Biology subjects

Mano, H.

Publications and source records attributed to Mano, H..

9 recordsLinked to original sources

bioRxiv2026

Personalized reference genome-based pipeline reveals comprehensive haplotype-resolved views of cancer genomes

Cancer genome analysis relies on standard human reference genomes, but detecting somatic alterations in highly repetitive or individual-specific regions remains challenging. We developed the Personalized Reference genome-based Cancer Genome Analysis Pipeline (PRCGAP, https://github.com/yos-sk/PRCGAP), to our knowledge, the first comprehensive pipeline integrating haplotype-resolved analyses of somatic point mutations, structural variants, copy number, and DNA methylation on personalized diploid reference genomes, with each variant phased and annotated by genomic features. We applied PRCGAP to eight tumor-normal cell line pairs and three newly collected pediatric B-cell acute lymphoblastic leukemia (B-ALL) clinical samples. PRCGAP recovered most standard reference-based variants (86.7-96.4% across variant types), validated by orthogonal short-read sequencing, while additionally detecting variants that standard references missed (23.4% of SNVs and 32.6% of SVs). Notably, PRCGAP uncovered variants in centromeric and telomeric regions, and somatic single-nucleotide variants were enriched in centromere dip regions, the putative kinetochore formation sites. Using PRCGAP outputs, we identified L1 retrotransposition source sites absent from standard references and showed that a B-ALL IGH::DUX4 fusion arose from a DUX4 pseudogene within an internal D4Z4 repeat unit, rather than the canonical full-length DUX4 gene. Within a single haplotype-resolved framework, PRCGAP opens regions of the cancer genome that are inaccessible to standard references.

bioinformatics↗

bioRxiv2026

Probabilistic coupling of cellular and microenvironmental heterogeneity by masked self-supervised learning

AO_SCPLOWBSTRACTC_SCPLOWSpatial omics technologies have advanced to single-cell resolution, enabling systematic analysis of tissue microenvironments alongside cellular-state heterogeneity. However, computationally defining microenvironmental states at single-cell resolution and identifying representations most informative for biological discovery remain major challenges. Here we present Mievformer, a Transformer-based masked self-supervised framework that learns microenvironmental embeddings by encoding neighboring cellular states and relative spatial configurations to parameterize the conditional distribution of continuous cell states at central spatial positions. Through InfoNCE optimization, Mievformer learns representations that capture the relative enrichment of cell states across microenvironments, formalized as a conditional density ratio, thereby enabling probabilistic inference of the coupling between microenvironmental and cellular heterogeneity. Mievformer outperformed existing methods in niche clustering on simulated spatial transcriptomics data and achieved the highest average performance across five real datasets spanning three spatial transcriptomics platforms when evaluated using DREC, a ground-truth-free metric that most strongly correlated with ground-truth performance in simulations. Beyond conventional clustering, Mievformer enables identification of cellular subpopulations based on their microenvironmental distribution and detection of gene-expression signatures associated with colocalization of specific cell populations. Together, these results establish Mievformer as a quantitatively robust and biologically informative framework for learning microenvironment representations in spatial omics.

bioinformatics↗

bioRxiv2025

High-Speed AFM Reveals IDR-Mediated Structural Plasticity of EML4-ALK Modulated by ALK Inhibitors

EML4-ALK is a key oncogenic driver in lung cancer, but variant-specific chemoresistance limits the efficacy of current ALK inhibitors. Because the N-terminus contains an intrinsically disordered region (IDR), how ALK inhibitors affect the structure and dynamics of full-length EML4-ALK remains unclear. Here, using high-speed atomic force microscopy (HS-AFM), we visualize the overall structures of three full-length EML4-ALK variants (v1, v3, and v5) at the single-molecule level. We identified a transient globular subdomain (residues 191-217) within the v3 IDR that contributes to distinct oligomerization patterns. Notably, ALK inhibitors compact the IDR subdomain and reduce oligomerization, whereas this effect is abolished by the resistance mutation ALKG1202R, suggesting that ALK inhibitors not only affect kinase activity but also modulate IDR dynamics. Our findings shed new light on the structural regulation of EML4-ALK and provide a foundation for developing next-generation targeted therapies that account for IDR-driven molecular behavior. TeaserHigh-speed AFM revealed the full-length EML4-ALK structures and their IDR-mediated plasticity caused by ALK kinase inhibitors

cancer biology↗

bioRxiv2020

Multi-sample Full-length Transcriptome Analysis of 22 Breast Cancer Clinical Specimens with Long-Read Sequencing

Although transcriptome alteration is considered as one of the essential drivers of carcinogenesis, conventional short-read RNAseq technology has limited researchers from directly exploring full-length transcripts, only focusing on individual splice sites. We developed a pipeline for Multi-Sample long-read Transcriptome Assembly, MuSTA, and showed through simulations that it enables construction of transcriptome from the transcripts expressed in target samples and more accurate evaluation of transcript usage. We applied it to 22 breast cancer clinical specimens to successfully acquire cohort-wide full-length transcriptome from long-read RNAseq data. By comparing isoform existence and expression between estrogen receptor positive and triple-negative subtypes, we obtained a comprehensive set of subtype-specific isoforms and differentially used isoforms which consisted of both known and unannotated isoforms. We have also found that exon-intron structure of fusion transcripts tends to depend on their genomic regions, and have found three-piece fusion transcripts that were transcribed from complex structural rearrangements. For example, a three-piece fusion transcript resulted in aberrant expression of an endogenous retroviral gene, ERVFRD-1, which is normally expressed exclusively in placenta and supposed to protect fetus from maternal rejection, and expression of which were increased in several TCGA samples with ERVFRD-1 fusions. Our analyses of real clinical specimens and simulated data provide direct evidence that full-length transcript sequencing in multiple samples can add to our understanding of cancer biology and genomics in general.

genomics↗

bioRxiv2020

High-Throughput Functional Evaluation of BRCA2 Variants of Unknown Significance

Numerous nontruncating missense variants of the BRCA2 gene have been identified, but there is a lack of convincing evidence, such as familial data, demonstrating their clinical relevance and they thus remain unactionable. To assess the pathogenicity of variants of unknown significance (VUSs) within BRCA2, we developed a novel method, the MANO-B method, for high-throughput functional evaluation utilizing BRCA2-deficient cells and poly(ADP-ribose) polymerase (PARP) inhibitors. The estimated sensitivity and specificity of this assay compared to those of the International Agency for Research on Cancer (IARC) classification system were 95% and 95%, respectively. We classified the pathogenicity of 186 BRCA2 VUSs with our original computational pipeline, resulting in the classification of 126 mutations as "neutral/likely neutral", 23 as "intermediate", and 37 as "deleterious/likely deleterious". We further invented a simplified, on-demand annotation system, the Accurate BRCA Companion Diagnostic (ABCD) test, as a companion diagnostic for PARP inhibitors in patients with unknown BRCA2 VUSs. The ABCD test classification was reproducible and consistent with that of a large-scale MANO-B method.

cancer biology↗

bioRxiv2018

Endogenous controllability of closed-loop brain-machine interfaces for pain.

The ultimate aim of closed-loop brain-machine systems for pain is to directly titrate the ongoing level of an intervention to pain-related neural activity. However pain is highly susceptible to endogenous modulation, raising the possibility that active or passive changes in neural activity provoked by the operation of the system could enhance or interfere with the signals upon which it is based. We studied healthy subjects receiving intermittent pain stimuli in a real-time fMRI-based closed-loop feedback-stimulation task. We showed that multi-voxel pattern decoding of pain intensity could be used to train a control algorithm to learn to deliver less painful stimuli (adaptive decoded neurofeedback). However, the system engaged two types of endogenous processes in the brain. First, despite the inherent incentive for subjects to enhance the neural decodability of pain, decodability was either reduced or unchanged in classic pain-processing regions, including insula, dorsolateral prefrontal, and somatosensory cortices. However, increased decodability was observed in a putative pain modulatory region - the pregenual anterior cingulate cortex (pgACC). Second, we found that pain perception itself was modulated by an endogenous computational uncertainty signal engaged as subjects learned the success rate of the system in reducing pain - an effect that also correlated with pgACC responses. The results illustrate how regionally and computationally specific co-adaptive brain-machine learning influences the efficacy of closed-loop systems for pain, and shows that pgACC acts as a key hub in the endogenous controllability of pain.

neuroscience↗

bioRxiv2017

Anterior cingulate cortex connectivity is associated with suppression of behavior in a rat model of chronic pain

A cardinal feature of persistent pain that follows injury is a general suppression of behavior, in which motivation is inhibited in a way that promotes energy conservation and recuperation. Across species, the anterior cingulate cortex (ACC) is associated with the motivational aspects of phasic pain, but whether it mediates motivational functions in persistent pain is less clear. Using burrowing behavior as an marker of non-specific motivated behavior in rodents, we studied the suppression of burrowing following painful CFA or control injection into the right knee-joint of 37 rats (18 with pain), and examined associated neural connectivity with ultra-high-field resting state functional MRI. We found that connectivity between ACC and the subcortex correlated with the reduction in burrowing behavior observed following the pain manipulation. In a full replication study we confirmed these findings in a group of 44 rats (23 with pain). Across both datasets, reduced burrowing was assosciated with increased connectivity between ACC and subcortical structures including hypothalamic/preoptic nuclei and the bed nucleus of the stria terminalis. Together, the findings implicate ACC connectivity as a robust correlate of the motivational aspect of persistent pain in rodents.

neuroscience↗

bioRxiv2017

Classification and characterisation of brain network changes in chronic back pain: a multicenter study.

Chronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

neuroscience↗

bioRxiv2017

The Control of Tonic Pain by Active Relief Learning

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning so that the cause of the pain can be reduced if possible. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system also uses an uncertainty ( associability) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that allows reduction of ongoing pain when learning about potential relief.

neuroscience↗