Biology subjects

Martis, S.

Publications and source records attributed to Martis, S..

9 recordsLinked to original sources

bioRxiv2026

Ensembles of in silico structures enable T cell peptide-MHC binding prediction

Adaptive immunity relies on T-cell receptor (TCR) recognition of peptides presented by the major histocompatibility complex (pMHC). Accurate prediction of TCR:pMHC binding pairs from sequence data remains a longstanding challenge in computational immunology, limiting the development of precision immunotherapies like cancer vaccines and adoptive cell therapies. Here, we present enFoldX (ensemble of Folded compleXes), a structure-based approach leveraging biophysical characterization of AlphaFold3-generated ensembles to classify TCR:pMHC sequence pairs as cognate versus non-cognate. Unlike previous methods reliant on only sequence data or a single, static predicted structure, enFoldX extracts features from an entire generated ensemble with a custom focus on the biophysical binding interface. Our model distinguishes T cell reactivity between peptides differing by a single amino acid substitution, the resolution required for cancer neoantigens, and generalizes to unseen peptides, MHCs, and TCRs, a major objective for artificial intelligence (AI) in immunology. Our performance on these crucial tasks demonstrates that diverse, structural sampling of biophysical interactions over an ensemble is fundamental for accurate AI-driven binding predictions and offers lessons for efficient future data generation to improve models. Our findings therefore offer a scalable framework to accelerate therapeutic binder design, and we provide access to a publicly available code repository.

immunology↗

bioRxiv2026

Unbiased identification of responding T cell clones from longitudinal repertoire sequencing with CloneSearch

T cells activate and expand upon interaction with cognate antigen, derived from pathogens or mutated proteins. T cell clones can be identified by their T cell receptor (TCR) which can act as a unique barcode to track their expansion. Longitudinal TCR sequencing can be used to track T cell responses to a large array of stimuli. However, experimental identification of T cell clones of interest is challenging, especially when information about the driving antigen is lacking. Computational identification based on clonal dynamics is an antigen-agnostic alternative. However, it is subject to sequencing noise and biological variability, and relies on the choice of particular time points that are compared to find expanding and contracting clones. We present CloneSearch, a method to identify expanding and contracting T cell clones from longitudinal TCR sequencing which is agnostic to the time of stimulus and can account for the noise these clones are subject to. We show that CloneSeach can recapitulate previously identified responses from published data, and expand the analysis to show identification of previously undetected responses from these same datasets. We make CloneSearch available at https://github.com/mm523/CloneSearch.

immunology↗

bioRxiv2026

Ecological determinants of disease and immunity in myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are clonal hematopoietic malignancies characterized by ineffective hematopoiesis, dysplastic morphology, and risk of progression to acute myeloid leukemia. While genomic alterations intrinsic to malignant MDS disease-initiating cells have been well-characterized, molecular assessment of the bone marrow in situ has been limited. Here we present single cell spatial assessment of gene expression, T cell receptors, as well as MDS-defining mutations and RNA isoforms within fixed, decalcified human bone marrow core biopsies (41 MDS, 15 controls) paired with single cell immunogenomic analysis of bone marrow aspirates (35 MDS, 6 controls). Bone marrow spatial analyses of >7.47x106 cells identified hematopoietic and non-hematopoietic populations not readily captured in dissociated tissue. We developed computational methods to compare ecological niche structures, revealing enriched hematopoietic niches and reorganization of T cell immunity in MDS patient bone marrow. In situ genotyping of mutant cells revealed increased TGF{beta} expression in malignant megakaryocytes suppressing local T cell cytotoxicity. By contrast, TGF{beta} signaling was disrupted in mutant cells due to aberrant splicing of multiple TGF{beta} signaling components. This study provides a spatially resolved landscape of human MDS bone marrow and uncovers mechanisms by which malignant cells simultaneously promote intrinsic clonal persistence while rewiring the microenvironment for immune escape.

cancer biology↗

bioRxiv2026

Evolution of oncogene amplification across 86,000 cancer cell genomes

High-level copy-number (CN) amplification (HLAMP) is a major mechanism of oncogene activation in human cancer. Despite progress in therapeutically targeting amplified oncogenes, the processes underlying amplicon evolution remain incompletely understood, leaving critical knowledge gaps in their etiology and mechanisms of therapeutic response. To address this, we analyzed the evolutionary trajectories of HLAMPs using single-cell whole-genome sequencing data from 86,239 cancer cells across 93 patients and 9 experimental systems. We found that cell-to-cell CN variability provides a quantifiable readout of HLAMP mechanism, clearly distinguishing extrachromosomal circular DNA (ecDNA) from intrachromosomal amplification (ICamp) through characteristic CN distributions that reflect distinct modes of segregation and correspond to clonal architecture. Notably, ICamp events frequently showed multiple amplitude peaks specific to subclones, indicating punctuated shifts in oncogene dosage through numeric or structural modulatory mechanisms with transcriptional impact. In contrast, ecDNAs exhibited broad, continuous CN distribution with extreme high-copy outliers, consistent with asymmetric segregation. The CN and structural diversity of ecDNA regions enabled systematic deconvolution of ecDNA subspecies and estimation of their per-cell abundance, revealing the history of ecDNA-mediated oncogenesis at single-nucleotide resolution. We observed ecDNA diversification through internal rearrangements across cases and, notably, convergent evolution in glioblastoma cases marked by multiple, recurrent acquisition of EGFR-targeting ecDNAs. Finally, single-cell genome-based identification of ecDNAs showed substantial discrepancy with bulk genome graph-based predictions and reliably distinguished actively maintained ecDNAs from historical genomic footprints after chromosomal re-integration. These findings reveal marked tissue-type specificity of ecDNAs, suggesting that ecDNA-mediated oncogenesis may depend on a permissive tissue context.

genomics↗

bioRxiv2024

Trait-space patterning is dictated by the tempo and mode of mutation

In large, natural ecosystems, many ({gtrsim} 1) phenotypically relevant mutants can emerge over the characteristic turnover time of the population. When this is the case, there can be eco-evolutionary feedback between the dynamical processes that underlie mutation, selection and ecology. In this paper, we show that, owing to such feedback, the precise details of the mutational process can have a qualitative impact on the long-term behavior of an eco-evolutionary system, in contrast to the classical population genetic assumption that all mutations can be modeled with an effective, homogeneous rate. We demonstrate this in the context of a version of MacArthurs consumer-resource model in which consumers mutate along a resource preference trait-space. Starting from a stochastic individual-based model, we simulate the system in the case where mutations are exogenously generated at a fixed rate (e.g. via external mutagens) and in the case where mutations are coupled to replication (e.g. via DNA copying errors). We find that, surprisingly, replication-coupled mutations are capable of generating a patterned phase in the limit of fast ecological relaxation - precisely the regime where classical population genetic models are expected to operate. We derive the appropriate mean-field description of the stochastic model and use it to show that the patterned phase comes about due to a Turing-like mechanism driven by the non-reciprocal and nonlinear nature of replicative mutations. We demonstrate that these results are robust to demographic noise and model choices and we discuss systems in which this phenomenology might be relevant.

evolutionary biology↗

bioRxiv2022

Eco-evolutionary feedback can stabilize diverse predator-prey communities

Ecological models with random interactions have provided insight into the problem of diversity, particularly showing that high variance in the distribution of interaction rates can lead to instability, chaos and extinction. However, these models have traditionally neglected evolution, which is central to the generation of biological variation and can act on timescales comparable to ecological change. We demonstrate that when a stochastic predator-prey system is coupled to high-dimensional evolutionary dynamics, high variance interactions counter-intuitively stabilize the population, delaying extinction and increasing the total population size. Using both stochastic and deterministic simulations and theory based on the statistical physics of disordered systems, this stabilizing effect is shown to be driven by an eco-evolutionary feedback loop which causes the population size to grow as a power law of the variance of the interactions. We show that the stable regime corresponds with the clonal interference regime of population genetics. We conjecture that qualitative aspects of our results generalize to other evolving complex systems.

ecology↗

bioRxiv2019

Longitudinal linked read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.

microbiology↗

bioRxiv2019

Impact of crowding on the diversity of expanding populations

Crowding effects are key to the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide they push each other apart, remodeling the structure and extent of the populations range. It has recently been shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes remains unclear, which controls the fate of new variants as long as they are rare. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria-Delbruck fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically "pushed out" of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power-law for low-frequency clones. Our model predicts that the distribution only depends on a single parameter, the characteristic growth layer thickness, and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales. Significance StatementGrowing cell populations become densely packed as cells proliferate and fill space. Crowding prevents spatial mixing of individuals, significantly altering the evolutionary outcome from established results for well-mixed populations. Despite the fundamental differences between spatial and well-mixed populations, little is known about the impact of crowding on genetic diversity. Looking at microbial colonies growing on plates, we show that the allele frequency spectrum is characterized by a simple power law for low frequencies. Using cell-based simulations and microfluidic experiments, we identify the origin of this distribution in the volume-exclusion interactions within the crowded cellular environment, enabling us to extend this findings to a broad range of densely packed populations. This study highlights the importance of cellular crowding for the emergence of rare genetic variants.

biophysics↗

bioRxiv2018

Directional selection limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources

Microbial communities can evade competitive exclusion by diversifying into distinct ecological niches. This spontaneous diversification often occurs amid a backdrop of directional selection on other microbial traits, where competitive exclusion would normally apply. Yet despite their empirical relevance, little is known about how diversification and directional selection combine to determine the ecological and evolutionary dynamics within a community. To address this gap, we introduce a simple, empirically motivated model of eco-evolutionary feedback based on the competition for substitutable resources. Individuals acquire heritable mutations that alter resource uptake rates, either by shifting metabolic effort between resources or by increasing overall fitness. While these constitutively beneficial mutations are trivially favored to invade, we show that the accumulated fitness differences can dramatically influence the ecological structure and evolutionary dynamics that emerge within the community. Competition between ecological diversification and ongoing fitness evolution leads to a state of diversification-selection balance, in which the number of extant ecotypes can be pinned below the maximum capacity of the ecosystem, while the ecotype frequencies and genealogies are constantly in flux. Interestingly, we find that fitness differences generate emergent selection pressures to shift metabolic effort toward resources with lower effective competition, even in saturated ecosystems. We argue that similar dynamical features should emerge in a wide range of models with a mixture of directional and diversifying selection.

evolutionary biology↗