ARTICLE: Powering cellular decision-making

Intracellular energy Variability Modulates cellular Decision-Making capacity
Ryan Kerr, Sara Jabbari, Iain G Johnston
Scientific Reports 9 1 (2019)

The ability to process information and make decisions is fundamental to life. Intelligent organisms use their brains to do this, but individual cells are also constantly making decisions, changing their behaviour in response to microscopic stimuli. Examples of this cellular decision-making abound in biology: stem cells decide which type of cell to become; some bacteria decide to become robust "persister" cells that can survive drug treatments; cells in plant seeds decide when to germinate.

Often, the "decisions" that cells make involve which genes to express. Genes contain information on how to build cellular machinery, and "expressing" a gene in a sense means turning it on so that its machinery gets built in the cell. We often see that two genes, say A and B, build proteins that switch each other's genes off. So if we have lots of A, it's very hard to produce B, and vice versa. These genes can determine the type of cell we have -- for example, cells with lots of A might be white blood cells, and cells with lots of B might be red blood cells. A blood stem cell could then become a white or a red cell depending on how the interaction between A and B plays out.

All this is reasonably common knowledge (though rather simplified!). But we got interested in how energy plays a role in these decisions. Gene expression requires energy, which in the cell is provided by a molecule called ATP. Different cells have different amounts of ATP, so the processes involved in the interaction of our genes A and B can take place at different rates. Following some ideas we laid out here, we asked, using maths, how this energy dependence might affect the decisions that cells make.

We found, in a new paper free to read in Scientific Reports, that ATP levels strongly influence the decision-making capacity of a cell. Consider the simple A-B case above. Four states are possible: no A or B (state 0), more A than B (state A), more B than A (state B), and high A and B (state AB). We found that, at low ATP, only state 0 is possible (the cell can't make any decisions). As ATP increases, states A and B become possible, and for high ATP the state AB also appears. So, the number of states a cell can choose between (for example, white, red, or stem blood cell) depends strongly on how much energy that cell has available to power these genetic interactions.

We also found that more energy stabilised the decisions that could be made (cells are noisy, so decisions can be randomly "overturned" by gene expression fluctuations), and mapped out the "landscape" of decisions that can be made as the biochemical features of the genes involved change. We're now going to the lab to explore these mathematical predictions in real cells -- particularly in bacterial persister cells -- and developing the theory further for more complicated decision-making circuits.

ARTICLE: The inheritance of mtDNA

Regulation of mother-to-offspring transmission of mtDNA heteroplasmy
Ana Latorre-Pellicer, Ana Victoria Lechuga-Vieco, Iain G Johnston, Riikka H Hämäläinen, Juan Pellico, Raquel Justo-Méndez, Jose María Fernández-Toro, Cristina Clavería, Adela Guaras, Rocío Sierra, Jordi Llop, Miguel Torres, Luis Miguel Criado, Anu Suomalainen, Nick S Jones, Jesús Ruíz-Cabello, José Antonio Enríquez
Cell Metabolism 30 1120 (2019)

Mitochondrial DNA (mtDNA) is inherited from mothers to children. If two or more types of mtDNA exist in a cell, the cell is called "heteroplasmic". Mothers may carry a heteroplasmic mixture of different mtDNA types in each of their oocytes (egg cells), so a mixture of different types may be passed on to children. Different oocytes may have different mixtures -- for example, one cel may have 50% type A and 50% type B, and another may have 70% A and 30% B. 

The inheritance of heteroplasmy depends both on a complicated "bottleneck" (see here and here) and whether either type has some advantage over the other -- a question that is hotly debated. The mechanisms that shape the inheritance of mtDNA populations remain poorly understood, so it's hard to predict which offspring will inherit which mixture. It's often the case that a disease is caused by a particular mixture -- for example, over 60% of type B -- so this complex inheritance makes it hard to plan fertility treatments too.

In a new paper in Cell Metabolism, we looked at the inheritance and consequences of heteroplasmy in mice. Strikingly, we found that the presence of any heteroplasmy has generally negative consequences for the cell. This is perhaps surprising, given the above view that we normally need a certain amount of a dangerous mtDNA type to cause disease. But it does match a prediction that we recently made by mathematically considering how a cell must invest energy in controlling mixed mtDNA populations. Correspondingly, we found that regardless of how much type A and type B there is, having a heteroplasmic mixture challenges metabolism in the embryo, and affects how readily induced pluripotent stem cells (iPSCs) can be produced from cells.

Given that heteroplasmy is a challenge, it seems that cells have evolved mechanisms to sense and address the inheritance of heteroplasmy. In addition to the bottleneck, we found (as in our previous work) that cell-to-cell variance of heteroplasmy increased in oocytes with age -- which will have the eventual effect of reducing heteroplasmy. We also found that particular mtDNA types had a selective advantage through inheritance, and identified a set of genes that shape this advantage. Variability in the expression of these genes, and variability in metabolic factors, led to differences in the strength of selection.

Some key findings from this paper, and links to our previous work.

This work was exciting because it provided some insights into the mechanisms that shape mtDNA populations between generations -- but also because it validated several predictions that our theoretical work had proposed in the past:

• Increasing heteroplasmy variance with age (predicted here, observed here)
• MtDNA selection occurs at different developmental stages (as we found here and here)
• Mixed mtDNA populations challenge the cell (predicted here)
• Genes related to mitochondrial dynamics shape mtDNA genetic makeup (predicted here)

We're continuing this exciting collaboration and looking in more depth at the behaviour of mtDNA over time.

ARTICLE: Learning pathways of disease progression

HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways
Sam F Greenbury, Mauricio Barahona, Iain G Johnston
Cell Systems (2019)

Many diseases that take a substantial human toll can be viewed as “progressive”. That is, a patient starts out healthy, then disease-related problems and/or symptoms develop over time. For example, a given case of cancer may begin with a patient acquiring a particular mutation, then other mutations building up in their genome over time.

How the same disease progresses in different patients often varies widely. Understanding this variability is important for precision medicine, where detailed knowledge of individual patients is used to design the best targeted treatments. However, learning the varied pathways of diseases and using them to predict future outcomes is challenging. Human researchers usually cannot hope to remember or analyse enough examples of patient data to provide the most reliable picture.

We previously developed an algorithm called HyperTraPS (hypercubic transition path sampling) to explore how biological systems evolve over time. We reasoned that HyperTraPS could also be used to learn the pathways of disease progression. In a new study in Cell Systems (free preprint available here) we used HyperTraPS to analyse biomedical data from many patients – hundreds, or thousands of individuals – to build a ‘road map’ of the different pathways that a disease takes over time.

Picture a river that branches out into a wide delta. Patients start out healthy – upstream in the river – and different patients go down different branches as the disease progresses and they acquire more symptoms. HyperTraPS learns the structure of the river delta from data, and predicts which river branches are more or less likely – and, importantly, where you'll end up if you're currently at a particular point.

By learning these branching patterns of disease progression, HyperTraPS has helped provide a refined risk assessment for malaria, based on data from thousands of Gambian children – as we’ve written about before. The approach also revealed diverse pathways of ovarian cancer progression, where the first mutation to occur appears to play a large role in determining subsequent mutations.

The "waterfall" in the foreground shows paths from one stage of a disease to the next, learnt by HyperTraPS using data from a high number of patients. Each dot of the illustration represents different stages of disease, for example a specific set of symptoms or a given set of mutations. The thickness of the lines indicate the probability of moving from one specific stage of disease to the next.

HyperTraPS is very generalisable and can be used to learn pathways by which mutations, symptoms, or other features develop over time from an initial state. We further used this generalisability to understand a biomedically important example of evolution – specifically, how tuberculosis evolves to become resistant to antibiotics.

Tuberculosis acquires resistance through mutations, and HyperTraPS has revealed the patterns of these mutations in TB bacteria reported from a group of 1000 Russian patients. These patterns help predict which mutation a bacterium will acquire next, and hence which drugs may be more effective for a given case. We’re following up with other applications of HyperTraPS, to learn about other progressive diseases, ageing, and evolution, and even to analyse how students complete tasks in online courses.

ARTICLES: Evolving cellular populations of mtDNA

Evolving mtDNA populations within cells
Iain G Johnston, Joerg P Burgstaller
Biochemical Society Transactions 47 1367 (2019)

and

Varied mechanisms and models for the varying mitochondrial bottleneck
Iain G Johnston
Frontiers in Cell and Developmental Biology 7 294 (2019)

We've recently written two review papers looking at the dynamics of mitochondrial DNA (mtDNA) in cells. As we've written about before, cells contain populations of hundreds or thousands of mtDNA molecules. These molecules replicate and degrade, so that over time, cellular populations of mtDNA change and evolve. The amount of disease-causing mutations, and the number and structure of mtDNA molecules, may all change as organisms develop and age, with different consequences.

The first article, in Biochemical Society Transactions, takes a broad look at how cellular mtDNA populations change over time, considering a range of organisms from humans and other animals to plants and fungi. We look at the different processes that change mtDNA populations, which include replication and degradation but may also include recombination (particularly in plants) and cell-to-cell exchange of mitochondria. The review particularly highlights the importance of understanding cell-to-cell variability in mtDNA populations -- as it only takes a few cells with lots of mutant mtDNA to cause disease, it's important to understand the statistics of mtDNA populations across cells. We review experiments and theory aiming to do so, including our recent work showing that cell-to-cell variability of mtDNA mutant load increases over time in a wide variety of circumstances.

The second article, in Frontiers in Cell and Developmental Biology, focuses on the so-called "mtDNA bottleneck", a process that shapes mtDNA populations in early mammalian development, and helps prevent the inheritance of mutant mtDNA. Specifically, mtDNA undergoes a "genetic bottleneck" between generations, meaning that mothers' egg cells, and offspring, often have dramatically different mtDNA populations. The review emphasises that this "genetic bottleneck" is an effective quantity, not a directly measurable observation, that arises from several physical processes, including but not limited to a "physical bottleneck" or mtDNA depletion during development. Different ways of modelling, analysing, and explaining the "genetic bottleneck" are reviewed, from human populations to mouse egg cells and Adélie penguins. We invest some time in trying to explain the different assumptions, symbols, and methods that researchers have used to quantify the bottleneck over the years. Again, the importance of understanding cell-to-cell variance in mtDNA populations is a core theme.

A. Different processes shaping mixed mtDNA populations inside cells. B. The "genetic bottleneck", increasing mtDNA variance between egg cells and offspring.  

Like all reviews, these articles don't have new results, but attempt to summarise existing knowledge and thinking on these topics. We hope that both papers provide some interesting insights, references, and (in the case of the bottleneck paper) visualisations that may help understand these often confusing topics.