Biology subjects

Feder, A. F.

Publications and source records attributed to Feder, A. F..

13 recordsLinked to original sources

bioRxiv2026

Haplotype and diversity signatures of ultra-soft selective sweeps in HIV-1

Many urgent medical and agricultural challenges are driven by resistance evolution via soft selective sweeps of multiple simultaneous mutations. Standard approaches to detect these mutations involve genome scans for regions with reduced diversity and increased haplotype lengths. However, it is unknown the extent to which those signatures persist as the number of mutations driving resistance grows. Here, we analyzed longitudinal linkage-resolved data from 10 intra-host HIV populations treated with broadly neutralizing antibody 10-1074. We found that HIV escapes 10-1074 with minimal perturbations to diversity and haplotype homozygosity in the region surrounding the sweep in the majority (8/10) of treated individuals. We matched these in vivo escape trajectories to forward simulations and found that adaptive mutations conferring escape must have been present on 20 or more genetic backgrounds to generate these signatures. These "ultra-soft" sweep signatures more closely resemble genetic patterns in a treatment non-responder without an adaptive response to 10-1074 than those of two other trial participants where adaptation occurred via harder selective sweeps. Our results demonstrate that HIV can adapt to a broadly neutralizing antibody treatment while retaining nearly all of its standing genetic diversity and that selection scans dependent on regional diversity and haplotype homozygosity signatures fail in this "ultra-soft" regime.

evolutionary biology↗

bioRxiv2026

Ultra-deep duplex sequencing reveals unique features of somatic evolution in the normal tissues of a family with Li-Fraumeni syndrome

Li-Fraumeni Syndrome (LFS) is caused by germline pathogenic variants in TP53 which predispose carriers to early onset cancer across multiple tissues. While genomically profiling those cancers has revealed factors contributing to their formation, little is understood about how LFS impacts clonal evolution in healthy tissues preceding cancer. Here, we use ultra-deep duplex sequencing (mean [~]15,000x depth) to investigate somatic mutation and selection in a family carrying the germline TP53 p.R181H pathogenic variant and a cohort of non-carrier controls. In blood samples, the germline variant is associated with more mutations in a panel designed to capture genomewide mutagenesis, and with reduced positive selection on somatic TP53 mutations, despite confounding by chemotherapy treatment in one individual. DNMT3A and TET2 mutations appear positively selected and GATA2 mutations negatively selected across the cohort, independent of the p.R181H status. Extensive multi-tissue sampling of 22 non-cancerous and 6 cancerous samples was also performed at autopsy in one individual with LFS who succumbed to esophageal cancer. Cross-tissue analysis reveals excess mutations in sun-exposed skin, esophagus and chronically-inflamed stomach tissue, and concordant mutations in the p.R248 hotspot of TP53 across most (18/28) tissue samples. Most somatic TP53 mutations in LFS that can be assessed for phase arose on the chromosomal copy lacking the p.R181H variant. Our study reveals how the germline p.R181H variant reshapes baseline somatic mutation and selection in normal tissues and highlights the importance of understanding early somatic evolution in LFS prior to cancer development and treatment.

cancer biology↗

bioRxiv2025

Distinct modes of evolution drive HIV escape from two broadly neutralizing antibodies.

Broadly neutralizing antibodies (bNAbs) show promise for HIV treatment and prevention, but are vulnerable to resistance evolution. Comprehensively understanding in vivo viral escape from individual bNAbs is necessary to design bNAb combinations that will provide durable responses. We characterize viral escape from two such bNAbs, 10-1074 and 3BNC117, using deep, longitudinal sequencing of full length HIV envelope (env) genes from study participants treated with bNAb monotherapy. Improved sequencing depth and computational evolutionary analyses permit us to identify in vivo routes and parallelism underlying HIV escape from each bNAb, providing new insights into this evolutionary process. We find that 10-1074 escape is restricted to a small number of previously documented pathways seen across participants, but these escape mutations 1) emerge via extensively recurrent mutation, 2) are not equally preferred, and 3) can pre-exist at low frequency in intra-host viral populations before therapy, although their detection does not predict rebound timing. In contrast, 3BNC117 escape follows background-specific patterns in which specific escape mutations present in one intra-host population rarely emerge or spread in other populations, except among highly related viruses. Despite this, 3BNC117 escape mutations often still emerge recurrently within their host. Our findings map longitudinal in vivo antibody escape across 20 diverse clade B HIV intra-host populations and reveal clinically relevant resistance dynamics that highlight how combination bNAb therapies will need to contend with extensively recurring escape mutations and dependence on genetic background. Significance StatementUsing recently developed techniques that capture viral genetic diversity and associations between mutations at depth, we deeply sequenced HIV from two clinical trials of broadly neutralizing antibody (bNAb) monotherapies, 3BNC117 and 10-1074. We computationally characterized HIV populations longitudinally with unprecedented resolution as they escaped these therapies in people living with HIV. Intra-host tracking of individual HIV genetic backgrounds reveals extensively recurrent mutations driving escape and suggests that HIV escape routes from certain bNAbs can depend sensitively on the genetic background of the virus. Our findings highlight the difficulties in evaluating pre-treatment resistance, provide an analysis blueprint for future trials, and inform the design of emerging combination antibody therapies to maximize the likelihood of durable efficacy.

molecular biology↗

bioRxiv2025

Social interactions shape antiviral resistance outcomes in poliovirus via eco-evolutionary feedback

Antiviral resistance evolution poses a major obstacle for controlling viral infections. A promising strategy is to target shared viral proteins that allow drug susceptible viruses to sensitize resistant ones during cellular coinfection, muting selection for resistance. Pocapavir, a poliovirus capsid inhibitor, employs this sociovirological strategy. While susceptible viruses significantly suppressed resistance in the presence of pocapavir in cell culture, a pocapavir clinical trial observed widespread resistance evolution and limited improvements to clearance times. To reconcile these findings, we present an intra-host eco-evolutionary model of poliovirus in the presence of pocapavir, which reproduces both the potent interference observed in vitro and the resistance emergence seen in patients. In the short term, our model predicts that a high density of susceptible viruses sensitizes resistant ones to pocapavir, mirroring cell culture results. However, over multiple replication cycles, pocapavirs high potency collapses viral density, which reduces coinfection and allows resistance to evolve as observed in the clinical trial. Since coinfection is essential to suppress resistance, enabling greater survival of susceptible viruses could offer therapeutic advantages. Counterintuitively, we demonstrate that this can be achieved by lessening antiviral potency, which can limit resistance evolution while also maintaining a low viral load. These findings suggest that antivirals that rely on viral intracellular interaction must balance immediate neutralization with the preservation of future coinfection, yielding more sustained inhibition. Explicitly considering the eco-evolutionary feedback encompassing viral density, shared phenotypes and absolute fitness not only provides new insights into designing effective therapies but also illuminates viral evolutionary dynamics more broadly.

evolutionary biology↗

bioRxiv2025

A signature-agnostic test for differences between tumor mutation spectra reveals carcinogen and ancestry effects

Despite dozens of tools to identify mutational signatures in cancer samples, there is not an established metric for quantifying whether signature exposures differ significantly between two heterogeneous groups of samples. We demonstrate that a signature-agnostic metric - the aggregate mutation spectrum distance permutation method (AMSD) - can rigorously determine whether mutational exposures differ between groups, a hypothesis that is not directly addressed by signature analysis. First, we reanalyze a study of carcinogen exposure in mice, determining that eleven of twenty tested carcinogens produce significant mutation spectrum shifts. Only three of these carcinogens were previously reported to induce distinct mutational signatures, suggesting that many carcinogens perturb mutagenesis by altering the composition of endogenous signatures. Next, we interrogate whether patient ancestry has a measurable impact on human tumor mutation spectra, finding significant ancestry-associated differences across ten cancer types. Some have been previously reported, such as elevated SBS4 in African lung adenocarcinomas, while some have not to our knowledge been reported, such as elevated SBS17a/b in European esophageal carcinomas. These examples suggest that AMSD is a robust tool for detecting differences among groups of tumors or other mutated samples, complementing descriptive signature deconvolution and enabling the discovery of environmental and genetic influences on mutagenesis.

cancer biology↗

bioRxiv2025

Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk

Fanconi Anemia (FA) is a heritable syndrome characterized by DNA damage repair deficits, frequent malformations and a significantly elevated risk of bone marrow failure, leukemia, and mucosal head and neck squamous cell carcinomas (HNSCC). Hematopoietic stem cell gene therapy can prevent marrow failure and lower leukemia risk, but mucosal gene therapy to lower HNSCC risk remains untested. Major knowledge gaps include an incomplete understanding of how rapidly gene-corrected cellular lineages could spread through the oral epithelium, and which delivery parameters are critical for ensuring efficient gene correction. To answer these questions, we extended an agent-based model of the oral epithelium to include the delivery of gene correction in situ to FA cells and the competitive dynamics between cellular lineages with and without gene correction. We found that only gene-corrected lineages with substantial proliferative advantages (probability of resisting displacement out of the basal layer [≥] 0. 1) could spread on clinically relevant timelines, and that these lineages were initially at high risk of loss in the generations following correction. Delivering gene correction to many cells minimizes the risk of loss, while delivery to many distinct locations within a tissue maximizes the rate of spread. To determine the impact of mucosal gene therapy in preventing the clonal expansion of pre-cancerous mutations, we compared the expected burden of TP53 mutations in simulated tissue sections with and without gene correction. We found that when FA cells have elevated genome instability or a TP53-dependent proliferative advantage, gene correction can substantially reduce the accumulation of pro-tumorigenic mutations. This model illustrates the power of computational frameworks to identify critical determinants of therapeutic success to enable experimental optimization and support novel and effective gene therapy applications. Author summaryWe investigated factors influencing the success of oral mucosal gene therapy for Fanconi Anemia (FA), a genetic syndrome marked by DNA repair defects in conjunction with a heightened risk of cancer. We used a computational model of the oral epithelium to determine how gene therapy corrected cells compete with FA background cells and the best gene delivery approaches to promote effective tissue replacement by gene-corrected cells. We find that gene-corrected cells require strong proliferative advantages to spread effectively, and that initially delivering gene correction to more cells reduces the chance that these cells are stochastically eliminated before they can spread. We also demonstrate that gene correction reduces the accumulation of pro-tumorigenic TP53 mutations in an FA context, where genomic instability can elevate the mutation rate and FA-specific selective pressures could favor accelerated TP53 clonal expansion. This research provides a useful framework for guiding mucosal gene therapy experiments and the development of effective oral gene therapy protocols for cancer prevention in FA.

cell biology↗

bioRxiv2024

Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data

Understanding variation in cellular growth rates among cells in tumors is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Advances in lineage tracing technologies enable the reconstruction of high-resolution, single-cell phylogenies of cancer cell populations, but methods to detect cellular growth rate differences on these phylogenies remain limited. Tree balance statistics offer a way forward, but it is unknown if and how these statistics are distorted when applied to phylogenetic reconstructions built from lineage tracing data, and if these distortions limit the utility of tree balance statistics to distinguish between evolutionary scenarios characterized by variable or homogeneous cellular growth rates. Here, we examined two tree balance statistics, J1 and the Sackin index, and benchmarked their performance in distinguishing lineage tracing trees derived from populations with and without variable cellular growth rates. We found that when tumor population sizes and lineage tracing editing rates are approximately known and in favorable ranges, J1 detects departures from homogenous growth rates just as well on lineage tracing trees as on true genealogical trees, while the Sackin index loses most of its power even under the most favorable conditions. We applied our J1-based test to data derived from cancer lineage tracing experiments and found widespread signals of growth rate heterogeneity in murine autochthonous lung cancers, and lung and PDAC xenograft experiments in mice. Our results demonstrate the potential and challenges of tree balance statistics in analyzing growth dynamics in lineage tracing data. Significance statementAlthough tree balance statistics are increasingly applied to examine tumor growth dynamics in trees built from bulk sequencing data, their application in tumor lineage tracing studies remains limited. In this study, we benchmarked J1 and the Sackin indexs potential to distinguish between tumor populations with and without varying growth rates among concurrent lineages using trees reconstructed from lineage tracing experiments. We describe conditions under which J1 can detect deviations from homogenous growth rates in lineage tracing trees, and find that the Sackin index is not well-suited to this objective. Our findings provide a first look at the complications associated with using tree balance statistics on trees reconstructed from lineage tracing data, and provide a practical guide for the application of these balance statistics in future lineage tracing studies.

evolutionary biology↗

bioRxiv2023

Elevated HIV viral load is associated with higher recombination rate in vivo.

HIVs exceptionally high recombination rate drives its intra-host diversification, enabling immune escape and multi-drug resistance within people living with HIV. While we know that HIVs recombination rate varies by genomic position, we have little understanding of how recombination varies throughout infection or between individuals as a function of the rate of cellular coinfection. We hypothesize that denser intra-host populations may have higher rates of coinfection and therefore recombination. To test this hypothesis, we develop a new approach (Recombination Analysis via Time Series Linkage Decay, or RATS-LD) to quantify recombination using autocorrelation of linkage between mutations across time points. We validate RATS-LD on simulated data under short read sequencing conditions and then apply it to longitudinal, high-throughput intra-host viral sequencing data, stratifying populations by viral load (a proxy for density). Among sampled viral populations with the lowest viral loads (< 26,800 copies/mL), we estimate a recombination rate of 1.5x10-5 events/bp/generation (95% CI: 7x10-6-2.9x10-5), similar to existing estimates. However, among samples with the highest viral loads (> 82,000 copies/mL), our median estimate is approximately 6 times higher. In addition to co-varying across individuals, we also find that recombination rate and viral load are associated within single individuals across different time points. Our findings suggest that rather than acting as a constant, uniform force, recombination can vary dynamically and drastically across intra-host viral populations and within them over time. More broadly, we hypothesize that this phenomenon may affect other facultatively asexual populations where spatial co-localization varies.

evolutionary biology↗

bioRxiv2022

State-dependent evolutionary models reveal modes of solid tumor growth

Spatial properties of tumor growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumor cell division remains difficult to evaluate in clinical tumors. Here, we demonstrate that elevated cellular growth rates on the tumor periphery leave characteristic patterns in the genomes of cells sampled from different parts of a tumor, which become evident when they are used to construct a tumor phylogenetic tree. Namely, rapidly-dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing lineages in the tumor center. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential cell division rates between peripheral and central cells jointly from the branching and mutational patterns of single-time point, multi-region sequencing data. We validate this approach on simulated tumors by demonstrating its ability to accurately infer spatially-varying birth rates under a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods which ignore differential mutational acquisition. Finally, we apply SDevo to multi-region sequencing data from clinical hepatocellular carcinomas and find evidence that cells on the tumor edge divide 3-6x faster than those in the center. As multi-region and single-cell sequencing increase in resolution and availability, we anticipate that SDevo will be useful in interrogating spatial restrictions on tumor growth and could be extended to model non-spatial factors that influence tumor progression, including hypoxia and immune infiltration.

cancer biology↗

bioRxiv2019

Challenging conventional wisdom on the evolution of resistance to multi-drug HIV treatment: Lessons from data and modeling.

1Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially-resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from patients on triple-drug therapy. Further, lessons from HIV may inform other systems.

evolutionary biology↗

bioRxiv2019

Evolutionary dynamics in structured populations under strong population genetic forces

High rates of migration between subpopulations result in little population differentiation in the long-term neutral equilibrium. However, in the short-term, even very abundant migration may not be enough for subpopulations to equilibrate immediately. In this study, we investigate dynamical patterns of short-term population differentiation in adapting populations via stochastic and analytical modeling through time. We characterize a regime in which selection and migration interact to create non-monotonic patterns of the population differentiation statistic FST when migration is weaker than selection, but stronger than drift. We demonstrate how these patterns can be leveraged to estimate high migration rates that would lead to panmixia in the long term equilibrium using an approximate Bayesian computation approach. We apply this approach to estimate fast migration in a rapidly adapting intra-host Simian-HIV population sampled from different anatomical locations. Notably, we find differences in estimated migration rates between different compartments, all above Nem = 1. This work demonstrates how studying demographic processes on the timescale of selective sweeps illuminates processes too fast to leave signatures on neutral timescales.

evolutionary biology↗

bioRxiv2018

The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to anti-retroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate them [1, 2, 3, 4]. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action [5, 6, 7]. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches.\n\nSpecifically, we focus on our recent paper [2], in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. We interpret this as evidence that resistance to early HIV drugs that failed quickly and predictably was driven by soft sweeps while evolution of resistance to better drugs is both less frequent and when it takes place it is associated with harder sweeps due to an effectively lower HIV population mutation rate ({theta}). Recently, Harris et al. have proposed an alternative interpretation [8]: the signal could be due to an increase in the selective benefit of mutations conferring resistance to better drugs. Therefore, better drugs lead to faster sweeps with less opportunity for recombination to rescue diversity. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.

evolutionary biology↗

bioRxiv2017

High resolution spatio-temporal assessment of simian/human immunodeficiency virus (SHIV) evolution reveals a highly dynamic process within the host

The process by which drug-resistant HIV-1 arises and spreads spatially within an infected individual is poorly understood. Studies have found variable results relating how HIV-1 in the blood differs from virus sampled in tissues, offering conflicting findings about whether HIV-1 throughout the body is homogeneously distributed. However, most of these studies sample only two compartments and few have data from multiple time points. To directly measure how drug resistance spreads within a host and to assess how spatial structure impacts its emergence, we examined serial sequences from four macaques infected with RT-SHIVmne027, a simian immunodeficiency virus encoding HIV-1 reverse transcriptase (RT), and treated with RT inhibitors. Both viral DNA and RNA (vDNA and vRNA) were isolated from the blood (including plasma and peripheral blood mononuclear cells), lymph nodes, gut, and vagina at a median of four time points and RT was characterized via single-genome sequencing. The resulting sequences reveal a dynamic system in which vRNA rapidly acquires drug resistance concomitantly across compartments through multiple independent mutations. Fast migration results in the same viral genotypes present across compartments, but not so fast as to equilibrate their frequencies through time. The blood and lymph nodes were found to be compartmentalized rarely, while both the blood and lymph node were more frequently different from mucosal tissues. There is some evidence for an increase in compartmentalization after the onset of selective pressure. This study suggests that even oft-sampled blood does not fully capture the viral dynamics in other parts of the body, especially the gut where vRNA turnover was faster than the plasma and vDNA retained fewer wild-type viruses than other sampled compartments. Our findings of transient compartmentalization across multiple tissues may help explain the varied results of previous compartmentalization studies in HIV-1.\n\nAuthor SummaryHIV-1 is difficult to treat because the virus can evolve to become drug-resistant within the body, but we have an incomplete understanding of where drug resistant viruses originate and how they spread within a person. In this study, four macaques were infected with RT-SHIV, a simian immunodeficiency virus with an HIV-1 reverse transcriptase coding region, which can be targeted with standard HIV drugs. We sampled virus from the macaques before, during and after they became resistant to administered drugs and determined the genetic viral sequences in several parts of the body: blood, lymph nodes, gut, and vagina. We found that drug resistance emerged across compartments nearly simultaneously, and drug resistance evolved multiple independent times within each macaque. Although migration of RT-SHIV between compartments is fast, compartments do not have the same distribution of viral genotypes. This is important because although studies typically sample virus from the blood to study how HIV-1 evolution in humans, our study suggests that it is not fully representative of other parts of the body, particularly the gut.

evolutionary biology↗