Biology subjects

Collins, A. G.

Publications and source records attributed to Collins, A. G..

6 recordsLinked to original sources

bioRxiv2026

The Curious Case of the Golden Orb -- Relict of Relicanthus daphneae (Cnidaria, Anthozoa, Hexacorallia), a deep sea anemone

The discovery and collection of the enigmatic Golden Orb by the NOAA Ship Okeanos Explorer and ROV Deep Discover in deep Alaskan waters during 2023 has yielded substantial interest by the scientific and public communities alike. Initial field identifications of the specimen collected at 3,250 meters depth ranged from an egg mass to sponge to microbial biofilm. Here we characterize the biology and ecology of the Golden Orb, as well as other specimens of similar appearance identified since the collection of the original material. Through an integrative taxonomic approach including morphological analysis and genomic characterization of the Golden Orb, we identified the presence of cnidocytes of the spirocyst type (restricted to Hexacorallia), as well as metazoan DNA, from which we were able to derive complete mitochondrial genomes and Ultra Conserved Elements. These results indicate that the Golden Orb and a similar specimen from deep equatorial waters represent remnant cuticles belonging to the geographically widespread deep-sea anemone ally Relicanthus daphneae. We also document the presence of cuticle from a collected specimen of R. daphneae from the Southern Ocean and in situ photographic evidence of similar cuticles beneath living individuals. These findings underscore the extent to which the biodiversity and organismal biology of obscure deep sea fauna broadly remain unresolved and highlight the value of whole-specimen collections and rigorous taxonomic follow-up in telepresence-enabled ocean exploration.

zoology↗

bioRxiv2026

Deep-sea siliceous sponges harbor distinct and functionally diverse microbiomes

Sponges, phylum Porifera, are long-lived and basal-branching metazoans that play important roles in ocean biogeochemistry and host diverse microbial communities. Siliceous sponges form a major clade of the Porifera, yet their microbiome is not well-characterized, particularly in the deep ocean. Here, we used shotgun metagenomics to investigate the composition of the microbial communities of 13 siliceous sponges collected from four sites near Puerto Rico from depths ranging from 400-1900 meters. Nine of the sponges in this study are from five sponge family taxa that have not previously been sequenced using shotgun metagenomics. We assembled a total of 176 metagenome-assembled-genomes from 20 bacterial and 1 archaeal phyla. Ammonia-oxidizing archaea (AOA) Nitrosopumilaceae dominated most siliceous sponge microbial communities and was strikingly the sole symbiont associated with one sponge (Farrea). Overall, microbiome diversity was relatively low across siliceous sponges, except for a Phloeodictyidae, which was likely a high microbial abundance (HMA) sponge. Our results suggest that host sponge phylogeny may shape microbial community structure, with limited evidence for environmental influence. The sponge-associated microbial communities contained genetic capabilities for diverse metabolic functions, particularly contributing to the carbon, nitrogen, and sulfur cycles. In addition to the AOA, evidence of potential for microbial autotrophy was found through the presence of genes for RuBisCO, methanotrophy, and ATP citrate lyase. These results reveal both conserved relationships and metabolic flexibility across siliceous sponge lineages, suggesting unique evolutionary dynamics and demonstrating the importance of microbial metabolism to sponge host health and nutrient cycling in the oligotrophic deep ocean. ImportanceMarine sponges, emerging [~]600 million years ago, have close relationships with microorganisms, but the microbiome of deep-sea siliceous sponges is not well understood. Siliceous sponges play essential roles in deep-sea ecosystems by providing habitats for other metazoans and mediating carbon, nitrogen, and sulfur cycling, yet they remain some of the least studied sponges. By shotgun sequencing DNA from 13 siliceous sponges collected near Puerto Rico, this study found that host sponge phylogeny influences microbial community composition and structure. Ammonia-oxidizing archaea dominated the microbial communities associated with marine sponges, likely playing key roles in utilizing metabolic byproducts and supporting host health. Other microbes also contributed to nutrient cycling and contained the potential to fix carbon, suggesting metabolic flexibility which may benefit sponge hosts in low-resource environments. These findings emphasize the ecological importance of siliceous sponge-microbe symbioses and contribute to our understanding of the drivers shaping their structure and function.

microbiology↗

bioRxiv2022

Phylogenomics suggests that larvae are ancestral in polyclads, but not homologous to the trochophore

Platyhelminthes (flatworms) are a diverse invertebrate phylum that are useful for exploring life history evolution. Within Platyhelminthes, only two clades develop through a larval stage: free-living polyclads and parasitic neodermatans. Neodermatan larvae are considered evolutionarily derived, whereas polyclad larvae are hypothesized to be retained from the last common ancestor of Platyhelminthes - and Spiralia - due to ciliary band similarities among polyclad and other spiralian larvae. However, larval evolution has been challenging to investigate within polyclads due to low support for deeper phylogenetic relationships. To investigate polyclad life history evolution, we generated transcriptomic data for 21 species of polyclads to build a well-supported phylogeny for the group. We then used ancestral state reconstruction to investigate ancestral modes of development (direct vs indirect) within Polycladida, and flatworms in general. The resulting tree provides strong support for deeper nodes and we recover a new monophyletic clade of early branching cotyleans. Early branching clades of acotyleans and cotyleans possess diverse modes of development, suggesting a complex history of larval evolution in polyclads that likely includes multiple losses and/or multiple gains. Our ancestral state reconstructions in a previous platyhelminth phylogeny also suggests that similarities in larval morphology between flatworms and other phyla may have re-emerged secondarily or are convergently evolved.

evolutionary biology↗

bioRxiv2020

Tackling the phylogenetic conundrum of Hydroidolina (Cnidaria: Medusozoa: Hydrozoa) by assessing competing tree topologies with targeted high-throughput sequencing.

Higher-level relationships of the Hydrozoan subclass Hydroidolina, which encompasses the vast majority of medusozoan cnidarian species diversity, have been elusive to confidently infer. The most widely adopted phylogenetic framework for Hydriodolina based on ribosomal RNA data received low support for several higher level relationships. To address this issue, we developed a set of RNA baits to target more than a hundred loci from the genomes of a broad taxonomic sample of Hydroidolina for high-throughput sequencing. Using these data, we inferred the relationships of Hydroidolina using maximum likelihood and Bayesian approaches. Both inference methods yielded well-supported phylogenetic hypotheses that largely agree with each other. Using maximum likelihood and Baysian hypothesis testing frameworks, we found that several alternate topological hypotheses proposed previously may be rejected in light of the genomic data generated for this study. Both the maximum likelihood and Bayesian topologies inferred herein consistently score well across testing frameworks, suggesting that their consensus represents the most likely phylogenetic hypothesis of Hydroidolina. This phylogenetic framework places Aplanulata as sister lineage to the remainder of Hydroidolina. This is a strong deviation from previous phylogenetic analyses that placed Capitata or Siphonophorae as sister group to the remainder of Hydroidolina. Considering that Aplanulata represents a lineage comprised of species that for the most part possess a life cycle involving a solitary polyp and free-swimming medusa stage, the phylogenetic hypotheses presented herein have potentially large implications for clarifying the evolution of life cycles, coloniality, and the division of labor in Hydrozoa as taxon sampling for phylogenetic analyses becomes more complete.

evolutionary biology↗

bioRxiv2019

Distentangling the systems contributing to changes in learning during adolescence

Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects use of WM and RL processes. Surprisingly, we found more robust changes in RL than WM during development. RL learning rate increased significantly with age across adolescence and WM parameters showed more subtle changes, many of them early in adolescence. These results underscore the importance of changes in RL processes for the developmental science of learning.\n\nHighlights- Subjects combine reinforcement learning (RL) and working memory (WM) to learn\n- Computational modeling shows RL learning rates grew with age during adolescence\n- When load was beyond WM capacity, weaker RL compensated less in younger adolescents\n- WM parameters showed subtler and more puberty-related changes\n- WM reliance, maintenance, and capacity had separable developmental trajectories\n- Underscores importance of RL processes in developmental changes in learning

neuroscience↗

bioRxiv2017

The tortoise and the hare: interactions between reinforcement learning and working memory.

Learning to make rewarding choices in response to stimuli depends on a slow but steady process, reinforcement learning, and a fast and flexible, but capacity limited process, working memory. Using both systems in parallel, with their contributions weighted based on performance, should allow us to leverage the best of each system: rapid early learning, supplemented by long term robust acquisition. However, this assumes that using one process does not interfere with the other. We use computational modeling to investigate the interactions between the two processes in a behavioral experiment, and show that working memory interferes with reinforcement learning. Previous research showed that neural representations of reward prediction errors, a key marker of reinforcement learning, were blunted when working memory was used for learning. We thus predicted that arbitrating in favor of working memory to learn faster in simple problems would weaken the reinforcement learning process. We tested this by measuring performance in a delayed testing phase where the use of working memory was impossible, and thus subject choices depended on reinforcement learning. Counter-intuitively, but confirming our predictions, we observed that associations learned most easily were retained worse than associations learned slower: using working memory to learn quickly came at the cost of long-term retention. Computational modeling confirmed that this could only be accounted for by working memory interference in reinforcement learning computations. These results further our understanding of how multiple systems contribute in parallel to human learning, and may have important applications for education and computational psychiatry.

neuroscience↗