A nanoscale, multi-parametric flow cytometry based platform to study mitochondrial heterogeneity and mitochondrial DNA dynamics

https://www.nature.com/articles/s42003-019-0513-4

Julie A. MacDonald, Alisha M. Bothun, Sofia N. Annis, Hannah Sheehan, Somak Ray, Yuanwei Gao, Alexander R. Ivanov, Konstantin Khrapko, Jonathan L. Tilly, and Dori C. Woods

• The authors describe a new technology for isolation and analysis of single mitochondria using flow cytometry, called "fluorescence-activated mitochondria sorting" (FAMS).
• Mitochondria isolated from liver tissue exhibited intact outer and inner membranes, and cristae structure, when evaluated by electron microscopy.
• Staining samples with the DNA stain DAPI, the authors found correlation between side-scatter of organelles and DNA content, suggesting that larger organelles, containing larger amounts of DNA, have larger side-scatter. 
• The authors used the membrane potential sensor dye JC-1 to categorise mitochondria into high/low membrane potential populations. They found that whilst both low and high-membrane potential populations generated ATP when provided with ADP, high-membrane potential mitochondria produced approximately x6 more ATP, and approximately x3 more Mt-ND1 and Mt-Nd4, than low-membrane potential mitochondria. The low-membrane potential mitochondria had ~2.5x lower FSC-PMT, potentially indicating their smaller size [Question: do differences in mitochondrial size confound the inference of differential membrane potential using the JC-1 dye, due to the surface area to volume ratio affecting the aggregation rate? If smaller mitochondria have a higher surface area to volume ratio then perhaps the true difference in mitochondrial membrane potential is even larger.]
• The authors generated mixed samples for two mouse strains, with two different mtDNA haplotypes, and performed single-molecule PCR. Of 54 organelles measured, 2 showed mixtures of mtDNA sequences, suggesting a relatively low rate of artificial fusion of mitochondrial in mixed samples.
• The authors measured the median number of mtDNAs per mitochondrion was 3, ranging from 1 to 22 molecules per sorted organelle.
• The authors used beads to calibrate FSC-PMT and SSC to define two gates: ~0.22-0.5 um, and 0.5-1um, and found that the small gate had approximately 1-2 mtDNAs per organelle, whereas the large gate had 6.5-7.5 mtDNAs per organelle. 

Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007023

Hanne Hoitzing, Payam A. Gammage, Lindsey Van Haute, Michal Minczuk, Iain G. Johnston, and Nick S. Jones


Background on mitochondrial DNA dynamics and control

Mitochondria have their own genomes (mtDNAs). These genomes can mutate upon division and at any one given time, mixture of normal (wildtype, w) and mutated (m) mtDNA can exist within a cell. Heteroplasmy is defined as the fraction of mutant mtDNA molecules.

The birth and death of mtDNAs is a stochastic process, their numbers fluctuating over time. Some kind of feedback control must be present, as mtDNA numbers in normal healthy cells tend to remain within certain bounds.

Treatments exist to reduce the load of mutant mtDNAs inside cells. For example, nucleases which are targeted to the specific sequence of a mutant mtDNA can be introduced in cells. They will bind to these mutant sequences and cut the (though off-target cutting of the wildtype genomes is a problem).

Thinking about controlling levels of mtDNA gives rise to various questions:

•  What exactly is this feedback control? What is the quantity that is being controlled (e.g. is it total mtDNA copy number, or is it the overall energy level)? 
• How does the type of control influence heteroplasmy levels? Does one type of control lead to faster mutant accumulation than another?
• How does the cell choose a particular feedback control? Does it do this randomly or does it minimize some 'cost function'?
• Can we somehow interfere with the cellular feedback control to reduce mutant loads?

 
Paper results

This paper investigates these questions a bit more closely.  Some of the main findings are:

• Many different forms of feedback control (e.g. linear, quadratic, etc..) can give rise to similar mtDNA dynamics and heteroplasmy dynamics.
• What makes all the difference, however, is which quantity is being controlled (rather than how it is controlled). Is it total copy number (w + m)? Is it only the number of wildtypes (w)? Is it some more general linear combination (w + 𝛿 m)?
• The more strongly one species is controlled, the more control is lost over the other
• A mitochondrial cost function is introduced, and it is shown that it can actually be more expensive for a cell to contain a mixture of mutant and wildtype molecules, rather than only mutants!
• A control based on energy levels seems to make more sense than blindly controlling total mtDNA copy number. This means that if mutants produce less energy, the quantity being controlled is (w + 𝛿 m) with 𝛿 < 1.
• Variance of mtDNA dynamics is important! An increase in variance in mutant and/or wildtype copy numbers (which will always occur over time) can lead to an increase in cost of maintaining a tissue
• Gene therapies specifically targeting mutant mtDNAs can successfully lower heteroplasmy levels, but this becomes hard when high tissue heteroplasmy levels are caused by only a small fraction of cells (i.e. a few cells have very high heteroplasmy levels and most cells are ok). Again, it's the mtDNA variance that's important!
• Long and weak gene therapies seem to reach lower overall heteroplasmy levels compared to short and strong therapies.

Mitochondrial Network State Scales mtDNA Genetic Dynamics

https://doi.org/10.1534/genetics.119.302423

Juvid Aryaman, Charlotte Bowles, Nick S. Jones and Iain G. Johnston

(Mirrored from Evolution, Energetics & Noise)

Mitochondrial DNA (mtDNA) populations within our cells encode vital energetic machinery. MtDNA is housed within mitochondria, cellular compartments lined by two membranes, that lead a very dynamic life. Individual mitochondria can fuse when they meet, and fused mitochondria can fragment to become individual smaller mitochondria, all the while moving throughout the cell. The reasons for this dynamic activity remain unclear (we’ve compared hypotheses about them before here and here, with blog articles here). But what influence do these physical mitochondrial dynamics have on the genetic composition of mtDNA populations?

MtDNA populations can, naturally or as a result of gene therapies, consist of a mixture of different mtDNA types. Typically, different cells will have different proportions of, say, type A and type B. For example, one cell may be 20% type A, another cell may be 40% type A, and a third may be 70% type A. This variability matters because when a certain threshold (often around 60%) is crossed for some mtDNA types, we get devastating diseases.

We previously showed mathematically (blog) and experimentally (blog) that this cell-to-cell variability in mtDNA proportions (often called “heteroplasmy variance” and sometimes referred to via the “mtDNA bottleneck”) is expected to increase linearly over time. However, this analysis pictured mtDNAs as individual molecules, outside of their mitochondrial compartments. When mitochondria fuse to form larger compartments, their mtDNA is more protected: smaller mitochondria (and their internal mtDNA) are subject to greater degradation. More degradation means more replication, and more opportunities for the fraction of a particular type of mtDNA to change per unit time. In a new paper here in Genetics, we show that this protection can dramatically influence cell-to-cell mtDNA variability. Specifically, the rate of heteroplasmy variance increase is scaled by the proportion of mitochondria that exist in a fragmented state. (It turns out that it's the proportion of mitochondria that are fragmented that's important -- not whether the rate of fission-fusion is fast or slow).

This has knock-on effects for how the cell can best get rid of low-quality mutant mtDNA. In particular, if mitochondria are allowed to fuse based on their quality (“selective fusion”), we show that intermediate rates of fusion are best for removing mutants. Too much fusion, and all mtDNA is protected; too little, and good mtDNA cannot be sorted from bad mtDNA using the mitochondrial network. This mechanism could help explain why we see different levels of mitochondrial fusion in different conditions. More broadly, this link between mitochondrial physics and genetics (which we’ve also speculated about here (blog) and here) suggests one way that selective pressures and tradeoffs could influence mitochondrial dynamics, giving rise to the wide variety of behaviours that remain unexplained. Juvid, Nick, and Iain

Respiratory Syncytial Virus co-opts host mitochondrial function to favour infectious virus production

https://elifesciences.org/articles/42448

MengJie Hu, Keith E Schulze, Reena Ghildyal, Darren C Henstridge, Jacek L Kolanowski, Elizabeth J New, Yuning Hong, Alan C Hsu, Philip M Hansbro, Peter AB Wark, Marie A Bogoyevitch, David A Jans

• Respiratory syncytial virus (RSV) is responsible for more deaths each year than influenza. Here, the authors investigate how RSV hijacks mitochondria for viral production.
• The authors suggest that RSV induces perinuclear clustering of mitochondria, reduction in mitochondrial respiration, impaired mitochondrial membrane potential, and increased reactive oxygen species production. 
• The authors find that inhibiting the dynein motor protein, or inhibiting mitochondrial ROS production, suppresses RSV production in vivo.

RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues

https://science.sciencemag.org/content/364/6444/eaaw0726?ijkey=747d2d8299edcfd1fdfe566522ccbcf3ba841b1f&keytype2=tf_ipsecsha

Keren Yizhak, François Aguet, Jaegil Kim, Paz Polak, Kristin G. Ardlie, Gad Getz and others

• The authors study the RNA sequence of >6000 samples across 29 normal tissues (using a method they call RNA-MuTect), and find multiple macroscopic somatic mutations in normal tissues.
• Genes which are highly expressed may be investigated for evidence of somatic mosaicism
• Sun-exposed skin, esophagus, and lung have a higher mutation load than other tested tissues, suggesting an evironmental role
• Mutation burden was associated with age and tissue-specific proliferation rate
• Normal tissues were found to harbour mutations in known cancer genes
• See also Cristian Tomasetti's summary here

Mitochondrial Stress Response in Neural Stem Cells Exposed to Electronic Cigarettes

https://www.sciencedirect.com/science/article/pii/S2589004219301713

Atena Zahedi, Rattapol Phandthong, Angela Chaili, Sara Leung, Esther Omaiye, Prue Talbot

A WORD ON MITOCHONDRIAL DYNAMICS (from this publication)

• Mitochondria of healthy cells continually divide and fuse with each other, forming an ever-changing mitochondrial network. This is referred to as mitochondrial dynamics.
• Fusion promotes exchange of mtDNA and other vital components, thus reinvigorating the mitochondrial network.
• Fission allows for disposal of faulty mitochondrial fragments through mitophagy. Moreover, when cells become committed to apoptosis, they shatter their mitochondrial networks.
• Modest levels of stress (well below the threshold to induce apoptosis) lead mitochondria to fuse extensively. This response was called stress‐induced mitochondrial hyperfusion (SIMH),  and might counter stress by optimizing mitochondrial ATP production.

FINDINGS OF THE PAPER

• Stem cells are critical to our wellbeing (controlling organ development and tissue renewal/repair) and the damage they accumulate over life can lead to disease.
• During development, neural stem cells are highly sensitive to toxicants and more vulnerable to stress than differentiated cells. Mitochondria are good indicators of stress in stem cells.
• Electronic cigarettes are marketed as a healthy substitute to cigarettes, and are targeted at youth and pregnant women.
• The authors exposed stem cells to EC fluid in a set of in vitro experiments. They argue that the nicotine present in EC fluid causes SIMH of stem cells. SIMH is a survival response in stem cells and is accompanied by increased oxidative stress and alterations in mitochondrial morphology and dynamics.
• Further, an interruption of autophagy was observed when stem cells were exposed to nicotine. Since autophagy is a defense mechanism of the cell, clearing damaged mitochondria, its inhibition is deleterious the the stem cell population.
• The main message of the study is that EC are not as harmless as they are claimed to be, and that similar findings could apply to any product containing nicotine.

DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction

https://www.sciencedirect.com/science/article/pii/S0092867419305471

Joshua A. Weinstein, Aviv Regev, and Feng Zhang

• The authors develop a novel method of determining spatial localisation of transcripts within the cell through "DNA Microscopy". 
• The method consists, firstly, of randomly tagging individual transcripts or DNA molecules with DNA unique molecular identifiers (UMIs), which are random nucleotide sequences of a particular length. 
• The UMI-concatenated molecules are then amplified through PCR, and diffuse in the cell. UMI tags are designed to contain overhanging complementary regions, such that tagged molecules are subsequently able to bind to another complementary molecule which is in close spatial proximity (called "beacon" and "target" amplicons). Through this process, "unique event identifiers" (UEIs) are generated. The cell can then be lysed, and sequenced through next-generation sequencing.
• The rate at which UMIs bound to a particular molecule concatenate indicates the distance between their points of origin.
• A computational algorithm then decodes molecular proximities from these UEIs to infer the spatial distribution of transcripts at cellular resolution.