Biology subjects

Yoneda, N.

Publications and source records attributed to Yoneda, N..

4 recordsLinked to original sources

bioRxiv2026

Mesoscopic cortical activities associated with pupil-linked perceptions inferred via explainable machine learning

Pupil dilation reflects arousal-related neural processes and is closely linked to sensory perception, attention, and cognitive state, but the mesoscopic cortical dynamics that accompany stimulus-evoked dilation remain unclear. Here, we combined simultaneous pupillometry and wide-field Ca{superscript 2} imaging in mice with explainable machine learning to identify cortical activity patterns predictive of pupil dilation. Cortical activity was recorded during hindpaw somatosensory stimulation, visual pattern change, and visual contextual stimulation using an visual oddball paradigm; spontaneous pupil dilation was additionally analyzed using a public resting-state dataset. To reduce multicollinearity in wide-field imaging signals, cortical activity was decomposed into independent components, which were used as inputs to recurrent neural network (RNN) decoders. Models reliably predicted sensory-evoked pupil dilation above shuffled-label controls when sessions with pre-existing pupil dilation were excluded, indicating that stimulus-evoked dilation was associated with distinct cortical activity patterns. Feature attribution using SHapley Additive exPlanations (SHAP) and spatial back-mapping revealed condition-dependent cortical signatures. Activity around the retrosplenial cortex and higher-order visual areas contributed to predictions during somatosensory and visual pattern stimulation, whereas secondary motor cortex activity was prominent during visual contextual stimulation. Spontaneous pupil dilation required lower-frequency cortical signals for decoding, suggesting partly distinct dynamics from sensory-evoked dilation. These findings demonstrate that explainable machine learning can extract candidate cortical activity patterns associated with pupil-linked arousal from high-dimensional wide-field Ca{superscript 2} imaging data. Because feature importance does not establish causality, the identified regions provide hypothesis-generating targets for future circuit manipulation studies.

neuroscience↗

bioRxiv2024

Systemic identification of functionally conserved lncRNA metabolic regulators in human and mouse livers

BACKGROUND & AIMSUnlike protein-coding genes, the majority of human long non-coding RNAs (lncRNAs) lack conservation based on their sequences, posing a challenge for investigating their role in a pathophysiological context for clinical translation. This study explores the hypothesis that non-conserved lncRNAs in human and mouse livers may share similar metabolic functions, giving rise to functionally conserved lncRNA metabolic regulators (fcLMRs). METHODSWe developed a sequence-independent strategy to select putative fcLMRs, and performed extensive analysis to determine the functional similarities of putative human and mouse LMR pairs (h/mLMRs). RESULTSWe found that several pairs of putative fcLMRs share similar functions in regulating gene expression. We further demonstrated that a pair of fcLMRs, h/mLMR1, robustly regulated triglyceride levels by modulating the expression of a similar set of lipogenic genes. Mechanistically, h/mLMR1 binds to PABPC1, a regulator of protein translation, via short motifs on either lncRNA with divergent sequences but similar structures. This interaction inhibits protein translation, activating an amino acid-mTOR-SREBP1 axis to regulate lipogenic gene expression. Intriguingly, PABPC1-binding motifs on each lncRNA fully rescued the functions of their corresponding LMRs in the opposite species. Given the elevated expression of h/mLMR1 in humans and mice with hepatic steatosis, the PABPC1-binding motif on hLMR1 emerges as a potential non-conserved human drug target whose functions can be fully validated in a physiologically relevant setting before clinical studies. CONCLUSIONSOur study supports that fcLMRs represent a novel and prevalent biological phenomenon, and deep phenotyping of genetic mLMR mouse models constitutes a powerful approach to understand the pathophysiological role of lncRNAs in the human liver.

physiology↗

bioRxiv2023

Single transcript-level metabolic responsive landscape of human liver transcriptome

Direct knowledge of gene regulation in human liver by metabolic stimuli could fundamentally advance our understanding of metabolic physiology. This information, however, is largely unknown, and this void is deeply rooted in a paradox that is caused by the inaccessibility of human liver to treatments and the insufficient annotation of liver transcriptome. Recent advances have uncovered immense complexity of transcriptome, i.e., multiple transcripts produced from one gene and extensive RNA modifications, which are all highly regulated and often condition-dependent. Establishing an inclusive annotation to study liver transcriptome dynamics thus requires human liver samples of diverse conditions, which are paradoxically unavailable due to the inaccessibility of human liver to treatments. In this work, we addressed these challenges by coupling an isogenic humanized mouse model with Nanopore single-molecule direct RNA sequencing (DRS). We first generated mice that carried humanized livers of identical genetic background, which were equivalent to clones of a single human liver, and then subjected the mice to representative metabolic treatments. We then analyzed the humanized livers with Nanopore DRS, which directly reads full-length native RNAs to determine the expression level, m6A modification and poly(A) tail length of all RNA transcript isoforms. Thus, our system allows for constructing a de novo annotation of human liver transcriptomes reflecting metabolic responses and studying transcriptome dynamics in conjunction. Our analysis uncovered a vast number of novel genes and transcripts that have not been previously reported. Our transcript-level analysis of human liver transcriptomes also identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional short read RNA-seq. We also revealed for the first time the dynamic changes in m6A and poly(A) tail length of human liver transcripts many of which are transcribed from key metabolic genes. Furthermore, we performed comparative analyses of gene regulation between human and mouse and between two individuals using the liver-specific humanized mice. This revealed that transcriptome dynamics are highly species- and genetic background-dependent, which may only be faithfully studied in a humanized system that entails clones of the same human liver. Hence our work revealed a complex metabolic responsive landscape of human liver transcriptome and also provided a framework to understand transcriptome dynamics of human liver in response to physiologically relevant metabolic stimuli.

systems biology↗

bioRxiv2020

Multilevel integrative transcriptome analyses in humans and humanized mice define in vivo human lncRNA metabolic regulators

A growing number of long non-coding RNAs (lncRNAs) have emerged as vital metabolic regulators in research animals suggesting that lncRNAs could also play an important role in human metabolism. However, most human lncRNAs are non-conserved, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). As the sequence-function relation of lncRNAs has yet to be established, the identification of lncRNA metabolic regulators in animals often relies on their regulations by experimental metabolic conditions. But it is very challenging to apply this strategy to human lncRNAs because well-controlled human data are much limited in scope and often confounded by genetic heterogeneity. In this study, we establish an efficient pipeline to identify putative hLMRs that are metabolically sensitive, disease-relevant, and population applicable. We first progressively processed human transcriptome data to select human liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a metabolic disease population, and response to dietary intervention in a small disease cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and metabolic disease populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we were able to validate the regulatory role of one non-conserved hLMR in cholesterol metabolism. Mechanistically, this hLMR binds to an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. In summary, our study provides a pipeline to overcome the variabilities intrinsic to human data to enable the efficient identification and functional definition of hLMRs. The combination of this bioinformatic framework and humanized murine model will enable broader systematic investigation of the physiological role of disease-relevant human lncRNAs in metabolic homeostasis.

physiology↗