Giants among Cnidaria: Large Nuclear Genomes and Rearranged Mitochondrial Genomes in Siphonophores.

Siphonophores (Cnidaria: Hydrozoa) are abundant predators found throughout the ocean and are important constituents of the global zooplankton community. They range in length from a few centimeters to tens of meters. They are gelatinous, fragile, and difficult to collect, so many aspects of the biology of these roughly 200 species remain poorly understood. To survey siphonophore genome diversity, we performed Illumina sequencing of 32 species sampled broadly across the phylogeny. Sequencing depth was sufficient to estimate nuclear genome size from k-mer spectra in six specimens, ranging from 0.7 to 2.3 Gb, with heterozygosity estimates between 0.69% and 2.32%. Incremental k-mer counting indicates k-mer peaks can be absent with nearly 20× read coverage, suggesting minimum genome sizes range from 1.4 to 5.6 Gb in the 25 samples without peaks in the k-mer spectra. This work confirms most siphonophore nuclear genomes are large relative to the genomes of other cnidarians, but also identifies several with reduced size that are tractable targets for future siphonophore nuclear genome assembly projects. We also assembled complete mitochondrial genomes for 33 specimens from these new data, indicating a conserved gene order shared among nonsiphonophore hydrozoans, Cystonectae, and some Physonectae, revealing the ancestral mitochondrial gene order of siphonophores. Our results also suggest extensive rearrangement of mitochondrial genomes within other Physonectae and in Calycophorae. Though siphonophores comprise a small fraction of cnidarian species, this survey greatly expands our understanding of cnidarian genome diversity. This study further illustrates both the importance of deep phylogenetic sampling and the utility of k-mer-based genome skimming in understanding the genomic diversity of a clade.

Cell type evolution reconstruction across species through cell phylogenies of single-cell RNA sequencing data.

The origin and evolution of cell types has emerged as a key topic in evolutionary biology. Driven by rapidly accumulating single-cell datasets, recent attempts to infer cell type evolution have largely been limited to pairwise comparisons because we lack approaches to build cell phylogenies using model-based approaches. Here we approach the challenges of applying explicit phylogenetic methods to single-cell data by using principal components as phylogenetic characters. We infer a cell phylogeny from a large, comparative single-cell dataset of eye cells from five distantly related mammals. Robust cell type clades enable us to provide a phylogenetic, rather than phenetic, definition of cell type, allowing us to forgo marker genes and phylogenetically classify cells by topology. We further observe evolutionary relationships between diverse vessel endothelia and identify the myelinating and non-myelinating Schwann cells as sister cell types. Finally, we examine principal component loadings and describe the gene expression dynamics underlying the function and identity of cell type clades that have been conserved across the five species. A cell phylogeny provides a rigorous framework towards investigating the evolutionary history of cells and will be critical to interpret comparative single-cell datasets that aim to ask fundamental evolutionary questions.

Normalizing need not be the norm: count-based math for analyzing single-cell data.

Counting transcripts of mRNA are a key method of observation in modern biology. With advances in counting transcripts in single cells (single-cell RNA sequencing or scRNA-seq), these data are routinely used to identify cells by their transcriptional profile, and to identify genes with differential cellular expression. Because the total number of transcripts counted per cell can vary for technical reasons, the first step of many commonly used scRNA-seq workflows is to normalize by sequencing depth, transforming counts into proportional abundances. The primary objective of this step is to reshape the data such that cells with similar biological proportions of transcripts end up with similar transformed measurements. But there is growing concern that normalization and other transformations result in unintended distortions that hinder both analyses and the interpretation of results. This has led to an intense focus on optimizing methods for normalization and transformation of scRNA-seq data. Here, we take an alternative approach, by avoiding normalization and transformation altogether. We abandon the use of distances to compare cells, and instead use a restricted algebra, motivated by measurement theory and abstract algebra, that preserves the count nature of the data. We demonstrate that this restricted algebra is sufficient to draw meaningful and practical comparisons of gene expression through the use of the dot product and other elementary operations. This approach sidesteps many of the problems with common transformations, and has the added benefit of being simpler and more intuitive. We implement our approach in the package countland, available in python and R.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

The evolution of ovary-biased gene expression in Hawaiian Drosophila.

With detailed data on gene expression accessible from an increasingly broad array of species, we can test the extent to which our developmental genetic knowledge from model organisms predicts expression patterns and variation across species. But to know when differences in gene expression across species are significant, we first need to know how much evolutionary variation in gene expression we expect to observe. Here we provide an answer by analyzing RNAseq data across twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that over evolutionary time, there exists a cohort of ovary specific genes that is stable and that largely corresponds to described expression patterns from laboratory model Drosophila species. Our results also provide a demonstration of the prediction that, as phylogenetic distance increases, variation between species overwhelms variation between tissue types. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression, and use this to identify gains and losses of ovary-biased expression across these twelve species. We then use this distribution to calculate the evolutionary correlation in expression changes between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that share evolutionary trajectories, and we present these results as a dataset of new testable hypotheses about genetic roles and interactions in the function and evolution of the Drosophila ovary.

Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic.

OBJECTIVES: Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. DESIGN: Retrospective cohort. METHODS: Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. RESULTS: Of 2153 individuals, 31% (strict criteria) - 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal. Normal clusters (83-87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15-18%) vs. small (∼5%) clusters. Semi-normal clusters (1-2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11-16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. CONCLUSIONS: Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.

Characterizing the secret diets of siphonophores (Cnidaria: Hydrozoa) using DNA metabarcoding.

Siphonophores (Cnidaria: Hydrozoa) are abundant and diverse gelatinous predators in open-ocean ecosystems. Due to limited access to the midwater, little is known about the diets of most deep-dwelling gelatinous species, which constrains our understanding of food-web structure and nutrient flow in these vast ecosystems. Visual gut-content methods can rarely identify soft-bodied rapidly-digested prey, while observations from submersibles often overlook small prey items. These methods have been differentially applied to shallow and deep siphonophore taxa, confounding habitat and methodological biases. DNA metabarcoding can be used to assess both shallow and deep species' diets under a common methodological framework, since it can detect both small and gelatinous prey. We (1) further characterized the diets of open-ocean siphonophores using DNA metabarcoding, (2) compared the prey detected by visual and molecular methods to evaluate their technical biases, and (3) evaluated tentacle-based predictions of diet. To do this, we performed DNA metabarcoding analyses on the gut contents of 39 siphonophore species across depths to describe their diets, using six barcode regions along the 18S gene. Taxonomic identifications were assigned using public databases combined with local zooplankton sequences. We identified 55 unique prey items, including crustaceans, gelatinous animals, and fish across 47 siphonophore specimens in 24 species. We reported 29 novel predator-prey interactions, among them the first insights into the diets of nine siphonophore species, many of which were congruent with the dietary predictions based on tentilla morphology. Our analyses detected both small and gelatinous prey taxa underrepresented by visual methods in species from both shallow and deep habitats, indicating that siphonophores play similar trophic roles across depth habitats. We also reveal hidden links between siphonophores and filter-feeders near the base of the food web. This study expands our understanding of the ecological roles of siphonophores in the open ocean, their trophic roles within the 'jelly-web', and the importance of their diversity for nutrient flow and ecosystem functioning. Understanding these inconspicuous yet ubiquitous predator-prey interactions is critical to predict the impacts of climate change, overfishing, and conservation policies on oceanic ecosystems.

Evolution of Gene Expression across Species and Specialized Zooids in Siphonophora.

Siphonophores are complex colonial animals, consisting of asexually produced bodies (zooids) that are functionally specialized for specific tasks, including feeding, swimming, and sexual reproduction. Though this extreme functional specialization has captivated biologists for generations, its genomic underpinnings remain unknown. We use RNA-seq to investigate gene expression patterns in five zooids and one specialized tissue across seven siphonophore species. Analyses of gene expression across species present several challenges, including identification of comparable expression changes on gene trees with complex histories of speciation, duplication, and loss. We examine gene expression within species, conduct classical analyses examining expression patterns between species, and introduce species branch filtering, which allows us to examine the evolution of expression across species in a phylogenetic framework. Within and across species, we identified hundreds of zooid-specific and species-specific genes, as well as a number of putative transcription factors showing differential expression in particular zooids and developmental stages. We found that gene expression patterns tended to be largely consistent in zooids with the same function across species, but also some large lineage-specific shifts in gene expression. Our findings show that patterns of gene expression have the potential to define zooids in colonial organisms. Traditional analyses of the evolution of gene expression focus on the tips of gene phylogenies, identifying large-scale expression patterns that are zooid or species variable. The new explicit phylogenetic approach we propose here focuses on branches (not tips) offering a deeper evolutionary perspective into specific changes in gene expression within zooids along all branches of the gene (and species) trees.

Rooting the Animal Tree of Life.

Identifying our most distant animal relatives has emerged as one of the most challenging problems in phylogenetics. This debate has major implications for our understanding of the origin of multicellular animals and of the earliest events in animal evolution, including the origin of the nervous system. Some analyses identify sponges as our most distant animal relatives (Porifera-sister hypothesis), and others identify comb jellies (Ctenophora-sister hypothesis). These analyses vary in many respects, making it difficult to interpret previous tests of these hypotheses. To gain insight into why different studies yield different results, an important next step in the ongoing debate, we systematically test these hypotheses by synthesizing 15 previous phylogenomic studies and performing new standardized analyses under consistent conditions with additional models. We find that Ctenophora-sister is recovered across the full range of examined conditions, and Porifera-sister is recovered in some analyses under narrow conditions when most outgroups are excluded and site-heterogeneous CAT models are used. We additionally find that the number of categories in site-heterogeneous models is sufficient to explain the Porifera-sister results. Furthermore, our cross-validation analyses show CAT models that recover Porifera-sister have hundreds of additional categories and fail to fit significantly better than site-heterogenuous models with far fewer categories. Systematic and standardized testing of diverse phylogenetic models suggests that we should be skeptical of Porifera-sister results both because they are recovered under such narrow conditions and because the models in these conditions fit the data no better than other models that recover Ctenophora-sister.

The evolution of siphonophore tentilla for specialized prey capture in the open ocean.

Predator specialization has often been considered an evolutionary "dead end" due to the constraints associated with the evolution of morphological and functional optimizations throughout the organism. However, in some predators, these changes are localized in separate structures dedicated to prey capture. One of the most extreme cases of this modularity can be observed in siphonophores, a clade of pelagic colonial cnidarians that use tentilla (tentacle side branches armed with nematocysts) exclusively for prey capture. Here we study how siphonophore specialists and generalists evolve, and what morphological changes are associated with these transitions. To answer these questions, we: a) Measured 29 morphological characters of tentacles from 45 siphonophore species, b) mapped these data to a phylogenetic tree, and c) analyzed the evolutionary associations between morphological characters and prey-type data from the literature. Instead of a dead end, we found that siphonophore specialists can evolve into generalists, and that specialists on one prey type have directly evolved into specialists on other prey types. Our results show that siphonophore tentillum morphology has strong evolutionary associations with prey type, and suggest that shifts between prey types are linked to shifts in the morphology, mode of evolution, and evolutionary correlations of tentilla and their nematocysts. The evolutionary history of siphonophore specialization helps build a broader perspective on predatory niche diversification via morphological innovation and evolution. These findings contribute to understanding how specialization and morphological evolution have shaped present-day food webs.

A genome-scale phylogeny of the kingdom Fungi.

Phylogenomic studies using genome-scale amounts of data have greatly improved understanding of the tree of life. Despite the diversity, ecological significance, and biomedical and industrial importance of fungi, evolutionary relationships among several major lineages remain poorly resolved, especially those near the base of the fungal phylogeny. To examine poorly resolved relationships and assess progress toward a genome-scale phylogeny of the fungal kingdom, we compiled a phylogenomic data matrix of 290 genes from the genomes of 1,644 species that includes representatives from most major fungal lineages. We also compiled 11 data matrices by subsampling genes or taxa from the full data matrix based on filtering criteria previously shown to improve phylogenomic inference. Analyses of these 12 data matrices using concatenation- and coalescent-based approaches yielded a robust phylogeny of the fungal kingdom, in which ∼85% of internal branches were congruent across data matrices and approaches used. We found support for several historically poorly resolved relationships as well as evidence for polytomies likely stemming from episodes of ancient diversification. By examining the relative evolutionary divergence of taxonomic groups of equivalent rank, we found that fungal taxonomy is broadly aligned with both genome sequence divergence and divergence time but also identified lineages where current taxonomic circumscription does not reflect their levels of evolutionary divergence. Our results provide a robust phylogenomic framework to explore the tempo and mode of fungal evolution and offer directions for future fungal phylogenetic and taxonomic studies.

Revising transcriptome assemblies with phylogenetic information.

A common transcriptome assembly error is to mistake different transcripts of the same gene as transcripts from multiple closely related genes. This error is difficult to identify during assembly, but in a phylogenetic analysis such errors can be diagnosed from gene phylogenies where they appear as clades of tips from the same species with improbably short branch lengths. treeinform is a method that uses phylogenetic information across species to refine transcriptome assemblies within species. It identifies transcripts of the same gene that were incorrectly assigned to multiple genes and reassign them as transcripts of the same gene. The treeinform method is implemented in Agalma, available at https://bitbucket.org/caseywdunn/agalma, and the general approach is relevant in a variety of other contexts.

Incorporating Within-Host Diversity in Phylogenetic Analyses for Detecting Clusters of New HIV Diagnoses.

BACKGROUND: Phylogenetic analyses of HIV sequences are used to detect clusters and inform public health interventions. Conventional approaches summarize within-host HIV diversity with a single consensus sequence per host of the pol gene, obtained from Sanger or next-generation sequencing (NGS). There is growing recognition that this approach discards potentially important information about within-host sequence variation, which can impact phylogenetic inference. However, whether alternative summary methods that incorporate intra-host variation impact phylogenetic inference of transmission network features is unknown. METHODS: We introduce profile sampling, a method to incorporate within-host NGS sequence diversity into phylogenetic HIV cluster inference. We compare this approach to Sanger- and NGS-derived pol and near-whole-genome consensus sequences and evaluate its potential benefits in identifying molecular clusters among all newly-HIV-diagnosed individuals over six months at the largest HIV center in Rhode Island. RESULTS: Profile sampling cluster inference demonstrated that within-host viral diversity impacts phylogenetic inference across individuals, and that consensus sequence approaches can obscure both magnitude and effect of these impacts. Clustering differed between Sanger- and NGS-derived consensus and profile sampling sequences, and across gene regions. DISCUSSION: Profile sampling can incorporate within-host HIV diversity captured by NGS into phylogenetic analyses. This additional information can improve robustness of cluster detection.

Empirical comparison of analytical approaches for identifying molecular HIV-1 clusters.

Public health interventions guided by clustering of HIV-1 molecular sequences may be impacted by choices of analytical approaches. We identified commonly-used clustering analytical approaches, applied them to 1886 HIV-1 Rhode Island sequences from 2004-2018, and compared concordance in identifying molecular HIV-1 clusters within and between approaches. We used strict (topological support ≥ 0.95; distance 0.015 substitutions/site) and relaxed (topological support 0.80-0.95; distance 0.030-0.045 substitutions/site) thresholds to reflect different epidemiological scenarios. We found that clustering differed by method and threshold and depended more on distance than topological support thresholds. Clustering concordance analyses demonstrated some differences across analytical approaches, with RAxML having the highest (91%) mean summary percent concordance when strict thresholds were applied, and three (RAxML-, FastTree regular bootstrap- and IQ-Tree regular bootstrap-based) analytical approaches having the highest (86%) mean summary percent concordance when relaxed thresholds were applied. We conclude that different analytical approaches can yield diverse HIV-1 clustering outcomes and may need to be differentially used in diverse public health scenarios. Recognizing the variability and limitations of commonly-used methods in cluster identification is important for guiding clustering-triggered interventions to disrupt new transmissions and end the HIV epidemic.

Phylogenetics is the New Genetics (for Most of Biodiversity).

Despite substantial progress in understanding the genetic basis for differences in morphology, physiology, and behavior, many phenotypes of interest are difficult to study with traditional genetic approaches because their origin traces to deep nodes in the tree of life. Moreover, many species are not amenable to either large-scale sampling or laboratory crosses. We argue that phylogenetic methods and theory provide tremendous power to identify the functional genetic variation underlying trait evolution. We anticipate that existing statistical comparative approaches will be more commonly applied to studying the genetic basis for phenotypic evolution as whole genomes continue to populate the tree of life. Nevertheless, new methods and approaches will be needed to fully capitalize on the power of clade-scale genomic datasets.

Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health.

Great efforts are devoted to end the HIV epidemic as it continues to have profound public health consequences in the United States and throughout the world, and new interventions and strategies are continuously needed. The use of HIV sequence data to infer transmission networks holds much promise to direct public heath interventions where they are most needed. As these new methods are being implemented, evaluating their benefits is essential. In this paper, we recognize challenges associated with such evaluation, and make the case that overcoming these challenges is key to the use of HIV sequence data in routine public health actions to disrupt HIV transmission networks.

Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data.

Phylogenetic trees and data are often stored in incompatible and inconsistent formats. The outputs of software tools that contain trees with analysis findings are often not compatible with each other, making it hard to integrate the results of different analyses in a comparative study. The treeio package is designed to connect phylogenetic tree input and output. It supports extracting phylogenetic trees as well as the outputs of commonly used analytical software. It can link external data to phylogenies and merge tree data obtained from different sources, enabling analyses of phylogeny-associated data from different disciplines in an evolutionary context. Treeio also supports export of a phylogenetic tree with heterogeneous-associated data to a single tree file, including BEAST compatible NEXUS and jtree formats; these facilitate data sharing as well as file format conversion for downstream analysis. The treeio package is designed to work with the tidytree and ggtree packages. Tree data can be processed using the tidy interface with tidytree and visualized by ggtree. The treeio package is released within the Bioconductor and rOpenSci projects. It is available at https://www.bioconductor.org/packages/treeio/.

Morphology and development of the Portuguese man of war, Physalia physalis.

The Portuguese man of war, Physalia physalis, is one of the most conspicuous, but poorly understood members of the pleuston, a community of organisms that occupy a habitat at the sea-air interface. Physalia physalis is a siphonophore that uses a gas-filled float as a sail to catch the wind. The development, morphology, and colony organization of P. physalis is very different from all other siphonophores. Here, we look at live and fixed larval and juvenile specimens, and use optical projection tomography to build on existing knowledge about the morphology and development of this species. We also propose a framework for homologizing the axes with other siphonophores, and also suggest that the tentacle bearing zooids should be called tentacular palpons. Previous descriptions of P. physalis larvae, especially descriptions of budding order, were often framed with the mature colony in mind. However, we use the simpler organization of larvae and the juvenile specimens to inform our understanding of the morphology, budding order, and colony organization in the mature specimen. Finally, we review what is known about the ecology and lifecycle of P. physalis.

We are not so special.

New sequence data from choanoflagellates improves our understanding of the genetic changes that occurred along the branch of the evolutionary tree that gave rise to animals.

Improved phylogenetic resolution within Siphonophora (Cnidaria) with implications for trait evolution.

Siphonophores are a diverse group of hydrozoans (Cnidaria) that are found at most depths of the ocean - from the surface, like the familiar Portuguese man of war, to the deep sea. They play important roles in ocean ecosystems, and are among the most abundant gelatinous predators. A previous phylogenetic study based on two ribosomal RNA genes provided insight into the internal relationships between major siphonophore groups. There was, however, little support for many deep relationships within the clade Codonophora. Here, we present a new siphonophore phylogeny based on new transcriptome data from 29 siphonophore species analyzed in combination with 14 publicly available genomic and transcriptomic datasets. We use this new phylogeny to reconstruct several traits that are central to siphonophore biology, including sexual system (monoecy vs. dioecy), gain and loss of zooid types, life history traits, and habitat. The phylogenetic relationships in this study are largely consistent with the previous phylogeny, but we find strong support for new clades within Codonophora that were previously unresolved. These results have important implications for trait evolution within Siphonophora, including favoring the hypothesis that monoecy arose at least twice.