HIGHLIGHTED PUBLICATIONS

papers that tell our story

  • Coral exists in a community, so coral disease outcomes are driven by interactions among the coral host, its microbial community, and its algal symbionts. This series of publications links immune traits to post-outbreak community structure to explain why some coral species are more resistant to disease than others.

  • As disease outbreaks and warming oceans threaten coral reefs, survival increasingly depends on the strength and flexibility of coral immune systems. The Mydlarz lab helped reveal the genetic architecture behind that resilience—showing how ancient immune receptors, lineage-specific variation, and gene duplication shape which corals are better equipped to withstand infection and heat.Description of Section 2

  • Coral disease is not a simple battle between one pathogen and one host, but a shifting process shaped by the coral, its microbiome, its symbionts, and the environment. Through collaborative efforts, the Mydlarz Lab helped build both the spectrum-based framework and the genomic and immune ‘omics tools now used to understand complex outbreaks such as stony coral tissue loss disease.

  • Machine learning can distinguish SCTLD from similar diseases. Using this precise diagnostic tool, scientists can detect molecular changes before visible tissue loss occurs.

The Holobiont Perspective

Coral immunity as a predictor of reef resilience

Meiling SS, Et al. 2021. Variable species responses to experimental stony coral tissue loss disease (SCTLD) exposure.  Front. Mar. Sci., 8: 464.

MacKnight NJ, Et al. Disease Resistance in Coral is Mediated by Distinct Adaptive and Plastic Gene Expression Profiles. 2022. Science Advances. 8:39.

MacKnight NJ, Et al. 2021. Microbial dysbiosis reflects disease resistance in diverse coral species. Communications Biology, 4,679.

Beavers, KM, Et al. 2023. Stony Coral Tissue Loss Disease Induces Transcriptional Signatures of in situ Degradation of Dysfunctional Symbiodiniaceae. Nature Communications. 14:2915

Rossin AM, Et al. 2026. Runaway coral-algal dysbiosis may be responsible for rapid coral tissue loss. Scientific Reports 16, 6415.

Williams S, Et al. 2026. From immunity to community: a predictive trait-space model for white plague disease on Caribbean reefs. Ecosphere, In press.

Coral disease outbreaks are among the greatest threats to reef ecosystems worldwide, driving declines in coral cover, altering community composition, and reducing the ecological functions that reefs provide. Understanding why some corals resist disease while others succumb is critical for predicting disease outcomes, and forecasting the future resilience of coral reef ecosystems. Through a series of complementary studies spanning experimental disease challenges, along with multi-layered data such as microbiome, transcriptomics, histopathology, and ecological modeling, this body of work examines the mechanisms underlying disease resistance across Caribbean reef-building corals and explores how immune variation scales from individuals to ecosystems.

Experimental transmission studies revealed consistent differences in susceptibility among coral species, establishing disease resistance as a key trait shaping coral responses to emerging diseases such as Stony Coral Tissue Loss Disease and White Plague Disease. Comparative analyses demonstrated that susceptibility is associated with microbial dysbiosis, variation in immune and cellular maintenance pathways, and both constitutive and plastic gene expression responses. Disease progression in SCTLD was linked to disruption of the coral–algal symbiosis, including shifts in Symbiodiniaceae function, induction of symbiophagy, and a characteristic sequence of histopathological changes that culminate in tissue loss. Together, these findings support a holobiont-based view of coral health in which disease outcomes emerge from interactions among the coral host, its microbial associates, and its algal symbionts.

In broader context, our work shows that immune traits can extend beyond their role in determining individual disease outcomes to serve as predictors of community-level responses to disturbance. By integrating molecular and immune phenotypes into trait-based models, we show that variation in immunity can forecast post-outbreak coral community structure and, ultimately, reef resilience.

Collectively, these studies establish coral immunity as a critical link between organismal health and ecosystem dynamics, providing a framework for predicting how coral communities will respond to future disease outbreaks in a rapidly changing ocean.

Decoding Coral Immunity

Dimos B, Et al. 2022. Adaptive Variation in Homolog Number Within Transcript Families Promotes Expression Divergence in Reef-Building Coral. Molecular Ecology. Vol. 31 Issue 9, pp. 2594–2610, 2022.

The evolution of coral defense

Emery, MA, Et al. 2021. Cnidarian pattern recognition receptor repertoires reflect both phylogeny and life history traits. Frontiers in Immunology, Comparative Immunology, 12, 2430.

Coral reefs face two imminent crises; disease outbreaks and warming oceans. A coral’s ability to survive both hinges on its immune system. These papers, together, show that corals are far from immunologically simple. They carry an innate immune system that detects danger both from outside (pathogens) and inside (cellular stress from heat), using receptor families with deep evolutionary roots shared with vertebrates. That immune capacity also isn't fixed: it varies across coral species and lineages, shaped by evolutionary history and life history traits like growth form and how they associate with symbiotic algae. Underlying that variation is a genomic process, gene duplication, that lets immune-related genes expand and diverge, giving some species more raw material to mount stronger or more flexible defenses than others.

What sets this body of work apart is that the Mydlarz lab moved coral immunology from broad description into specific, testable molecular detail. We worked to catalog coral immunity, identifying specific NLR and PRR gene repertoires in Caribbean coral species, then showing how these immune genes vary by lineage and are driven by gene duplication.

Collectively, these papers connect coral immune gene content and genomic architecture to real differences in disease and thermal resilience across species, giving conservation and restoration efforts a genetic basis for prioritizing which corals or lineages might be more resilient.

Van Buren, Et al. 2024. Structural and Evolutionary Relationships of Melanin Cascade Proteins in Cnidarian Innate Immunity. Integrative and Comparative Biology, 64: 5, Pp 1320–1337.

Emery MA, Et. al. 2024. Immune system components in cnidarians. Reference Module in Life Sciences, Encyclopedia Chapter, Elsevier, 2025

Beyond One Host, One Pathogen

Conceptual frameworks for understanding and studying coral disease

Brown A, Et al. 2026. Adapting the disease triad to a spectrum for coral disease: highlighting variation in coral disease. Book Chapter in: The Ecology and Evolution of Marine Parasites and Disease. Oxford University Press. In press.

Vega-Thurber RL, Et al. 2020. Deciphering Coral Disease Dynamics: Integrating Host, Microbiome, and the Changing Environment. Frontiers in Ecology and Evolution - Coevolution, 8, 402.

Traylor-Knowles N, Et al. 2022. Advances in Immunity ‘Omics in Response to Coral Disease Outbreaks. Front. Mar. Sci. Volume 9 - 2022

Coral disease is never a simple case of one pathogen attacking one host. Dr. Laura Mydlarz and members of the Mydlarz Lab have been fortunate to be part of several large collaborative efforts to reframe disease as a dynamic interplay between the coral, its microbiome, and a changing environment, showing that different diseases fall along a spectrum rather than fitting a single fixed model. Building on this framework, the lab has helped drive the field's adoption of genomic and immune 'omics tools to study coral disease outbreaks like stony coral tissue loss disease.

Together, the story of these papers over the years reflects a conceptual shift: coral disease isn't a fixed host-versus-pathogen event but a variable, context-dependent process shaped by host, symbiont and microbiome state and environment. The lab is proud to have been part of large collaborative groups such as the Marine Disease Research Coordination Network and Florida's Disease Advisory Committee (DAC), working with the field to build both the theoretical framework, the spectrum model, and the molecular tools, 'omics, needed to study it.

Burge, CA Et al. 2016. Complementary approaches to diagnosing marine diseases: a union of the modern and the classic. Phil. Trans. R. Soc. B 20150207

Reading Coral Disease

Machine learning reveals not only which coral disease is present, but how far it has progressed

Van Buren EW, Et al. 2025. Leveraging machine learning to classify and characterize gene expression patterns in two coral diseases. Discover Applied Sciences (2025) 7:1003

Beavers K, Et al. 2025. Characterizing gene expression profiles of various tissue states in stony coral tissue loss disease using a feature selection algorithm. Royal Society Open Science 12 (7), 241993.

Together, these studies show how machine learning can improve both the diagnosis and biological understanding of coral disease. The first study distinguishes stony coral tissue loss disease (SCTLD) from white plague by identifying disease-specific patterns of coral gene expression across multiple species. The second examines how gene expression changes within SCTLD-affected colonies, comparing healthy tissue, apparently healthy tissue on diseased colonies, and active lesions. Both studies identify strong immune disruption, inflammation, metabolic stress, and breakdown of the coral–algal symbiosis as key features of SCTLD. Importantly, the second study shows that molecular changes can appear in tissue before visible tissue loss occurs, suggesting the potential for earlier detection. Samples from that study were also used to help validate the diagnostic model developed in the first. Together, the findings support a future two-step approach that could identify both the type of disease present and the stage of disease progression. This combined framework could help reef managers detect SCTLD earlier, distinguish it from visually similar diseases, and better target treatment and conservation efforts.