Biology subjects

Fong, S.

Publications and source records attributed to Fong, S..

17 recordsLinked to original sources

bioRxiv2026

Diet Quality from Midlife to Later Life Relates to Late-Life Brain Health and Verbal Memory in the SG70 Cohort

Healthy diet across adulthood is associated with better late-life cognition, but how life-course diet quality relates to brain integrity, and whether brain measures mediate diet-cognition associations, remains unclear as studies with long-term diet records and detailed neurocognitive measures are lacking. We studied 892 participants from the SG70 study, nested within the Singapore Chinese Health Study, with adherence to the Dietary Approaches to Stop Hypertension diet (DASH) assessed between 1993--2025. Dietary quality during midlife, ages 44--55 years, and early elderhood, ages 61--73 years, was examined in relation to seven cognitive domains, brain morphometry, white matter hyperintensities and free-water MRI markers in late life, ages 68--82 years. Higher DASH adherence at both life stages was significantly associated with better late-ife verbal memory, and remained so when both life stages were modelled jointly. Higher midlife DASH adherence was associated with greater white matter volume in association tracts, whereas higher early-elderhood DASH adherence was associated with lower white matter hyperintensity (deep basal ganglia and anterior periventricular regions) and lower frontal and occipital grey matter free water, suggesting lower neurovascular and inflammatory burden. Mediation analyses indicated that white matter volume accounted for 12.3% in mediating the midlife DASH--verbal memory association, while cortical free water accounted for 12.5% in mediating the early-elderhood DASH--verbal memory association. Importantly, participants whose DASH adherence improved from lower adherence in midlife to better adherence in later life showed better verbal memory and more favourable brain integrity than those with persistently low adherence. These findings identify midlife and post-midlife diet quality as modifiable life-course exposures associated with late-life cognitive resilience through differences in macrostructural and microstructural brain integrity.

neuroscience↗

bioRxiv2026

Functional characterization of bat limb regulatory elements

Bats are the only mammals capable of powered flight and roost head-down. However, the molecular changes shaping bat limbs remain largely unknown. Here, we used comparative functional genomics coupled with mouse-bat sequence swaps to identify key regulatory elements important in bat limb development. We generated and compared bat and mouse forelimb and hindlimb genomic datasets at key wing developmental timepoints, followed by mouse enhancer assays to characterize sequences showing differences between species. We then swapped six mouse enhancer sequences with their corresponding bat sequences, obtaining a variety of bat limb associated phenotypes, including ossification delay, longer digits, thicker skin and symmetrical hindlimb digits. Our work provides a genomic catalog of genes and regulatory elements involved in bat limb development and through extensive characterization in mice shows how changes in regulatory elements lead to small phenotypic changes that together contribute to bat limb development.

developmental biology↗

bioRxiv2025

PRDM16 controls smooth muscle cell fate in atherosclerosis

Vascular smooth muscle cells (SMCs) normally exist in a contractile state but can undergo fate switching to produce various cell phenotypes in response to pathologic stimuli1-3. In atherosclerosis, these phenotypically modulated SMCs regulate plaque composition and influence the risk of major adverse cardiovascular events4,5. We found that PRDM16, a transcription factor that is genetically associated with cardiovascular disease, is highly expressed in arterial SMCs and downregulated during SMC fate switching in human and mouse atherosclerosis. Loss of Prdm16 in SMCs of mice activates a synthetic modulation program under homeostatic conditions. Single cell analyses show that loss of Prdm16 drives a synthetic program in all SMC populations. Upon exposure to atherogenic stimuli, SMC-selective Prdm16 deficient mice develop SMC-rich, fibroproliferative plaques that contain few foam cells. Acute loss of Prdm16 results in the formation of collagen-rich lesions with thick fibrous caps. Reciprocally, increasing PRDM16 expression in SMCs blocks synthetic processes, including migration, proliferation, and fibrosis. Mechanistically, PRDM16 binds to chromatin and decreases activating histone marks at synthetic genes. Altogether, these results define PRDM16 as a critical determinant of SMC identity and atherosclerotic lesion composition.

molecular biology↗

bioRxiv2024

Methods for detection of cardiac glycogen-autophagy

Glycogen-autophagy ( glycophagy) is a selective autophagy process involved in delivering glycogen to the lysosome for bulk degradation. Glycophagy protein intermediaries include STBD1 as a glycogen tagging receptor, delivering the glycogen cargo into the forming phagosome by partnering with the Atg8 homolog, GABARAPL1. Glycophagy is emerging as a key process of energy metabolism and development of reliable tools for assessment of glycophagy activity is an important priority. Here we show that antibodies raised against the N-terminus of the GABARAPL1 protein (but not the full-length protein) detected a specific endogenous GABARAPL1 immunoblot band at 18kDa. A stable GFP-GABARAPL1 cardiac cell line was used to quantify GABARAPL1 lysosomal flux via measurement of GFP puncta in response to lysosomal inhibition with bafilomycin. Endogenous glycophagy flux was quantified in primary rat ventricular myocytes by the extent of glycogen accumulation with bafilomycin combined with chloroquine treatment (no effect observed with bafilomycin or chloroquine alone). In wild-type isolated mouse hearts, bafilomycin alone and bafilomycin combined with chloroquine (but not chloroquine alone) elicited a significant increase in glycogen content signifying basal glycophagy flux. Collectively, these methodologies provide a comprehensive toolbox for tracking cardiac glycophagy activity to advance research into the role of glycophagy in health and disease.

molecular biology↗

bioRxiv2024

The protein interactome of Escherichia coli carbohydrate metabolism.

Carbohydrate metabolism is strictly regulated by multiple mechanisms to meet cellular needs (i.e. energy production). Several mechanisms modulate the amount and activity of metabolic enzymes. Here, we investigate how carbohydrate metabolism (CHM) in E. coli is regulated by their interaction properties with other proteins and their quantities. We computationally analyze 378 protein-enzyme interactions (PEIs) potentially involved in carbohydrate metabolism. We identified 20 enzymes and 19 interactors that occur at stoichiometries that are highly likely to affect CHM and 174 interactions that are possibly conserved across thousands of bacteria. These PPIs are predicted to be of global importance, including pathogens. Author summaryIn systems biology, "big" data are used to reconstruct biological networks which are then investigated in detail to uncover their functional dynamics. Understanding functional dynamics finally helps in identifying biomarkers in the big networks. We apply this concept to the biological network which is a crosstalk of protein-protein interaction network and carbohydrate metabolic pathway in Escherichia coli K-12. We investigate if proteins regulate enzymes when they bind them and we predict that protein-enzyme interactions (PEIs) regulate carbohydrate metabolism in E. coli.

systems biology↗

bioRxiv2023

Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans

Gene regulatory divergence between species can result from cis-acting local changes to regulatory element DNA sequences or global trans-acting changes to the regulatory environment. Understanding how these mechanisms drive regulatory evolution has been limited by challenges in identifying trans-acting changes. We present a comprehensive approach to directly identify cis- and trans-divergent regulatory elements between human and rhesus macaque lymphoblastoid cells using ATAC-STARR-seq. In addition to thousands of cis changes, we discover an unexpected number (~10,000) of trans changes and show that cis and trans elements exhibit distinct patterns of sequence divergence and function. We further identify differentially expressed transcription factors that underlie >50% of trans differences and trace how cis changes can produce cascades of trans changes. Overall, we find that most divergent elements (67%) experienced changes in both cis and trans, revealing a substantial role for trans divergence--alone and together with cis changes--to regulatory differences between species.

genomics↗

bioRxiv2021

Fast Medical Image Auto-segmentation for Bleeding Gastric Tissue Detection based on Deep DuS-KFCM Clustering

BackgroundDetection and classification of gastric bleeding tissues are one of the challenging tasks in endoscopy image analysis. Lesion detection plays an important role in gastric cancer (GC) diagnosis and follow-up. Manual segmentation of endoscopy images is a very time-consuming task and subject to intra- and interrater variability. Accurate GB segmentation in abdominal sequences is an essential and crucial task for surgical planning and navigation in gastric lesion ablation. However, GB segmentation in endoscope is a substantially challenging work because the intensity values of gastric blood are similar to those of adjacent structures. ObjectiveIn this paper the idea is to combine two parts: Neural Network and Fuzzy Logic--Hybrid Neuro-Fuzzy system. The objective of this manuscript is to provide an efficient way to segment the gastric bleeding lesion area. This work focuses on design and development of an automated diagnostic system using gastric bleeding cancer endoscopy images. MethodsIn this paper, a coarse-to-fine method was applied to segment gastric bleeding lesion from endoscopy images, which consists of two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm combined with spatial gray level co-occurrence matrix (GLCM) and the refined segmentation is implemented with deeplabv3+ (backbone with resnet50) algorithm to improve the overall accuracy. ResultsExperimental results for gastric bleeding segmentation show that the method provides an accuracy of 87.9476% with specificity of 96.3343% and performs better than other related methods. onclusionsThe performance of the method was evaluated using two benchmark datasets: The GB Segmentation and the healthy datasets. Then use the gastric red spots (GRS) dataset to do the final test to verify weak bleeding symptoms. Our method achieves high accuracy in gastric bleeding lesion segmentation. The work describes an innovative way of using GLCM based textural features to extract underlying information in gastric bleeding cancer imagery. Modified deep DuS-KFCM endoscopy image segmentation method based on GLCM feature, The experimental results shown to be effective in image segmentation and has good performance of resisting noise, segmentation effect more ideal.

bioinformatics↗

bioRxiv2021

A Deep Learning Approach for NeoAG-Specific Prediction Considering Both HLA-Peptide Binding and Immunogenicity: Finding Neoantigens to Making T-Cell Products More Personal

BackgroundAn emerging type of cancer treatment, known as cell immunotherapy, is gaining popularity over chemotherapy or other radiation therapy that causes mass destruction to our body. One favourable approach in cell immunotherapy is the use of neoantigens as targets that help our body immune system identify the cancer cells from healthy cells. Neoantigens, which are non-autologous proteins with individual specificity, are generated by non-synonymous mutations in the tumor cell genome. Owing to its strong immunogenicity and lack of expression in normal tissues, it is now an important target for tumor immunotherapy. Neoantigens are some form of special protein fragments excreted as a by-product on the surface of cancer cells during the DNA mutation at the tumour. In cancer immunotherapies, certain neoantigens which exist only on cancer cells elicit our white blood cells (bodys defender, anti-cancer T-cell) responses that fight the cancer cells while leaving healthy cells alone. Personalized cancer vaccines therefore can be designed de novo for each individual patient, when the specific neoantigens are found to be relevant to his/her tumour. The vaccine which is usually coded in synthetic long peptides, RNA or DNA representing the neoantigens trigger an immune response in the body to destroy the cancer cells (tumour). The specific neoantigens can be found by a complex process of biopsy and genome sequencing. Alternatively, modern technologies nowadays tap on AI to predict the right neoantigen candidates using algorithms. However, determining the binding and non-binding of neoantigens on T-cell receptors (TCR) is a challenging computational task due to its very large search space. ObjectiveTo enhance the efficiency and accuracy of traditional deep learning tools, for serving the same purpose of finding potential responsiveness to immunotherapy through correctly predicted neoantigens. It is known that deep learning is possible to explore which novel neoantigens bind to T-cell receptors and which ones dont. The exploration may be technically expensive and time-consuming since deep learning is an inherently computational method. one can use putative neoantigen peptide sequences to guide personalized cancer vaccines design. MethodsThese models all proceed through complex feature engineering, including feature extraction, dimension reduction and so on. In this study, we derived 4 features to facilitate prediction and classification of 4 HLA-peptide binding namely AAC and DC from the global sequence, and the LAAC and LDC from the local sequence information. Based on the patterns of sequence formation, a nested structure of bidirectional long-short term memory neural network called local information module is used to extract context-based features around every residue. Another bilstm network layer called global information module is introduced above local information module layer to integrate context-based features of all residues in the same HLA-peptide binding chain, thereby involving inter-residue relationships in the training process. introduced ResultsFinally, a more effective model is obtained by fusing the above two modules and 4 features matric, the method performs significantly better than previous prediction schemes, whose overall r-square increased to 0.0125 and 0.1064 on train and increased to 0.0782 and 0.2926 on test datasets. The RMSE for our proposed models trained decreased to approximately 0.0745 and 1.1034, respectively, and decreased to 0.6712 and 1.6506 on test dataset. ConclusionOur work has been actively refining a machine-learning model to improve neoantigen identification and predictions with the determinants for Neoantigen identification. The final experimental results show that our method is more effective than existing methods for predicting peptide types, which can help laboratory researchers to identify the type of novel HLA-peptide binding.

bioinformatics↗

bioRxiv2021

An inactivated multivalent influenza A virus vaccine is broadly protective in mice and ferrets

Influenza A viruses (IAVs) present major public health threats from annual seasonal epidemics, from pandemics caused by novel virus subtypes, and from viruses adapted to a variety of animals including poultry, pigs and horses. Vaccines that broadly protect against all such IAVs, so-called "universal" influenza vaccines, do not currently exist, but are urgently needed. This study demonstrates that an inactivated, multivalent whole virus vaccine, delivered intramuscularly or intranasally, is broadly protective against challenges with multiple IAV HA/NA subtypes in both mice and ferrets, including challenges with IAV subtypes not contained in the vaccine. This vaccine approach indicates the feasibility of eliciting broad "universal" IAV protection, and identifies a promising candidate for influenza vaccine clinical development. One-Sentence SummaryAn inactivated, whole avian influenza virus vaccine delivered intramuscularly or intranasally provides extremely broad protection against antigenically divergent viral challenge and is a promising candidate for a "universal" influenza virus vaccine.

microbiology↗

bioRxiv2021

Artificial mosaic brain evolution of relative telencephalon size improves cognitive performance in the guppy (Poecilia reticulata)

The telencephalon is a brain region believed to have played an essential role during cognitive evolution in vertebrates. However, till now, all the evidence on the evolutionary association between telencephalon size and cognition stem from comparative studies. To experimentally investigate the potential evolutionary association between cognitive abilities and telencephalon size, we used male guppies artificially selected for large and small telencephalon relative to the rest of the brain. In a detour task, we tested a functionally important aspect of executive cognitive ability; inhibitory control abilities. We found that males with larger telencephalon outperformed males with smaller telencephalon. They showed faster improvement in performance during detour training and were more successful in reaching the food reward without touching the transparent barrier. Together, our findings provide the first experimental evidence showing that evolutionary enlargements of relative telencephalon size confer cognitive benefits, supporting an important role for mosaic brain evolution during cognitive evolution.

ecology↗

bioRxiv2021

Rapid mosaic brain evolution under artificial selection for relative telencephalon size in the guppy (Poecilia reticulata)

The vertebrate brain displays enormous morphological variation and the quest to understand the evolutionary causes and consequences of this variation has spurred much research. The mosaic brain evolution hypothesis, stating that brain regions can evolve relatively independently, is an important idea in this research field. Here we provide experimental support for this hypothesis through an artificial selection experiment in the guppy (Poecilia reticulata). After four generations of selection on relative telencephalon volume (relative to brain size) in replicated up-selected, down-selected and control-lines, we found substantial changes in telencephalon size, but no changes in other regions. Comparisons revealed that up-selected lines had larger telencephalon while down-selected lines had smaller telencephalon than wild Trinidadian populations. No cost of increasing telencephalon size was detected in offspring production. Our results support that independent evolutionary changes in specific brain regions through mosaic brain evolution can be important facilitators of cognitive evolution.

evolutionary biology↗

bioRxiv2019

Deep functional synthesis: a machine learning approach to gene functional enrichment

Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from its associated network of literature and data, conditioned on the disease and drug context of the current experiment. Using a knowledge graph with 3,048,803 associations between genes, diseases, drugs, and functions, DeepSyn obtained accurate performance (range 0.74 AUC to 0.96 AUC) on a variety of biological applications including drug target identification, gene set functional enrichment, and disease gene prediction. AvailabilityThe DeepSyn codebase is available on GitHub at http://github.com/wangshenguiuc/DeepSyn/ under an open source distribution license.

systems biology↗

bioRxiv2019

Quantitative translation of dog-to-human aging by conserved remodeling of epigenetic networks

Mammals progress through similar physiological stages during life, from early development to puberty, aging, and death. Yet, the extent to which this conserved physiology reflects conserved molecular events is unclear. Here, we map common epigenetic changes experienced by mammalian genomes as they age, focusing on evolutionary comparisons of humans to dogs, an emerging model of aging. Using targeted sequencing, we characterize the methylomes of 104 Labrador retrievers spanning a 16 year age range, achieving >150X coverage within mammalian syntenic blocks. Comparison with human methylomes reveals a nonlinear relationship which translates dog to human years, aligns the timing of major physiological milestones between the two species, and extends to mice. Conserved changes center on specific developmental gene networks which are sufficient to capture the effects of anti-aging interventions in multiple mammals. These results establish methylation not only as a diagnostic age readout but as a cross-species translator of physiological aging milestones.

evolutionary biology↗

bioRxiv2019

A VP2/3-derived peptide exhibits potent antiviral activity against BK and JC polyomaviruses by targeting a novel VP1 binding site

In pursuit of effective therapeutics for human polyomaviruses, we identified a peptide derived from the BK polyomavirus (BKV) minor structural proteins VP2/3 that is a potent inhibitor of BKV infection with no observable cellular toxicity. The thirteen amino acid peptide binds to major structural protein VP1 in a new location within the pore with a low nanomolar KD. Alanine scanning of the peptide identified three key residues, substitution of each of which results in [~]1000-fold loss of affinity with a concomitant reduction in antiviral activity. NMR spectroscopy and an X-ray structurally-guided model demonstrate specific binding of the peptide to the pore of the VP1 pentamer that constitutes the BKV capsid. Cell-based assays with the peptide demonstrate nanomolar inhibition of BKV infection and suggest that the peptide likely blocks the viral entry pathway between endocytosis and escape from the host cell ER. The peptide motif is highly conserved among the polyomavirus clade, and homologous peptides exhibit similar binding properties for JC polyomavirus and inhibit infection with similar potency to BKV in a model cell line. Substitutions within VP1 or VP2/3 residues involved in VP1-peptide interaction negatively impact viral infectivity, potentially indicating the peptide-binding site within the VP1 pore is relevant for VP1-VP2/3 interactions. The inhibitory potential of the peptide-binding site first reported here may present a novel target for development of new anti-polyomavirus therapies. In summary, we present the first anti-polyomavirus inhibitor that acts via a novel mechanism of action by specifically targeting the pore of VP1.

microbiology↗

bioRxiv2019

Metabolic stress is a primary pathogenic event in transgenic Caenorhabditis elegans expressing neuronal human amyloid-β

Alzheimers disease (AD) is the most common neurodegenerative disease affecting the elderly worldwide. Mitochondrial dysfunction has been proposed as a key event in the etiology of AD. We have previously modeled amyloid-beta (A{beta})-induced mitochondrial dysfunction in a transgenic Caenorhabditis elegans strain by expressing human A{beta} peptide specifically in neurons (GRU102). Here, we focus on a deeper analysis of these metabolic changes associated with A{beta}-induced mitochondrial dysfunction. Integrating metabolomics, transcriptomics, biochemical studies and computational modeling, we identify alterations in Tricarboxylic Acid (TCA) cycle metabolism following even low-level A{beta} expression. In particular, GRU102 show reduced activity of a rate-limiting TCA cycle enzyme, alpha-ketoglutarate dehydrogenase. These defects are associated with elevation of protein carbonyl content specifically in mitochondria. Importantly, metabolic failure occurs before any significant increase in global protein aggregate is detectable. Treatment with an antidiabetes drug, Metformin, reverses A{beta}-induced metabolic defects, reduces protein aggregation and normalizes the lifespan of GRU102. Our results point to metabolic dysfunction as an early and causative event in AD pathology and a promising target for intervention.

neuroscience↗

bioRxiv2019

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment. The process of deliberation on the evidence can be described by a time-varying decision variable (DV), decoded from neural activity, that predicts a subjects decision at the end of a trial. However, within trials, large moment-to-moment fluctuations of the DV are observed. The behavioral significance of these fluctuations and their role in the decision process remain unclear. Here we show that within-trial DV fluctuations decoded in real time from motor cortex are tightly linked to choice behavior, and that robust changes in DV sign have the statistical regularities expected from behavioral studies of changes-of-mind. Furthermore, we find single-trial evidence for absorbing decision bounds. As the DV builds up, heavily favoring one or the other choice, moment-to-moment variability in the DV is reduced, and both neural DV and behavioral decisions become resistant to additional pulses of sensory evidence as predicted by diffusion-to-bound and attractor models of the decision process.

neuroscience↗

bioRxiv2019

Genetic risk for major depressive disorder and loneliness in gender-specific associations with coronary artery disease

ImportanceEpidemiological evidence indicates that major depressive disorder (MDD) and loneliness both reduce life expectancies, but mechanisms underlying the excess morbidity are unclear. Electronic health records (EHRs) linked to genetic data offer new opportunities to address this knowledge gap.\n\nObjectiveTo determine the medical morbidity pattern associated with genetic risk factors for MDD and loneliness, two common psychological traits with adverse health outcomes.\n\nDesignPhenome-wide association study using EHRs spanning 1990 to 2017 from the Vanderbilt University Medical Center biobank, BioVU. Top associations with coronary artery disease (CAD) were replicated in the Atherosclerosis Risk in Communities (ARIC) cohort.\n\nSettingHospital-based EHR study, with replication in a population-based cohort study.\n\nParticipants18,385 genotyped adult patients in BioVU. Replication in ARIC included 7,197 genotyped participants. All participants were of European ancestry.\n\nExposuresPolygenic scores for MDD and loneliness were developed for each individual using previously published meta-GWAS summary statistics.\n\nMain Outcomes and MeasuresThe phenome-wide association study included 882 clinical diagnoses ascertained via billing codes in the EHR. ARIC included 1598 incident CAD cases.\n\nResultsBioVU patients had a median EHR length of 9.91 years. In the phenome-wide association study, polygenic scores for MDD and loneliness were significantly associated with psychiatric and cardiac phenotypes. Targeted analyses of CAD in 3,893 cases and 4,197 controls in BioVU found odds ratios of 1.11 (95% CI, 1.04-1.18; P=8.43x10-4) and 1.13 (95% CI, 1.07-1.20; P=4.51x10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Comparable hazard ratios in ARIC were 1.07 (95% CI, 0.99-1.14; P=0.07) and 1.07 (1.01-1.15; P=0.03). Across both studies, the increased risk persisted in women after adjusting for multiple conventional risk factors, a polygenic score for CAD, and psychiatric symptoms (available in BioVU). Controlling for genetic risk factors shared between MDD and loneliness, the polygenic score for loneliness conditioned on MDD remained associated with CAD risk, but the polygenic score for MDD conditioned on loneliness did not.\n\nConclusions and RelevanceGenetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in women. Continued research into the biological and clinical connections between the heart and mind is warranted.

genetics↗