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文章數：86 |
 永心鳳茶尾牙氣氛熱鬧嗎？》公益路最值得吃的10家餐廳｜實訪整理 |
| 休閒生活｜旅人手札 2026/04/21 21:24:15 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
身為一個熱愛美食、喜歡在城市裡挖掘驚喜的人，臺中公益路一直是我最常出沒的地方之一。這條路可說是「臺中人的美食戰場」，從精緻西餐到創意火鍋，從日式丼飯到義式早午餐，每走幾步，就會有完全不同的特色料理餐廳。 這次我特別花了一整個月，實際造訪了公益路上十間口碑不錯的餐廳。有的是網友熱推的打卡名店，也有隱藏在巷弄裡的小驚喜。我以環境氛圍、口味表現、價格CP值與再訪意願為基準，整理出這篇實測評比。希望能幫正在猶豫去哪裡吃飯的你，找到那一間「吃完會想再來」的餐廳。 評比標準與整理方向
這次我走訪的10家餐廳橫跨不同料理類型，從高質感牛排館到巷弄系早午餐，每一間都有自己獨特的風格。為了讓整體比較更客觀，我依照以下四大面向進行評比，並搭配實際用餐體驗來打分。
整體而言，我希望這份評比不只是「哪家好吃」，而是幫你在不同情境下（約會、家庭聚餐、朋友小聚、商業午餐）都能快速找到合適的選擇。畢竟，美食不只是味覺的滿足，更是一段段與朋友共享的生活記憶。 10間臺中公益路餐廳評比懶人包公益路向來是臺中人聚餐的首選地段，從火鍋、燒肉到中式料理與早午餐，每走幾步就有驚喜。以下是我實際造訪過的10間代表性餐廳清單，橫跨平價、創意、高級各路風格。
一頭牛日式燒肉｜炭香濃郁的和牛饗宴，約會聚餐首選
走在公益路上，很難不被 一頭牛日式燒肉 的木質外觀吸引。低調卻不失質感的門面，搭配昏黃燈光與暖色調的內裝，讓人一進門就感受到濃濃的日式職人氛圍。店內空間不大，但桌距規劃得宜，每桌皆設有獨立排煙設備，烤肉時完全不怕滿身油煙味。 餐點特色
一頭牛的靈魂，絕對是他們招牌的「三國和牛拼盤」。 用餐體驗整體節奏掌握得非常好。店員會在你剛想烤下一片肉時貼心遞上夾子、幫忙換烤網，讓人完全不用分心。整場用餐過程就像一場表演，從視覺、嗅覺到味覺都被滿足。 綜合評分
地址：408臺中市南屯區公益路二段162號電話：04-23206800 小結語一頭牛日式燒肉不僅是「吃肉的地方」，更像是一場五感盛宴。從進門那一刻到最後一道甜點，都能感受到他們對細節的用心。 TANG Zhan 湯棧｜文青系火鍋代表，麻香湯底與視覺美感並重
在公益路這條美食戰線上，TANG Zhan 湯棧 是讓人一眼就會想走進去的那一種。 餐點特色
湯棧最有名的當然是它的「麻香鍋」。 用餐體驗整體氛圍比一般火鍋店更有質感。 綜合評分
地址：408臺中市南屯區公益路二段248號電話：04-22580617 官網：https://www.facebook.com/TangZhan.tw/ 小結語TANG Zhan 湯棧 把傳統火鍋做出新的樣貌保留臺式鍋物的溫度，又結合現代風格與細節服務，讓吃鍋這件事變得更有品味。 如果你想找一間兼具「好吃、好拍、好放鬆」的火鍋店，湯棧會是公益路上最有風格的選擇之一。 NINI 尼尼臺中店｜明亮寬敞的義式早午餐天堂
如果說前兩間是肉食愛好者的天堂，那 NINI 尼尼臺中店 絕對是想放鬆、聊聊天的好地方。餐廳外觀以白色系與大片玻璃窗為主，陽光灑進室內，讓人一踏入就有種度假般的輕盈感。假日早午餐時段特別熱鬧，建議提早訂位。 餐點特色
NINI 的菜單融合義式與臺灣人口味，選擇多樣且份量十足。主打的 松露燉飯 濃郁卻不膩口，米芯保留微Q口感；而 香蒜海鮮義大利麵 則以新鮮白蝦、花枝與淡菜搭配微辣蒜香，口感層次豐富。 用餐體驗店內氣氛輕鬆不拘謹，無論是一個人帶電腦工作、或朋友聚餐，都能找到舒服角落。餐點上桌速度穩定，服務人員態度親切、補水與收盤都非常主動。整體節奏讓人覺得「時間變慢了」，很適合想遠離忙碌日常的人。 綜合評分
地址：40861臺中市南屯區公益路二段18號電話：04-23288498 小結語NINI 尼尼臺中店是一間能讓人放下手機、慢慢吃飯的餐廳。餐點不追求浮誇，而是以「剛剛好」的份量與風味，陪伴每個平凡午後。如果你在找一間能邊吃邊聊天、拍照也漂亮的早午餐店，NINI 會是你在公益路上最不費力的幸福選擇。 加分100%浜中特選昆布鍋物｜平價卻用心的湯頭系火鍋，家庭聚餐好選擇
在公益路這條高質感餐廳林立的戰場上，加分100%浜中特選昆布鍋物 走的是截然不同的路線。它沒有浮誇的裝潢、也沒有高價位的套餐，但靠著實在的湯頭與親切的服務，默默吸引許多回頭客。每到用餐時間，總能看到家庭或情侶三兩成群地圍著鍋邊聊天。 餐點特色
主打 北海道浜中昆布湯底，湯頭清澈卻不單薄，越煮越能喝出海藻與柴魚的自然香氣。 用餐體驗整體氛圍偏家庭取向，桌距寬敞、座位舒適，帶小孩來也不覺擁擠。店員態度親切，補湯、收盤都很勤快，給人一種「被照顧著」的安心感。 綜合評分
地址：403臺中市西區公益路288號電話：0910855180 小結語加分100%浜中特選昆布鍋物是一間「不浮誇、但會讓人想再訪」的火鍋店。它不追求豪華擺盤，而是用最簡單的湯頭與新鮮食材，傳遞出家常卻不平凡的溫度。 印月餐廳｜中式料理的藝術演繹，宴客與家庭聚會首選
說到臺中公益路的中式料理代表，印月餐廳 絕對是榜上有名。這間開業多年的餐廳以「中菜西吃」的概念聞名，把傳統中式料理以現代手法重新詮釋。從建築外觀到餐具擺設，每個細節都散發著低調的典雅氣息。 餐點特色
印月最令人印象深刻的是他們將傳統中菜融入創意手法。 用餐體驗服務方面完全對得起餐廳的高級定位。從入座、點餐到上菜節奏，都拿捏得恰如其分。每道菜都會有服務人員細心介紹食材與吃法，讓人感受到「被款待」的尊榮感。 綜合評分
地址：408臺中市南屯區公益路二段818號電話：0422511155 小結語印月餐廳是一間「不只吃飯，更像品味生活」的地方。 KoDō 和牛燒肉｜極致職人精神，專為儀式感與頂級味覺而生
若要形容 KoDō 和牛燒肉 的用餐體驗，一句話足以總結——「像在欣賞一場關於肉的表演」。 餐點特色
這裡主打 日本A5和牛冷藏肉，以「精切厚燒」的方式呈現。 用餐體驗KoDō 的最大特色是「儀式感」。 綜合評分
地址：403臺中市西區公益路260號電話：0423220312 官網：https://www.facebook.com/kodo2018/ 小結語KoDō 和牛燒肉不是日常餐廳，而是一場體驗。 永心鳳茶｜在茶香裡用餐的優雅時光，臺味早午餐的新詮釋
走進 永心鳳茶公益店，彷彿進入一間有氣質的茶館。 餐點特色
永心鳳茶的餐點結合中式靈魂與西式擺盤，無論是「炸雞腿飯」還是「紅玉紅茶拿鐵」，都能讓人感受到熟悉卻不平凡的味道。 用餐體驗店內服務人員態度溫和，對茶品介紹詳盡。上餐節奏剛好，不急不徐。 綜合評分
地址：40360臺中市西區公益路68號三樓(勤美誠品)電話：0423221118 小結語永心鳳茶讓人重新定義「臺味」。 三希樓｜老饕級江浙功夫菜，穩重又帶人情味的中式饗宴
位於公益路上的 三希樓 是許多臺中老饕的口袋名單。 餐點特色
三希樓的菜色以 江浙與港式料理 為主，兼顧傳統與現代風味。 用餐體驗三希樓的服務給人一種老派但貼心的感覺。 綜合評分
地址：408臺中市南屯區公益路二段95號電話：0423202322 官網：https://www.sanxilou.com.tw/ 小結語三希樓是一間「吃得出功夫」的餐廳。 一笈壽司｜低調奢華的無菜單日料，職人手藝詮釋旬味極致
在熱鬧的公益路上，一笈壽司 低調得幾乎不顯眼。 餐點特色
一笈壽司採 Omakase（無菜單料理） 形式，每一餐都由主廚根據當日食材設計。 用餐體驗整場用餐約90分鐘，節奏緩慢但沉穩。 綜合評分
地址：408臺中市南屯區公益路二段25號電話：0423206368 官網：https://www.facebook.com/YIJI.sushi/ 小結語一笈壽司是一間真正讓人「放慢呼吸」的餐廳。 茶六燒肉堂｜人氣爆棚的和牛燒肉聖地，肉香與幸福感同時滿分
若要票選公益路上「最難訂位」的餐廳，茶六燒肉堂 絕對名列前茅。 餐點特色
茶六主打 和牛燒肉套餐，價格約落在 $700–$1000 間，份量與品質兼具。 用餐體驗茶六的服務效率相當高。店員親切、換網勤快、補水速度快，整場用餐流程流暢無壓力。 綜合評分
地址：403臺中市西區公益路268號電話：0423281167 官網：https://inline.app/booking/-L93VSXuz8o86ahWDRg0:inline-live-karuizawa/-LUYUEIOYwa7GCUpAFWA 小結語茶六燒肉堂用「穩定品質＋輕奢氛圍」抓住了臺中年輕族群的心。 吃完10家公益路餐廳後的心得與結語吃完這十家餐廳後，臺中公益路不只是一條美食街，而是一段生活風景線。 有的餐廳講究細膩與儀式感，像 一頭牛日式燒肉 與 一笈壽司，讓人感受到食材最純粹的美好 有的則以親切與溫度打動人心，像 加分昆布鍋物、永心鳳茶，讓人明白吃飯不只是為了飽足，而是一種被照顧的幸福。 而像茶六燒肉堂、TANG Zhan 湯棧 這類人氣名店，則用穩定的品質與熱絡的氛圍，成為許多臺中人心中「想吃肉就去那裡」的代名詞。 這十家店，構成了公益路最動人的縮影 有華麗的，也有溫柔的；有傳統的，也有創新的。 每一家都在自己的風格裡發光，讓人吃到的不只是料理，而是一種生活的溫度與節奏。 對我而言，這不僅是一場美食旅程，更是一趟關於「臺中味道」的回憶之旅。 FAQ：關於臺中公益路美食常見問題Q1：公益路哪一區的餐廳最集中？ Q2：需要提前訂位嗎？ 最後的話若要用一句話形容這趟美食之旅，我會說： 加分100%浜中特選昆布鍋物網路評價符合期待嗎？ 如果你也和我一樣喜歡用味蕾探索一座城市，那就把這篇公益路美食攻略收藏起來吧。永心鳳茶婚前派對適合嗎？ 無論是約會、慶生、家庭聚餐，或只是想犒賞一下辛苦的自己——這條路上永遠會有一間剛剛好的餐廳在等你。TANG Zhan 湯棧會太油嗎？ 下一餐，不妨從這10家開始。三希樓慶生氣氛夠嗎？ 打開手機、約上朋友，讓公益路成為你生活裡最容易抵達的小確幸。三希樓值得排隊嗎？ 如果你有私心愛店，也歡迎留言分享，永心鳳茶再訪意願高嗎？ 你的推薦，可能讓我下一趟美食旅程變得更精彩。KoDō 和牛燒肉價位會不會太高？ Dr. Eric A. Vitriol. Credit: Michael Holahan, Augusta University A new “image analysis pipeline” is giving scientists rapid new insight into how disease or injury have changed the body, down to the individual cell. It’s called TDAExplore, which takes the detailed imaging provided by microscopy, pairs it with a hot area of mathematics called topology, which provides insight on how things are arranged, and the analytical power of artificial intelligence to give, for example, a new perspective on changes in a cell resulting from ALS and where in the cell they happen, says Dr. Eric Vitriol, cell biologist and neuroscientist at the Medical College of Georgia. It is an “accessible, powerful option” for using a personal computer to generate quantitative — measurable and consequently objective — information from microscopic images that likely could be applied as well to other standard imaging techniques like X-rays and PET scans, they report in the journal Patterns. “We think this is exciting progress into using computers to give us new information about how image sets are different from each other,” Vitriol says. “What are the actual biological changes that are happening, including ones that I might not be able to see, because they are too minute, or because I have some kind of bias about where I should be looking.” At least in the analyzing data department, computers have our brains beat, the neuroscientist says, not just in their objectivity but in the amount of data they can assess. Computer vision, which enables computers to pull information from digital images, is a type of machine learning that has been around for decades, so he and his colleague and fellow corresponding author Dr. Peter Bubenik, a mathematician at the University of Florida and an expert on topological data analysis, decided to partner the detail of microscopy with the science of topology and the analytical might of AI. Topology and Bubenik were key, Vitriol says. Topology is “perfect” for image analysis because images consist of patterns, of objects arranged in space, he says, and topological data analysis (the TDA in TDAExplore) helps the computer also recognize the lay of the land, in this case where actin — a protein and essential building block of the fibers, or filaments, that help give cells shape and movement — has moved or changed density. It’s an efficient system, that instead of taking literally hundreds of images to train the computer how to recognize and classify them, it can learn on 20 to 25 images. Part of the magic is the computer is now learning the images in pieces they call patches. Breaking microscopy images down into these pieces enables more accurate classification, less training of the computer on what “normal” looks like, and ultimately the extraction of meaningful data, they write. No doubt microscopy, which enables close examination of things not visible to the human eye, produces beautiful, detailed images and dynamic video that are a mainstay for many scientists. “You can’t have a college of medicine without sophisticated microscopy facilities,” he says. But to first understand what is normal and what happens in disease states, Vitriol needs detailed analysis of the images, like the number of filaments; where the filaments are in the cells — close to the edge, the center, scattered throughout — and whether some cell regions have more. The patterns that emerge in this case tell him where actin is and how it’s organized — a major factor in its function — and where, how and if it has changed with disease or damage. As he looks at the clustering of actin around the edges of a central nervous system cell, for example, the assemblage tells him the cell is spreading out, moving about, and sending out projections that become its leading edge. In this case, the cell, which has been essentially dormant in a dish, can spread out and stretch its legs. Some of the problems with scientists analyzing the images directly and calculating what they see include that it’s time-consuming and the reality that even scientists have biases. As an example, and particularly with so much action happening, their eyes may land on the familiar, in Vitriol’s case, that actin at the leading edge of a cell. As he looks again at the dark frame around the cell’s periphery clearly indicating the actin clustering there, it might imply that is the major point of action. “How do I know that when I decide what’s different that it’s the most different thing or is that just what I wanted to see?” he says. “We want to bring computer objectivity to it and we want to bring a higher degree of pattern recognition into the analysis of images.” AI is known to be able to “classify” things, like recognizing a dog or a cat every time, even if the picture is fuzzy, by first learning many millions of variables associated with each animal until it knows a dog when it sees one, but it can’t report why it’s a dog. That approach, which requires so many images for training purposes and still doesn’t provide many image statistics, does not really work for his purposes, which is why he and his colleagues made a new classifier that was restricted to topological data analysis. The bottom line is that the unique coupling used in TDAExplore efficiently and objectively tells the scientists where and how much the perturbed cell image differs from the training, or normal, image, information which also provides new ideas and research directions, he says. Back to the cell image that shows the actin clustering along its perimeter, while the “leading edge” was clearly different with perturbations, TDAExplore showed that some of the biggest changes actually were inside the cell. “A lot of my job is trying to find patterns in images that are hard to see,” Vitriol says, “Because I need to identify those patterns so I can find some way to get numbers out of those images.” His bottom lines include figuring out how the actin cytoskeleton, which the filaments provide the scaffolding for and which in turn provides support for neurons, works and what goes wrong in conditions like ALS. Some of those machine learning models that require hundreds of images to train and classify images don’t describe which part of the image contributed to the classification, the investigators write. Such huge amounts of data that need analyzing and might include like 20 million variables, require a supercomputer. The new system instead needs comparatively few high-resolution images and characterizes the “patches” that led to the selected classification. In a handful of minutes, the scientist’s standard personal computer can complete the new image analysis pipeline. The unique approach used in TDAExplore objectively tells the scientists where and how much the perturbed image differs from the training image, information which also provides new ideas and research directions, he says. The ability to get more and better information from images ultimately means that information generated by basic scientists like Vitriol, which often ultimately changes what is considered the facts of a disease and how it’s treated, is more accurate. That might include being able to recognize changes, like those the new system pointed out inside the cell, that have been previously overlooked. Currently, scientists apply stains to enable better contrast then use software to pull out information about what they are seeing in the images, like how the actin is organized into bigger structure, he says. “We had to come up with a new way to get relevant data from images and that is what this paper is about.” Reference: “TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning” by Parker Edwards, Kristen Skruber, Nikola Milicevic, James B. Heidings, Tracy-Ann Read, Peter Bubenik and Eric A. Vitriol, 12 October 2021, Patterns. DOI: 10.1016/j.patter.2021.100367 The published study provides all the pieces for other scientists to use TDAExplore. The research was supported by the National Institutes of Health. The LSD algorithm spotlights actively responding lung cells (green). Credit: Matthias Schmitt, Gargiulo Lab, Max Delbrück Center A new computer program allows scientists to design synthetic DNA segments that indicate, in real time, the state of cells. Reported by the Gargiulo lab in Nature Communications, it will be used to screen for anti-cancer or viral infections drugs, or to improve gene and cell-based immunotherapies. All the cells in our body have the same genetic code, and yet they can differ in their identities, functions, and disease states. Telling one cell apart from another in a simple manner, in real time, would prove invaluable for scientists trying to understand inflammation, infections or cancers. Now, scientists at the Max Delbrück Center have created an algorithm that can design such tools that reveal the identity and state of cells using segments of DNA called “synthetic locus control regions” (sLCRs). They can be used in a variety of biological systems. The findings, by the lab of Dr. Gaetano Gargiulo, head of the Molecular Oncology Lab, are reported in Nature Communications. “This algorithm enables us to create precise DNA tools for marking and studying cells, offering new insights into cellular behaviors,” says Gargiulo, senior author of the study. “We hope this research opens doors to a more straightforward and scalable way of understanding and manipulating cells.” This effort began when Dr. Carlos Company, a former graduate student at the Gargiulo lab and co-first author of the study, started to invest energy into making the design of the DNA tools automated and accessible to other scientists. He coded an algorithm that can generate tools to understand basic cellular processes as well as disease processes such as cancers, inflammation, and infections. “This tool allows researchers to examine the way cells transform from one type to another. It is particularly innovative because it compiles all the crucial instructions that direct these changes into a simple synthetic DNA sequence. In turn, this simplifies studying complex cellular behaviors in important areas like cancer research and human development,” says Company. Algorithm to make a tailored DNA tool The computer program is named “logical design of synthetic cis-regulatory DNA” (LSD). The researchers input the known genes and transcription factors associated with the specific cell states they want to study, and the program uses this to identify DNA segments (promoters and enhancers) controlling the activity in the cell of interest. This information is sufficient to discover functional sequences, and scientists do not have to know the precise genetic or molecular reason behind a cell’s behavior; they just have to construct the sLCR. The program looks within the genomes of either humans or mouse to find places where transcription factors are highly likely to bind, says Yuliia Dramaretska, a graduate student at the Gargiulo lab and co-first author. It spits out a list of 150-basepair long sequences that are relevant, and which likely act as the active promoters and enhancers for the condition being studied. “It’s not giving a random list of those regions, obviously,” she says. “The algorithm is actually ranking them and finding the segments that will most efficiently represent the phenotype you want to study.” Like a lamp inside the cells Scientists can then make a tool, called a “synthetic locus control region” (sLCR), which includes the generated sequence followed by a DNA segment encoding a fluorescent protein. “The sLCRs are like an automated lamp that you can put inside of the cells. This lamp switches on only under the conditions you want to study,” says Dr. Michela Serresi, a researcher at the Gargiulo lab and co-first author. The color of the “lamp” can be varied to match different states of interest, so that scientists can look under a fluorescence microscope and immediately know the state of each cell from its color. “We can follow with our eyes the color in a petri dish when we give a treatment,” Serresi says. The scientists have validated the utility of the computer program by using it to screen for drugs in SARS-CoV-2 infected cells, as published last year in “Science Advances.” They also used it to find mechanisms implicated in brain cancers called glioblastomas, where no single treatment works. “In order to find treatment combinations that work for specific cell states in glioblastomas, you not only need to understand what defines these cell states, but you also need to see them as they arise,” says Dr. Matthias Jürgen Schmitt, the researcher at the Gargiulo lab and co-first author, who used the tools in the lab to showcase their value. Now, imagine immune cells engineered in the lab as a gene therapy to kill a type of cancer. When infused into the patient, not all these cells will work as intended. Some will be potent and while others may be in a dysfunctional state. Funded by an European Research Council grant, the Gargiulo lab will be using this system to study the behavior of these delicate anti-cancer cell-based therapeutics during manufacturing. “With the right collaborations, this method holds potential for advancing treatments in areas like cancer, viral infections, and immunotherapies,” Gargiulo says. Reference: “Logical design of synthetic cis-regulatory DNA for genetic tracing of cell identities and state changes” by Carlos Company, Matthias Jürgen Schmitt, Yuliia Dramaretska, Michela Serresi, Sonia Kertalli, Ben Jiang, Jiang-An Yin, Adriano Aguzzi, Iros Barozzi and Gaetano Gargiulo, 5 February 2024, Nature Communications. DOI: 10.1038/s41467-024-45069-6 Worms exposed to a cannabinoid show heightened food preferences, displaying behavior akin to humans experiencing “the munchies,” according to a study led by neuroscientist Shawn Lockery at the University of Oregon. The research highlights the potential of using worms as a model for understanding the endocannabinoid system, a signaling network that regulates various body functions such as appetite, mood, and pain sensation. By understanding this system better, scientists could potentially develop drugs that target specific aspects of the endocannabinoid system, leading to more effective treatments with fewer side effects. Worms could be a good research model for understanding the endocannabinoid system—and possibly developing better drugs. Worms exposed to a cannabinoid exhibit behavior similar to humans experiencing “the munchies,” suggesting that they could be a useful model for studying the endocannabinoid system and potentially developing better drugs with fewer side effects. If you give a worm some weed, he might just need a snack to go with it. Worms exposed to a cannabinoid become even more interested in the kind of food that they’d already prefer, new University of Oregon (UO) research shows. The effect is similar to craving potato chips and ice cream after a few puffs of marijuana—a phenomenon known scientifically as “hedonic feeding,” but colloquially called “the munchies.” The study, led by neuroscientist Shawn Lockery in the University of Oregon College of Arts and Sciences, points to worms as a useful tool for understanding more about the many roles that cannabinoids naturally play in the body. And it could help researchers develop better drugs that target this system. He and his team published their findings on April 20 in the journal Current Biology. Cannabinoids and Food Preferences The endocannabinoid system is a far-reaching signaling network that helps regulate key body systems like appetite, mood, and pain sensation. Molecules called endocannabinoids send chemical messages by interacting with cannabinoid receptors, special proteins that are sprinkled throughout the body and brain. Normally, these messages help keep different body systems in balance. But molecules in marijuana—like THC—also interact with cannabinoid receptors, making you feel high after partaking and causing other effects, too. When Lockery and his team started this research, marijuana had just been legalized recreationally in Oregon, “so we thought, well heck, let’s just try this!” said Lockery. “We thought it would be amusing if it worked.” Image of a worm that is genetically engineered so that certain neurons and muscles are fluorescent. Green dots are neurons that respond to cannabinoids. Magenta dots are other neurons. Credit: Stacy Levichev The idea wasn’t totally out of left field. Research in the Lockery lab focuses on the neurobiology of decision-making, using a species of tiny bacteria-eating worms called C. elegans that eats bacteria as a simple system to test hypotheses. He often uses food choice experiments, tempting the animals with bacterial blends to see which they prefer under different conditions. To see how marijuana-like substances might affect the worms’ food preferences, Lockery’s team soaked them in anandamide. Anandamide is an endocannabinoid, a molecule made by the body that activates the body’s cannabinoid receptors. Then, they put the worms into a T-shaped maze. On one side of the maze was high-quality food; on the other side, lower-quality food. Previous research has shown that on high-quality food sources, the worms grow quickly; on lower-quality ones, they grow more slowly. Worms also find high-quality food more desirable, and preferentially seek it out. In the T-maze experiment, under normal conditions, the worms indeed preferred the higher-quality food. But when soaked in anandamide, that preference became even stronger — they flocked to the high-quality food and stayed there longer than they usually did. “We suggest that this increase in existing preference is analogous to eating more of the foods you would crave anyway,” Lockery said. “It’s like choosing pizza versus oatmeal.” Higher-quality food might call to mind a nutritious spread of fruits, veggies, and whole grains. But evolutionarily, “higher quality” food is the kind packed with calories to ensure survival. So in this case, “higher quality” worm food is more like human junk food—it packs in a lot of calories quickly. “The endocannabinoid system helps make sure that an animal that’s starving goes for high fat and sugar content food,” Lockery said. It’s one reason why, after consuming cannabis, you’re more likely to reach for chocolate pudding, but not necessarily hungry for a salad. Anandamide’s Effects on Neural Sensitivity and Preferences In follow-up experiments, Lockery’s team was able to identify some of the neurons affected by anandamide. Under the influence, these neurons became more sensitive to the smell of higher quality food, and less sensitive to the smell of lower-quality food. The results drive home just how old the endocannabinoid system is, evolutionarily speaking. Worms and humans last shared a common ancestor more than 600 million years ago, yet cannabinoids affect our food preferences in a similar way. “It’s a really beautiful example of what the endocannabinoid system was probably for at the beginning,” Lockery said. The similarity in response between worms and humans also suggests that worms can be a useful model for studying the endocannabinoid system. In particular, one current limitation of tapping into the medicinal properties of cannabinoids is their broad-ranging effects. Cannabinoid receptors are found throughout the body, so a drug targeting these receptors could help the problem at hand, but might also have lots of undesired side effects. For instance, smoking weed might relieve your pain, but could also make it hard to focus on work. But the other nearby proteins that are also involved in the cascade of chemical messages varies depending on the body system at play. So better drugs could aim at these other proteins, narrowing the effects of the drug. Because scientists know so much about worm genetics, they’re are a good study system for picking apart these kinds of pathways, Lockery suggests. “The ability to rapidly find signaling pathways in the worm could help identify better drug targets, with fewer side effects.” For more on this research, see Worms Get the Munchies From Cannabinoids Just Like Humans. Reference: “The conserved endocannabinoid anandamide modulates olfactory sensitivity to induce hedonic feeding in C. elegans” by Anastasia Levichev, Serge Faumont, Rachel Z. Berner, Zhifeng Purcell, Amanda M. White, Kathy Chicas-Cruz and Shawn R. Lockery, 20 April 2023, Current Biology. DOI: 10.1016/j.cub.2023.03.013 Funding: National Institute on Drug Abuse RRG455KLJIEVEWWF |
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