Biology subjects

Johnson, M. M.

Publications and source records attributed to Johnson, M. M..

15 recordsLinked to original sources

bioRxiv2026

Entrenchment of germline amino-acid differences in antibody affinity maturation

Entrenchment -- epistasis that locks in amino acid differences between homologous proteins, so each disfavors substitutions toward the others state -- has been demonstrated along individual protein lineages over deep evolutionary time. Antibodies offer a unique system for studying entrenchment: multiple homologous germline V gene paralogs provide diverse starting points, and the rapid somatic evolution of affinity maturation generates dense phylogenies from which selection on germline-encoded residues can be inferred. Using DASM, a deep-learning model that separates selection from mutation in antibody repertoire data, we test for entrenchment across immunoglobulin heavy chain variable (IGHV) genes. We detect entrenchment at two levels of germline divergence, driven by different sources of epistasis. Within V gene families (up to [~]20% amino acid divergence), entrenched sites cluster at the borders of the complementarity-determining regions (CDRs, the antigen-binding loops) and show high germline diversity. These sites contact antigen, light chain, and the heavy chain CDR3 loop, all of which are encoded independently of the IGHV germline. This pattern is consistent with epistasis from genetically uncoupled partners. Between V gene families, at deeper levels of divergence (25-40%), entrenchment additionally includes positions in the framework scaffold distant from binding interfaces, suggesting a larger contribution from intra-heavy-chain structural constraints. Observed mutation frequencies in human repertoires corroborate these predictions where data are sufficient. Together, these results demonstrate that the rapid somatic evolution of antibodies can serve as a lens for revealing epistatic constraints acting on germline-encoded residues, including constraints imposed by genetically uncoupled partners assembled during B cell development.

evolutionary biology↗

bioRxiv2025

Novel compounds derived from AR-12 that demonstrate host-directed clearance of intracellular Salmonella enterica Serovar Typhimurium

Salmonella infections, including typhoid and paratyphoid fevers, impose a substantial burden in low-income countries, contributing to significant morbidity and mortality associated with enteric and systemic infections. Developing effective treatments for Salmonella infections requires further consideration, especially due to widespread antibiotic resistance. Host-targeted therapies, such as AR-12, offer a promising solution to combat drug resistance. AR-12 has demonstrated broad-spectrum antimicrobial activity against various bacterial pathogens, including Salmonella enterica serovar Typhimurium, S. Typhi, Francisella tularensis, and F. novicida, as well as protozoan parasites and fungal pathogens. To expand its HDT potential against S. Typhimurium, we conducted a medicinal chemistry campaign using AR-12 as a scaffold with systematic optimization of various points of diversity and the pyrazole core to develop analogs informed by structure-activity relationship. This work led to the development of 81 AR-12 analogs. Primary screening identified 38 analogs that are both more potent and less cytotoxic than parent compound AR-12, while only three were less potent and more cytotoxic. Further, only seven compounds affected planktonic Salmonella growth below 20 {micro}M, suggesting host-directed activity in most of the compounds. Twelve analogs were chosen for secondary screening in MDR S. Typhimurium. Compounds 372, 373, and 378 demonstrated remarkable selectivity, with values exceeding 1500 for both susceptible and MDR S. Typhimurium, compared to AR-12s selectivity of around 20. This approximately 100-fold improvement, coupled with improved potency against intracellular Salmonella, suggests these analogs have significantly greater host-directed activity than direct antibacterial effects. Proteomic analysis for the two most potent compounds, 341 and 370 revealed enrichment of vesicle-mediated transport proteins, specifically with respect to retrograde transport at the trans-Golgi-network and intra-Golgi traffic. These results suggest that the analogs reduce intracellular S. Typhimurium replication by disrupting its exploitation of the host cells vesicle-mediated transport system.

microbiology↗

bioRxiv2025

Discovery of Host-Directed Small Molecules with Broad Anti-Leishmanial Efficacy

Leishmaniasis, a neglected tropical disease affecting nearly 10% of the global population, suffers from limited therapeutic options and rising drug resistance. To address this, we developed 343 analogs of AR-12, a compound that has previously illustrated host-directed anti-leishmanial effects. Primary screening using a luminescence-based assay revealed 66 analogs with greater selectivity than the parent compound, AR-12. Sixteen promising candidates, selected for high potency (IC < 1 {micro}M) or high selectivity (>15), underwent secondary screening via Giemsa staining. Four lead compounds (53, 134, 197, and 354) demonstrated therapeutic indices greater than 40. Tertiary assays confirmed their broad in vitro efficacy against both Leishmania donovani and L. mexicana. Notably, 197 exhibited potent host-directed activity and proteomic analysis identified lysozyme as a mechanistic target, implicating it in the host-mediated clearance of intracellular parasites. These findings highlight the dual host- and pathogen-directed mechanisms of these compounds and support their potential as the basis for new therapeutic strategies. Further optimization and clinical exploration of these leads are warranted to meet the urgent need for effective leishmaniasis treatments. AUTHOR SUMMARYLeishmaniasis is a parasitic infectious disease with limited therapeutic options and rising drug resistance. Drugs that act directly against Leishmania can further drive drug resistance. Host-directed therapies work to enable the host responses to enhance pathogen clearance and reduce disease progression. Host-directed therapies both limit the emergence of new drug resistance and combat drug resistant infections. In this work we screened a library of 343 AR-12 analogs for host-directed anti-leishmanial activity. This work identified 4 hit compounds. We then did proteomic analysis to understand the mechanism of action of the primary lead compound, identifying the protein lysozyme as having a role in host-directed activity against Leishmania infection.

biochemistry↗

bioRxiv2025

Separating selection from mutation in antibody language models

Antibodies are encoded by nucleotide sequences that are generated by V(D)J recombination and evolve according to mutation and selection processes. Existing antibody language models, however, focus exclusively on antibodies as strings of amino acids and are fitted using standard language modeling objectives such as masked or autoregressive prediction. In this paper, we first show that fitting models using this objective implicitly incorporates nucleotide-level mutation processes as part of the protein language model, which degrades performance when predicting effects of mutations on functional properties of antibodies. To address this limitation, we devise a new framework: a Deep Amino acid Selection Model (DASM) that learns the selection effects of amino-acid mutations while explicitly factoring out the nucleotide-level mutation process. By fitting selection as a separate term from the mutation process, the DASM exclusively quantifies functional effects: effects that change some aspect of the function of the antibody. This factorization leads to substantially improved performance on standard functional benchmarks. Moreover, our model is an order of magnitude smaller and multiple orders of magnitude faster to evaluate than existing approaches, as well as being readily interpretable.

evolutionary biology↗

bioRxiv2025

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

Antibodies play a crucial role in adaptive immunity. They develop as B cell receptors (BCRs): membrane-bound forms of antibodies that are expressed on the surfaces of B cells. BCRs are refined through affinity maturation, a process of somatic hypermutation (SHM) and natural selection, to improve binding to an antigen. Computational models of affinity maturation have developed from two main perspectives: molecular evolution and language modeling. The molecular evolution perspective focuses on nucleotide sequence context to describe mutation and selection; the language modeling perspective involves learning patterns from large data sets of protein sequences. In this paper, we compared models from both perspectives on their ability to predict the course of antibody affinity maturation along phylogenetic trees of BCR sequences. This included models of SHM, models of SHM combined with an estimate of selection, and protein language models. We evaluated these models for large human BCR repertoire data sets, as well as an antigen-specific mouse experiment with a pre-rearranged cognate naive antibody. We demonstrated that precise modeling of SHM, which requires the nucleotide context, provides a substantial amount of predictive power for predicting the course of affinity maturation. Notably, a simple nucleotide-based convolutional neural network modeling SHM outperformed state-of-the-art protein language models, including one trained exclusively on antibody sequences. Furthermore, incorporating estimates of selection based on a custom deep mutational scanning experiment brought only modest improvement in predictive power. To support further research, we introduce EPAM (Evaluating Predictions of Affinity Maturation), a benchmarking framework to integrate evolutionary principles with advances in language modeling, offering a road map for understanding antibody evolution and improving predictive models.

immunology↗

bioRxiv2024

Thrifty wide-context models of B cell receptor somatic hypermutation

Somatic hypermutation (SHM) is the diversity-generating process in antibody affinity maturation. Probabilistic models of SHM are needed for analyzing rare mutations, for understanding the selective forces guiding affinity maturation, and for understanding the underlying biochemical process. High throughput data offers the potential to develop and fit models of SHM on relevant data sets. In this paper we model SHM using modern frameworks. We are motivated by recent work suggesting the importance of a wider context for SHM, however, assigning an independent rate to each k-mer leads to an exponential proliferation of parameters. Thus, using convolutions on 3-mer embeddings, we develop "thrifty" models of SHM of various sizes; these can have fewer free parameters than a 5-mer model and yet have a significantly wider context. These offer a slight performance improvement over a 5-mer model, and other modern model elaborations worsen performance. We also find that a per-site effect is not necessary to explain SHM patterns given nucleotide context. Also, the two current methods for fitting an SHM model -- on out-of-frame sequence data and on synonymous mutations -- produce significantly different results, and augmenting out-of-frame data with synonymous mutations does not aid out-of-sample performance.

immunology↗

bioRxiv2024

The battle of the sexes in humans is highly polygenic

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a novel method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover polygenic signals of genome-wide SDS for both viability and fecundity. An interesting life-history tradeoff emerges: alleles that increase viability more in one sex increase fecundity more in the other sex. Lastly, we find evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome. Significance statementSelection often acts differently on females and males, as evidenced by the striking sexual dimorphism found in many taxa. As a result, alleles can have different fitness effects in each sex. Consequences can include higher levels of genetic variation and higher disease burdens in populations. This study introduces a novel method to quantify this sex-differential selection (SDS) and reveals that it acts throughout the human genome. We discovered a life history tradeoff between survival and fecundity in females and males and that SDS on fecundity acts on alleles affecting arm fat-free mass.

evolutionary biology↗

bioRxiv2023

Growth-dependent gene expression variation influences the strength of codon usage biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

evolutionary biology↗

bioRxiv2022

Maternal body condition and season influence RNA deposition in the oocytes of alfalfa leafcutting bees (Megachile rotundata)

Maternal effects are an important source of phenotypic variance, whereby females influence offspring developmental trajectory beyond direct genetic contributions, often in response to changing environmental conditions. However, relatively little is known about the mechanisms by which maternal experience is translated into molecular signals that shape offspring development. One such signal may be maternal RNA transcripts (mRNAs and miRNAs) deposited into maturing oocytes. These regulate the earliest stages of development of all animals, but are understudied in most insects. Here we investigated the effects of female internal (body condition) and external (time of season) environmental conditions on maternal RNA in the maturing oocytes and 24 hr old eggs of alfalfa leafcutting bees. Using gene expression and WGCNA analysis, we found that females adjust the quantity of mRNAs related to protein phosphorylation, transcriptional regulation, and nuclease activity deposited into maturing oocytes in response to both poor body condition and shorter day lengths that accompany the late season. However, the magnitude of these changes was higher for time of season. Females also adjusted miRNA deposition in response to seasonal changes, but not body condition. We did not observe significant changes in maternal RNAs in response to either body condition or time of season in 24-hr-old eggs, which were past the maternal-to-zygotic transition. Our results suggest that females adjust the RNA transcripts they provide for offspring to regulate development in response to both internal and external environmental cues. Variation in maternal RNAs may, therefore, be important for regulating offspring phenotype in response to environmental change.

developmental biology↗

bioRxiv2022

Recombination rate inference via deep learning is limited by sequence diversity

A common inference task in population genetics is to estimate recombination rate from multiple sequence alignments. Traditionally, recombination rate estimators have been developed from biologically-informed, statistical models, but more recently deep learning models have been employed for this task. While deep learning approaches offer unique advantages, their performance is inconsistent across the range of potential recombination rates. Here, we generate and characterize data sets (genotype alignments with known recombination rates) for use by deep learning estimators and assess how their features limit estimator performance. We find that certain input parameter regimes produce genotype alignments with low sequence diversity, which are inherently information-limited. We next test how estimator performance is impacted by training and evaluating neural networks on data sets with varying degrees of diversity. The inclusion of genotype alignments with low diversity at high frequency results in considerable performance declines across two different network architectures. In aggregate, our results suggest that genotype alignments have inherent information limits when sequence diversity is low, and these limitations need to be considered both when training deep learning recombination rate estimators and when using them in inference applications.

evolutionary biology↗

bioRxiv2020

Composition and acquisition of the microbiome in solitary, ground-nesting alkali bees

Increasing evidence suggests the microbiome plays an important role in bee ecology and health. However, the relationship between bees and their bacterial symbionts has only been explored in a handful of species. We characterized the microbiome across the life cycle of solitary, ground-nesting alkali bees (Nomia melanderi). We find that feeding status is a major determinant of microbiome composition. The microbiome of feeding larvae was similar to that of pollen provisions, but the microbiome of post-feeding larvae (pre-pupae) was similar to that of the brood cell walls and newly-emerged females. Feeding larvae and pollen provisions had the lowest beta diversity, suggesting the composition of larval diet is highly uniform. Comparisons between lab-reared, newly-emerged, and nesting adult females suggest that the hindgut bacterial community is largely shaped by the external environment. However, we also identified taxa that are likely acquired in the nest or which increase or decrease in relative abundance with age. Although Lactobacillus micheneri was highly prevalent in pollen provisions, it was only detected in one lab-reared female, suggesting it is primarily acquired from environmental sources. These results provide the foundation for future research on metagenomic function and development of probiotics for these native pollinators.

ecology↗

bioRxiv2020

Resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotypes PPI networks resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks resilience. We use publicly-available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms--random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

systems biology↗

bioRxiv2020

Multiscale Characterization of Complex Binding Interactions of Cellulolytic Enzymes Highlights Limitations of Classical Approaches

Cellulolytic microorganisms, like Trichoderma reesei or Clostridium thermocellum, frequently have non-catalytic carbohydrate-binding modules (CBMs) associated with secreted or cell surface bound multidomain carbohydrate-active enzymes (CAZymes) like cellulases. Mostly type-A family CBMs are known to promote cellulose deconstruction by increasing the substrate-bound concentration of cognate cellulase catalytic domains. However, due to the interfacial nature of cellulose hydrolysis and the structural heterogeneity of cellulose, it has been challenging to fully understand the role of CBMs on cellulase activity using classical protein-ligand binding assays. Here, we report a single-molecule CAZyme assay for an industrially relevant processive cellulase Cel7A (from T. reesei) to reveal how subtle CBM1 binding differences can drastically impact cellulase motility/velocity and commitment to initial processive motion for deconstruction of two well-studied crystalline cellulose allomorphs (namely cellulose I and III). We take a multifaceted approach to characterize the complex binding interactions of all major type-A family representative CBMs including CBM1, using an optical-tweezers based single-molecule CBM-cellulose bond rupture assay to complement several classical bulk ensemble protein-ligand binding characterization methods. While our work provides a basis for the cautious use of Langmuir-type adsorption models to characterize classical protein-ligand binding assay data, we highlight the critical limitations of using such overly simplistic models to gain a truly molecular-level understanding of interfacial protein binding interactions at heterogeneous solid-liquid interfaces. Finally, molecular dynamics simulations provided a theoretical basis for the complex binding behavior seen for CBM1 towards two distinct cellulose allomorphs reconciling experimental findings from multiscale analytical methods. Significance StatementMultimodal biomolecular binding interactions involving carbohydrate polymers (e.g., cellulose, starch, chitin, glycosaminoglycans) are fundamental molecular processes relevant to the recognition, biosynthesis, and degradation of all major terrestrial and aquatic biomass. Protein-carbohydrate binding interactions are also critical to industrial biotechnology operations such as enzymatically-catalyzed bioconversion of starch and lignocellulose into biochemicals like ethanol. However, despite the ubiquitous importance of such interfacial processes, we have a poor molecular-level understanding of protein-polysaccharide binding interactions. Here, we provide a comprehensive experimental and theoretical analysis of bulk ensemble versus single-molecule binding interactions of enzyme motors and associated non-catalytic binding domains with cellulosic polysaccharides to highlight the critical limitations of applying classical biochemical assay techniques alone to understanding protein adsorption or biological activity at solid-liquid interfaces.

biochemistry↗

bioRxiv2018

The many nuanced evolutionary consequences of duplicated genes

Gene duplication is seen as a major source of structural and functional divergence in genome evolution. Under the conventional models of sub- or neofunctionalizaton, functional changes arise in one of the duplicates after duplication. However, we suggest here that the presence of a duplicated gene can result in functional changes to its interacting partners. We explore this hypothesis by in-silico evolution of a heterodimer when one member of the interacting pair is duplicated. We examine how a range of selection pressures and protein structures leads to differential patterns of evolutionary divergence. We find that a surprising number of distinct evolutionary trajectories can be observed even in a simple three member system. Further, we observe that selection to correct dosage imbalance can affect the evolution of the initial function in several unexpected ways. For example, if a duplicate is under selective pressure to avoid binding its original binding partner, this can lead to changes in the binding interface of a non-duplicated interacting partner to exclude the duplicate. Hence, independent of the fate of the duplicate, its presence can impact how the original function operates. Additionally, we introduce a conceptual framework to describe how interacting partners cope with dosage imbalance after duplication. Contextualizing our results within this framework reveals that the evolutionary path taken by a duplicates interacting partners is highly stochastic in nature. Consequently, the fate of duplicate genes may not only be controlled by their own ability to accumulate mutations but also by how interacting partners cope with them.

evolutionary biology↗

bioRxiv2017

Juvenile Hormone, But Not Nutrition Or Social Cues, Affects Reproductive Maturation In Solitary Alkali Bees (Nomia melanderi)

Eusocial insect colonies are defined by extreme variation in reproductive activity among castes, but the ancestral conditions from which this variation arose are unknown. Investigating the factors that contribute to variation in reproductive physiology among solitary insects that are closely related to social species can help to fill this gap. We experimentally tested the role of nutrition, juvenile hormone, and social cues on reproductive maturation in solitary alkali bees (Halictidae: Nomia melanderi). We find that alkali bee females emerge from overwintering with small Dufour's glands and small ovaries, containing oocytes in the early stages of development. Oocyte maturation occurs rapidly, and is staggered between the two ovaries. Lab-reared females reached reproductive maturity without access to mates or nesting opportunities, and many had resorbed oocytes. Initial activation of these reproductive structures does not depend on pollen consumption, though dietary protein or lipids may be necessary for long-term reproductive activity. JH is likely to be a limiting factor in alkali bee reproductive activation, as females treated with JH were more likely to develop mature oocytes and Dufour's glands. Unlike for related social bees, the effects of JH were not suppressed by the presence of older, reproductive females. These results provide important insight into the factors that influence reproductive activity in an important native pollinator, and those that may have been particularly important in the evolution of reproductive castes.

physiology↗