New worlds: Genes and green potatoes

Eating a potato that has turned green from exposure to sunlight can be toxic and even fatal.

Potato fritters (photo credit: Courtesy)
Potato fritters
(photo credit: Courtesy)
Eating a potato that has turned green from exposure to sunlight can be toxic and even fatal. In 1924, Science magazine reported on one such fatal case.
James Matheney of Vandalia, Illinois, had gathered about one and a half bushels of tubers, which had turned green. Two days after eating the potatoes, most of his family – wife, two daughters and four sons – showed symptoms of poisoning; the only exceptions were James himself, who didn’t eat the potatoes, and a breast-fed baby boy. His wife, aged 45, died a week later, followed by their 16-year-old daughter.
The other five members of the family recovered.
Although such fatalities are rare among human beings, farm animals often get sick or die after eating green potatoes. Symptoms include damage to the digestive system as well as loss of sensation, hallucinations and other neurological disturbances.
Death can be caused by a disruption of the heartbeat. The culprits are the toxic substances solanine and chaconine; their concentration rises sharply with exposure to light or during sprouting, and they protect the tubers from insects and disease.
Solanine and chaconine belong to the large family of glycoalkaloids, which includes thousands of toxins found in small amounts in other edible plants, including tomatoes and eggplant. These substances have been known for over 200 years, but only recently has Prof. Asaph Aharoni of the Weizmann Institute of Science’s plant sciences department begun to unravel how they are produced in plants.
He and his team have mapped out the biochemical pathway responsible for manufacturing glycoalkaloids from cholesterol.
Their findings will facilitate the breeding of toxin-free crops and the development of new crop varieties from wild strains that contain such large amounts of glycoalkaloids they are currently considered inedible. On the other hand, causing plants to produce glycoalkaloids if they don’t do so naturally or increasing their glycoalkaloid content can help protect them against disease.
Two years ago, in research reported in The Plant Cell, the scientists identified the first gene in the chain of reactions that leads to the production of glycoalkaloids.
In a new study published recently in Science, they have now managed to identify nine other genes in the chain by using the original gene as a marker and comparing gene expression patterns in different parts of tomatoes and potatoes. Disrupting the activity of one of these genes, they found, prevented the accumulation of glycoalkaloids in potato tubers and tomatoes. The team then revealed the function of each of the genes and outlined the entire pathway, consisting of 10 stages, in which cholesterol molecules turn into glycoalkaloids.
An analysis of the findings produced an intriguing insight – most of the genes involved are grouped on chromosome 7 of the potato and tomato genome. Such grouping apparently prevents the plants from passing on to their offspring an incomplete glycoalkaloid pathway, which can result in the manufacture of chemicals harmful to the plants.ONLINE DECISIONMAKING
How do we decide which purchases, news stories and political candidates are worth our time, money and support? In the Age of Internet, we increasingly rely on the opinions of others as expressed through aggregated online ratings. But does the so-called wisdom of crowds produce unbiased information that helps us make better decisions? Or does subtle online peer pressure create a group-think mentality with negative consequences for markets, politics and health? To quantify how social influence affects online decision-making, Israeli and American researchers designed a large-scale experiment on a popular news aggregation website similar to and Reddit.
Their study, titled “Social Influence Bias: A Randomized Experiment,” appeared recently in the journal Science. It was conducted by Dr. Lev Muchnik of Hebrew University’s School of Business Administration, Prof. Sinan Aral at the Massachusetts Institute of Technology’s Sloan School of Management and Sean Taylor at New York University’s Stern School of Business.
Over five months, the researchers randomly assigned over 100,000 comments submitted on the site to one of three groups – a positively manipulated group (up-treated), a negatively manipulated group (down-treated) and a control group.
To reflect the natural proportions of votes on the site, they artificially up-voted (+1 rating) 4,049 comments upon their creation and down-voted (-1 rating) 1,942 others. This created a small random signal of positive or negative judgment by prior raters, while holding all other factors constant.
The experimental comments were viewed over 10 million times and rated over 300,000 times by subsequent users.
The researchers found that positively manipulating a single vote created significant bias in the rating behavior of subsequent users. This positive herding was topic dependent: in the seven most active topic categories on the website, there were significant positive herding effects for comment ratings in politics, culture and business, but no detectable herding behavior for comments in economics, IT, entertainment and general news. There was no significant negative herding in any category. The positive herding was also affected by whether individuals were viewing the opinions of friends or enemies.
Interestingly, while positive manipulation had a significant effect, negative manipulation resulted in final ratings that were not statistically different from the control group. The researchers explained that although artificial negative ratings generated negative bias in subsequent user ratings, they also stimulated a countervailing phenomenon of users fixing the inadequate negative score, thus neutralizing the effect of social influence.
Muchnik concluded:“As new communication and information processing technologies assume a more dominant role in our decisionmaking, this research has implications for electoral polling, stock market prediction, product recommendation and many other areas. To interpret collective judgment more accurately and make better use of collective intelligence, we need to adapt online rating and review technologies to account for social influence bias.”