Biology subjects

Fung, M.

Publications and source records attributed to Fung, M..

5 recordsLinked to original sources

bioRxiv2026

Integrated Multiomics Reveals Intergenerational Brain Sensory System Remodeling Induced by Parental Intermittent Fasting

Intermittent fasting (IF) is increasingly recognized as a potent modulator of metabolic health, but its effects on brain function across generations remain poorly understood. Here, we show that parental IF, administered to both mothers and fathers before mating, is associated with coordinated molecular remodeling in the cerebral cortex of fathers and their offspring, with shared molecular signatures identified between these generations and a pronounced enrichment of sensory-associated pathways. Using whole-genome bisulfite sequencing, transcriptomic profiling, and proteomic analysis, we found that IF elicited widespread cortical epigenetic remodeling in fathers, accompanied by transcriptional and protein-level changes enriched for olfactory, taste, synaptic, and neurometabolic pathways. Strikingly, related molecular signatures were detected in F1 offspring that had never been directly exposed to fasting, consistent with intergenerational transmission of IF-associated cortical remodeling. Offspring responses were sex dependent, with female and male progeny exhibiting distinct patterns of DNA methylation, gene expression, and protein abundance. Integrated multiomic analyses identified conserved candidate networks involved in sensory perception, glutamatergic signaling, synaptic organization, intracellular trafficking, cytoskeletal regulation, and stress-response pathways. Together, these findings identify sensory-associated cortical remodeling as a prominent molecular feature associated with parental IF and reveal persistent, sex-dependent molecular alterations in the offspring brain.

genomics↗

bioRxiv2026

CortexDecoder: fMRI-to-image Reconstruction from Cortical Flatmaps

Recent advances in brain decoding work has shown the potential of Vision Transformers (ViTs) to learn mappings from neural representation to visual features. Here we present CortexDecoder, a brain decoding framework mapping fMRI flatmaps to images viewed by subjects. Flatmaps are neural responses projected onto cortical surfaces and then unfolded into 2D images. We demonstrate that (1) flatmaps preserve sufficient signal to achieve high performance in brain decoding; raw fMRI data has noisy signals and high-dimensionality which makes decoding a difficult task. (2) Reshaping raw fMRI data into flatmaps allows for clean patch-based tokenization. This allows us to harness state-of-the-art Vision Transformers to learn meaningful neural representations and preserve spatial encoding. To do this, we pass flatmap patches into a pretrained CortexMAE encoder to obtain brain tokens. A lightweight cross-attention bridge then learns to map these brain tokens to image features. Our cross-attention bridge is split into two modules; the semantic module for predicting CLIP embeddings and the lower-level module to generate low-level image reconstruction. Finally, a fine-tuned SDXL (Stable Diffusion XL) takes this low-level reconstruction image as the initial image. Conditioned by the CLIP semantic embedding, the SDXL produces the final reconstruction image. In single-subject evaluations, CortexDecoder achieves performance comparable to state-of-the-art brain decoding methods, demonstrating the effectiveness of flatmap based neural representations for visual reconstruction.

neuroscience↗

bioRxiv2026

Harnessing Inflammatory Monocytes to Overcome Resistance to Anti-PD-1 Immunotherapy

BackgroundResistance to immune checkpoint inhibitors represents a major therapeutic challenge, as less than 50% of patients with melanoma achieve long-term response to immune checkpoint inhibitor therapy. One mechanism of acquired resistance involves somatic mutations, such as loss of beta-2 microglobulin (B2m), that enable tumor cells to evade T cell-mediated killing. MethodsThis study used single-cell RNA-seq, flow cytometry, and ex vivo functional assays to characterize tumor-infiltrating immune cells in antigen presentation-deficient tumors. Tumor-bearing mice were treated with anti-PD-1 or CD40 agonist antibodies and cell depletion or cytokine blocking antibodies to define mechanisms of action. Analysis of published human RNA-seq datasets was performed to dissect the contributions of inflammatory monocytes to patient outcomes. ResultsWe found an increase in immunosuppressive macrophages in B2m-null tumors. We hypothesized that repolarizing myeloid cells may restore control of tumor growth. Treatment with CD40 agonist antibody, which promotes differentiation of monocytes and macrophages towards a proinflammatory phenotype, reduced tumor growth and improved survival in B2m-null melanoma and colorectal cancer models. Unexpectedly, both CD8+ T cells and NK cells, but not CD4+ T cells, were required for the efficacy of CD40 agonist, even though CD8+ T cells cannot directly recognize antigen presentation-deficient tumor cells. Instead, these lymphocytes control tumor growth via secretion of IFN{gamma}, as depletion of IFN{gamma} inhibited the therapeutic effect of CD40 agonist. IFN{gamma} receptor (Ifngr1) expression was required on host cells, not tumor cells, for CD40 agonist-mediated tumor control. Single-cell analysis identified a distinct population of inflammatory monocytes that were enriched for an IFN{gamma} response signature in CD40 agonist-treated tumors, suggesting that these cells may be important for tumor control. Analysis of human bulk and single-cell RNA-seq datasets demonstrated that an inflammatory monocyte signature derived from our data was associated with improved patient outcomes and response to immune checkpoint inhibitors. ConclusionsThese data demonstrate that CD8+ T cells contribute to tumor control even in the absence of direct antigen presentation by tumor cells. More broadly, our work suggests that strategies to activate the effector functions of inflammatory monocytes may limit tumor growth and overcome acquired resistance to immune checkpoint inhibitors.

immunology↗

bioRxiv2023

Magnetic Resonance Fingerprinting based Thermometry (MRFT): application to ex vivo imaging near DBS leads

The purpose of this study is to demonstrate the first work of T1-based magnetic resonance thermometry using magnetic resonance fingerprinting (dubbed MRFT). We compared temperature estimation of MRFT with proton resonance frequency shift (PRFS) thermometry on ex vivo bovine muscle. We demonstrated MRFTs feasibility in predicting temperature on ex vivo bovine muscles with deep brain stimulation (DBS) lead. B0 maps generated from MRFT were compared with gold standard B0 maps near the DBS lead. All experiments were performed on a 3 Tesla whole-body GE Premier system equipped with a 21-channel receive head coil (GE Healthcare, Milwaukee, WI). Four fluoroptic probes were used to measure the temperature at the center of a cold muscle (probe 1), the room temperature water bottle (probe 2), and the center and periphery of the heated muscle (probes 3 and 4). We selected regions of interest (ROIs) around the location of the probes and used simple linear regression to generate the temperature sensitivity calibration equations that convert T1 maps and {Delta}s maps to temperature maps. We then repeated the same setup and compared MRFT, PRFS thermometry temperature estimation with gold standard probe measurements. For the MRFT experiment on DBS lead, we taped the probe to the tip of the DBS lead and used a turbo spin echo (TSE) sequence to induce heating near the lead. We selected ROIs around the tip of the lead to compare MRFT temperature estimation with probe measurements. Vendor-supplied B0 mapping sequence was acquired to compare with MRFT-generated B0 maps. We found strong linear relationships (R2>0.958) between T1 and temperature and {Delta}s and temperatures in our temperature sensitivity calibration experiment. MRFT and PRFS thermometry both accurately predict temperature (RMSE<1.55 {degrees}C) compared to probe measurements. MRFT estimated temperature near DBS lead has a similar trend as the probe temperature. Both B0 maps show inhomogeneities around the lead.

bioengineering↗

bioRxiv2018

Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category(1). Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests(2). In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the lung microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 - 0.98), and the host transcriptional classifier with an AUC of 0.91 (95% CI = 0.80 - 1.00). Combining all three achieved an AUC of 0.99 (95% CI = 0.97 - 1.00) and negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.\n\nSIGNIFICANCE STATEMENTLower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, lung microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.\n\nFundingNHLBI K12HL119997 (Langelier C), NHLBI K23HL123778 (Christensen S), NIAID P01AI091575 and the Chan Zuckerberg Biohub (DeRisi JL), NHLBI K23 HL136844 (Moazed F), NHLBI R01HL110969, K24HL133390, R35HL140026 (Calfee C), Gladstone Institutes (Pollard KS).

genomics↗