C9orf72-mediated ALS and FTD: multiple pathways to disease

The discovery that repeat expansions in the C9orf72 gene are a frequent cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) has revolutionized our understanding of these diseases. Substantial headway has been made in characterizing C9orf72-mediated disease and unravelling its underlying aetiopathogenesis. Three main disease mechanisms have been proposed: loss of function of the C9orf72 protein and toxic gain of function from C9orf72 repeat RNA or from dipeptide repeat proteins produced by repeat-associated non-ATG translation. Several downstream processes across a range of cellular functions have also been implicated. In this article, we review the pathological and mechanistic features of C9orf72-associated FTD and ALS (collectively termed C9FTD/ALS), the model systems used to study these conditions, and the probable initiators of downstream disease mechanisms. We suggest that a combination of upstream mechanisms involving both loss and gain of function and downstream cellular pathways involving both cell-autonomous and non-cell-autonomous effects contributes to disease progression

Modelling approaches for studying the microbiome

Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health