Journal Articles - UP - MSI

Permanent URI for this collectionhttps://repository.unesco.gov.ph/handle/123456789/50

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Differentiating two closely related Alexandrium species using comparative quantitative proteomics
Subong, Bryan John J.; Lluisma, Arturo O.; Azanza, Rhodora V.; Salvador-Reyes, Lilibeth A. (MDPI AG, 2020-12-23)
Alexandrium minutum and Alexandrium tamutum are two closely related harmful algal bloom (HAB)-causing species with different toxicity. Using isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics and two-dimensional differential gel electrophoresis (2D-DIGE), a comprehensive characterization of the proteomes of A. minutum and A. tamutum was performed to identify the cellular and molecular underpinnings for the dissimilarity between these two species. A total of 1436 proteins and 420 protein spots were identified using iTRAQ-based proteomics and 2D-DIGE, respectively. Both methods revealed little difference (10–12%) between the proteomes of A. minutum and A. tamutum, highlighting that these organisms follow similar cellular and biological processes at the exponential stage. Toxin biosynthetic enzymes were present in both organisms. However, the gonyautoxin-producing A. minutum showed higher levels of osmotic growth proteins, Zn-dependent alcohol dehydrogenase and type-I polyketide synthase compared to the non-toxic A. tamutum. Further, A. tamutum had increased S-adenosylmethionine transferase that may potentially have a negative feedback mechanism to toxin biosynthesis. The complementary proteomics approach provided insights into the biochemistry of these two closely related HAB-causing organisms. The identified proteins are potential biomarkers for organismal toxicity and could be explored for environmental monitoring.
Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model
Yñiguez, Aletta T.; Ottong, Zheina J. (Elsevier BV, 2020-03)
Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problems that appear to be increasing in frequency and expanding in area. One way to help mitigate their impacts on people's health and livelihoods is to develop early-warning systems. Models to predict and manage HABs typically make use of complex multi-model structures incorporating satellite imagery and frequent monitoring data with different levels of detail into hydrodynamic models. These relatively more sophisticated methods are not necessarily applicable in countries like the Philippines. Empirical statistical models can be simpler alternatives that have also been successful for HAB forecasting of toxic blooms. Here, we present the use of the random forest, a machine learning algorithm, to develop an early-warning system for the prediction of two different types of HABs: fish kill and toxic bloom occurrences in Bolinao-Anda, Philippines, using data that can be obtained from in situ sensors. This site features intensive and extensive mariculture activities, as well as a long history of HABs. Data on temperature, salinity, dissolved oxygen, pH and chlorophyll from 2015 to 2017 were analyzed together with shellfish ban and fish kill occurrences. The random forest algorithm performed well: the fish kill and toxic bloom models were 96.1% and 97.8% accurate in predicting fish kill and shellfish ban occurrences, respectively. For both models, the most important predictive variable was a decrease in dissolved oxygen. Fish kills were more likely during higher salinity and temperature levels, whereas the toxic blooms occurred more at relatively lower salinity and higher chlorophyll conditions. This demonstrates a step towards integrating information from data that can be obtained through real-time sensors into a an early-warning system for two different types of HABs. Further testing of these models through times and different areas are recommended.