Biology subjects

Hughes, K. J.

Publications and source records attributed to Hughes, K. J..

3 recordsLinked to original sources

bioRxiv2026

Measurement Equivalence of On-Scalp OPM-MEG and Cryogenic MEG for Auditory and Somatosensory Cortical Mapping Across Development

Wearable optically pumped magnetometer magnetoencephalography (OPM-MEG) reduces sensor-to-cortex distance compared with conventional cryogenic SQUID-MEG, but whether the two technologies yield equivalent neurophysiological conclusions remains unclear. We recorded auditory and somatosensory evoked fields in 18 participants (10-45 years) using a 128-sensor FieldLine HEDscan OPM-MEG system and a 275-channel CTF SQUID-MEG system within the same individuals. Equivalent current dipole source models were estimated using identical preprocessing and modeling procedures and compared using paired permutation testing. Both systems localized canonical auditory and somatosensory cortical generators with matched peak latencies and modest cross-system spatial differences. Auditory sources showed a consistent medial bias in SQUID-MEG localization, whereas somatosensory sources exhibited a small systematic offset ([~]4 mm), indicating stable coordinate differences rather than localization error. Dipole moments were larger for SQUID-MEG and goodness-of-fit higher for OPM-MEG; however, the increased moment was explained by a medial localization bias, demonstrating inverse-model effects rather than physiological disagreement. Auditory dipole moment increased with age in both systems, whereas somatosensory responses showed no age-related change. Together, these observations indicate preserved developmental physiology across platforms. These findings demonstrate that OPM-MEG and SQUID-MEG recover the same cortical generators and support equivalent biological interpretations despite predictable geometry-dependent coordinate differences. OPM-MEG therefore represents a measurement-equivalent implementation of MEG suitable for sensory functional mapping.

neuroscience↗

bioRxiv2021

Distinct mechanoreceptor pezo-1 isoforms modulate food intake in the nematode Caenorhabditis elegans

Two PIEZO mechanosensitive cation channels, PIEZO1 and PIEZO2, have been identified in mammals, where they are involved in numerous sensory processes. While structurally similar, PIEZO channels are expressed in distinct tissues and exhibit unique properties. How different PIEZOs transduce force, how their transduction mechanism varies, and how their unique properties match the functional needs of the tissues they are expressed in remain all-important unanswered questions. The nematode Caenorhabditis elegans has a single PIEZO ortholog (pezo-1) predicted to have twelve isoforms. These isoforms share many transmembrane domains but differ in those that distinguish PIEZO1 and PIEZO2 in mammals. We used transcriptional and translational reporters to show that putative promoter sequences immediately upstream of the start codon of long pezo-1 isoforms predominantly drive GFP expression in mesodermally derived tissues (such as muscle and glands). In contrast, sequences upstream of shorter pezo-1 isoforms resulted in GFP expression primarily in neurons. Putative promoters upstream of different isoforms drove GFP expression in different cells of the same organs of the digestive system. The observed unique pattern of complementary expression suggests that different isoforms could possess distinct functions within these organs. We used mutant analysis to show that pharyngeal muscles and glands require long pezo-1 isoforms to respond appropriately to the presence of food. The number of pezo-1 isoforms in C. elegans, their putative differential pattern of expression, and roles in experimentally tractable processes make this an attractive system to investigate the molecular basis for functional differences between members of the PIEZO family of mechanoreceptors.

genetics↗

bioRxiv2017

Biological classification with RNA-Seq data: Can alternative splicing enhance machine learning classifier?

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data.\n\nIn this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis.

bioinformatics↗