Nat Commun. 2024 Jun 11;15(1):4862. doi: 10.1038/s41467-024-48841-w.

ABSTRACT

As spaceflight becomes more common with commercial crews, blood-based measures of crew health can guide both astronaut biomedicine and countermeasures. By profiling plasma proteins, metabolites, and extracellular vesicles/particles (EVPs) from the SpaceX Inspiration4 crew, we generated "spaceflight secretome profiles," which showed significant differences in coagulation, oxidative stress, and brain-enriched proteins. While >93% of differentially abundant proteins (DAPs) in vesicles and metabolites recovered within six months, the majority (73%) of plasma DAPs were still perturbed post-flight. Moreover, these proteomic alterations correlated better with peripheral blood mononuclear cells than whole blood, suggesting that immune cells contribute more DAPs than erythrocytes. Finally, to discern possible mechanisms leading to brain-enriched protein detection and blood-brain barrier (BBB) disruption, we examined protein changes in dissected brains of spaceflight mice, which showed increases in PECAM-1, a marker of BBB integrity. These data highlight how even short-duration spaceflight can disrupt human and murine physiology and identify spaceflight biomarkers that can guide countermeasure development.

PMID:38862464 | DOI:10.1038/s41467-024-48841-w

Genomics Proteomics Bioinformatics. 2024 May 9;22(1):qzad005. doi: 10.1093/gpbjnl/qzad005.

ABSTRACT

The microbiome plays a critical role in the process of conception and the outcomes of pregnancy. Disruptions in microbiome homeostasis in women of reproductive age can lead to various pregnancy complications, which significantly impact maternal and fetal health. Recent studies have associated the microbiome in the female reproductive tract (FRT) with assisted reproductive technology (ART) outcomes, and restoring microbiome balance has been shown to improve fertility in infertile couples. This review provides an overview of the role of the microbiome in female reproductive health, including its implications for pregnancy outcomes and ARTs. Additionally, recent advances in the use of microbial biomarkers as indicators of pregnancy disorders are summarized. A comprehensive understanding of the characteristics of the microbiome before and during pregnancy and its impact on reproductive health will greatly promote maternal and fetal health. Such knowledge can also contribute to the development of ARTs and microbiome-based interventions.

PMID:38862423 | DOI:10.1093/gpbjnl/qzad005

Learn Mem. 2024 Jun 11;31(5):a053815. doi: 10.1101/lm.053815.123. Print 2024 May.

ABSTRACT

Drug addiction and the circuitry for learning and memory are intimately intertwined. Drugs of abuse create strong, inappropriate, and lasting memories that contribute to many of their destructive properties, such as continued use despite negative consequences and exceptionally high rates of relapse. Studies in Drosophila melanogaster are helping us understand how drugs of abuse, especially alcohol, create memories at the level of individual neurons and in the circuits where they function. Drosophila is a premier organism for identifying the mechanisms of learning and memory. Drosophila also respond to drugs of abuse in ways that remarkably parallel humans and rodent models. An emerging consensus is that, for alcohol, the mushroom bodies participate in the circuits that control acute drug sensitivity, not explicitly associative forms of plasticity such as tolerance, and classical associative memories of their rewarding and aversive properties. Moreover, it is becoming clear that drugs of abuse use the mushroom body circuitry differently from other behaviors, potentially providing a basis for their addictive properties.

PMID:38862166 | DOI:10.1101/lm.053815.123

Nature. 2024 Jun 11. doi: 10.1038/s41586-024-07639-y. Online ahead of print.

ABSTRACT

Spaceflight induces molecular, cellular, and physiological shifts in astronauts and poses myriad biomedical challenges to the human body, which are becoming increasingly relevant as more humans venture into space1-6. Yet, current frameworks for aerospace medicine are nascent and lag far behind advancements in precision medicine on Earth, underscoring the need for rapid development of space medicine databases, tools, and protocols. Here, we present the Space Omics and Medical Atlas (SOMA), an integrated data and sample repository for clinical, cellular, and multi-omic research profiles from a diverse range of missions, including the NASA Twins Study7, JAXA CFE study8,9, SpaceX Inspiration4 crew10-12, plus Axiom and Polaris. The SOMA resource represents a >10-fold increase in publicly available human space omics data, with matched samples available from the Cornell Aerospace Medicine Biobank. The Atlas includes extensive molecular and physiological profiles encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiome data sets, which reveal some consistent features across missions, including cytokine shifts, telomere elongation, and gene expression changes, as well as mission-specific molecular responses and links to orthologous, tissue-specific murine data sets. Leveraging the datasets, tools, and resources in SOMA can help accelerate precision aerospace medicine, bringing needed health monitoring, risk mitigation, and countermeasures data for upcoming lunar, Mars, and exploration-class missions.

PMID:38862028 | DOI:10.1038/s41586-024-07639-y

Nature. 2024 Jun 11. doi: 10.1038/s41586-024-07648-x. Online ahead of print.

ABSTRACT

Human spaceflight has historically been managed by government agencies, such as the NASA Twins Study1, but new commercial spaceflight opportunities have opened spaceflight to a broader population. In 2021, the SpaceX Inspiration4 mission launched the first-ever all civilian crew to low Earth orbit, which included the youngest American astronaut (age 29), novel in-flight experimental technologies (handheld ultrasound imaging, smartwatch wearables, and immune profiling), ocular alignment measurements, and new protocols for in-depth, multi-omic molecular and cellular profiling. Here we report the primary findings from the 3-day spaceflight mission, which induced a broad range of physiological and stress responses, neurovestibular changes indexed by ocular misalignment, and altered neurocognitive functioning, some of which match long-term spaceflight2, but almost all of which did not differ from baseline (pre-flight) after return to Earth. Overall, these preliminary civilian spaceflight data suggest that short-duration missions do not pose a significant health risk, and moreover present a rich opportunity to measure the earliest phases of adaptation to spaceflight in the human body at anatomical, cellular, physiologic, and cognitive levels. Finally, these methods and results lay the foundation for an open, rapidly expanding biomedical database for astronauts3, which can inform countermeasure development for both private and government-sponsored space missions.

PMID:38862026 | DOI:10.1038/s41586-024-07648-x

Cell Syst. 2024 Jun 6:S2405-4712(24)00149-2. doi: 10.1016/j.cels.2024.05.005. Online ahead of print.

ABSTRACT

Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:38861992 | DOI:10.1016/j.cels.2024.05.005

Cell Rep Methods. 2024 Jun 3:100794. doi: 10.1016/j.crmeth.2024.100794. Online ahead of print.

ABSTRACT

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

PMID:38861988 | DOI:10.1016/j.crmeth.2024.100794

ISME J. 2024 Jun 11:wrae102. doi: 10.1093/ismejo/wrae102. Online ahead of print.

ABSTRACT

Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidisers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarising recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidisers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidising bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidisers as model organisms and to provide an outlook for future studies.

PMID:38861460 | DOI:10.1093/ismejo/wrae102

Cell Rep. 2024 Jun 10;43(6):114328. doi: 10.1016/j.celrep.2024.114328. Online ahead of print.

ABSTRACT

A key issue for research on COVID-19 pathogenesis is the lack of biopsies from patients and of samples at the onset of infection. To overcome these hurdles, hamsters were shown to be useful models for studying this disease. Here, we further leverage the model to molecularly survey the disease progression from time-resolved single-cell RNA sequencing data collected from healthy and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected Syrian and Roborovski hamster lungs. We compare our data to human COVID-19 studies, including bronchoalveolar lavage, nasal swab, and postmortem lung tissue, and identify a shared axis of inflammation dominated by macrophages, neutrophils, and endothelial cells, which we show to be transient in Syrian and terminal in Roborovski hamsters. Our data suggest that, following SARS-CoV-2 infection, commitment to a type 1- or type 3-biased immunity determines moderate versus severe COVID-19 outcomes, respectively.

PMID:38861386 | DOI:10.1016/j.celrep.2024.114328

Int J Cancer. 2024 Jun 11. doi: 10.1002/ijc.35054. Online ahead of print.

ABSTRACT

Previous studies suggest a role for inflammation in hepatocarcinogenesis. However, no study has comprehensively evaluated associations between circulating inflammatory proteins and risk of hepatocellular carcinoma (HCC) among the general population. We conducted a nested case-control study in the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) with 56 pairs of incident HCC cases and controls. External validation was performed in the UK Biobank (34 HCC cases and 48,471 non-HCC controls). Inflammatory protein levels were measured in pre-diagnostic plasma using the Olink® Inflammation Panel. We used conditional logistic regression to calculate multivariable odds ratios (ORs) with 95% confidence intervals (CIs) for associations between a 1-standard deviation (SD) increase in biomarker levels and HCC risk, considering a statistically significant threshold of false discovery rate (FDR)-adjusted p < .05. In the NHS/HPFS, among 70 analyzed proteins with call rates >80%, 15 proteins had significant associations with HCC risk (pFDR < .05). Two proteins (stem cell factor, OR per SD = 0.31, 95% CI = 0.16-0.58; tumor necrosis factor superfamily member 12, OR per SD = 0.51, 95% CI = 0.31-0.85) were inversely associated whereas 13 proteins were positively associated with risk of HCC; positive ORs per SD ranged from 1.73 for interleukin (IL)-10 to 2.35 for C-C motif chemokine-19. A total of 11 proteins were further replicated in the UK Biobank. Seven of the eight selected positively associated proteins also showed positive associations with HCC risk by enzyme-linked immunosorbent assay, with ORs ranging from 1.56 for IL-10 to 2.72 for hepatocyte growth factor. More studies are warranted to further investigate the roles of these observed inflammatory proteins in HCC etiology, early detection, risk stratification, and disease treatment.

PMID:38861327 | DOI:10.1002/ijc.35054

Microbiol Resour Announc. 2024 Jun 11:e0027324. doi: 10.1128/mra.00273-24. Online ahead of print.

ABSTRACT

Here, we report the draft genome sequence of Xanthomonas arboricola pv. pruni strain PVCT 262.1, isolated from almond (Prunus dulcis) leaves affected by bacterial spots in Italy in 2020. Genome size is 5,076,418 bp and G+C content is 65.44%. A total of 4,096 protein-coding genes and 92 RNAs are predicted.

PMID:38860797 | DOI:10.1128/mra.00273-24

Elife. 2024 Jun 11;13:e85914. doi: 10.7554/eLife.85914. Online ahead of print.

ABSTRACT

The autoimmune disease lupus erythematosus (lupus) is characterized by photosensitivity, where even ambient ultraviolet radiation (UVR) exposure can lead to development of inflammatory skin lesions. We have previously shown that Langerhans cells (LCs) limit keratinocyte apoptosis and photosensitivity via a disintegrin and metalloprotease 17 (ADAM17)-mediated release of epidermal growth factor receptor (EGFR) ligands and that LC ADAM17 sheddase activity is reduced in lupus. Here, we sought to understand how the lupus skin environment contributes to LC ADAM17 dysfunction and, in the process, differentiate between effects on LC ADAM17 sheddase function, LC ADAM17 expression, and LC numbers. We show through transcriptomic analysis a shared IFN-rich environment in non-lesional skin across human lupus and three murine models: MRL/lpr, B6.Sle1yaa, and imiquimod (IMQ) mice. IFN-I inhibits LC ADAM17 sheddase activity in murine and human LCs, and IFNAR blockade in lupus model mice restores LC ADAM17 sheddase activity, all without consistent effects on LC ADAM17 protein expression or LC numbers. Anti-IFNAR-mediated LC ADAM17 sheddase function restoration is associated with reduced photosensitive responses that are dependent on EGFR signaling and LC ADAM17. Reactive oxygen species (ROS) is a known mediator of ADAM17 activity; we show that UVR-induced LC ROS production is reduced in lupus model mice, restored by anti-IFNAR, and is cytoplasmic in origin. Our findings suggest that IFN-I promotes photosensitivity at least in part by inhibiting UVR-induced LC ADAM17 sheddase function and raise the possibility that anifrolumab ameliorates lupus skin disease in part by restoring this function. This work provides insight into IFN-I-mediated disease mechanisms, LC regulation, and a potential mechanism of action for anifrolumab in lupus.

PMID:38860651 | DOI:10.7554/eLife.85914

J Integr Bioinform. 2024 Jun 11. doi: 10.1515/jib-2024-0002. Online ahead of print.

ABSTRACT

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.

PMID:38860571 | DOI:10.1515/jib-2024-0002

Mol Omics. 2024 Jun 11. doi: 10.1039/d3mo00187c. Online ahead of print.

ABSTRACT

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

PMID:38860509 | DOI:10.1039/d3mo00187c

J Biopharm Stat. 2024 Jun 11:1-16. doi: 10.1080/10543406.2024.2358797. Online ahead of print.

ABSTRACT

Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.

PMID:38860461 | DOI:10.1080/10543406.2024.2358797

Front Cell Dev Biol. 2024 May 24;12:1295403. doi: 10.3389/fcell.2024.1295403. eCollection 2024.

ABSTRACT

Patients with breast cancer show altered expression of genes within the pectoralis major skeletal muscle cells of the breast. Through analyses of The Cancer Genome Atlas (TCGA)-breast cancer (BRCA), we identified three previously uncharacterized putative novel tumor suppressor genes expressed in normal muscle cells, whose expression was downregulated in breast tumors. We found that NEDD4 binding protein 2-like 1 (N4BP2L1), pleckstrin homology domain-containing family A member 4 (PLEKHA4), and brain-enriched guanylate kinase-associated protein (BEGAIN) that are normally highly expressed in breast myoepithelial cells and smooth muscle cells were significantly downregulated in breast tumor tissues of a cohort of 50 patients with this cancer. Our data revealed that the low expression of PLEKHA4 in patients with menopause below 50 years correlated with a higher risk of breast cancer. Moreover, we identified N4BP2L1 and BEGAIN as potential biomarkers of HER2-positive breast cancer. Furthermore, low BEGAIN expression in breast cancer patients with blood fat, heart problems, and diabetes correlated with a higher risk of this cancer. In addition, protein and RNA expression analysis of TCGA-BRCA revealed N4BP2L1 as a promising diagnostic protein biomarker in breast cancer. In addition, the in silico data of scRNA-seq showed high expression of these genes in several cell types of normal breast tissue, including breast myoepithelial cells and smooth muscle cells. Thus, our results suggest their possible tumor-suppressive function in breast cancer and muscle development.

PMID:38859961 | PMC:PMC11163233 | DOI:10.3389/fcell.2024.1295403

Front Mol Biosci. 2024 May 27;11:1378656. doi: 10.3389/fmolb.2024.1378656. eCollection 2024.

ABSTRACT

Autophagy mediates the degradation of intracellular macromolecules and organelles within lysosomes. There are three types of autophagy: macroautophagy, microautophagy, and chaperone-mediated autophagy. Heat shock protein 70.1 (Hsp70.1) exhibits dual functions as a chaperone protein and a lysosomal membrane stabilizer. Since chaperone-mediated autophagy participates in the recycling of ∼30% cytosolic proteins, its disorder causes cell susceptibility to stress conditions. Cargo proteins destined for degradation such as amyloid precursor protein and tau protein are trafficked by Hsp70.1 from the cytosol into lysosomes. Hsp70.1 is composed of an N-terminal nucleotide-binding domain (NBD) and a C-terminal domain that binds to cargo proteins, termed the substrate-binding domain (SBD). The NBD and SBD are connected by the interdomain linker LL1, which modulates the allosteric structure of Hsp70.1 in response to ADP/ATP binding. After the passage of the Hsp70.1-cargo complex through the lysosomal limiting membrane, high-affinity binding of the positive-charged SBD with negative-charged bis(monoacylglycero)phosphate (BMP) at the internal vesicular membranes activates acid sphingomyelinase to generate ceramide for stabilizing lysosomal membranes. As the integrity of the lysosomal limiting membrane is critical to ensure cargo protein degradation within the acidic lumen, the disintegration of the lysosomal limiting membrane is lethal to cells. After the intake of high-fat diets, however, β-oxidation of fatty acids in the mitochondria generates reactive oxygen species, which enhance the oxidation of membrane linoleic acids to produce 4-hydroxy-2-nonenal (4-HNE). In addition, 4-HNE is produced during the heating of linoleic acid-rich vegetable oils and incorporated into the body via deep-fried foods. This endogenous and exogenous 4-HNE synergically causes an increase in its serum and organ levels to induce carbonylation of Hsp70.1 at Arg469, which facilitates its conformational change and access of activated μ-calpain to LL1. Therefore, the cleavage of Hsp70.1 occurs prior to its influx into the lysosomal lumen, which leads to lysosomal membrane permeabilization/rupture. The resultant leakage of cathepsins is responsible for lysosomal cell death, which would be one of the causative factors of lifestyle-related diseases.

PMID:38859931 | PMC:PMC11163108 | DOI:10.3389/fmolb.2024.1378656

Brief Bioinform. 2024 May 23;25(4):bbae264. doi: 10.1093/bib/bbae264.

ABSTRACT

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

PMID:38859767 | DOI:10.1093/bib/bbae264

Biotechnol Bioeng. 2024 Jun 10. doi: 10.1002/bit.28758. Online ahead of print.

ABSTRACT

The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.

PMID:38859573 | DOI:10.1002/bit.28758

J Fish Biol. 2024 Jun 10. doi: 10.1111/jfb.15828. Online ahead of print.

ABSTRACT

Visual signals are involved in many fitness-related tasks and are therefore essential for survival in many species. Aquatic organisms are ideal systems to study visual evolution, as the high diversity of spectral properties in aquatic environments generates great potential for adaptation to different light conditions. Flatfishes are an economically important group, with over 800 described species distributed globally, including halibut, flounder, sole, and turbot. The diversity of flatfish species and wide array of environments they occupy provides an excellent opportunity to understand how this variation translates to molecular adaptation of vision genes. Using models of molecular evolution, we investigated how the light environments inhabited by different flatfish lineages have shaped evolution in the rhodopsin gene, which is responsible for mediating dim-light visual transduction. We found strong evidence for positive selection in rhodopsin, and this was correlated with both migratory behavior and several fundamental aspects of habitat, including depth and freshwater/marine evolutionary transitions. We also identified several mutations that likely affect the wavelength of peak absorbance of rhodopsin, and outline how these shifts in absorbance correlate with the response to the light spectrum present in different habitats. This is the first study of rhodopsin evolution in flatfishes that considers their extensive diversity, and our results highlight how ecologically-driven molecular adaptation has occurred across this group in response to transitions to novel light environments.

PMID:38859571 | DOI:10.1111/jfb.15828