains than losses, demonstrating a dominance of gains due to insertion sequences and prophage integration. Our results suggest the majority of synteny changes and insertions in our dataset are likely deleterious and only persist for a short time before being removed by purifying selection.

Affinity Maturation (AM) is the process through which the immune system is able to develop potent antibodies against new pathogens it encounters, and is at the base of the efficacy of vaccines. At its core AM is analogous to a Darwinian evolutionary process, where B-cells mutate and are selected on the base of their affinity for an Antigen (Ag), and Ag availability tunes the selective pressure. In cases when this selective pressure is high the number of B-cells might quickly decrease and the population might risk extinction in what is known as a population bottleneck. Here we study the probability for a B-cell lineage to survive this bottleneck scenario as a function of the progenitor affinity for the Ag. Using recursive relations and probability generating functions we derive expressions for the average extinction time and progeny size for lineages that go extinct. We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size. Our study suggests the population bottleneck phenomenology might represent a limit case in the space of biologically plausible maturation scenarios, whose characterization could help guide the process of vaccine development.

Understanding how healthcare-associated pathogens adapt in clinical environments can inform strategies to reduce their burden. Here, we investigate the hypothesis that insertion sequences (IS), prokaryotic transposable elements, are a dominant mediator of rapid genomic evolution in healthcare-associated pathogens. Among 28,207 publicly available pathogen genomes, we find high copy numbers of the replicative ISL3 family in healthcare-associated . In , the ESKAPE pathogen with the highest IS density, we find that ISL3 proliferation has increased in the last 30 years. To enable better identification of structural variants, we long read-sequenced a new, single hospital collection of 282 infection isolates collected over three years. In these samples, we observed extensive, ongoing structural variation of the genome, largely mediated by active replicative ISL3 elements. To determine if ISL3 is actively replicating in clinical timescales in its natural, gut microbiome reservoir, we long read-sequenced a collection of 28 longitudinal stool samples from patients undergoing hematopoietic cell transplantation, whose gut microbiomes were dominated by . We found up to six structural variants of a given strain within a single stool sample. Examining longitudinal samples from one individual in further detail, we find ISL3 elements can replicate and move to specific positions with profound regulatory effects on neighboring gene expression. In particular, we identify an ISL3 element that upon insertion replaces an imperfect -35 promoter sequence at a gene locus with a perfect -35 sequence, which leads to substantial upregulation of expression of , driving highly effective folate scavenging. As a known folate auxotroph, depends on other members of the microbiota or diet to supply folate. Enhanced folate scavenging may enable to thrive in the setting of microbiome collapse that is common in HCT and other critically ill patients. Together, ISL3 expansion has enabled to rapidly evolve in healthcare settings, and this likely contributes to its metabolic fitness and may strongly influence its ongoing trajectory of genomic evolution.

Bacterial genomes primarily diversify via gain, loss, and rearrangement of genetic material in their flexible accessory genome. Yet the dynamics of accessory genome evolution are very poorly understood, in contrast to the core genome where diversification is readily described by mutations and homologous recombination. Here, we tackle this problem for the case of very closely related genomes. We comprehensively describe genome evolution within n=222 genomes of E. coli ST131, which likely shared a common ancestor around one hundred years ago. After removing putative recombinant diversity, the total length of the phylogeny is 6000 core genome mutations. Within this diversity, we find 22 modifications to core genome synteny and estimate around 2000 structural changes within the accessory genome, i.e. one structural change for every 3 core genome mutations. 63% of loci with structural diversity could be resolved into individual gain and loss events with ten-fold more gains than losses, demonstrating a dominance of gains due to insertion sequences and prophage integration. Our results suggest the majority of synteny changes and insertions in bacterial genomes are likely deleterious and only persist for a short time before being removed by purifying selection.

Bacteriophages, the viruses that infect bacteria, are the most abundant and diverse biological entities on our planet. They play a critical role in shaping ecosystems and are increasingly recognized for their potential in treating bacterial infections. Yet, our comprehension of their biology and evolutionary dynamics is limited, largely because research has concentrated on a select few well-characterized phages or relies on broad metagenomic studies with limited follow-up analysis of individual phages. This knowledge gap hinders our capacity to exploit their therapeutic and ecological possibilities – and while some studies have attempted to bridge it, such efforts typically require a lot of manual labor, highlighting the need for high-throughput, reproducible methods for in-depth study of phage evolution. To address this gap, we introduce the Aionostat, a novel automated continuous culture device designed to facilitate bacteriophage directed evolution experiments at scale. The Aionostat’s potential is showcased through two example experiments. In the first, phages from the BASEL collection rapidly adapted to a challenging E. coli strain, acquiring mutations and deletions that improved their infectivity. In the second experiment, we evolved a mixture of these phages on the same E.coli strain, leading to the emergence of recombinant phages with increased fitness. By automating these experiments, the Aionostat enables faster, more reproducible studies of phage evolution that would be impractical to perform by hand, thereby opening new avenues for investigating viral dynamics, engineering phage therapies, and studying evolutionary principles in broader biological contexts.

The genomic diversity of microbes is commonly parameterized as single nucleotide polymorphisms relative to a reference genome of a well-characterized, but arbitrary, isolate. However, any reference genome contains only a fraction of the microbial pangenome, the total set of genes observed in a given species. Reference-based approaches are thus blind to the dynamics of the accessory genome, as well as variation within gene order and copy number. With the wide-spread usage of long-read sequencing, the number of high-quality, complete genome assemblies has increased dramatically. Traditional computational approaches towards whole-genome analysis either scale poorly with the number of genomes, or treat genomes as dissociated ``bags of genes'', and thus are not suited for this new era. Here, we present PanGraph, a Julia-based library and command line interface for aligning whole genomes into a graph. Each genome is represented as an undirected path along vertices, which in turn, encapsulate homologous multiple sequence alignments. The resultant data structure succinctly summarizes population-level nucleotide and structural polymorphisms and can be exported into a several common formats for either downstream analysis or immediate visualization.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Added support for additional alignment kernel, added more validation.* https://neherlab.github.io/pangraph/

Following a newly introduced approach by Rasetti and Merelli we investigate the possibility to extract topological information about the space where interacting systems are modelled. From the statistical datum of their observable quantities, like the correlation functions, we show how to reconstruct the activities of their constitutive parts which embed the topological information. The procedure is implemented on a class of polymer models on hypergraphs with hard-core interactions. We show that the model fulfils a set of iterative relations for the partition function that generalise those introduced by Heilmann and Lieb for the monomer-dimer case. After translating those relations into structural identities for the correlation functions we use them to test the precision and the robustness of the inverse problem. Finally the possible presence of a further interaction of peer-to-peer type is considered and a criterion to discover it is identified.