Did Someone Say Cheese?

Those funky cheese smells allow microbes to 'talk' to and feed each other

Oct 2020, phys.org

https://phys.org/news/2020-10-funky-cheese-microbes.html

The more you learn, the more complicated it gets. So we know that the funky cheese smell of isovaleric acid is produced by bacteria, and we know that (some) cheese has fungus growing on it. But now we're told that they're all talking to each other, using other odor molecules.

The "cheese microbiome." Cheese doesn't house only one bacteria species, and it doesn't make only one smell, although isovaleric acid is a pretty good representative. There's a whole cheese microbiome, made of bacteria, yeast and fungi. Each one of these organisms secretes goop that digests their food, in this case that's the cheese. Their goop is kind of like making the whole world your stomach, where your digestive juices aid your digesting al fresco. As their external digestion approach does its thing, the target nutrients get broken down, and a by-product of that breakdown are odorous volatile organic compounds. 

What these researchers have discovered is that fungi also release VOCs, but instead of being an important part of the smell of cheese, they communicate to bacteria. Some bacteria accelerate their growth in the presence of the right fungi-gas. Others get real shy and shut down. These fungus VOCs do real-time genetic modification on the bacteria, changing the way they metabolize nutrients. They also eat the VOCs themselves. They eat smells. We can't eat smells. 

Anyway, the cheese microbiome now contains a VOC-ome sub-component, and this study hints that one day we may be looking very carefully at these VOC-omes, especially in the bio-factories of the future. 

via Tufts University: Casey M. Cosetta et al, Fungal volatiles mediate cheese rind microbiome assembly, Environmental Microbiology (2020). DOI: 10.1111/1462-2920.15223

Artificial Olfactory Perception and the Olfactome

Chemical informatics, machine learning and the indispensable fruit-fly, Drosophila melanogaster have been used by researchers at University of California Riverside to predict odor perception. 

Olfactory prediction is kind of a holy grail of sensory perception. Sounds sus. Let's get into the data.

image credit: Diatom, by Dr. Jan Michels for Nikon Small World 2020

Using artificial intelligence to smell the roses

Aug 2020, phys.org

https://phys.org/news/2020-07-artificial-intelligence-roses.html

First sentence they're referencing Asifa Majid. That's a great start. Her work shows us that culture, language and experience influence individual odor perception. Nonetheless, the search for the human odor code continues.

After reducing a larger dataset of 84 olfactory receptors and 54 allelic variants (138 total), they took 34 receptors, each of which is controlled by a single gene, and trained machines to predict their descriptors. The descriptors, or "the words we would use to describe the smell," came from the Vosshall Keller Rockefeller University 2016 lexicon. They've got about 170 odorants, working on 34 receptors. 

Remember that each odor receptor gene can be activated by a number of chemicals, sometimes by only one, but usually by more than one. This is what makes things complicated. Olfaction is a combinatorial affair that breaks down at the granular level.

And they made a model for each receptor, 34 different models, and fed those models the odorants. They found that you could predict chemical properties of the molecules that match each receptor tested. So now, we can use the 450,000 library of chemicals, run them through each of the 36 artificial receptors, and predict what those receptors would perceive.

Figure 5A: Few Key ORs or Chemical Features Sensibly Cluster the Perceptual Descriptors

(A) Dendrogram representation of the Euclidean distances among perceptual descriptors based on overlap of perceptual response data (% Usage) from chemicals in the ATLAS study.

(B) Dendrogram from the top five ORs picked per perceptual descriptor.

(C) Dendrogram created from five randomly chosen ORs per perceptual descriptor.

(D) Dendrogram from the five best overall predictors including OR and chemical features per perceptual descriptor. Clustering is hierarchical and based on Euclidean distance (A) or the Jaccard distance (B–D). Cluster number (colored branches) inferred from gap statistic across bootstrap samples. [find the pdf for fine-resolution]

-source

I think, and I could be wrong, but it seems the big deal here is that they made a model for each receptor, instead of just making one model for all receptors. Whereas others have created an n-dimensional predictive space to collapse the behemoth of the chemosphere into a single equation, this team just reverse-engineered the receptors themselves.

They haven't found the odor code, but they did write 34 of them. We have hundreds of olfactory receptors. That's not everything, but we are definitely getting there.

What it CAN do? It can help us discover new chemicals, and also to discover substitutes for other chemicals that are expensive, rare, or ethically-troublesome (fear-pheromones from tortured cats for example).

What it CAN'T do? It can't predict how an odor will smell to you, as an individual. It can approximate, however, and pretty good. They mention only getting 20% of the human olfactome, or human olfactory receptor repertoire.

via UC Riverside: Joel Kowalewski et al. Predicting Human Olfactory Perception from Activities of Odorant Receptors, iScience (2020). DOI: 10.1016/j.isci.2020.101361

http://dx.doi.org/10.1016/j.isci.2020.101361

Post Script:

They mention something called the ATLAS dataset, but I don't know what that is, other than a proprietary data analysis software. Maybe it's their own dataset through ATLAS?

And for fun, I'll report that they do mention "substantive portion of odor identity arises early in the processing stream" which is a good way of describing the the two-layer perception process of olfaction.

The second layer, and this is the one that Asifa Majid tells us is influenced by culture, experience, and language: "It is likely that the remaining portion depends on experience-dependent modulation, supporting a downstream model with reliance on distributed neuronal networks for human perceptual coding."

Further: "Unlike the retinotopic and tonotopic patterning observed in the visual and auditory cortices, representing spatiotemporal properties of visual and auditory stimuli as they are processed at sensory neurons, piriform activity appears randomly distributed, without a clear mapping of physicochemical features (Stettler and Axel, 2009)."

Interesting: "In our analyses, the OR specialized for musk was not a top candidate for

musk predictions but contributed strongly to predictions of 'sweaty.'"

Perhaps because the model isn't "smelling" it among other calculated fragrant mixtures such as perfumes, but rather "in the wild?" 

Post Post Script:

Can't talk about the odor code without mentioning code smell, a term for when something is wrong with your code, but we're not sure what it is. 

https://en.wikipedia.org/wiki/Code_smell

Also, going deep on the topic here:

The Dream of Olfaction Prediction

http://www.limbicsignal.com/2020/01/the-dream-of-olfaction-prediction.html