A myosin-based mechanism for stretch activation and its possible role revealed by varying phosphate concentration in fast and slow mouse skeletal muscle fibers.

Stretch activation (SA) is a delayed increase in force following a rapid muscle length increase. SA is best known for its role in asynchronous insect flight muscle, where it has replaced calcium's typical role of modulating muscle force levels during a contraction cycle. SA also occurs in mammalian skeletal muscle but has previously been thought to be too low in magnitude, relative to calcium-activated (CA) force, to be a significant contributor to force generation during locomotion. To test this supposition, we compared SA and CA force at different Pi concentrations (0-16 mM) in skinned mouse soleus (slow-twitch) and extensor digitorum longus (EDL; fast-twitch) muscle fibers. CA isometric force decreased similarly in both muscles with increasing Pi, as expected. SA force decreased with Pi in EDL (40%), leaving the SA to CA force ratio relatively constant across Pi concentrations (17-25%). In contrast, SA force increased in soleus (42%), causing a quadrupling of the SA to CA force ratio, from 11% at 0 mM Pi to 43% at 16 mM Pi, showing that SA is a significant force modulator in slow-twitch mammalian fibers. This modulation would be most prominent during prolonged muscle use, which increases Pi concentration and impairs calcium cycling. Based upon our previous Drosophila myosin isoform studies and this work, we propose that in slow-twitch fibers a rapid stretch in the presence of Pi reverses myosin's power stroke, enabling quick rebinding to actin and enhanced force production, while in fast-twitch fibers, stretch and Pi cause myosin to detach from actin.

Age-associated changes in lineage composition of the enteric nervous system regulate gut health and disease.

The enteric nervous system (ENS), a collection of neural cells contained in the wall of the gut, is of fundamental importance to gastrointestinal and systemic health. According to the prevailing paradigm, the ENS arises from progenitor cells migrating from the neural crest and remains largely unchanged thereafter. Here, we show that the lineage composition of maturing ENS changes with time, with a decline in the canonical lineage of neural-crest derived neurons and their replacement by a newly identified lineage of mesoderm-derived neurons. Single cell transcriptomics and immunochemical approaches establish a distinct expression profile of mesoderm-derived neurons. The dynamic balance between the proportions of neurons from these two different lineages in the post-natal gut is dependent on the availability of their respective trophic signals, GDNF-RET and HGF-MET. With increasing age, the mesoderm-derived neurons become the dominant form of neurons in the ENS, a change associated with significant functional effects on intestinal motility which can be reversed by GDNF supplementation. Transcriptomic analyses of human gut tissues show reduced GDNF-RET signaling in patients with intestinal dysmotility which is associated with reduction in neural crest-derived neuronal markers and concomitant increase in transcriptional patterns specific to mesoderm-derived neurons. Normal intestinal function in the adult gastrointestinal tract therefore appears to require an optimal balance between these two distinct lineages within the ENS.

Universal prediction of cell-cycle position using transfer learning.

BACKGROUND: The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. RESULTS: Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays. CONCLUSIONS: Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.