Biology subjects

Goldman, N.

Publications and source records attributed to Goldman, N..

At least 19 recordsLinked to original sources

bioRxiv2026

DGM Is Dead; Long Live DGM-Star! A Quantization-Theoretic Approach to Gamma Rate Discretization

Modeling substitution rate heterogeneity as a discretized Gamma distribution is standard in phylogenetic inference. Ferretti et al. (2026) have recently demonstrated that the most commonly used equiprobable Gamma rate discretization, known simply as the discrete Gamma model (DGM), substantially overestimates average branch lengths in reconstructed phylogenies when the true rates are drawn from a continuous Gamma distribution. We propose an alternative method to discretize the Gamma rate distribution that considerably reduces the bias in estimated branch lengths and can be used as a drop-in replacement for the standard DGM. The method works especially well on large trees with short branches, yielding up to a 20-fold reduction in bias on simulated phylogenies. It is developed by applying quantization-theoretic tools to the analysis of a simplified star-phylogeny abstraction introduced by Ferretti et al. (2026), where the new method is unbiased by construction. We explain Ferretti et al.'s counterintuitive observation that increasing the number of rate categories can exacerbate the bias of the standard DGM, even though this should in principle make DGM a better approximation to the continuous case, and offer our method as an immediately available alternative.

evolutionary biology↗

bioRxiv2026

Phylogeny-aided detection of contamination in nearly 5 million SARS-CoV-2 genomes

Contamination can occur during genome sequencing when a contaminant genome is accidentally mixed with the intended genome to be sequenced. Contamination can lead to incorrect consensus genome calling, disrupting analyses of pathogen evolution and transmission. To investigate the extent of this issue, we developed PhyCD, a phylogeny-aided computational approach to investigate contamination in SARS-CoV-2 genome sequencing data. PhyCD masks consensus genome positions associated with suspicious sequencing read coverage drops, then leverages pandemic-scale phylogenetic placement techniques to identify putative contamination events. Applying PhyCD to nearly 5 million SARS-CoV-2 genomes, we identified 10,942 putative contamination events under conservative parameters. Across the flagged genomes, PhyCD flagged -- and so permits masking of -- a total of 64,753 substitutions that could cause errors in downstream genome data analyses.

bioinformatics↗

bioRxiv2026

CMAPLE 2: Fast and Accurate Phylogenetic Inference for Millions of Pathogen Genomes

Phylogenetic analysis is essential to genomic epidemiology, for example in tracing the origin and evolution of SARS-CoV-2 variants during the COVID-19 pandemic. We previously introduced CMAPLE, a single-threaded implementation of the MAPLE algorithm designed for large-scale epidemiological genomic datasets. CMAPLE can reconstruct phylogenetic trees from up to one million SARS-CoV-2 genomes. Here, we present CMAPLE 2, a multi-threaded version of CMAPLE with parallel sample placement and subtree pruning and regrafting (SPR) search algorithms. CMAPLE 2 also reduces memory consumption by compressing data structures using multiple references along the tree instead of a single reference genome. It further implements two advanced models of highly site- and nucleotide-specific mutation patterns as observed in pandemic-scale genome data. Additionally, CMAPLE 2 parallelizes SPR-based Tree Assessment (SPRTA), an efficient and interpretable approach for assessing phylogenetic tree uncertainty, and supports ancestral state and mutation inference via mutation-annotated tree (MAT) reconstruction. When inferring a phylogeny from 500,000 SARS-CoV-2 genomes using 48 CPU cores, CMAPLE 2 reduces runtime from 5 days (with sequential CMAPLE) to 9 hours (a 13-fold speedup) while decreasing peak RAM usage from 11.1 GB to 7.3 GB. CMAPLE 2 can now reconstruct a tree of nearly four million SARS-CoV-2 genomes from scratch within 12 days using 41 GB of RAM, a task that the sequential CMAPLE and MAPLE cannot realistically complete. CMAPLE 2 is applicable to many pathogen genome datasets and enhances our preparedness for future pandemics.

evolutionary biology↗

bioRxiv2026

Modeling Site-Specific Mutation Patterns in Pandemic-Scale Phylogenetics

Models of genome evolution often account for different evolutionary rates at different genome positions due to, e.g., varying selective pressures or mutation rates. Recent evidence from millions of publicly shared SARS-CoV-2 genomes has revealed a more complex mutational landscape than can be modeled with existing approaches. Here, mutation rates are in fact not only highly position-specific, as currently modeled, but also nucleotide-specific; for example, specific mutations can occur very often at certain determined genome positions, while at the same positions other mutations might not be highly recurrent. Here, we propose and investigate a general model of genome evolution where each genome position is allowed to evolve under an independent, non-normalized substitution rate matrix describing site-specific rates of all mutation types ("Site-Specific Matrix" model, or SSM). We implement SSM in the efficient pandemic-scale phylogenetic inference software CMAPLE. Large-scale genomic epidemiological simulations suggest that, given enough data, SSM can accurately infer position- and nucleotide-specific substitution rates for more frequently observed nucleotides (typically the reference nucleotide), while other rates require higher levels of divergence. Simulations also show that SSM has a modest impact on the accuracy of phylogenetic tree estimation. We use SSM to analyze the evolution of millions of SARS-CoV-2 genomes and observe substantial mismatches between the substitution rates of classical rate variation models and our SSM estimates. These results suggest that classical models of rate variation are inadequate for modeling site-specific mutation patterns and that SSM is a useful alternative for large-scale genome analyses.

evolutionary biology↗

bioRxiv2025

The transcription factor BCL11A restores differentiation potential to aged oligodendrocyte progenitor cells

In young animals, oligodendrocyte progenitor cells (OPCs) undergo robust differentiation, progressing through stages to become pre-myelinating oligodendrocytes (Pre-OL) and ultimately myelinating oligodendrocytes (OLs). However, OPCs from aged animals have reduced differentiation ability. This disrupts myelin maintenance in the central nervous system (CNS), leading to a lack of remyelination following demyelinating injury and impaired adaptive myelination as a mechanism of learning. To uncover novel factors essential for restoring resilience in aged OPCs, we employed a data-driven approach involving the development of a computational pipeline, gSWITCH (accessible at: https://altoslabs.shinyapps.io/gSWITCH/), that allows for capture of precise dynamic gene expression patterns to pinpoint potential switch genes during lineage progression. Using gSWITCH to identify potential switch genes crucial for OPCs, we conducted a comparative analysis of gene expression in OPCs isolated from young and aged animals. This revealed a group of transcription factors with decreased expression in aged OPCs. Further analysis of transcription factor binding site enrichment in OPC switches highlighted Bcl11a, a zinc finger transcription factor that could potentially serve as a master regulator of many switch genes. Ectopic overexpression of Bcl11a in aged OPCs did not enhance their proliferation; however, it significantly enhanced their differentiation into OLs. Overexpression of Bcl11a in aged mice, followed by demyelination injury in spinal cord white matter, significantly increased the differentiation of OPCs into OLs within the injury region compared to control aged mice. Furthermore, we found that Bcl11a is absent in invertebrates and has undergone pervasive purifying selection throughout vertebrate evolution, constraining its amino-acid sequence by eliminating deleterious mutations. Our study shows that reversing the age-related decline of this evolutionarily conserved factor in aged OPCs restores their impaired capacity for differentiation.

neuroscience↗

bioRxiv2025

Depletion of extracellular asparagine impairs self-reactive T cells and ameliorates autoimmunity in a murine model of multiple sclerosis

Amino acids play critical roles in the activation and function of lymphocytes. Here we show that the non-essential amino acid, asparagine, is essential for optimal activation and proliferation of CD4+ T cells. We demonstrate that asparagine depletion at different time points after CD4+ T cell activation reduces mitochondrial membrane potential and function. Furthermore, asparagine depletion at specific time points during CD4+ T cell differentiation reduces cytokine production in multiple CD4+ T cell subsets. In an adoptive transfer model of experimental autoimmune encephalomyelitis (EAE), myelin oligodendrocyte-specific pathogenic T helper 17 cells differentiated under Asn-deficient conditions exhibited reduced encephalitogenic potential and attenuated EAE severity. In a model of EAE induced by active immunization, therapeutic depletion of extracellular Asn significantly reduced disease severity. These results identify asparagine as a key metabolic regulator of the pathogenicity of autoreactive CD4+ T cells and suggest that targeting asparagine metabolism may be a novel therapeutic strategy for autoimmunity.

immunology↗

bioRxiv2024

Highly recurrent multi-nucleotide mutations in SARS-CoV-2

Multi-nucleotide mutations (MNMs) simultaneously replace multiple nu-cleotides. They are a significant contributor to evolution and disease, as well as to misdiagnosis, misannotation and other biases in genome data analysis. MNMs are generally thought to be rare and random events. However, by processing millions of publicly shared genomes, we show that certain MNMs are highly recurrent in SARS-CoV-2: they repeatedly and consistently modify the same multiple nucleotides at the same genome position in the same way. The most frequent of these MNMs have independently occurred hundreds of times across all SARS-CoV-2 lineages. The vast majority of these recurrent MNMs are linked to transcription regulatory sequences. We propose a mechanism that explains them through template switching as part of the natural transcription process of the virus. This previously unknown mutational pattern increases our understanding of the evolution of SARS-CoV-2 and potentially many other nidoviruses. It also has important consequences for computational evolutionary biology: we show that for example recurrent MNMs cause approximately 14% of false positives during inference of recombination in SARS-CoV-2.

evolutionary biology↗

bioRxiv2024

Detecting interspecific positive selection using convolutional neural networks

Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. Leveraging the feature-detection capabilities of convolutional neural networks (CNNs), we achieve higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident with noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore generalisability and identify future avenues to achieve broader utility. Once trained, our CNN model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multi-gene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.

evolutionary biology↗

bioRxiv2024

This is SPRTA: assessing phylogenetic confidence at pandemic scales

Phylogenetics plays a central role in evolutionary biology and genomic epidemiology. Assessing phylogenetic confidence and reliability is therefore crucial and methods to do this, such as Felsensteins bootstrap, are among the most used in modern science. However, methods based on Felsensteins bootstrap suffer from excessive computational demand, and are unsuitable for large datasets. Furthermore, most of these methods emerge from a cladistic framework which makes their results hard to interpret in the context of genomic epidemiology. We propose SPRTA (" SPR-based Tree Assessment"), an efficient and interpretable approach to assess confidence in phylogenetic trees. SPRTA shifts the paradigm of phylogenetic support measurement from evaluating the confidence in clades (groupings of taxa) to genome evolution histories, for example assessing if a lineage evolved from another considered lineage or not. This focus on evolutionary histories is particularly valuable in genomic epidemiology, where typically the evolutionary and transmission history of a pathogen are of interest, not clade content. We illustrate the use of SPRTA by investigating a global SARS-CoV-2 phylogenetic tree relating > 2M genomes, highlighting plausible alternative evolutionary origins of many SARS-CoV-2 variants. We have implemented SPRTA within the free and open source maximum likelihood phylogenetic software MAPLE, available from https://github.com/NicolaDM/MAPLE.

bioinformatics↗

bioRxiv2024

Rate variation and recurrent sequence errors in pandemic-scale phylogenetics

Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the COVID-19 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present new algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct reliable and public sequence alignment and phylogenetic tree of > 2 million SARS-CoV-2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023.

bioinformatics↗

bioRxiv2024

TET2 regulates early and late transitions in exhausted CD8+ T-cell differentiation and limits CAR T-cell function

CD8+ T-cell exhaustion hampers disease control in cancer and chronic infections and limits efficacy of T-cell-based therapies, such as CAR T-cells. Epigenetic reprogramming of CAR T-cells by targeting TET2, a methylcytosine dioxygenase that mediates active DNA demethylation, has shown therapeutic potential; however, the role of TET2 in exhausted T-cell (TEX) development is unclear. In CAR T-cell exhaustion models and chronic LCMV infection, TET2 drove the conversion from stem cell-like, self-renewing TEX progenitors towards terminally differentiated and effector (TEFF)-like TEX. In mouse T-cells, TET2-deficient terminally differentiated TEX retained aspects of TEX progenitor biology, alongside decreased expression of the transcription factor TOX, suggesting that TET2 potentiates terminal exhaustion. TET2 also enforced a TEFF-like terminally differentiated CD8+ T-cell state in the early bifurcation between TEFF and TEX, indicating a broad role for TET2 in mediating the acquisition of an effector biology program that could be exploited therapeutically. Finally, we developed a clinically actionable strategy for TET2- targeted CAR T-cells, using CRISPR/Cas9 editing and site-specific adeno-associated virus transduction to simultaneously knock-in a CAR at the TRAC locus and a functional safety switch within TET2. Disruption of TET2 with this safety switch in CAR T-cells restrained terminal TEX differentiation in vitro and enhanced anti-tumor responses in vivo. Thus, TET2 regulates pivotal fate transitions in TEX differentiation and can be targeted with a safety mechanism in CAR T-cells for improved tumor control and risk mitigation. One Sentence SummaryModulation of exhausted CD8+ T-cell differentiation by targeting TET2 improves therapeutic potential of CAR T-cells in cancer.

immunology↗

bioRxiv2024

Specialization in Molecular Functions and Secondary Metabolite Production Potential in Cyanobacterial Symbionts

Cyanobacteria are globally occurring photosynthetic bacteria notable for their contribution to primary production and their production of toxins which have detrimental impacts on ecosystems. Beyond this, cyanobacteria can form mutualistic symbiotic relationships with a diverse set of eukaryotes, ranging from land plants to fungi. Nevertheless, not all cyanobacteria are found in symbiotic associations suggesting symbiotic cyanobacteria have evolved specializations that facilitate host-interactions. Photosynthetic capabilities, nitrogen fixation, and the production of complex biochemicals are key functions provided by host-associated cyanobacterial symbionts. To explore if additional specializations are associated with such lifestyles in cyanobacteria, we have conducted comparative phylogenomics of molecular functions and of biosynthetic gene clusters (BGCs) in 977 cyanobacterial genomes. Cyanobacteria with host-associated and symbiotic lifestyles were concentrated in the family Nostocaceae, where eight monophyletic clades correspond to specific host taxa. In agreement with previous studies, symbionts are likely to provide fixed nitrogen to their eukaryotic partners. Additionally, our analyses identified chitin metabolising pathways in cyanobacteria associated with specific host groups, while obligate symbionts had fewer BGCs. The conservation of molecular functions and BGCs between closely related symbiotic and free-living cyanobacteria suggests that there is the potential for additional cyanobacteria to form symbiotic relationships than is currently known.

genomics↗

bioRxiv2024

A generalised protein identification method for novel and diverse sequencing technologies

Protein sequencing is a rapidly evolving field with much progress towards the realisation of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Using a hypothetical sequencing device that can simulate several novel devices, we show that on the human protein database (N=20,181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.

bioinformatics↗

bioRxiv2022

SWAMPy: Simulating SARS-CoV-2 Wastewater Amplicon Metagenomes with Python

MotivationTracking SARS-CoV-2 variants through genomic sequencing has been an important part of the global response to the pandemic. As well as whole-genome sequencing of clinical samples, this surveillance effort has been aided by amplicon sequencing of wastewater samples, which proved effective in real case studies. Because of its relevance to public healthcare decisions, testing and benchmarking wastewater sequencing analysis methods is also crucial, which necessitates a simulator. Although metagenomic simulators exist, none are fit for the purpose of simulating the metagenomes produced through amplicon sequencing of wastewater. ResultsOur new simulation tool, SWAMPy (Simulating SARS-CoV-2 Wastewater Amplicon Metagenomes with Python), is intended to provide realistic simulated SARS-CoV-2 wastewater sequencing datasets with which other programs that rely on this type of data can be evaluated and improved. AvailabilityThe code for this project is available at https://github.com/goldman-gp-ebi/SWAMPy It can be installed on any Unix-based operating system and is available under the GPL-v3 license.

bioinformatics↗

bioRxiv2022

A multi-enhancer hub at the Ets1 locus controls T cell differentiation and allergic inflammation through 3D genome topology

Multi-enhancer hubs are spatial clusters of enhancers which have been recently characterized across numerous developmental programs. Yet, the functional relevance of these three-dimensional (3D) structures is poorly understood. Here we show that the multiplicity of enhancers interacting with the transcription factor Ets1 is essential to control the precise expression level of this gene in response to cellular cues, and the failure to do so can lead to allergic diseases. Focusing on T cells as a model, we identified a highly connected multi-enhancer hub at the Ets1 locus, comprising a noncoding regulatory element that is a hotspot for sequence variation associated with allergic diseases. We deleted this hotspot and found that the multi-enhancer connectivity is dispensable for T cell development but required for CD4+ T helper (Th1) differentiation in response to changes in the cytokine milieu. Mice lacking this hotspot are thus protected from Th1-mediated colitis but demonstrate an overt allergic response to house dust mites, a T cell-mediated response which is dampened by Th1 cells. Mechanistically, the multi-enhancer hub controls the expression level of Ets1 that is dispensable for the active enhancer landscape but required for the Th1-specific genome topology through recruitment of CTCF. Together, we establish a paradigm for the functional and mechanistic relevance of multi-enhancer hubs controlling cellular competence to respond specifically to an inductive cue.

genomics↗

bioRxiv2022

Maximum likelihood pandemic-scale phylogenetics

Phylogenetics plays a crucial role in the interpretation of genomic data1. Phylogenetic analyses of SARS-CoV-2 genomes have allowed the detailed study of the viruss origins2, of its international3,4 and local4-9 spread, and of the emergence10 and reproductive success11 of new variants, among many applications. These analyses have been enabled by the unparalleled volumes of genome sequence data generated and employed to study and help contain the pandemic12. However, preferred model-based phylogenetic approaches including maximum likelihood and Bayesian methods, mostly based on Felsensteins pruning algorithm13,14, cannot scale to the size of the datasets from the current pandemic4,15, hampering our understanding of the viruss evolution and transmission16. We present new approaches, based on reworking Felsensteins algorithm, for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. We exploit near-certainty regarding ancestral genomes, and the similarities between closely related and densely sampled genomes, to greatly reduce computational demands for memory and time. Combined with new methods for searching amongst candidate evolutionary trees, this results in our MAPLE ( MAximum Parsimonious Likelihood Estimation) software giving better results than popular approaches such as FastTree 217, IQ-TREE 218, RAxML-NG19 and UShER15. Our approach therefore allows complex and accurate proba-bilistic phylogenetic analyses of millions of microbial genomes, extending the reach of genomic epidemiology. Future epidemiological datasets are likely to be even larger than those currently associated with COVID-19, and other disciplines such as metagenomics and biodiversity science are also generating huge numbers of genome sequences20-22. Our methods will permit continued use of preferred likelihood-based phylogenetic analyses.

bioinformatics↗

bioRxiv2022

A retroviral origin of vertebrate myelin

Myelin, the insulating sheath that surrounds neuronal axons, is produced by oligodendrocytes in the central nervous system (CNS). This evolutionary innovation, which first appears in jawed vertebrates, enabled rapid transmission of nerve impulses, more complex brains and greater morphological diversity. Here we report that RNA level expression of RNLTR12-int, a retrotransposon of retroviral origin, is essential for myelination. We show RNLTR12-int-encoded non-coding RNA binds to the transcription factor SOX10 to regulate transcription of myelin basic protein (Mbp, the major constituent of myelin) in rodents. RNLTR12-int like sequences (which we name RetroMyelin) are found in all jawed-vertebrates and we further demonstrate their function in regulating myelination in two different vertebrate phyla (zebrafish and frogs). Our study therefore suggests that retroviral endogenization was a key step in the emergence of vertebrate myelin.

neuroscience↗

bioRxiv2021

matUtils: Tools to Interpret and Manipulate Mutation Annotated Trees

The vast scale of SARS-CoV-2 sequencing data has made it increasingly challenging to comprehensively analyze all available data using existing tools and file formats. To address this, we present a database of SARS-CoV-2 phylogenetic trees inferred with unrestricted public sequences, which we update daily to incorporate new sequences. Our database uses the recently-proposed mutation-annotated tree (MAT) format to efficiently encode the tree with branches labeled with parsimony-inferred mutations as well as Nextstrain clade and Pango lineage labels at clade roots. As of June 9, 2021, our SARS-CoV-2 MAT consists of 834,521 sequences and provides a comprehensive view of the virus evolutionary history using public data. We also present matUtils - a command-line utility for rapidly querying, interpreting and manipulating the MATs. Our daily-updated SARS-CoV-2 MAT database and matUtils software are available at http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/ and https://github.com/yatisht/usher, respectively.

bioinformatics↗