Harnessing the power of multi‐omics data for predicting climate change response

Layton, Kara K S; Bradbury, Ian

doi:10.1111/1365-2656.13619

Cited by 26 publications

(22 citation statements)

References 120 publications

(133 reference statements)

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“…Understanding microbe–ecosystem interactions and functions is therefore central to their utilization in ecological models and biotechnologies for intervening on climate change. The generation of high-resolution spatiotemporal dynamics data and incorporation of different omics data sets can provide important insights into the molecular mechanisms behind climate changes responses and improve the accuracy of forecasting models ( Herold et al, 2020 ; Layton and Bradbury, 2021 ). Together with their ubiquitous nature, the core roles of microbial communities afford us with a broad framework for potential microbiological tools with which the fundamental impacts of global climate change can be understood, monitored, predicted, and conceivably, mitigated.…”

Section: Main Bodymentioning

confidence: 99%

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

et al. 2022

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Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.

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Section: Main Bodymentioning

confidence: 99%

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

et al. 2022

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“…With most of Eastern Canada experiencing moderate increases in mean annual temperatures and temperature extremes, fish catch is predicted to decrease and considerable declines in fish stocks are forecast due to long-term increases in temperature [3]. Therefore, a thorough understanding of the evolutionary mechanisms through which fishes can respond to climate change is a priority for the conservation of fish stocks [4–6].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a thorough understanding of the evolutionary mechanisms through which fishes can respond to climate change is a priority for the conservation of fish stocks [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Such instances of epigenetic inheritance have the potential to prime offspring for an environment based on parental experience, though they could prove maladaptive for offspring if parental environment is too different from that of the offspring and if the offspring have limited capacity to overwrite inherited methylation. Since climate change will pose a significant threat to brook charr in the coming years [43,47,48,[50][51][52], a thorough understanding of the mechanisms of plasticity through which fish can cope with changing environments is needed [6]. From an applied standpoint, the conservation implications of epigenetic inheritance remain unclear, particularly after release of stocked fish into natural environments.…”

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confidence: 99%

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Thermal regime during parental sexual maturation, but not during offspring rearing, modulates DNA methylation in brook charr (Salvelinus fontinalis)

Venney

Wellband

Normandeau

et al. 2022

Preprint

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Epigenetic inheritance can result in plastic responses to changing environments being faithfully transmitted to offspring. However, it remains unclear how epigenetic mechanisms such as DNA methylation can contribute to multigenerational acclimation and adaptation to environmental stressors. Brook charr (Salvelinus fontinalis), an economically important salmonid, is highly sensitive to thermal stress, and is of conservation concern in the context of climate change. We studied the effects of temperature during parental sexual maturation and offspring rearing on whole-genome DNA methylation in brook charr juveniles (fry). Parents were split between warm and cold temperatures during sexual maturation, mated in controlled breeding designs, then offspring from each family were split between warm (8°C) and cold (5°C) rearing environments. We found 188 differentially methylated regions (DMRs) due to parental maturation temperature after controlling for family structure. In contrast, offspring rearing temperature had a negligible effect on offspring methylation. Stable intergenerational inheritance of DNA methylation and minimal plasticity in progeny could result in transmission of acclimatory epigenetic states to offspring, priming them for a warming environment. Our findings have implications pertaining to the role of intergenerational epigenetic inheritance in response to ongoing climate change.

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“…Yet, one major challenge moving forward will be ground truthing these predictions to adapt conservation strategies accordingly. Genomic and epigenomic datasets represent powerful resources for conservationists in these efforts (Formenti et al, 2022; Layton & Bradbury, 2021; Waldvogel et al, 2020). The use of genomic tools in conservation is already widespread, allowing for assessments of population structure and connectivity (Wright et al, 2020), inbreeding depression (Dussex et al, 2021; Kardos et al, 2016), contemporary evolutionary responses to environmental change (Bi et al, 2019; Catullo et al, 2019), and adaptive capacities to cope with future change (Bay et al, 2018; Flanagan et al, 2018; Harrisson et al, 2014; Layton et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide DNA methylation patterns harbor signatures of hatchling sex and past incubation temperature in a species with environmental sex determination

Bock

Smaga

McCoy

et al. 2022

Preprint

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Conservation of thermally sensitive species depends on monitoring organismal and population-level responses to environmental change in real time. Epigenetic processes are increasingly recognized as key integrators of environmental conditions into developmentally plastic responses, and attendant epigenomic datasets hold potential for revealing cryptic phenotypes relevant to conservation efforts. Here, we demonstrate the utility of genome-wide DNA methylation (DNAm) patterns in the face of climate change for a group of especially vulnerable species, those with temperature-dependent sex determination (TSD). Due to their reliance on thermal cues during development to determine sexual fate, contemporary shifts in temperature are predicted to skew offspring sex ratios and ultimately destabilize sensitive populations. Using reduced-representation bisulfite sequencing, we profiled the DNA methylome in blood cells of hatchling American alligator (Alligator mississippiensis), a TSD species lacking reliable markers of sexual dimorphism in early life-stages. We identified 120 sex-associated differentially methylated cytosines (DMCs; FDR < 0.1) in hatchlings incubated under a range of temperatures, as well as 707 unique temperature-associated DMCs. We further developed DNAm-based models capable of predicting hatchling sex with 100% accuracy and past incubation temperature with a mean absolute error of 1.2°C based on the methylation status of 20 and 24 loci, respectively. Though largely independent of epigenomic patterning occurring in the embryonic gonad during TSD, DNAm patterns in blood cells may serve as non-lethal markers of hatchling sex and past incubation conditions in conservation applications. These findings also raise intriguing questions regarding tissue-specific epigenomic patterning in the context of developmental plasticity.

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Harnessing the power of multi‐omics data for predicting climate change response

Cited by 26 publications

References 120 publications

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Thermal regime during parental sexual maturation, but not during offspring rearing, modulates DNA methylation in brook charr (Salvelinus fontinalis)

Genome-wide DNA methylation patterns harbor signatures of hatchling sex and past incubation temperature in a species with environmental sex determination

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