Biology subjects

Sui, J.

Publications and source records attributed to Sui, J..

At least 19 recordsLinked to original sources

bioRxiv2026

sORF-Trans2MS: a two-module deep learning framework for sORF translation and MS-supported microprotein prediction

Ribosome profiling is widely used to identify translated small open reading frames (sORFs), but existing prediction models often generalize poorly to newly collected sORF datasets. In addition, many microproteins with strong mass spectrometry (MS) evidence lack support in public Ribo-seq resources, suggesting that Ribo-supported sORF and MS-supported microprotein detection may represent complementary prediction tasks. Here, we developed sORF-Trans2MS, a two-module deep learning framework for predicting Ribo-supported sORFs and MS-supported microproteins. Module I uses RNA-FM representations for upstream, ORF, and downstream regions with a Transformer encoder for Ribo-supported sORF translation prediction. Module II integrates pretrained RNA-FM representations of RNA context with ESM-2 protein representations using multi-scale CNN encoders for predicting translated microproteins supported by MS but not Ribo-seq data. Module I was trained on 9,579 Ribo-supported sORFs and matched background sORFs, while Module II used 14,697 MS-supported (MS++Ribo-) microproteins and matched backgrounds. Module I achieved an AUROC of 0.870 internally and 0.930 on an independent GENCODE dataset. Ablation analysis highlighted upstream RNA context, including a distinct signal around 50 nt upstream of AUG. Module II achieved an AUROC/AUPR of 0.804/0.797 internally and 0.785/0.773 on an independent mass spectrometry dataset. Model interpretation highlighted upstream RNA context and the protein N terminus, while literature-supported microproteins further illustrated the complementary roles of the two modules. Overall, sORF-Trans2MS improves prediction and generalization of Ribo-supported sORFs and extends computational discovery to MS-supported candidates that are not represented in current public Ribo-seq resources.

bioinformatics↗

bioRxiv2026

Identification of smoking-enabled blood miRNA regulatory networks

Cigarette smoking induces complex signaling disruptions that contribute to diseases such as COPD and lung cancer, yet the molecular mechanisms underlying these effects remain incompletely understood. To address this gap, we analyzed peripheral blood from 3190 COPDGene participants using LIONESS and PUMA and constructed miRNA-mRNA regulatory networks associated with smoking status. Comparing networks for active versus former smokers uncovered a striking shift in regulatory architecture: active smokers exhibited elevated miRNA targeting of the mitochondrial complex I protein NDUFA12. This finding was validated in lung tissue expression data from the Lung Genomics Research Consortium (LGRC), where we observed that ever-smokers showed consistent dysregulation of Ndufa12-targeting miRNAs compared to never-smokers. This allowed us to identify a set of smoking-defined circulating and tissue-associated miRNAs. To investigate the specific cellular compartment, we analyzed cell-type deconvoluted expression data from COPDGene blood and LTRC (Lung Tissue Research Consortium) lung tissue, as well as lung transcriptomics data from cigarette smoke-exposed mice, and identified the monocyte/macrophage compartment as a principal site of NDUFA12/Ndufa12 expression. Human THP-1 macrophages treated with cigarette smoke extract demonstrated selective inhibition of NDUFA12 by network-defined miRNAs. These distinct, NDUFA12-targeting, smoking-associated miRNA signatures, revealed through network analysis, describe new smoking-mitochondrial interactions that may serve as novel targets for therapeutic intervention.

Molecular Biology↗

bioRxiv2026

NeuroMark-SZ: A Holistic Resting-State-fMRI-Based Model for Divergent Functional Circuitry in Schizophrenia

BackgroundSchizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity. MethodsWith these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset (N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC. ResultsOur analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia. ConclusionsThis work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-access resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

neuroscience↗

bioRxiv2025

Scavenger Receptor class F member 2 is an intracellular receptor for Hepatitis B virus

Hepatitis B virus (HBV) infects hepatocytes by specific binding to the cell-surface receptor --sodium taurocholate cotransporting polypeptide (NTCP)--through the preS1 region of its large envelop protein, followed by a less well understood transport process across the cytoplasm to the nucleus. Here, we report that scavenger receptor class F member 2 (SCARF2), a single-pass transmembrane protein, functions as an intracellular receptor for HBV. SCARF2 binds to a preS1 region downstream of the NTCP binding site through its N-terminal EGF-like domains 4-6, and its proline-rich C-terminal domain also plays an indispensable role in the infection. The internalized HBV virions are transported to the cytoplasmic side of nuclear pore complexes (NPCs) within the SCARF2-containing endosomes. HBV capsid release from the endosomal vesicles is significantly impaired by knockdown of SCARF2. These results suggest a model that SCARF2 conveys HBV to the periphery of NPCs and ultimately leads to viral capsid release for nuclear entry.

microbiology↗

bioRxiv2025

Cellular and molecular underpinnings of functional networks in the human brain

Understanding how cellular and molecular architecture underpins the large-scale organization of human brain function is a central challenge in neuroscience. By integrating transcriptomic (microarray and single-nucleus RNA-sequencing), molecular imaging, and neuroimaging datasets, we observe spatial correspondences indicating that the distributions of diverse cell types, neurotransmitter systems, and mitochondrial phenotypes align with intrinsic connectivity networks (ICNs). These associations extend beyond local correspondence to reflect network-level structure: inter-ICN similarity networks derived from cellular and molecular profiles recapitulate static and dynamic patterns of functional network connectivity (FNC), mirroring canonical functional domains. Mediation analyses reveal that specific ICNs mediate the relationship between microscale cell-type architecture and domain-specific cognitive processes, while FNCs capture mediating pathways linking cell-type and neurotransmitter similarity networks to cognitive organization. Together, our findings show that the brains functional architecture systematically aligns with cellular and molecular organization, which may constrain functional network formation and contribute to the neural basis of cognition.

neuroscience↗

bioRxiv2025

Physical Activity and Depressive Mood Shared the Structural Connectivity between Motor and Reward Network: a population-based study from the UK Biobank

BackgroundExercise has been revealed to leave a positive effect on alleviating depressive symptoms by various existing studies. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism.AimsUsing a large neuroimaging cohort from UK Biobank, this study aimed to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as its biological interpretation.Method492 participants with major depression disorder (MDD) and 535 healthy controls (HC) were extracted from the UK Biobank dataset. Partial least squares regression was first utilized to explore a replicable SC pattern simultaneously associated with physical activity and depressive mood. The neuromaps toolbox was applied to interpret the biological ontologies of the identified SC pattern. Neuroimaging-transcriptome association and enrichment analyses were conducted to explore its underlying genetic basis, pathways and cell types. The reproducibility and generalizability were tested on another independent MDD dataset (N=3,496) and bipolar disorder (BD) dataset (N=81).ResultsA SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discriminative between MDDs and HCs, which primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and astrocytes cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset and present commonalities with BD.ConclusionsThis study not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhanced our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.

neuroscience↗

bioRxiv2024

Evidence for white matter intrinsic connectivity networks at rest and during a task: a large-scale study and templates

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

neuroscience↗

bioRxiv2024

Self-association enhances early attentional selection through automatic prioritization of socially salient signals

Efficiently processing self-related information is critical for cognition, yet the earliest mechanisms enabling this self-prioritization remain unclear. By combining a temporal order judgement task with computational modelling based on the Theory of Visual Attention (TVA), we show how mere, arbitrary associations with the self can fundamentally alter attentional selection of sensory information into short-term memory/awareness, by enhancing the attentional weights and processing capacity devoted to encoding socially loaded information. This self-prioritization in attentional selection occurs automatically at early perceptual stages but reduces when active social decoding is required. Importantly, the processing benefits obtained from attentional selection via self-relatedness and via physical salience were additive, suggesting that social and perceptual salience captured attention via separate mechanisms. Furthermore, intra-individual correlations revealed an obligatory self-prioritization effect, whereby self-relatedness overpowered the contribution of perceptual salience in guiding attentional selection. Together, our findings provide evidence for the influence of self-relatedness during earlier, automatic stages of attentional section at the gateway to perception, distinct from later post-attentive processing stages.

neuroscience↗

bioRxiv2024

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimers dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimers disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimers individuals.

neuroscience↗

bioRxiv2024

LRG1 promotes atherosclerosis by activating macrophages

BackgroundAtherosclerosis is a chronic inflammatory disease of the arterial wall characterized by the accumulation of cholesterol-rich lipoproteins in macrophages. Leucine-rich alpha-2 glycoprotein 1 (LRG1) is a circulating protein associated with inflammation, however, its role in atherosclerosis remains unclear. This study identified its role in macrophage pro-inflammatory differentiation and revealed the relationship between LRG1 and atherosclerosis. MethodWe evaluated the impact of LRG1 on atherosclerosis progression by analyzing atherosclerotic tissue and serum samples from patients with coronary artery disease (CAD) and healthy individuals and analyzed its role in such a process using two types of mice models: Apoe knock-out mice (Apoe-/-) and Apoe and Lrg1 double knock-out mice (Apoe-/-/Lrg1-/-). These mice were fed with a high-fat diet for 16 to 32 weeks to simulate conditions exacerbating atherosclerosis. To examine the effects of inhibiting LRG1 on atherogenesis, we administered intraperitoneal injections of LRG1 neutralizing antibody (50g/kg) weekly to Apoe-/- mice for 8 weeks. We conducted in vitro assays using bone marrow-derived macrophages isolated from wild-type mice and analyzed transcriptional signatures using RNA sequencing. Additionally, we utilized small molecular inhibitors to validate the signaling pathway through which LRG1 promotes macrophage-driven inflammation. ResultsLRG1 levels were found to be elevated in patients with atherosclerosis and correlated with higher levels of a plasma pro-inflammatory biomarker high-sensitive C-reactive protein (hsCRP), and several macrophage-related pro-inflammatory markers including CD68, VE-Cadherin and VCAM-1. In a high fat diet induced Apoe-/- mouse atherosclerosis model, the deletion of LRG1 gene significantly delayed atherogenesis progression and reduced levels of macrophage-related pro-inflammatory cytokines. Addition of purified LRG1 to cultured macrophages stimulated those macrophages to pro-inflammatory M1-like polarization regulated by the activation of ERK and JNK pathways. An anti-LRG1 neutralizing antibody effectively blocked LRG1-induced macrophage M1-like polarization in vitro and conferred therapeutic benefits to animals with ApoE deficiency-induced atherosclerosis. ConclusionLRG1 plays an important pro-inflammatory role in atherosclerosis by influencing macrophage polarization towards a pro-inflammatory state. The inhibition of LRG1 with neutralizing antibodies may offer a potential therapeutic strategy for patients with atherosclerosis by mitigating the pro-inflammatory response and delaying disease progression, offering a novel therapy in atherosclerosis management. Translational PerspectiveAtherosclerosis, a persistent inflammatory condition affecting the arterial wall, serves as the underlying pathophysiological basis for acute ischemic cardiovascular events. The involvement of macrophages is crucial in the advancement of atherosclerosis. In this investigation, heightened levels of plasma LRG1 were observed in individuals with coronary artery disease. Moreover, this study presents initial evidence highlighting LRG1 as a pivotal activator of macrophages, instigating a pro-inflammatory M1 polarization during atherogenesis through the activation of ERK1/2 and JNK pathways. The use of an anti-LRG1 neutralizing antibody demonstrated a delay in atherosclerosis progression in an animal model, suggesting a potential therapeutic target for atherosclerosis treatment. Suppression of LRG1 production could impede atherosclerosis advancement and enhance plaque stability. Utilizing neutralizing antibodies against LRG1 emerges as a promising therapeutic approach for treating atherosclerosis.

pathology↗

bioRxiv2024

Single-cell RNA sequencing reveals recruitment of the M2-like CCL8high macrophages in Lewis lung carcinoma-bearing mice following hypofractionated radiotherapy

Tumor-associated macrophages (TAMs) are a crucial factor in reprogramming the tumor microenvironment following radiotherapy. The mechanisms underlying this process remain to be elucidated. Here, we seek to investigate the effects of hypofractionated radiotherapy on macrophages dynamics in a subcutaneous Lewis lung carcinoma murine model. Utilizing single-cell RNA sequencing, we identified a distinct M2-like population of macrophages with high Ccl8 expression level post-hypofractionated radiotherapy. Remarkbly, hypofractionated radiotherapy promoted CCL8high macrophages infiltration and reprogrammed them by upregulating immunosuppressive genes and downregulating antigen-presenting genes, leading to an immunosuppressive tumor microenvironment. Further, we demonstrated that hypofractionated radiotherapy amplified the CCL signaling pathway, enhancing the pro-tumorigenic functions of CCL8high macrophages and promoting macrophages recruitment. The combination therapy of hypofractionated radiotherapy with the CCL signal inhibitor Bindarit was effective in reducing M2 macrophages infiltration and extending the duration of local tumor control. This research highlights the potential of targeting TAMs and introduces a novel combination to improve the efficacy of hypofractionated radiotherapy.

immunology↗

bioRxiv2024

Gray Matters: An Efficient Vision Transformer GAN Framework for Predicting Functional Network Connectivity Biomarkers from Brain Structure

The field of brain connectivity research has under-gone revolutionary changes thanks to state-of-the-art advancements in neuroimaging, particularly regarding structural and functional magnetic resonance imaging (MRI). To navigate the intricate neural dynamics, one must possess a keen comprehension of the interdependent links between structure and function. Such relationships are understudied as they are complex and likely nonlinear. To address this, we created a new generative deep learning architecture using a conditional efficient vision transformer generative adversarial network (cEViTGAN) to capture the distinct information in structural and functional MRI of the human brain. Our model generates functional network connectivity (FNC) matrices directly from three-dimensional sMRI data. Two pioneering innovations are central to our approach. First, we use a novel linear embedding method for structural MRI (sMRI) data that retains the 3D spatial detail. This embedding is best for representative learning, and when used on a consistent dataset, and shows that it is good at upstream classification assignments. To estimate neural biomarkers, we need to process much smaller patches using ViT-based architectures, which usually makes the computations more difficult because of the self-attention operations. We present a new, lightweight self-attention mechanism to address this challenge. Our mechanism not only overcomes computational shortcomings of traditional softmax self-attention but also surpasses pure linear self-attention models in accuracy and performance. This optimization enables us to analyze even the tiniest neuroanatomical details with exceptional precision. Our model allows for the identification of functional network connectivity (FNC) with 74.2% accuracy and also predicts subject differences in FNC for schizophrenia patients versus controls. The results are intriguing and suggest the links between gray matter volume and brain function may be stronger than previously considered.

neuroscience↗

bioRxiv2023

An actomyosin network organizes niche morphology and responds to feedback from recruited stem cells

SummaryStem cells often rely on signals from a niche, which in many tissues adopts a precise morphology. What remains elusive is how niches are formed, and how morphology impacts function. To address this, we leverage the Drosophila gonadal niche, which affords genetic tractability and live-imaging. We have previously shown mechanisms dictating niche cell migration to their appropriate position within the gonad, and the resultant consequences on niche function. Here, we show that once positioned, niche cells robustly polarize filamentous actin (F-actin) and Non-muscle Myosin II (MyoII) towards neighboring germ cells. Actomyosin tension along the niche periphery generates a highly reproducible smoothened contour. Without contractility, niches are misshapen and exhibit defects in their ability to regulate germline stem cell behavior. We additionally show that germ cells aid in polarizing MyoII within niche cells, and that extrinsic input is required for niche morphogenesis and function. Our work reveals a feedback mechanism where stem cells shape the niche that guides their behavior.

developmental biology↗

bioRxiv2023

Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion

Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.

neuroscience↗

bioRxiv2023

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease

In this paper we focus on estimating the joint relationship between structural MRI (sMRI) gray matter (GM) and multiple functional MRI (fMRI) intrinsic connectivity networks (ICN) using a novel approach called multi-link joint independent component analysis (ml-jICA). The proposed model offers several improvements over the existing joint independent component analysis (jICA) model. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimers disease (AD) versus controls. The results yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for Alzheimers disease versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to gray matter components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

neuroscience↗

bioRxiv2022

Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks

ObjectiveMulti-site collaboration is essential for overcoming small-sample problems when exploring reproducible biomarkers in MRI studies. However, various scanner-specific factors dramatically reduce the cross-scanner replicability. Moreover, existing harmony methods mostly could not guarantee the improved performance of downstream tasks. Methodswe proposed a new multi-scanner harmony framework, called maximum classifier discrepancy generative adversarial network, or MCD-GAN, for removing scanner effects in the original feature space while preserving substantial biological information for downstream tasks. Specifically, the adversarial generative network was utilized for persisting the structural layout of each sample, and the maximum classifier discrepancy module was introduced for regulating GAN generators by incorporating the downstream tasks. ResultsWe compared the MCD-GAN with other state-of-the-art data harmony approaches (e.g., ComBat, CycleGAN) on simulated data and the Adolescent Brain Cognitive Development (ABCD) dataset. Results demonstrate that MCD-GAN outperformed other approaches in improving cross-scanner classification performance while preserving the anatomical layout of the original images. SignificanceTo the best of our knowledge, the proposed MCD-GAN is the first generative model which incorporates downstream tasks while harmonizing, and is a promising solution for facilitating cross-site reproducibility in various tasks such as classification and regression. The codes of the MCD-GAN are available at https://github.com/trendscenter/MCD-GAN.

bioengineering↗

bioRxiv2022

Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from fMRI data

Brain functional networks identified from resting fMRI data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale. In this work, we present an approach using scICA to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales (ICA model orders), which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N>1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into spatially constrained ICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. We also used the msFNC features as input to a classification model in cross-validated hold-out data and also in an independent test data. Visualization of predictive features was performed by evaluating their feature weights. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.

neuroscience↗

bioRxiv2022

Loss of ANT1 Increases Fibrosis and Epithelial Cell Senescence in Idiopathic Pulmonary Fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is an interstitial lung disease characterized by progressive lung scarring and remodeling. Although treatments exist that slow disease progression, IPF is irreversible and there is no cure. Cellular senescence, a major hallmark of aging, has been implicated in IPF pathogenesis, and mitochondrial dysfunction is increasingly recognized as a driver of senescence. Adenine nucleotide translocases (ANTs) are abundant mitochondrial ATP-ADP transporters critical for regulating cell fate and maintaining mitochondrial function. We sought to determine how alterations in ANTs influence cellular senescence in pulmonary fibrosis. We found SLC25A4 (ANT1) and SLC25A5 (ANT2) expression is reduced in the lungs of IPF patients and particularly within alveolar type II cells by single cell RNA sequencing. Loss of ANT1 by siRNA in lung epithelial cell lines resulted in increased senescence markers such as beta-galactosidase staining and p21 by Western Blot and RT-qPCR. Bleomycin treated ANT1 knockdown cells also had increased senescence markers when compared to bleomycin treated control cells. Global loss of ANT1 resulted in worse lung fibrosis and increased senescence in the bleomycin and asbestos-induced mouse models of pulmonary fibrosis. This data supports the concept that loss of ANT1 drives IPF pathogenesis through mitochondrial dysfunction associated cellular senescence (MiDaS). In summary, loss of ANT1 induces cellular senescence, leading to abnormal tissue remodeling and enhanced lung fibrosis in IPF. Modulation of ANTs presents a new therapeutic avenue that may alter cellular senescence pathways and limit pulmonary fibrosis.

cell biology↗