In a special blog post, Molly-Ann Williams(@WilliamsMolly_9) and Anne Parle-McDermott (@anne_parle) from the School of Biotechnology and DCU Water Institute, Dublin City University provide an overview of how CRISPR-Cas works and how it can be applied to ecology and monitoring in particular. Read their recently published Molecular Ecology Resources paper here.
The field of CRISPR-Cas for genome editing has simply exploded since its introduction in 2012. The discovery of many different Cas enzymes with additional natural or genetically engineered functionalities, is resulting in an increase in CRISPR-Cas applications across all fields from food security to medicine.
So how can we join the revolution and apply CRISPR-Cas to the field of Ecology?
CRISPR-Cas systems consist of two main elements: a guide and a nuclease. Guides (made of RNA) direct the nuclease (Cas enzyme) to specific nucleic acid sequences (DNA or RNA). Upon target recognition the nuclease carries out the desired response, most commonly cleavage of the target sequence. The initially discovered CRISPR-Cas system relied on a nuclease called Cas9. This enzyme is involved in highly specific cleavage of target sequences that allow genome editing to occur by activating the natural repair system of the cell. More recently the applications of this system have been expanded beyond genome editing by the discovery of several new Cas enzymes with a secondary function i.e., the indiscriminate cleavage of single stranded nucleic acids upon target recognition. The discovery of these Cas enzymes has revolutionised nucleic acid diagnostics due to two main features:
Protein-guide and cleavage molecules (Cas): able to specifically recognise target nucleic acids, cleave the target sequence and subsequently cleave other non-specific nucleic acids.
Nucleic acids as reporters: the non-specific nucleic acids can be designed as a reporter molecule that releases measurable signal when cleaved. This allows us to visualise when the initial target sequence has been detected and apply it to diagnostics and species monitoring.
Two main elements of a CRISPR-Cas diagnostic system: Cas enzyme and guide RNA effector complex and single stranded (ss) nucleic acid reporter molecule. In this example, the nuclease is Cas12a specific to DNA detection downstream from a TTTV PAM site.
The three main Cas enzymes of interest for diagnostics are Cas12, Cas13 and Cas14 each with unique functions applicable to different types of tests (for a more detailed discussion of these enzymes visit this blog).
The Cas enzyme most relevant for single species detection from environmental DNA is the enzyme Cas12a. This nuclease can detect both ssDNA and dsDNA but can only recognise DNA sequences downstream from a TTTV protospacer adjacent motif (PAM). Importantly, Cas12a cannot detect DNA sequences missing this PAM site. This is vital when designing single species detection assays.
Do you have two closely related species that you want to distinguish? Searching your target species sequence for a site downstream of a PAM site found ONLY in your target, and not in sympatric species, will ensure highly specific recognition and prevent detection of non-target species.
What if you work with environmental RNA? Well there is a CRISPR-Cas system for you too! The Cas enzyme Cas13 differs from Cas12a in that it recognises single stranded RNA molecules with non-specific cleavage of ssRNA following target cleavage i.e., it works the same as Cas12a but targets RNA rather than DNA.
The world of CRISPR diagnostics is still in its early stages but with the discovery of new CRISPR-Cas systems with unique functions, there is no reason ecologists cannot utilise these diagnostic tools to enhance environmental monitoring using molecular techniques. For more information on using CRISPR-Cas diagnostics for single species detection from environmental DNA read our paper here.
RADseq is a great method for gathering genomic data to answer biological questions across many different scales, from phylogenetics to population and landscape genetics. It is fast, inexpensive, and requires no previous knowledge about the species’ genomic architecture. However, with this flexibility comes challenges. In this paper we develop and bench test an approach to address what may be the biggest RADseq challenge: how to choose the right sequence similarity threshold that defines whether two non-identical sequencing reads arose from the same or different genomic locations. This problem goes to the heart of evolutionary genetics— if two sequences are considered to be homologous, or derived from the same ancestral genomic location with subsequent modification through time, then they tell us a great deal about evolutionary history. If they are paralogous, and map to separate locations, then they lack that shared evolutionary history. Getting this straight is perhaps the single most important step in using genomic data for evolutionary inference.
Studies that include relatively distantly related samples, such as those asking phylogenetic or biogeographical questions, should expect that homologous sequences will have diverged over time and therefore require lower similarity thresholds that allow for that divergence. However, if the threshold is set too low, paralogs will be falsely assigned to the same genomic locus, leading to problems ranging from inflated missing data rates to inaccurate measures of genetic diversity. Rather than relying on rough guesses that are preset in software packages, our approach attempts to balance these two competing forces by quantifying the relationship between pairwise genetic relatedness (as estimated directly from the data) and summaries of the RADseq dataset including pairwise data missingness and the slope of isolation by distance among samples. The relationship between pairwise genetic distance and pairwise data missingness is particularly informative—although some positive correlation is expected as mutations accumulate in enzyme restriction sites that RAD relies on, there is often a clear pattern of increased pairwise missingness that occurs when the most divergent homologous allelic variants begin to be erroneously oversplit into different presumptive loci. By explicitly looking for this breakpoint as a function of clustering threshold, researchers can choose a value that allows them to maximize the number of genomic regions recovered while minimizing the erroneous oversplitting of highly divergent, but homologous loci.
Citation: McCartney‐Melstad, E, Gidiş, M, Shaffer, HB. An empirical pipeline for choosing the optimal clustering threshold in RADseq studies. Mol Ecol Resour. 2019; 19: 1195– 1204. https://doi.org/10.1111/1755-0998.13029
We were challenged to design and build a simple and rapid species monitoring system. Why do we need such a system? Biodiversity loss is at an all-time high and such a system would help to support the management and conservation of fish species within aquatic environments by acquiring knowledge of species distribution that traditionally is gained through visual detection and counting. These methods are expensive, time consuming and can lead to harm of the species of interest. We decided that environmental DNA (eDNA) was the way to go but we had to solve the ‘PCR problem’ i.e., avoid having to do cyclical high temperatures as that would see us ending up with a costly, once-off device that would likely not be applied outside our lab. This got us brainstorming and led us to a novel isothermal detection method, combining Recombinase Polymerase Amplification with CRISPR-Cas detection, which simplifies the adaptation of nucleic acid detection on to a biosensor device.
This innovative methodology utilises the collateral cleavage activity of Cas12a, a ribonuclease guided by a highly specific single CRISPR RNA, to detect specific species from eDNA. We proved it could work for eDNA by applying the technology to the detection of Salmo salar from eDNA samples collected in Irish rivers, where presence or absence had been previously confirmed using conventional field sampling. The beauty of this advance is that it can be applied to any species in the environment. Not only does this assay solve the ‘PCR problem’, it is also is a better approach for distinguishing very closely related species. We look forward to others in the field adapting it to their own favourite species of interest.
Citation: Williams, M‐A, O’Grady, J, Ball, B, et al. The application of CRISPR‐Cas for single species identification from environmental DNA. Mol Ecol Resour. 2019; 19: 1106– 1114. https://doi.org/10.1111/1755-0998.13045
Adding of methyl groups to a DNA molecule or methylation has the interesting ability to alter the activity of a DNA segment without changing the sequence. In this behind the scenes look, Zachary Laubach and colleagues test if this valuable biomarker is impacted by differences in hyena social status or other ecological factors early in life. What’s particularly impressive is that they garnered insights into methylation from a wild population. They find some surprising results, such as that high ranking mums can confer higher levels of methylation to their cubs that disappears when they get older. Why? Find out below and read the full article here.
What led to your interest in this topic / what was the motivation for this study?
Across a broad taxonomic
spectrum, social experiences, particularly those early in life, seem to have a
profound impact on organisms’ development. The idea that during sensitive
periods of development, social experiences and early life environment can have
lasting impacts on the later life phenotype and health is known as the
Developmental Origins of Health and Disease (DOHaD) hypothesis, and was
formalized in the 1980s by epidemiologists, namely David Barker and his
research on cardiovascular disease. Among social mammals, including humans and
non-human primates, an individual’s social rank affects their behavior,
physiology, and related health outcomes. For example, in humans, low
socioeconomic status is widely recognized as a risk factor for cardiovascular
complications and other chronic diseases. In non-human primates, low social
rank is risk factor for elevated chronic stress and immune dysregulation. So, although
we observe that social status affects biology, we still know little about how
this all works. To better understand a potential mechanism for how early life
environment affects biology, we investigated possible early environmental
determinants of a molecular biomarker (DNA methylation) over the course of
development in a population of wild spotted hyenas. Similar to many primates,
hyenas live in groups organized by a social dominance hierarchy, and whether or
not a hyena is born high or low ranking has lifelong consequences.
What difficulties did you run into
along the way?
In this study, we
focused on measuring DNA methylation, which is generally of interest to
researchers because it is responsive to environmental stimuli and associated
with gene expression. Still, while spotted hyenas present a unique opportunity
to investigate how various social experiences and ecological factors early in
life are associated with biological characteristics later in life, there were
no previous studies (at least of which we were aware) that measured DNA
methylation in this species. In other words, this was not like working with a
well characterized molecular biology model organism, like fruit flies or lab
rats. In fact, when we were conducting our lab work there was no publicly
available draft hyena genome. In our attempt to assess a potentially
informative biomarker in hyenas, we measured multiple types of DNA methylation with
varying degrees of success. Finally, the hyenas we study live freely in a large
reserve in Kenya, so much of our data were observational and collected under a
variety of field conditions making collection of samples non-trivial.
What is the biggest or most surprising innovation highlighted in this study?
This work represents one
of a handful of studies conducted in a wild population that measures DNA
methylation to better understand how early life environment may influence
organisms’ biology over the course of development. Taking advantage of our
approximately 30 years’ worth of continuously collected data on individually
recognizable hyenas from the Masai Mara Hyena Project, we not only amassed a
particularly large sample size for a long-lived, wild mammal, but we were also
able to compare patterns of DNA methylation at various stages of development
with respect to multiple early life environmental factors. We found that being
born to a higher-ranking mom corresponded with greater global DNA methylation
in young but not older hyenas. One interpretation of this result is that high
ranking moms confer some advantage to their cubs early in life, but that the
effect of maternal rank per se is not evident in global DNA methylation of
subadult or adult hyenas. We also found some associations between global DNA
methylation and litter size, human disturbance, and prey availability in the
year a hyena was born, and these associations were strongest in the youngest
age group of hyenas.
Moving forward, what are the next steps in this area of research?
In our next steps we are working to understand whether specific types of early life social environments, like maternal care and how well socially connected an animal is within its group, correspond with variation in DNA methylation and adult stress. We are also utilizing more advanced techniques for measuring DNA methylation, so that we might home in on functional pathways that are involved in the development of an adverse stress phenotype. As part of our broader research agenda looking at general biological principles related to DOHaD hypothesis, we have also teamed up with epidemiologists to ask how social status in humans affects biology. In fact, we have recently published another a paper looking at the associations between maternal socioeconomic status and patterns of DNA methylation over the course of development in children who are part of the Project Viva pre-birth cohort study (check out the paper here).
What would your message be for students about to start developing or using novel techniques in Molecular Ecology?
This project was part of
my PhD work, and from this experience I have learned just how fast molecular
biology advances as a field. Given that this technology is constantly changing,
it is critical to find mentors and collaborators with up-to-date expertise who
are willing to support you. I was fortunate to work in a cutting-edge molecular
laboratory, and to receive training from internationally recognized experts in Dr.
Dana Dolinoy’s lab who specialize in studying DNA methylation. Additionally, in
studies like these that involve large observational data sets and that aim to
understand biological mechanisms, the value of clearly defined study questions,
hypotheses and a complimentary analytical strategy cannot be understated. In my
opinion, novel technology will not substitute for a thoughtful and well-planned
What have you learned about methods and resources development over the course of this project?
Working in a novel system, like investigating DNA methylation in wild spotted hyenas, presents challenges and limitations that are unique from those encountered in laboratory settings and when working with model organisms. However, there are deep insights and rich perspective to be gained at the three-way interface between molecular biology, behavioral ecology and evolutionary biology from study populations with intact life histories and that are subject to natural selection. I have also learned that long-term field studies with uninterrupted data collection, like the Masai Mara Hyena Project, provide an invaluable resource and an unmatched opportunity to combine molecular techniques with vast collections of behavioral, demographic and ecological data. In addition, while long-term field studies represent a substantial investment of time and resources, they also present a chance for comparative research that can help elucidate basic biological principals that span taxa, like the DOHaD hypothesis. As such, I believe I have been fortunate to work with Dr. Kay Holekamp’s hyenas and that these types of long-term field studies are an asset to be prioritized and preserved.
Describe the significance of this research for the general scientific community in one sentence.
Social and ecological
factors experienced early in life can correspond to changes in molecular
biomarkers, like DNA methylation, that are detected over the course of
development, and that may affect patterns of gene expression.
Describe the significance of this research for your scientific community in one sentence.
Findings from this research suggests that maternal rank, anthropogenic disturbance, and prey availability around the time of birth are associated with later life global DNA methylation in spotted hyenas, particularly in cubs.
Citation: Laubach, ZM, Faulk, CD, Dolinoy, DC, et al. Early life social and ecological determinants of global DNA methylation in wild spotted hyenas. Mol Ecol. 2019; 28: 3799– 3812. https://doi.org/10.1111/mec.15174
Plant pathogens are a major factor in farming and forestry, and also play a key role in ecosystem health. Understanding pathogens at national scales is critical for appropriate prevention and management strategies and for a sustainable provision of future ecosystem services and agroecosystem productivity. Despite this, at present we have little knowledge of the diversity patterns of plant pathogens and how they change with land use at a broad scale.
In our study we show how land uses such as farming and plantation forestry affected the variety of plant pathogens in soil, roots and on plant leaves – and we show there are many more species of plant pathogens in land that’s been modified by pasture, cropping, and plantation forestry than there are in natural forest. The patterns of pathogen diversity are distinct from other microbes.
These are some of the first landscape level insights into these critically important communities including fungal, oomycete and bacterial pathogens in seemingly healthy ecosystems. Our results give scientists new insights into where pathogens exist, and how pathogen communities are structured.
Andreas Makiola and Ian Dickie (Bio-Protection Research Centre, New Zealand)
As genomic and ecological data sets grow larger in size, researchers are flooded with far more information than was available when many conventional model-based approaches were designed. To deal with these massive amounts of data, many researchers have turned to machine learning techniques, which promise the ability to help find signals within the noise of the complex data sets generated by modern sequencing approaches. Applications for machine learning in molecular ecology are broad and include global studies of biodiversity patterns, species delimitation studies, and studies of the genomic architecture of adaptation, among many others. Here at Molecular Ecology Resources, we are excited to highlight research that applies supervised and unsupervised machine learning algorithms to answer questions of interest to the readership of molecular ecology. This special issue will also highlight the nuances and limitations of machine-learning techniques. Rather than focusing on the supposed differences between machine-learning and model-based approaches, this issue would aim to highlight the broad spectrum of machine-learning approaches, many of which can incorporate model-based expectations and predictions.
We are soliciting original research
that applies novel robust applications of machine learning methods on molecular
data to address questions across ecological disciplines.
Manuscripts should be submitted in the usual way through the Molecular Ecology Resources website. Submissions should clearly state in the cover letter accompanying the submission that you wish the manuscript to be considered for publication as part of this special issue. Pre-submission inquiries are not necessary, but any questions can be directed to: email@example.com
Special issue editors: Nick Fountain-Jones, Megan Smith & Frédéric Austerlitz