With Environmental DNA, Small Water Samples Can Find Really Big Animals

In an article posted by SciTechDaily, a team of scientists from California State University (CUNY), the Wildlife Conservation Society (WCS), and Columbia University used an emerging genetic tool to detect whales and dolphins in the New York Bight. This technique searched for environmental DNA (eDNA), or trace amounts of genetic material left behind by wildlife in the water. The results of this study were published in the journal Frontiers.

The scientists said that eDNA can be used to support other efforts to locate whales and dolphins, such as visual observations and acoustic monitoring. According to the study’s lead author, Dr. Elizabeth Alter, “Determining how cetaceans and other threatened marine animals use coastal habitats is critical to their effective conservation. By generating eDNA data in parallel with survey data, it will be possible to gain a clearer understanding of how this tool can be used in management…”

This technique also detected baitfish present in the area preyed on by whales and dolphins in addition to finding the mammals themselves. The authors claim that in the future, as technology improves, this technique could eventually be used to identify individual animals. Because eDNA drops to lower levels over time, the authors also state that additional research is needed to better understand how behavior and oceanic conditions contribute to the longevity of eDNA signals.

Though there are some signs of promising recovery for many whale populations, whales continue to face a range of modern-day threats such as ship strikes, ocean noise, entanglement with nets, and a general loss of habitat. For example, there are currently plans to scale up massive renewable energy projects to meet energy demands in the United States, including a wind energy auction for more than 488,000 acres in the New York Bight. The use of emerging techniques such as eDNA can provide a new perspective on the current status of whale populations and their prey in and around lease areas as offshore wind operations scale up along the east coast. The WCS has also extended these eDNA techniques to detect critically endangered wildlife such as Swinhoe’s softshell turtle, in the Bolivian Amazon, and in some of the most rugged areas on the planet including Mt. Everest.

Related article: https://www.forbes.com/sites/grrlscientist/2018/05/18/the-power-of-environmental-dna-for-monitoring-whales/?sh=64ccf93f264c

The First Genome-Scale Network of Rice Genes Can Predict Their Functions to Help Make Biofuels

[caption id="attachment_3077" align="alignright" width="357" caption="This schematic is of a full-size view of a RiceNet layout. It's color-coded to show the probability of gene network links (red = higher probability and blue = lower probability). (Image source: Insuk Lee, Yonsei University)"][/caption]

An international team of researchers, including scientists in the U.S. Department of Energy (DOE)’s Joint BioEnergy Institute (JBEI), created the world’s first genome-scale model for predicting genes and gene networks in a plant species – rice, to be precise. This model, dubbed RiceNet, allows researchers to view multiple sets of data of genes and genetic pathways, which then allows them to predict the functions of entire gene networks. Professor Pamela Ronald and her co-researchers of the University of California (UC) Davis conducted experiments proving that RiceNet is indeed able to accurately determine gene functions in rice.

One may wonder why it would be so crucial to discover the functions of certain genes in rice. Well, rice is a food staple for about half of the world’s population and can be used as a model plant for the perennial plants, or those that live for longer than two years, that seem to be perfect candidates for creating cellulosic biofuels (fuels whose energy is derived from organic materials such as grass, corn, and vegetable oil through the process of carbon fixation), which would decrease dependence on other, non-renewable energy sources. Ronald said it best: “The ability to identify key genes that control simple or complex traits in rice has important biological, agricultural, and economic consequences. RiceNet offers an attractive and potentially rapid route for focusing crop engineering efforts on the small sets of genes that are deemed most likely to affect the traits of interest.”

Personally, I think that the ability to access multiple data sets at one time is very useful and efficient. Networks similar to RiceNet may well be the future of genetics research; in fact, RiceNet is already accessible for this very purpose: the creators of RiceNet also made an online model that can select ideal rice genes based on RiceNet’s data, which can be used here.