The wandering Lilliputians in our machine: Part 1 – Exosomes
In the great adventure book by Jonathan Swift, “Gulliver’s Travels”, there all sorts of humans: Lilliputians who stood about 6 inches tall; Brobdignagians who were about 50 or 60 feet tall; Yahoos who were uncivilized and filthy and the floating-city Laputans who were both normal-sized. Then there was the civilized horse society, the Houyhnhnms.
If we scale this to the human cellular and physiological system, the Lilliputians would be the microbiome, exosomes and lysosomes; Laputans would be the various secreted proteins; Yahoos would be the bad cells and the Brobdignagians the much larger protein and tissue systems. The Houyhnhnms? Well, perhaps the “known unknowns”.
This is a short story of our exosomes, one group of wandering Lilliputians within our machine (lysosomes being the ones that stay within the cell), told here in four literary tidbits…
Tidbit 1 – Brobdignagians: The human tissue proteome
In their comprehensive pan-tissue-proteome mapping project, Uhlen, et al, in the journal Science studied 44 tissues and organ types with about 17,000 protein encoding genes, with predicted secreted proteins (n = 3171) and membrane bound proteins (n = 5570). Normalization of the most abundant genes is done using fragments per kilobase of exon per million fragments mapped (FPKM). A quick summary of the results: The housekeeping proteome had about 9000 genes; the regulatory proteome about 1500 transcription factors; the “druggable” proteome can be encoded from 618 genes with about 535 proteins that are targeted by small chemical molecules, whereas 108 proteins are targeted by biotech drugs (biologics). Within the cancer proteome 259 genes have been shown to be mutated across 21 tumor types, 290 genes have been reported as cancer driver genes across 12 tumor types, and 525 genes have been implicated in malignant transformation.
I took an arbitrary threshold (the red line in the chart below) of 10,000 in the FPKM normalization scale and looked at the tissues that were larger than this threshold. These were all affected by secreted proteins without exception (the wandering Brobdignagians).
Fig. 1: Tissue-enriched proteins are shown for 13 representative tissues or groups of tissues, stratified according to their predicted subcellular localization. Derived from Uhlen M, et al., "Tissue-based map of the human proteome", Science, 23 Jan 2015, Vol 347 Issue 6220, pp 394-403.
The tissues implicated to the right of the FPKM threshold are the toughest cancers to resolve and treat by their 5-year to 20-year survival rates (highest to lowest): Endocrine, Blood and Immune System, Liver, GI Tract, and Pancreas. This begs the question: What are the regulatory mechanisms of the secreted proteome?
Tidbit 2 – wandering Lilliputians: Autophagy and exosomes
Exosomes are 30nm to 100nm diameter “vesicles” that are created in each of the more than 30 trillion cells in our bodies; they wander around (hence “exo”) in perhaps all of the fluids outside eukaryotic cells (and prokaryotes) in the larger container which is our human house. Exosomes influence functions in cells nearby and far away by transferring bioactive lipids, nucleic acids, and proteins. Their basic structure, like viruses, are RNA and lipids. The types of extracellular vesicles are shown in the figure below:
Fig. 2: Types of extracellular vesicles. Source: Gyorgy B, et al, "Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles", Cellular and Molecular Life Sciences, (2011) 68:2667–2688, doi: 10.1007/s00018-011-0689-3
How does our cellular system maintain a clean house? "Autophagy" comes from the Greek root words auto meaning "self" and phagein meaning to "eat". This has been studied since the mid-1960s as the "garbage collector" of the cellular system where the garbage is collected using specialized bags (membranes) called autophagosomes. One researcher, Yoshinori Ohsumi, who has been studying protein degradation in special compartments called vacuoles. Professor Ohsumi won the Nobel Prize in Physiology or Medicine in 2016. Dysregulation of autophagy has been implicated in chronic diseases like Type2 Diabetes. Baixauli, et al, explored the coordinated dance between autophagy and exosomes in the journal Frontiers in Immunology (other than the autophagy-lysosomal pathways within the cell) by becoming the messenger of systemic responses to cells and tissues far away in the system.
Tidbit 3 – wandering mini-Yahoos: Malicious cancer exosomes
Exosomes contain messenger RNA (mRNA). When they carry microRNA (mRNA) from cancer cells they are not only messengers, but can also be protein factories that can regulate gene expression. Is this the soil that metastasizes cancer? Quoting Anastasiadou and Slack, in the journal Science on the properties of “malicious” exosomes: “secreted by cancer cells inhibit exosome release from the normal counterparts; may trigger extracellular acidity in which cancer cells (but not healthy cells) can survive; activate hypoxia dependent angiogenesis during tumor development; induce drug resistance of cancer cells by sequestering chemotherapeutic agents and simulate metastasis.” This is elegantly illustrated in the figure below:
Fig. 3: Malicious cancer exosomes in a mouse model. Source: O’Driscoll L. “Expanding on Exosomes and Ectosomes in Cancer”, N Engl J Med, Jun 2015; 372:2359-2362.
Tidbit 4 – the travels and stories in strange tongue: Exosomes in the gut and interaction with microbiota
The gut microbiome is a fantastic collection of hundreds to thousands of species of bacteria, archea, protozoa and fungi whose total population exceeds 100 trillion. This mucosal tract in the human body is about 300 square meters, larger than the area of an average American home.
The Smythies-es summarized the role of exosomes in the gut in the journal Frontiers in Immunology with two observations and two communication methods:
“Exosomes released from the apical or basolateral surface of the gastrointestinal epithelium may contribute to antimicrobial defenses in the gut lumen; Exosomes may be transported to the mesenteric lymph nodes (MLN) where they modulate, by the epigenetic mechanisms, host adaptive responses to luminal antigens.”
“The first communication method transmits information (“software”) reflecting the contents of the gut, obtained and transmitted by dendritic cells (DCs); the second channel transmits epigenetic instructions, in particular specific miRNAs, via exosomes to the T cell, so that it can develop the optimum molecular mechanisms or reactions (“hardware”) to process the incoming software.”
Epilogue: Sequencing and understanding exosomes
In 1712, “Dean” Jonathan Swift wrote a letter to Robert, Earl of Oxford and Mortimer and Lord High Treasurer of Great Britain about "A Proposal for Correcting, Improving and Ascertaining the English Tongue." This standardization, validation and verification process (which was then mostly about maintaining language purity) is as important to science as it is has been to language which shifts as it absorbs from and interacts with other systems.
Lord William Thomson Kelvin famously quoted in 1883 that “to measure is to know.” How do we measure exosomes? Since three primary components of exosomes are their RNA (exRNA), messenger RNA (mRNA) for gene regulation and micro RNA (miRNA) from other cells, the primary sequencing method is RNA-sequencing (RNA-seq). The process involves RNA extraction, Sequence Library preparation and Data Analysis (which is more complex than DNA sequencing). An example set of databases for exomes are Exocarta, EVpedia and Vesiclepedia.
Other than the microbiome, exosomes provide the other language that defines cross-species communication between plants, animals and humans. Let us measure exosomes thoroughly to understand this new, cross-cellular and cross-species language better! Part 2 of these travels will address the “denizen” Lilliputians (perhaps in Laputa) – the microbiome; stay tuned…
Principal Partner at Fountainbase Engineering Limited
5 年.
Director of AI and Data Engineering @ Intuit : Consumer Group AI + Growth & Engagement AI
8 年That's a good analogy.