Biology subjects

Simpson, S. D.

Publications and source records attributed to Simpson, S. D..

15 recordsLinked to original sources

bioRxiv2025

Evidence of ecosystem process recovery across a large-scale coral reef restoration programme using AI accelerated soundscape analysis

1.Coral reef restoration efforts are on the increase globally. However, reporting on the ecological outcomes of these efforts is rare and typically focuses on coral related metrics. As a result, understanding of whether restoration can recover broader aspects of reef functioning remains limited. In this study we use passive acoustic monitoring coupled with human-in-the-loop artificial intelligence to analyse >12 months of soundscape recordings from 45 sites across five biogeographically independent regions to investigate the impact of active restoration on reef functioning. We trained and rigorously evaluated machine learning models to identify 34 biological sound types within this data, generating >912,000 high-confidence detections. These detections were used to infer four key functions across healthy, degraded, early-stage (<3 months) and mid-stage (32-53 months) restored reefs. Restoration significantly enhanced: (i) biological sounds at night, key to recruiting juvenile fish; (ii) diversity of biological sounds, an indicator of fish community diversity; and (iii) snapping shrimp activity, an indicator of bioturbation. However, effects varied by region, and audible parrotfish grazing, key to algal control and bioerosion, did not differ among habitat types in four of the five regions. Our findings provide evidence that restoration can support recovery of broader ecosystem functioning when carefully implemented in the right contexts. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=174 SRC="FIGDIR/small/678197v4_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@7d5462org.highwire.dtl.DTLVardef@2efa7dorg.highwire.dtl.DTLVardef@3f4b9eorg.highwire.dtl.DTLVardef@17d829c_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2025

Group personality, rather than acoustic noise, causes variation in group decision-making in guppy shoals

Group living has essential fitness benefits for many species. While numerous studies have explored how environmental conditions impact collective movement, their impact on decisions made in a social context--a central component of group-living--is poorly documented. In this study, we assess how acoustic noise impacts group decision-making, cohesion and activity in fish shoals, using Trinidadian guppies (Poecilia reticulata) as a model species. Movements within a radially symmetric five-armed maze were measured using high-resolution trajectory data from video-tracking software. The behaviour of groups with and without continuous acoustic white noise were measured over a four-day testing period in a repeated-measures design. We found no significant change in swimming speed or group cohesion with additional acoustic noise. However, there was evidence for fewer following events (moves into already occupied arms) in the noise treatment compared to the control, but this additional noise had no effect on leadership attempts (moves into empty arms). We found strong evidence for consistent, repeatable differences between groups in all behavioural parameters indicating strong personality variation at the group level. Rather than environmental factors, these results provide evidence that consistent group-level differences dominate variation in collective behaviour, including group decision-making, in fish shoals.

animal behavior and cognition↗

bioRxiv2024

Unlocking the soundscape of coral reefs with artificial intelligence

Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison of whole soundscape properties can rapidly deliver broad insights from acoustic data, in contrast to the more detailed but time-consuming analysis of individual bioacoustic signals. However, a lack of effective automated analysis for whole soundscape data has impeded progress in this field. Here, we show that machine learning (ML) can be used to unlock greater insights from reef soundscapes. We showcase this on a diverse set of tasks using three biogeographically independent datasets, each containing fish community, coral cover or depth zone classes. We show supervised learning can be used to train models that can identify ecological classes and individual sites from whole soundscapes. However, we report unsupervised clustering achieves this whilst providing a more detailed understanding of ecological and site groupings within soundscape data. We also compare three different approaches for extracting feature embeddings from soundscape recordings for input into ML algorithms: acoustic indices commonly used by soundscape ecologists, a pretrained convolutional neural network (P-CNN) trained on 5.2m hrs of YouTube audio and a CNN trained on individual datasets (T-CNN). Although the T-CNN performs marginally better across the datasets, we reveal that the P-CNN is a powerful tool for identifying marine soundscape ecologists due to its strong performance, low computational cost and significantly improved performance over acoustic indices. Our findings have implications for soundscape ecology in any habitat. Author SummaryArtificial intelligence has the potential to revolutionise bioacoustic monitoring of coral reefs. So far, a limited set of work has used machine learning to train detectors for specific sounds such as individual fish species. However, building detectors is a time-consuming process that involves manually annotating large amounts of audio followed by complicated model training, this must then be repeated all over again for any new dataset. Instead, we explore machine learning techniques for whole soundscape analysis, which compares the acoustic properties of raw recordings from the entire habitat. We identify multiple machine learning methods for whole soundscape analysis and rigorously test these using datasets from Indonesia, Australia and French Polynesia. Our key findings show use of a neural network pretrained on 5.2m hours of unrelated YouTube audio offers a powerful tool to produce compressed representations of reef audio data, conserving the datas key properties whilst being executable on a standard personal laptop. These representations can then be used to explore patterns in reef soundscapes using "unsupervised machine learning", which is effective at grouping similar recordings periods together and dissimilar periods apart. We show these groupings hold relationships with ground truth ecological data, including coral coverage, the fish community and depth.

ecology↗

bioRxiv2023

Metagenomics for pathogen detection during a wildlife mortality event in songbirds

Mass mortality events in wildlife can be indications of an emerging infectious disease. During the spring and summer of 2021, hundreds of dead passerines were reported across the eastern US. Birds exhibited a range of clinical signs including swollen conjunctiva, ocular discharge, ataxia, and nystagmus. As part of the diagnostic investigation, high-throughput metagenomic next-generation sequencing was performed across three molecular laboratories on samples from affected birds. Many potentially pathogenic microbes were detected, with bacteria comprising the largest proportion; however, no singular agent was consistently identified, with many of the detected microbes also found in unaffected (control) birds, and thus considered to be subclinical infections. Congruent results across laboratories have helped drive further investigation into alternative causes including environmental contaminants and nutritional deficiencies. This work highlights the utility of metagenomic approaches in investigations of emerging diseases and provides a framework for future wildlife mortality events. Article Summary LineThe causative agent of a mass mortality event in passerines remains inconclusive after metagenomic high-throughput sequencing with results prompting further investigation into non-pathogenic causes.

genomics↗

bioRxiv2023

Autotrophic adaptive laboratory evolution of the acetogen Clostridium autoethanogenum delivers the gas-fermenting strain LAbrini with superior growth, products, and robustness

Microbes able to convert gaseous one-carbon (C1) waste feedstocks are increasingly important to transition to the sustainable production of renewable chemicals and fuels. Acetogens are interesting biocatalysts since gas fermentation using Clostridium autoethanogenum has already been commercialised. However, most acetogen strains need complex nutrients, display slow growth, and are not robust for routine bioreactor fermentations. In this work, we used three different and independent adaptive laboratory evolution (ALE) strategies to evolve the wild-type C. autoethanogenum to grow faster, without yeast extract and to be robust in operating continuous bioreactor cultures. Multiple evolved strains with improved phenotypes were isolated on a minimal medium with one strain, named "LAbrini" (LT1), exhibiting superior performance regarding the maximum specific growth rate, product profile, and robustness in continuous cultures. Whole-genome sequencing of the evolved strains identified 25 mutations. Of particular interest are two genes that acquired seven different mutations across the three ALE strategies, potentially as a result of convergent evolution. Reverse genetic engineering of sporulation-related mutations in genes CLAU_3129 (spo0A) and CLAU_1957 recovered all three superior features of our ALE strains through triggering significant proteomic rearrangements. This work provides a robust C. autoethanogenum strain to accelerate phenotyping and genetic engineering and to better understand acetogen metabolism, which we named "LAbrini".

bioengineering↗

bioRxiv2022

Cell-free prototyping enables implementation of optimized reverse beta-oxidation pathways in heterotrophic and autotrophic bacteria

Carbon-negative synthesis of biochemical products has the potential to mitigate global CO2 emissions. An attractive route to do this is the reverse {beta}-oxidation (r-BOX) pathway coupled to the Wood-Ljungdahl pathway. Here, we optimized and implemented r-BOX for the synthesis of C4-C6 acids and alcohols. With a high-throughput in vitro prototyping workflow, we screened 762 unique pathway combinations using cell-free extracts tailored for r-BOX to identify enzyme sets for enhanced product selectivity. Implementation of these pathways into Escherichia coli generated designer strains for the selective production of butanoic acid (4.9 {+/-}0.1 gL-1), hexanoic acid (3.06 {+/-} 0.03 gL-1) and 1-hexanol (1.0 {+/-} 0.1 gL-1) at the best performance reported to date in this bacterium. We also generated Clostridium autoethanogenum strains able to produce 1-hexanol from syngas, achieving a titer of 0.26 gL-1 in a 1.5-L continuous fermentation. Our strategy enables optimization of rBOX derived products for biomanufacturing and industrial biotechnology.

synthetic biology↗

bioRxiv2021

Application of transposon-insertion sequencing to determine gene essentiality in the acetogen Clostridium autoethanogenum

The majority of the genes present in bacterial genomes remain poorly characterised with up to one third of those that are protein encoding having no definitive function. Transposon insertion sequencing represents a high-throughput technique that can help rectify this deficiency. The technology, however, can only be realistically applied to easily transformable species leaving those with low DNA-transfer rates out of reach. Here we have developed a number of approaches that overcome this barrier in the autotrophic species Clostridium autoethanogenum using a mariner-based transposon system. The inherent instability of such systems in the Escherichia coli conjugation donor due to transposition events was counteracted through the incorporation of a conditionally lethal codA marker on the plasmid backbone. Relatively low frequencies of transformation of the plasmid into C. autoethanogenum were circumvented through the use of a plasmid that is conditional for replication coupled with the routine implementation of an Illumina library preparation protocol that eliminates plasmid-based reads. A transposon library was then used to determine the essential genes needed for growth using carbon monoxide as a sole carbon and energy source. IMPORTANCEAlthough microbial genome sequences are relatively easily determined, assigning gene function remains a bottleneck. Consequently, relatively few genes are well characterised, leaving the function of many as either hypothetical or entirely unknown. High-throughput, transposon sequencing can help remedy this deficiency, but is generally only applicable to microbes with efficient DNA-transfer procedures. These exclude many microorganisms of importance to humankind either as agents of disease or as industrial process organisms. Here we developed approaches to facilitate transposon-insertion sequencing in the acetogen Clostridium autoethanogenum, a chassis being exploited to convert single-carbon waste gases, CO and CO2, into chemicals and fuels at an industrial scale. This allowed the determination of gene essentiality under heterotrophic and autotrophic growth providing insights into the utilisation of CO as a sole carbon and energy source. The strategies implemented are translatable and will allow others to apply transposon-insertion sequencing to other microbes where DNA-transfer has until now represented a barrier to progress.

molecular biology↗

bioRxiv2021

Absolute proteome quantification in the gas-fermenting acetogen Clostridium autoethanogenum

Microbes that can recycle one-carbon (C1) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO2 and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model-acetogen Clostridium autoethanogenum grown on three gas mixtures. We detect prioritisation of proteome allocation for C1 fixation and significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are important. Integration of proteomic and metabolic flux data demonstrated that enzymes catalyse high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied with changing enzyme catalytic rates rather than concentrations. Our work serves as a reference dataset and advances systems-level understanding and engineering of acetogens.

systems biology↗

bioRxiv2021

Temperature and O2, but not CO2, interact to affect aerobic performance of European sea bass (Dicentrarchus labrax)

Climate change causes warming, decreased O2, and increased CO2 in marine systems and responses of organisms will depend on interactive effects between these factors. We provide the first experimental assessment of the interactive effects of warming (14 to 22{degrees}C), reduced O2 ([~]3 - 21 kPa O2), and increased CO2 ([~]400 or [~]1000 {micro}atm ambient CO2) on four indicators of aerobic performance (standard metabolic rate, SMR, maximum metabolic rate, MMR, aerobic scope, and hypoxia tolerance, O2crit), blood chemistry, and O2 transport (P50) of a marine fish, the European sea bass (Dicentrarchus labrax). Warming increased SMR and O2crit (i.e. reduced hypoxia tolerance) as well as MMR in normoxia but there was an interactive effect with O2 so that hypoxia caused larger reductions in MMR and aerobic scope at higher temperatures. Increasing CO2 had minimal effects on SMR, MMR and O2crit and did not show interactive effects with temperature or O2 for any measured variables. Aerobic performance was not linked to changes in blood chemistry or P50. Despite lack of effects of CO2 on aerobic performance, increased CO2 induced 30% mortality of fish exercised in low O2 at 22{degrees}C indicating important threshold effects independent of aerobic performance. Overall, our results show temperature and O2, but not CO2, interact to affect aerobic performance of sea bass, disagreeing with predictions of the oxygen- and capacity-limited thermal tolerance hypothesis.

ecology↗

bioRxiv2020

Modular cell-free expression plasmids to accelerate biological design in cells

Industrial biotechnology aims to produce high-value products from renewable resources. This can be challenging because model microorganisms--organisms that are easy to use like Escherichia coli--often lack the machinery required to utilize desired feedstocks like lignocellulosic biomass or syngas. Non-model organisms, such as Clostridium, are industrially proven and have the desired metabolic features but have several hurdles to mainstream use. Namely, these species grow more slowly than conventional laboratory microbes and genetic tools for engineering them are far less prevalent. To address these hurdles for accelerating cellular design, cell-free synthetic biology has emerged as an approach for characterizing non-model organisms and rapidly testing metabolic pathways in vitro. Unfortunately, cell-free systems can require specialized DNA architectures with minimal regulation that are not compatible with cellular expression. In this work, we develop a modular vector system that allows for T7 expression of desired enzymes for cell-free expression and direct Golden Gate assembly into Clostridium expression vectors. Utilizing the Joint Genome Institutes DNA Synthesis Community Science Program, we designed and synthesized these plasmids and genes required for our projects allowing us to shuttle DNA easily between our in vitro and in vivo experiments. We next validated that these vectors were sufficient for cell-free expression of enzymes, performing on par with the previous state-of-the-art. Lastly, we demonstrated automated six-part DNA assemblies for C. autoethanogenum expression with efficiencies ranging from 68-90%. We anticipate this system of plasmids will enable a framework for facile testing of biosynthetic pathways in vitro and in vivo by shortening development cycles.

synthetic biology↗

bioRxiv2020

Development of a clostridia-based cell-free system for prototyping genetic parts and metabolic pathways

Gas fermentation by autotrophic bacteria, such as clostridia, offers a sustainable path to numerous bioproducts from a range of local, highly abundant, waste and low-cost feedstocks, such as industrial flue gases or syngas generated from biomass or municipal waste. Unfortunately, designing and engineering clostridia remains laborious and slow. The ability to prototype individual genetic parts, gene expression, and biosynthetic pathway performance in vitro before implementing them in cells could help address these bottlenecks by speeding up design. Unfortunately, a high-yielding cell-free gene expression (CFE) system from clostridia has yet to be developed. Here, we report the development and optimization of a high-yielding (236 {+/-} 24 {micro}g/mL) batch CFE platform from the industrially relevant anaerobe, Clostridium autoethanogenum. A key feature of the platform is that both circular and linear DNA templates can be applied directly to the CFE reaction to program protein synthesis. We demonstrate the ability to prototype gene expression, and quantitatively map cell-free metabolism in lysates from this system. We anticipate that the C. autoethanogenum CFE platform will not only expand the protein synthesis toolkit for synthetic biology, but also serve as a platform in expediting the screening and prototyping of gene regulatory elements in non-model, industrially relevant microbes.

synthetic biology↗

bioRxiv2020

Enhancing CO2-valorization using Clostridium autoethanogenum for sustainable fuel and chemicals production

Acetogenic bacteria can convert waste gases into fuels and chemicals. Design of bioprocesses for waste carbon valorization requires quantification of steady-state carbon flows. Here, steady-state quantification of autotrophic chemostats containing Clostridium autoethanogenum grown on CO2 and H2 revealed that captured carbon (460 {+/-} 80 mmol/gDCW/day) had a significant distribution to ethanol (54 {+/-} 3 mol% with a 2.4 {+/-} 0.3 g/L titer). We were impressed with this initial result, but also observed limitations to biomass concentration and growth rate. Metabolic modelling predicted culture performance and indicated significant metabolic adjustments when compared to fermentation with CO as the carbon source. Moreover, modelling highlighted flux to pyruvate, and subsequently reduced ferredoxin, as a target for improving CO2 and H2 fermentation. Supplementation with a small amount of CO enabled co-utilisation with CO2, and enhanced CO2 fermentation performance significantly, while maintaining an industrially relevant product profile. Additionally, the highest specific flux through the Wood-Ljungdahl pathway was observed during co-utilization of CO2 and CO. Furthermore, the addition of CO led to superior CO2-valorizing characteristics (9.7 {+/-} 0.4 g/L ethanol with a 66 {+/-} 2 mol% distribution, and 540 {+/-} 20 mmol CO2/gDCW/day). Similar industrial processes are commercial or currently being scaled up, indicating CO-supplemented CO2 and H2 fermentation has high potential for sustainable fuel and chemical production. This work also provides a reference dataset to advance our understanding of CO2 gas fermentation, which can contribute to mitigating climate change.

bioengineering↗

bioRxiv2019

In vitro prototyping and rapid optimization of biosynthetic enzymes for cellular design

Microbial cell factories offer an attractive approach for production of biobased products. Unfortunately, designing, building, and optimizing biosynthetic pathways remains a complex challenge, especially for industrially-relevant, non-model organisms. To address this challenge, we describe a platform for in vitro Prototyping and Rapid Optimization of Biosynthetic Enzymes (iPROBE). In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE with two examples. First, we tested and ranked 54 different pathways for 3-hydroxybutyrate production, improving in vivo production in Clostridium by 20-fold to 14.63 {+/-} 0.48 g/L and identifying a new biosynthetic route to (S)-(+)-1,3-butanediol. Second, we used iPROBE and data-driven design to optimize a 6-step n-butanol pathway, increasing titers 4-fold across 205 pathways, and showed strong correlation between cell-free and cellular performance. We expect iPROBE to accelerate design-build-test cycles for industrial biotechnology.

synthetic biology↗

bioRxiv2019

A TetR-family protein activates transcription from a new promoter motif associated with essential genes for autotrophic growth in acetogens

Acetogens can fix carbon (CO or CO2) into acetyl-CoA via the Wood-Ljungdahl pathway (WLP) that also makes them attractive cell factories for the production of fuels and chemicals from waste feedstocks. Although most biochemical details of the WLP are well understood and systems-level characterisation of acetogen metabolism has recently improved, key transcriptional features such as promoter motifs and transcriptional regulators are still unknown in acetogens. Here, we use differential RNA-sequencing to identify a previously undescribed promoter motif associated with essential genes for autotrophic growth of the model-acetogen Clostridium autoethanogenum. RNA polymerase was shown to bind to the new promoter motif using a DNA-binding protein assay and proteomics enabled the discovery of four candidates to potentially function directly in control of transcription of the WLP and other key genes of C1 fixation metabolism. Next, in vivo experiments showed that a TetR-family transcriptional regulator (CAETHG_0459) and the housekeeping sigma factor ({sigma}A) activate expression of a reporter protein (GFP) in-frame with the new promoter motif from a fusion vector in E. coli. Lastly, a protein-protein interaction assay with the RNA polymerase (RNAP) shows that CAETHG_0459 directly binds to the RNAP. Together, the data presented here advance the fundamental understanding of transcriptional regulation of C1 fixation in acetogens and provide a strategy for improving the performance of gas-fermenting bacteria by genetic engineering.

bioengineering↗

bioRxiv2017

Cross-Site Comparison of Ribosomal Depletion Kits for Illumina RNAseq Library Construction

Ribosomal RNA (rRNA) comprises at least 90% of total RNA extracted from mammalian tissue or cell line samples. Informative transcriptional profiling using massively parallel sequencing technologies requires either enrichment of mature poly-adenylated transcripts or targeted depletion of the rRNA fraction. The latter method is of particular interest because it is compatible with degraded samples such as those extracted from FFPE and also captures transcripts that are not poly-adenylated such as some non-coding RNAs. Here we provide a cross-site study that evaluates the performance of ribosomal RNA removal kits from Illumina, Takara/Clontech, Kapa Biosystems, Lexogen, New England Biolabs and Qiagen on intact and degraded RNA samples. We find that all of the kits are capable of performing significant ribosomal depletion, though there are differences in their ease of use. All kits were able to remove ribosomal RNA to below 20% with intact RNA and identify [~]14,000 protein coding genes from the Universal Human Reference RNA sample at >1FPKM. Analysis of differentially detected genes between kits suggests that transcript length may be a key factor in library production efficiency. These results provide a roadmap for labs on the strengths of each of these methods and how best to utilize them.

genomics↗