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Hughes, H.

Publications and source records attributed to Hughes, H..

2 recordsLinked to original sources

Novel iGluSnFR3 variants with improved kinetics and dynamic range for tracking high frequency firing at glutamatergic synapses

The genetically-encoded fluorescent glutamate sensor, iGluSnFR3, characterised by a high dynamic range and rapid on-kinetics, is an attractive sensor for glutamate imaging in the central nervous system. However, kinetic variants with ultrafast or slow off-kinetics are needed to broaden the spectrum of applications from monitoring rapid synaptic glutamate transients to mesoscale imaging of brain activity. Here we report binding-site variant S72T (iGlu3Fast) optimised for reporting fast glutamate release at individual sites with a fluorescence dynamic range of 57 and a decay t1/2 of 2 ms in solution, [~]5-fold faster than iGluSnFR3. In contrast, variants D25A and D25R (termed iGlu3Slow1 and iGlu3Slow2, respectively) presented with slow off-kinetics (decay t1/2 43 ms and 30 ms, respectively, at 20{degrees}C), while retaining high dynamic ranges (48 and 65, respectively). These values were reduced when expressed in HEK293T cells, glutamate dissociation from iGlu3Fast slowed to a decay t1/2 of 6.9 ms, while decay t1/2-s of 115 and 234 ms were measured for iGlu3Slow1 and iGlu3Slow2, respectively. The slow decay rates in cells were in accordance with reduced Kd-s in the range of 200 M for iGlu3Fast and 6.4 and 8.4 M for iGlu3Slow1 and iGlu3Slow2, respectively. 2-photon imaging in organotypic hippocampal slices reported spontaneous activity with high sensitivity by both iGlu3Fast and iGlu3Slow2. Strikingly, different kinds of glutamate transient were detected by the fast- and slow-decay sensors indicating that the rapid kinetics and increased fluorescence dynamic range make iGlu3Fast an excellent candidate for imaging high-frequency glutamate release at synapses while iGlu3Slow2 has the potential for mesoscale imaging of brain activity to record global events with high sensitivity.

neuroscience↗

Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases

Misdiagnosis of enteric fever is a major global health problem resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine-learning algorithm to host gene expression profiles, we identified a diagnostic signature which could accurately distinguish culture-confirmed enteric fever cases from other febrile illnesses (AUROC<95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host-response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR highlighting their utility as PCR-based diagnostic for use in endemic settings.

molecular biology↗