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Nakamura, W.

Publications and source records attributed to Nakamura, W..

3 recordsLinked to original sources

Rare k-mers reveal centromere haplogroups underlying human diversity and cancer translocations

Centromeres are among the most diverse and dynamically evolving regions of the human genome and are commonly affected in various human cancers. However, organized into highly repetitive -satellite higher-order repeats (HORs), human centromere sequences have long resisted detailed genomic analysis. Although the development of long-read sequencing platforms has enabled the analysis of complete centromere sequences, their application to a large set of samples is still largely limited, preventing our understanding of centromere variation and haplotype structures across large human populations and the structural basis of centromere-involving translocations in cancer. Here we show that rare k-mers present in centromeric regions can serve as effective markers for dissecting the complexity of centromere structure, particularly that of active -satellite HOR arrays (aHOR arrays), across human populations and for understanding centromere-involving abnormalities in cancer. Based on rare k-mer-based clustering, centromere aHOR arrays are clustered into discrete haplogroups (aHOR-HGs) with distinct structural features. These k-mers were also used to develop a framework that enables the inference of haplogroups in a given sample based on short-read whole genome sequencing (WGS) data (ascairn). By applying ascairn to large-scale human population datasets (n > 3,300), we revealed the diversity of aHOR-HGs and their geographic histories across populations. The rare k-mer-based approach was also applied to investigate the structure of 1p/19q co-deletion, a highly recurrent centromere-involving translocation in IDH-mutated oligodendrogliomas. Analyzing short-read WGS data from 142 cases with 1p/19q co-deletion using rare k-mers, we showed that breakpoints of 1p/19q co-deletion were mapped to aHOR arrays in chromosomes 1 (D1Z7) and 19 (D19Z3), which was validated by long-read sequencing of two 1p/19q co-deletion-positive cases. Notably, the translocation preferentially involved haplogroups composed of haplotypes containing larger regions susceptible to rearrangement. These results highlight the role of rare k-mers in dissecting the complexity of centromere sequences and their evolutionary history as well as understanding centromere-involving abnormalities associated with human diseases.

genomics↗

A mouse model of insomnia with sleep apnea

Obstructive sleep apnea (OSA) patients are exposed to nighttime hypoxia during sleep by intermittent airway closure and feel daytime strong sleepiness. Strangely, insomnia co-occur in some OSA patients, which is called co-morbid insomnia and sleep apnea (COMISA). Here, we show activity responses to daytime hypoxia (DHx) in nocturnal mice were comparable to daytime sleepiness and co-occurring nighttime insomnia in COMISA. DHx reduced activity in active phase (AP) and increased following activity in activity ending phase (AEP). This down-and-up activity response (DUR) by DHx was also observed in molecular clock deficient Cry1 and Cry2 double knockout mice (CryDKO) expressing nighttime activity rise under light-dark cycle (LD) and not observed in arrhythmic CryDKO under constant darkness (DD). When daytime timing hypoxia was exposed at transition from LD to DD, about every 6 h down and up and down wavelike activity responses appeared in arrhythmic CryDKO. Results indicate this wavelike response and AP activity overlap and cause DUR in rhythmic mice. DHx increased plasma corticosterone and this increase antagonized AP activity reduction by DHx. DHx reduced forebrain adenosine and morning adenosine inhibition by caffeine induced DUR. Adenosine inhibition by caffeine or istradefylline at transition from LD to DD induced wavelike response in CryDKO. It is possible that wavelike response is damped oscillation because, interestingly, chronic caffeine treatment induced circasemidian and/or circadian activity rhythms in arrhythmic CryDKO. Evening caffeine attenuated DUR by DHx, which suggested adenosine inhibition chronotherapy may improve OSA/COMISA symptoms. Our animal model will be useful to understand COMISA. SignificanceObstructive sleep apnea patients (OSA) are exposed to nighttime hypoxia during sleep. OSA feels daytime strong sleepiness and increases risk of many diseases. Insomnia occurs in not a few OSA, which is called comorbid insomnia and sleep apnea (COMISA). We show here a mouse model of COMISA. In mice, daytime hypoxia exposure induced following down and up activity response (DUR), activity reduction in active phase and increase in activity ending phase, which corresponded to sleepiness and insomnia in COMISA. We found DUR was clock gene independent and might be driven by circasemidian system. Glucocorticoid and forebrain adenosine response were involved in DUR. Caffeine chronotherapy was effective in DUR. Our model may be useful to understand COMISA.

physiology↗

The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

neuroscience↗