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Hughes, M. A.

Publications and source records attributed to Hughes, M. A..

2 recordsLinked to original sources

White pupae genes in the Tephritids Ceratitis capitata, Bactrocera dorsalis and Zeugodacus cucurbitae: a story of parallel mutations

Mass releases of sterilized male insects, in the frame of sterile insect technique programs, have helped suppress insect pest populations since the 1950s. In the major horticultural pests Bactrocera dorsalis, Ceratitis capitata, and Zeugodacus cucurbitae, a key phenotype white pupae (wp) has been used for decades to selectively remove females before releases, yet the gene responsible remained unknown. Here we use classical and modern genetic approaches to identify and functionally characterize causal wp− mutations in these distantly related fruit fly species. We find that the wp phenotype is produced by parallel mutations in a single, conserved gene. CRISPR/Cas9-mediated knockout of the wp gene leads to the rapid generation of novel white pupae strains in C. capitata and B. tryoni. The conserved phenotype and independent nature of the wp− mutations suggest that this technique can provide a generic approach to produce sexing strains in other major medical and agricultural insect pests.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

Coordinated cerebellar climbing fiber activity is gated by behavioral context in a voluntary motor regime

The prevailing model of cerebellar learning states that climbing fibers (CFs) are both driven by, and serve to correct, erroneous motor output. However, this model is grounded largely in studies of behaviors that utilize hardwired neural pathways to link sensory input to motor output. To test whether this model applies to more flexible learning regimes that require arbitrary sensorimotor associations, we have developed a cerebellar-dependent motor learning paradigm compatible with both mesoscale and single dendrite resolution calcium imaging in mice. Here, we find that CFs are preferentially driven by and more time-locked to correctly executed movements and other task parameters that predict reward outcome, exhibiting widespread correlated activity within parasagittal processing zones that is governed by these predictions. Together, such CF activity patterns are well-suited to drive learning by providing predictive instructional input consistent with an unsigned reinforcement learning signal that does not rely exclusively on motor errors.

neuroscience↗