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Ryan, C.

Publications and source records attributed to Ryan, C..

At least 19 recordsLinked to original sources

Pan-cancer discovery of driver mutations in long noncoding RNAs reveals widespread functional rewiring of RNA regulatory elements

Most somatic mutations in cancer occur outside protein-coding genes, yet the functional impact of these mutations remains largely unknown. Long noncoding RNAs (lncRNAs) represent a major class of cancer-promoting genes whose molecular mechanisms are poorly understood. While individual driver mutations in lncRNAs have been identified, detecting such driver lncRNAs at scale requires large tumour genome cohorts. We analyse 12,631 cancer genomes from the 100,000 Genomes Project (100kGP) and identify 121 lncRNAs under positive selection across 19 cancer types. These driver lncRNAs are independently supported by functional genomic screens, germline predisposing variants, mutual exclusivity with protein-coding drivers, and independent oncogenic lncRNA catalogues. Overall, approximately two-thirds of analysed tumours harbour at least one lncRNA driver mutation. Leveraging the depth of this dataset, we demonstrate that somatic mutations preferentially target and remodel RNA-binding protein (RBP) interaction sites to potentiate oncogenic lncRNAs, including MALAT1, SNHG14 and NEAT1. From these data, we derive a model in which somatic mutations liberate oncogenic lncRNAs from repressive RNA:protein interactions. This work expands the number and nature of cancer driver genes, identifies targets for RNA-directed therapies, and demonstrates that with large tumour mutation catalogues we can dissect the molecular mechanisms of noncoding genes.

Cancer Biology↗

Integrating image-based phenotyping and GWAS to map tolerance to Spittlebug nymphs in interspecific Urochloa grasses

Urochloa grasses are among the most widely used forage grasses across the tropics. Spittlebugs (Hemiptera: Cercopidae) are major pests of tropical Urochloa (syn. Brachiaria) pastures, severely reducing forage productivity and quality. Understanding the genetic basis of host-plant resistance is essential for developing durable resistant cultivars. Here, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of tolerance to Aeneolamia varia nymphs in 339 interspecific F hybrids derived from crosses between resistant sexual and susceptible apomictic Urochloa parents. Digital image analysis using both unsupervised (DQU) and supervised (DTR) quantification pipelines enabled precise estimation of plant damage, yielding moderate to high broad-sense heritability estimates (H{superscript 2} = 0.49-0.66). In contrast, insect survival (NTS) exhibited low to moderate correlations with all damage traits and lower heritability estimates (H{superscript 2} = 0.42). Using 57,051 high-quality SNPs aligned to the genome of the hybrid cultivar Basilisk, GWAS models identified 18 quantitative trait loci (QTL) for plant damage traits, but none for insect survival (antibiosis). Six robust QTL on chromosomes 1, 6, 7, 27, 29, and 36 were consistently detected across models and phenotyping methods, explaining up to 21.5% of phenotypic variance. Candidate gene analysis revealed proteins involved in hormone signalling, oxidative stress response, and cell wall modification, suggesting multifaceted tolerance mechanisms. These results provide a foundational set of molecular markers associated with spittlebug tolerance in Urochloa, useful for marker-assisted and genomic selection in our forage breeding programme.

plant biology↗

A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional analyses. With application to translational cancer research, these computational tools have positioned 3D patient-derived tumor avatars front and center as crucial data input sources. However, a major challenge remains: the lack of standardization in media composition in 3D patient-derived tumor models unpredictably affects cell behavior and limit the utility beyond predicting treatment responses. To address this unmet need, we developed a simple, reproducible perfusion circuit system to approximate in vivo physiology using autologous patient plasma. With peritoneal metastases and core needle biopsies across multiple tumor histologies, we demonstrate preservation of the tumor microenvironment for up to 48 hours using multi-modal interrogation techniques. With proof-of-concept experiments, we display the systems ability to unveil complex drug-dependent biology within this time window. Standardizable, physiologically relevant platforms for 3D patient-derived tumor avatars will yield unprecedented insights through the integration of data from broad groups of patients and the use of an expanding armamentarium of artificial intelligence capabilities.

cancer biology↗

Benchmarking Genetic Interaction Scoring Methods for Identifying Synthetic Lethality from Combinatorial CRISPR Screens

Synthetic lethality (SL) is an extreme form of negative genetic interaction, where simultaneous disruption of two non-essential genes causes cell death. SL can be exploited to develop cancer therapies that target tumour cells with specific mutations, potentially limiting toxicity. Pooled combinatorial CRISPR screens, where two genes are simultaneously perturbed and the resulting impacts on fitness estimated, are now widely used for the identification of SL targets in cancer. Various scoring methods have been developed to infer SL genetic interactions from these screens, but there has been no systematic comparison of these approaches. Here, we performed a comprehensive analysis of 5 scoring methods for SL detection using 5 combinatorial CRISPR datasets. We assessed the performance of each algorithm on each screen dataset using two different benchmarks of paralog synthetic lethality. We find that no single method performs best across all screens but identify two methods that perform well across most datasets. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/645224v1_fig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@b66b64org.highwire.dtl.DTLVardef@44871dorg.highwire.dtl.DTLVardef@102b24borg.highwire.dtl.DTLVardef@77416_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Graphical Abstract. Benchmarking Scoring Methods for Synthetic Lethality Detection from CRISPR screen data. Experimental setup for benchmarking experiments. Five different CRISPR double knockout (DKO) screens are scored for genetic interaction using 5 different scoring methods. The calculated scores are analysed using two different benchmarks (De Kegel Hits and Koferle Hits). Area under the receiver operating characteristic curve (AUROC) and Area under the precision recall curve (AUPR) for each scoring method on each dataset are calculated and compared. C_FIG

bioinformatics↗

Evaluating Sequence and Structural Similarity Metrics for Predicting Shared Paralog Functions

Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over evolutionary time scales, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional similarity and can predict shared functions between paralogs as revealed by synthetic lethal experiments. However, the advent of alternative protein representations, including embeddings from protein language models (PLMs) and predicted structures from AlphaFold, raises the possibility that alternative similarity metrics could better capture functional similarity between paralogs. Here, using two species (budding yeast and human) and two different definitions of shared functionality (shared protein-protein interactions, synthetic lethality) we evaluated a variety of alternative similarity metrics. For some tasks, predicted structural similarity or PLM embedding similarity outperform sequence identity, but more importantly these similarity metrics are not redundant with sequence identity, i.e. combining them with sequence identity leads to improved predictions of shared functionality. By adding contextual features, representing similarity to homologous proteins within and across species, we can significantly enhance our predictions of shared paralog functionality. Overall, our results suggest that alternative similarity metrics capture complementary aspects of functional similarity beyond sequence identity alone. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/617835v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@21ed4org.highwire.dtl.DTLVardef@136184eorg.highwire.dtl.DTLVardef@75e0e5org.highwire.dtl.DTLVardef@1001932_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

A haplotype-resolved chromosome-level genome assembly of Urochloa decumbens cv. Basilisk resolves its allopolyploid ancestry and composition

Haplotyped-resolved phased assemblies aim to capture the full allelic diversity in heterozygous and polyploid species to enable accurate genetic analyses. However, building non-collapsed references still presents a challenge. Here, we used long-range interaction Hi-C reads (high-throughput chromatin conformation capture) and HiFi PacBio reads to assemble the genome of the apomictic cultivar Basilisks from Urochloa decumbens (2n = 4x = 36), an outcrossed tetraploid Paniceae grass widely cropped to feed livestock in the tropics. We identified and removed Hi-C reads between homologous unitigs to facilitate their scaffolding and employed methods for the manual curation of rearrangements and misassemblies. Our final phased assembly included the four haplotypes in 36 chromosomes. We found that 18 chromosomes originated from diploid U. brizantha and the other 18 from either U. ruziziensis or diploid U. decumbens. We also identified a chromosomal translocation between chromosomes 5 and 32, as well as evidence of pairing exclusively within subgenomes, except for a homoeologous exchange in chromosome 21. Our results demonstrate that haplotype-aware assemblies accurately capture the allelic diversity in heterozygous species, making them the preferred option over collapsed-haplotype assemblies.

plant biology↗

The effects of genome size on cell size and the functional composition and morphology of leaves: a case study in Rhododendron (Ericaceae)

Background and AimsDespite the predominance of scaling photosynthetic metabolism by two-dimensional leaf surface area, leaves are three-dimensional structures composed of multiple tissues that directly and indirectly influence photosynthetic metabolism. The structure of leaf surfaces for CO2 diffusion and light transmission and the internal volume of tissues that process energy and matter work together to control rates of resource acquisition and turnover. Here we investigate the influence of cell size and packing density on resource acquisition as measured by surface conductance to CO2 and water vapor and on resource turnover as measured by leaf water turnover time. MethodsWe sampled wild and cultivated congeneric species in the genus Rhododendron (Ericaceae) and measured genome size, anatomical traits related to cell sizes and packing densities, and morphological traits related to water content and dry mass allocation. ResultsAmong Rhododendron, anatomical traits related to cell size and morphological traits related to water content and dry mass investment varied largely orthogonally to each other, allowing for many combinations of leaf traits to exist. However, there was a strong, negative relationship between the leaf water residence time ({tau}) and the maximum leaf surface conductance per leaf volume (gmax,vol), both of which are influenced by cell size and cell packing densities. ConclusionsDespite leaf function being controlled by many potential combinations of leaf cell- and tissue-level traits, cell size has a pervasive effect on leaf function. Small cells allow for higher diffusion of CO2 and water vapor per unit leaf volume (gmax,vol) even at constant leaf thickness, but small cells also result in shorter leaf water residence times ({tau}). The strong tradeoff between gmax,vol and ({tau}) illuminates how genome size-cell size allometry influences the fast-slow continuum of plant carbon and water economy.

plant biology↗

Loss of Polycomb Repressive Complex 2 function causes asparaginase resistance in T-acute lymphoblastic leukemia through decreased WNT pathway activity

Loss-of-function mutations and deletions in core components of the epigenetic Polycomb Repressive Complex 2 (PRC2) are associated with poor prognosis and treatment resistance in T-acute lymphoblastic leukemia (T-ALL). We leveraged clinical mutational and transcriptional data to identify a functional link between PRC2 alterations and changes in WNT signaling pathway activity in leukemia cells. Computational integration of transcriptomic, proteomic and phosphoproteomic data from an isogenic T-ALL cellular model revealed reduced activity of the WNT-dependent stabilization of proteins (WNT/STOP) pathway in cells lacking core PRC2 factor EZH2. We discovered that PRC2 loss significantly reduced sensitivity to key T-ALL treatment asparaginase, and that this was mechanistically linked to increased cellular ubiquitination levels that bolstered leukemia cell asparagine reserves. We further found that asparaginase resistance in PRC2-depleted leukemic blasts could be mitigated by pharmaceutical proteasome inhibition, thereby providing a novel and clinically tractable means to tackle induction treatment failure in high-risk T-ALL.

cancer biology↗

Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity

Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.

bioinformatics↗

Unraveling a Bacterial Starvation Response Through the Direct Targets of a Starvation-Induced Transcriptional Activator

Organisms frequently encounter environments with nutrient shortages and their survival depends on changes in physiology and the ability to conserve resources. In bacteria, many physiological changes associated with starvation have been identified, but the underlying genetic components and regulatory networks that direct these physiological changes are often poorly defined. Here, we aimed to better define the gene regulatory networks that mediate the starvation response in Myxococcus xanthus, a bacterium that copes with starvation by producing fruiting bodies filled with dormant and stress-resistant spores. We focused on the direct promoter/gene targets of Nla28, a transcriptional activator/enhancer binding protein (EBP) that is important for early rounds of gene expression following starvation. Using expression profiling to identify genes that are downregulated in nla28 mutant cells and bioinformatics to identify the putative promoters of these genes, 12 potential promoter targets (37 genes) of Nla28 were identified. The results of in vitro promoter binding assays, coupled with in vitro and in vivo mutational analyses, suggested that the 12 promoters are in vivo targets of Nla28 and that Nla28 dimers use tandem, imperfect repeats of an 8-bp sequence for binding. Interestingly, nine of the Nla28 target promoters are intragenic, located in the protein coding sequence of an upstream gene or in the protein coding sequence of one gene within an operon (internal promoters). Based on mutational analyses, we concluded that the 12 Nla28 target loci contain at least one gene important for production of stress-resistant spores following starvation. Most of these loci contain genes predicted to be involved in regulatory or defense-related functions. Using the consensus Nla28 binding sequence, followed by bioinformatics and expression profiling, 58 additional promoters and 102 genes were tagged as potential Nla28 targets. Among these putative Nla28 targets, functions such as regulatory, metabolic and cell envelope biogenesis were commonly assigned to genes.

microbiology↗

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumour proteomic profiles

Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analysing replicate profiles of tumours and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlation across multiple studies. Finally, we show that pathways previously reported to have higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.

systems biology↗

Deciphering Multi-way Interactions in the Human Genome

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types.

bioinformatics↗

Role of Ori in Thermococcus barophilus

The mechanisms underpinning replication of genomic DNA in Archaea have recently been challenged. Species belonging to two different taxonomic orders grow well in the absence of an origin of replication, challenging the role of the replication origin in these organisms. Here, we pursue the investigation of the particular way some archaea manage their DNA replication with Thermococcus barophilus and the role of Ori in this Archaea. Surprisingly we discovered that T. barophilus uses its Ori all along the growth curve with marked increase at the end of exponential phase. Through gene deletion, we show that Ori utilization requires Cdc6, and that origin deletion results in increased time in lag phase and a moderate decrease of growth rate in mutants. The number of chromosomes are quite similar between both strains during exponential and early stationary phases but differs after 24h of growth where {Delta}TbOriC has only 6 chromosomes/cell compared to 10 for the reference strain (WT). Following 1hr of growth in fresh media, {Delta}TbOriC strains contains 3 chromosome copies/cell, whereas the WT contains only 1. We hypothesize that the T. barophilus might degrade DNA to obtain energy to start replication and cell division, whereas the {Delta}TbOriC must maintain more chromosomal copies in order to initiate DNA replication in the absence of an origin or replication. Finally, we analyzed the role of Ori at temperatures above or below the optimal temperature, revealing that Ori is important to start growth at those temperatures, suggesting that replication origins may be involved in stress response.

microbiology↗

Decadal changes in fire frequencies shift tree communities and functional traits

Global change has resulted in chronic shifts in fire regimes, increasing fire frequency in some regions and decreasing it in others. Predicting the response of ecosystems to changing fire frequencies is challenging because of the multi-decadal timescales over which fire effects emerge and the variability in environmental conditions, fire types, and plant composition across biomes. Here, we address these challenges using surveys of tree communities across 29 sites that experienced multi-decadal alterations in fire frequencies spanning ecosystems and environmental conditions. Relative to unburned plots, more frequently burned plots had lower tree basal area and stem densities that compounded over multiple decades: average fire frequencies reduced basal area by only 4% after 16 years but 57% after 64 years, relative to unburned plots. Fire frequency had the largest effects on basal area in savanna ecosystems and in sites with strong wet seasons. Analyses of tree functional-trait data across North American sites revealed that frequently burned plots had tree communities dominated by species with low biomass nitrogen and phosphorus content and with more efficient nitrogen acquisition through ectomycorrhizal symbioses (rising from 85% to nearly 100%). Our data elucidate the impact of long-term fire regimes on tree community structure and composition, with the magnitude of change depending on climate, vegetation type, and fire history. The effects of widespread changes in fire regimes underway today will manifest in decades to come and have long-term consequences for carbon storage and nutrient cycling.

ecology↗

Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is timeconsuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder). Author SummaryLinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI de-velopments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.

bioinformatics↗

An inhomogeneous Poisson model of the spatial intensity of opioid overdoses, as represented by EMS naloxone use

1From 1999 to 2017, the age-adjusted annual death rate in the United States from opioid overdose has increased several-fold, to about 18.3 per 100,000 population. The federal government has declared opioid overdose a public health emergency. Spatially-aware analyses and surveillance may contribute to control of this growing problem. As naloxone has only one clinical use--the treatment of opioid overdose--its administration by emergency medical services (EMS) personnel can serve as a surveillance indicator for opioid overdose. I previously demonstrated spatial clustering of naloxone-involved EMS calls, over and above that to be expected fromEMS calls in general. To better understand the nature of that clustering, I modelled the spatial distribution of naloxone-involved EMS calls in a three-county region in south-central New York State as an inhomogeneous Poisson process, using as predictors several census-tract-level sociodemographic variables and the point locations of convenience stores (minimarts). In Monte Carlo simulations, I examined how well the model explained the observed clustering. Although it assumes no interaction between event locations, the inhomogeneous Poisson model was nevertheless able to explain much of the observed spatial clustering. The spatial intensity of EMS calls for opioid overdose, in events per square kilometer, was signficantly higher in census tracts with lower rates of owner-occupancy of housing. Neither the proportion of households in poverty nor the proportion of residents in the 20-44 age band were signficant predictors. Spatial intensity of overdose events decreased by about 10% for each kilometer of distance from the nearest minimart, but this was of borderline signfificance at conventional levels. These findings cast some doubt on the utility of real-time surveillance for apparent spatial clusters of opioid overdose and instead favor a longer-term, systemic strategy comprising efforts to improve neighborhood conditions.

epidemiology↗

Strong and lasting impacts of past global warming on baleen whale and prey abundance

AbstractThe demography of baleen whales and their prey during the past 30 thousand years was assessed to understand the effects of past rapid global warming on marine ecosystems. Mitochondrial and genome-wide DNA sequence variation in eight baleen whale and seven prey species revealed strong, ocean-wide demographic changes that were correlated with changes in global temperatures and regional oceanographic conditions. In the Southern Ocean baleen whale and prey abundance increased exponentially and in apparent synchrony, whereas changes in abundance varied among species in the more heterogeneous North Atlantic Ocean. The estimated changes in whale abundance correlated with increases in the abundance of prey likely driven by reductions in sea-ice cover and an overall increase in primary production. However, the specific regional oceanographic environment, trophic interactions and species ecology also appeared to play an important role. Somewhat surprisingly the abundance of baleen whales and prey continued to increase for several thousand years after global temperatures stabilized. These findings warn of the potential for dramatic, long-term effects of current climate changes on the marine ecosystem.\n\nOne Sentence SummaryThe effects of past global warming on marine ecosystems were drastic, system-wide and long-lasting.

evolutionary biology↗

Fin whale (Balaenoptera physalus) mitogenomics: A cautionary tale of defining sub-species from mitochondrial sequence monophyly

HighlightsO_LIMitochondrial monophyly is commonly employed to define evolutionary significant units.\nC_LIO_LIMonophyly may be caused by insufficient sampling or a recent common ancestor.\nC_LIO_LIMitogenomic studies are generally based on few samples and prone to sampling issues.\nC_LIO_LIExpanded mitogenome sampling negates previous monophyly in fin whales.\nC_LI\n\nAbstractThe advent of massive parallel sequencing technologies has resulted in an increase of studies based upon complete mitochondrial genome DNA sequences that revisit the taxonomic status within and among species. Spatially distinct monophyly in mitogenomic genealogies, i.e., the sharing of a recent common ancestor among con-specific samples collected in the same region has been viewed as evidence for subspecies. Several recent studies in cetaceans have employed this criterion to suggest subsequent intraspecific taxonomic revisions. We reason that employing intra-specific, spatially distinct monophyly at non-recombining, clonally inherited genomes is an unsatisfactory criterion for defining subspecies based upon theoretical (genetic drift) and practical (sampling effort) arguments. This point is illustrated by a re-analysis of a global mitogenomic assessment of fin whales, Balaenoptera physalus spp., published by Archer et al. (2013) which proposed to further subdivide the Northern Hemisphere fin whale subspecies, B. p. physalus. The proposed revision was based upon the detection of spatially distinct monophyly among North Atlantic and North Pacific fin whales in a genealogy based upon complete mitochondrial genome DNA sequences. The extended analysis conducted in this study (1,676 mitochondrial control region, 162 complete mitochondrial genome DNA sequences and 20 microsatellite loci genotyped in 358 samples) revealed that the apparent monophyly among North Atlantic fin whales reported by Archer et al. (2013) to be due to low sample sizes. In conclusion, defining sub-species from monophyly (i.e., the absence of para-or polyphyly) can lead to erroneous conclusions due to relatively \"trivial\" aspects, such as sampling. Basic population genetic processes (i.e., genetic drift and migration) also affect the time to most recent common ancestor and hence the probability that individuals in a sample are monophyletic.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC=\"FIGDIR/small/488130v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1e38e54org.highwire.dtl.DTLVardef@9533eborg.highwire.dtl.DTLVardef@183b5b5org.highwire.dtl.DTLVardef@eae09_HPS_FORMAT_FIGEXP M_FIG C_FIG

evolutionary biology↗