Biology subjects

Goldman, N.

Publications and source records attributed to Goldman, N..

32 records · Page 2Linked to original sources

bioRxiv2021

The SARS-CoV-2 replication-transcription complex is a priority target for broad-spectrum pan-coronavirus drugs

In the absence of effective treatment, COVID-19 is likely to remain a global disease burden. Compounding this threat is the near certainty that novel coronaviruses with pandemic potential will emerge in years to come. Pan-coronavirus drugs - agents active against both SARS-CoV-2 and other coronaviruses - would address both threats. A strategy to develop such broad-spectrum inhibitors is to pharmacologically target binding sites on SARS-CoV-2 proteins that are highly conserved in other known coronaviruses, the assumption being that any selective pressure to keep a site conserved across past viruses will apply to future ones. Here, we systematically mapped druggable binding pockets on the experimental structure of fifteen SARS-CoV-2 proteins and analyzed their variation across twenty-seven - and {beta}-coronaviruses and across thousands of SARS-CoV-2 samples from COVID-19 patients. We find that the two most conserved druggable sites are a pocket overlapping the RNA binding site of the helicase nsp13, and the catalytic site of the RNA-dependent RNA polymerase nsp12, both components of the viral replication-transcription complex. We present the data on a public web portal (https://www.thesgc.org/SARSCoV2_pocketome/) where users can interactively navigate individual protein structures and view the genetic variability of drug binding pockets in 3D.

pharmacology and toxicology↗

bioRxiv2021

phastSim: efficient simulation of sequence evolution for pandemic-scale datasets

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, as well as being part of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. < 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software is available from https://github.com/NicolaDM/phastSim and allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutatability models that we developed to more realistically represent SARS-CoV-2 genome evolution. Author summaryOne of the most influential responses to the SARS-CoV-2 pandemic has been the widespread adoption of genome sequencing to keep track of viral spread and evolution. This has resulted in vast availability of genomic sequence data, that, while extremely useful and promising, is also increasingly hard to store and process efficiently. An important task in the processing of this genetic data is simulation, that is, recreating potential histories of past and future virus evolution, to benchmark data analysis methods and make statistical inference. Here, we address the problem of efficiently simulating large numbers of closely related genomes, similar to those sequenced during SARS-CoV-2 pandemic, or indeed to most scenarios in genomic epidemiology. We develop a new algorithm to perform this task, that provides not only computational efficiency, but also extreme flexibility in terms of possible evolutionary models, allowing variation in mutation rates, non-stationary evolution, and indels; all phenomena that play an important role in SARS-CoV-2 evolution, as well as many other real-life epidemiological scenarios.

bioinformatics↗

bioRxiv2021

Multiomics single-cell analysis of human pancreatic islets reveals novel cellular states in health and type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease of only partially defined etiology in which immune cells destroy insulin-producing beta cells. Using single-cell transcriptomics and an advanced analytical strategy to assess pancreatic islets of T1D, autoantibody-positive, and non-diabetic organ donors, we identified both canonical cell types and rare insulin-expressing cells with a hybrid mixture of endocrine and exocrine gene signatures within all donors. We further found elevated expression of MHC Class II pathway genes in exocrine ductal cells of T1D donors, which we confirmed through CyTOF, in situ imaging mass cytometry, and immunofluorescence analysis. Taken together, our multimodal analyses identify novel cell types and processes that may contribute to T1D immunopathogenesis and provide new cellular and molecular insights into human pancreas function.

genomics↗

bioRxiv2021

Mutation rates and selection on synonymous mutations in SARS-CoV-2

AO_SCPLOWBSTRACTC_SCPLOWThe COVID-19 pandemic has seen an unprecedented response from the sequencing community. Leveraging the sequence data from more than 140,000 SARS-CoV-2 genomes, we study mutation rates and selective pressures affecting the virus. Understanding the processes and effects of mutation and selection has profound implications for the study of viral evolution, for vaccine design, and for the tracking of viral spread. We highlight and address some common genome sequence analysis pitfalls that can lead to inaccurate inference of mutation rates and selection, such as ignoring skews in the genetic code, not accounting for recurrent mutations, and assuming evolutionary equilibrium. We find that two particular mutation rates, G[->]U and C[->]U, are similarly elevated and considerably higher than all other mutation rates, causing the majority of mutations in the SARS-CoV-2 genome, and are possibly the result of APOBEC and ROS activity. These mutations also tend to occur many times at the same genome positions along the global SARS-CoV-2 phylogeny (i.e., they are very homoplasic). We observe an effect of genomic context on mutation rates, but the effect of the context is overall limited. While previous studies have suggested selection acting to decrease U content at synonymous sites, we bring forward evidence suggesting the opposite.

evolutionary biology↗

bioRxiv2020

Phylogenetic Novelty Scores: a New Approach for Weighting Genetic Sequences

BackgroundMany important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are novel compared to the others in the same dataset, and low weights to sequences that are over-represented. ResultsWe formalise this principle by rigorously defining the evolutionary novelty of a sequence within an alignment. This results in new sequence weights that we call phylogenetic novelty scores. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column -- important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they improve the accuracy of character frequency estimation compared to existing sequence weighting schemes. ConclusionsOur phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.

bioinformatics↗

bioRxiv2020

Short-range template switching in great ape genomes explored using a pair hidden Markov model

Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons.

bioinformatics↗

bioRxiv2020

Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summaryAs shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.

evolutionary biology↗

bioRxiv2020

High-resolution influenza mapping of a city reveals socioeconomic determinants of transmission within and between urban quarters

With two-thirds of the global population projected to be living in urban areas by 2050, understanding the transmission patterns of viral pathogens within cities is crucial for effective prevention strategies. Here, in unprecedented spatial resolution, we analysed the socioeconomic determinants of influenza transmission in a European city. We combined geographical and epidemiological data with whole genome sequencing of influenza viruses at the scale of urban quarters and statistical blocks, the smallest geographic subdivisions within a city. We observed annually re-occurring geographic clusters of influenza incidences, mainly associated with net income, and independent of population density and living space. Vaccination against influenza was also mainly associated with household income and was linked to the likelihood of influenza-like illness within an urban quarter. Transmissions patterns within and between quarters were complex. High-resolution city-level epidemiological studies combined with social science surveys such as this will be essential for understanding seasonal and pandemic transmission chains and delivering tailored public health information and vaccination programs at the municipal level.

microbiology↗

bioRxiv2020

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography -- with location data provided in the form of latitude and longitude coordinates -- describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreaks spatial distribution. We therefore consider an alternative model, the spatial {Lambda}-Fleming-Viot process ({Lambda}FV), which has recently gained popularity in population genetics. Despite the {Lambda}FVs robustness to sampling biases, we find that the different assumptions of the {Lambda}FV and BMP models result in different applicabilities, with the {Lambda}FV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.

evolutionary biology↗

bioRxiv2019

Ambiguity coding allows accurate inference of evolutionary parameters from alignments in an aggregated state-space

How can we best learn the history of a proteins evolution? Ideally, a model of sequence evolution should capture both the process that generates genetic variation and the functional constraints determining which changes are fixed. However, in practical terms the most suitable approach may simply be the one that combines the convenience of easily available input data with the ability to return useful parameter estimates. For example, we might be interested in a measure of the strength of selection (typically obtained using a codon model) or an ancestral structure (obtained using structural modelling based on inferred amino acid sequence and side chain configuration).\n\nBut what if data in the relevant state-space are not readily available? We show that it is possible to obtain accurate estimates of the outputs of interest using an established method for handling missing data. Encoding observed characters in an alignment as ambiguous representations of characters in a larger state-space allows the application of models with the desired features to data that lack the resolution that is normally required. This strategy is viable because the evolutionary path taken through the observed space contains information about states that were likely visited in the \"unseen\" state-space. To illustrate this, we consider two examples with amino acid sequences as input.\n\nWe show that{omega} , a parameter describing the relative strength of selection on non-synonymous and synonymous changes, can be estimated in an unbiased manner using an adapted version of a standard 61-state codon model. Using simulated and empirical data, we find that ancestral amino acid side chain configuration can be inferred by applying a 55-state empirical model to 20-state amino acid data. Where feasible, combining inputs from both ambiguity-coded and fully resolved data improves accuracy. Adding structural information to as few as 12.5% of the sequences in an amino acid alignment results in remarkable ancestral reconstruction performance compared to a benchmark that considers the full rotamer state information. These examples show that our methods permit the recovery of evolutionary information from sequences where it has previously been inaccessible.

evolutionary biology↗

bioRxiv2019

Integrated evolutionary and structural analysis reveals xenobiotics and pathogens as the major drivers of mammalian adaptation

Understanding the molecular basis of adaptation to the environment is a central question in evolutionary biology, yet linking detected signatures of positive selection to molecular mechanisms remains challenging. Here we demonstrate that combining sequence-based phylogenetic methods with structural information assists in making such mechanistic interpretations on a genomic scale. Our integrative analysis shows that positively selected sites tend to co-localise on protein structures and that positively selected clusters are found in functionally important regions of proteins, indicating that positive selection can contravene the well-known principle of evolutionary conservation of functionally important regions. This unexpected finding, along with our discovery that positive selection acts on structural clusters, opens new strategies for the development of better models of protein evolution. Remarkably, proteins where we detect the strongest evidence of clustering belong to just two functional groups: components of immune response and metabolic enzymes. This gives a coherent picture of immune response and xenobiotic metabolism as the drivers of adaptive evolution of mammals.

evolutionary biology↗

bioRxiv2019

Modelling structural constraints on protein evolution via side-chain conformational states

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally-aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well a rotamer state: the discrete geometric pattern of permitted side-chain atomic positions. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference and ancestral state reconstruction. We conclude that the concomitant evolution of sequence and structure is a valuable source of phylogenetic information.

evolutionary biology↗

bioRxiv2019

Oncogenic Notch promotes long-range regulatory interactions within hyperconnected 3D cliques

Chromatin loops enable transcription factor-bound distal enhancers to interact with their target promoters to regulate transcriptional programs. Although developmental transcription factors, such as active forms of Notch, can directly stimulate transcription by activating enhancers, the effect of their oncogenic subversion on the 3-dimensional (3D) organization of the cancer genome is largely undetermined. By mapping chromatin looping genome-wide in Notch-dependent triple-negative breast cancer and B-cell lymphoma, we show that far beyond the well-characterized role of Notch as an activator of distal enhancers, Notch regulates its direct target genes through establishing new long-range regulatory interactions. Moreover, a large fraction of Notch-promoted regulatory loops forms highly interacting enhancer and promoter spatial clusters, termed "3D cliques". Loss-and gain-of-function experiments show that Notch preferentially targets hyperconnected 3D cliques that regulate the expression of crucial proto-oncogenes. Our observations suggest that oncogenic hijacking of developmental transcription factors can dysregulate transcription through widespread effects on the spatial organization of cancer genomes.

cancer biology↗

bioRxiv2017

Avoiding ascertainment bias in the maximum likelihood inference of phylogenies based on truncated data

Some phylogenetic datasets omit data matrix positions at which all taxa share the same state. For sequence data this may be because of a focus on single nucleotide polymorphisms (SNPs) or the use of a technique such as restriction site-associated DNA sequencing (RADseq) that concentrates attention onto regions of differences. With morphological data, it is common to omit states that show no variation across the data studied. It is already known that failing to correct for the ascertainment bias of omitting constant positions can lead to overestimates of evolutionary divergence, as the lack of constant sites is explained as high divergence rather than as a deliberate data selection technique. Previous approaches to using corrections to the likelihood function in order to avoid ascertainment bias have either required knowledge of the omitted positions, or have modified the likelihood function to reflect the omitted data. In this paper we indicate that the technique used to date for this latter approach is a conditional maximum likelihood (CML) method. An alternative approach -- unconditional maximum likelihood (UML) -- is also possible. We investigate the performance of CML and UML and find them to have almost identical performance in the phylogenetic SNP dataset context. We also make some observations about the nucleotide frequencies observed in SNP datasets, indicating that these can differ systematically from the overall equilibrium base frequencies of the substitution process. This suggests that model parameters representing base frequencies should be estimated by maximum likelihood, and not by empirical (counting) methods.

evolutionary biology↗