Biology subjects

Huang, Y. J.

Publications and source records attributed to Huang, Y. J..

13 recordsLinked to original sources

bioRxiv2026

The NMR Exchange Format (NEF): Specification and Applications

The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEFs growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.

biochemistry↗

bioRxiv2025

Modeling Alternative Conformational States in CASP16

The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score >0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality based selection. While the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score <0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains a critical frontier for structural biology.

biochemistry↗

bioRxiv2025

Divergent ontogeny of Tissue Resident Memory and Tissue Resident Exhausted CD8+ T cells underlies distinct functional potential

Persistent antigen stimulation promotes differentiation of exhausted CD8+ T (TEX) cells. TEX cells are distinct from circulating memory T (TCIRCM) cells but share many features with tissue-resident memory (TRM) cells established following infection resolution. CD8+ T cells co-expressing residency- and exhaustion-associated molecules in chronic diseases often correlate with clinical outcomes. However, the relationship between these cells and conventional TRM or TEX cells remains unclear. Here, we show that chronic antigen stimulation drives development of tissue-resident TEX (TR-TEX) cells that are ontologically and functionally distinct from TRM cells generated after antigen clearance. TR-TEX phenotypically resembled TRM cells but were regulated by distinct transcriptional networks and were uniquely dependent on Tox for residency programming. Although TEX progenitor cells acquired residency features upon entering chronically infected tissues, they failed to generate conventional TRM cells after antigen withdrawal. Conversely, TRM cells were able to differentiate into TEX cells during chronic antigen stimulation. Deriving cell-state specific transcriptional signatures revealed a selective association of TR-TEX cells with patient responses to immune checkpoint blockade, and only TR-TEX but not TRM cells responded to PD-1 pathway inhibition in vivo. These data suggest that TR-TEX and TRM cells are developmentally distinct cell types that share a tissue-residency program but have distinct roles in disease control.

immunology↗

bioRxiv2024

Hidden Structural States of Proteins Revealed byConformer Selection with AlphaFold-NMR

We introduce AlphaFold-NMR, a novel approach to NMR structure determination that reveals previously undetected protein conformational states. Unlike conventional NMR methods that rely on NOE-derived spatial restraints, AlphaFold-NMR combines AI-driven conformational sampling with Bayesian scoring of realistic protein models against NOESY and chemical shift data. This method uncovers alternative conformational states of the enzyme Gaussia luciferase, involving large-scale changes in the lid, binding pockets, and other surface cavities. It also identifies similar yet distinct conformational states of the human tumor suppressor Cyclin-Dependent Kinase 2-Associated Protein 1. These studies demonstrate the potential of AI-based modeling with enhanced sampling to generate diverse structural models followed by conformer selection and validation with experimental data as an alternative to traditional restraint-satisfaction protocols for protein NMR structure determination. The AlphaFold-NMR framework enables discovery of conformational heterogeneity and cryptic pockets that conventional NMR analysis methods do not distinguish, providing new insights into protein structure-function relationships. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/600902v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1d52566org.highwire.dtl.DTLVardef@8a8c14org.highwire.dtl.DTLVardef@1f28ffeorg.highwire.dtl.DTLVardef@1cba82a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

In Vitro Modeling of CD8 T Cell Exhaustion Enables CRISPR Screening to Reveal a Role for BHLHE40

Identifying novel molecular mechanisms of exhausted CD8 T cells (Tex) is a key goal of improving immunotherapy of cancer and other diseases. However, high-throughput interrogation of in vivo Tex can be costly and inefficient. In vitro models of Tex are easily customizable and quickly generate high cellular yield, offering an opportunity to perform CRISPR screening and other high-throughput assays. We established an in vitro model of chronic stimulation and benchmarked key phenotypic, functional, transcriptional, and epigenetic features against bona fide in vivo Tex. We leveraged this model of in vitro chronic stimulation in combination with pooled CRISPR screening to uncover transcriptional regulators of T cell exhaustion. This approach identified several transcription factors, including BHLHE40. In vitro and in vivo validation defined a role for BHLHE40 in regulating a key differentiation checkpoint between progenitor and intermediate subsets of Tex. By developing and benchmarking an in vitro model of Tex, we demonstrate the utility of mechanistically annotated in vitro models of Tex, in combination with high-throughput approaches, as a discovery pipeline to uncover novel Tex biology.

immunology↗

bioRxiv2023

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. HighlightsO_LIAF2 models assessed against NMR data for 9 monomeric proteins not used in training. C_LIO_LIAF2 models fit NMR data almost as well as the experimentally-determined structures. C_LIO_LIRPF-DP, PSVS, and PDBStat software provide structure quality and RDC assessment. C_LIO_LIRPF-DP analysis using AF2 models suggests multiple conformational states. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/525096v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@be0a41org.highwire.dtl.DTLVardef@9336e9org.highwire.dtl.DTLVardef@fa1d24org.highwire.dtl.DTLVardef@8f4efe_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Enhanced STAT5a activation rewires exhausted CD8 T cells during chronic stimulation to acquire a hybrid durable effector like state

Rewiring exhausted CD8 T cells (TEX) towards more functional states is a major goal of cancer immunotherapy but has proven challenging due to the epigenetic stability of TEX. Indeed, TEX are epigenetically programmed by the transcription factor Tox. However, epigenetic changes continue to occur as TEX transition from progenitor (TEXprog), to intermediate (TEXint) and terminal (TEXterm) subsets, suggesting potential developmental flexibility in mature TEX subsets. By examining the transition of TEXprog into TEXint cells, we discovered a reciprocally antagonistic circuit between Stat5a and Tox in TEX cells. Stat5-activity controlled TEXint development, antagonized Tox, and instigated partial effector biology. Stat5 was also essential for TEX reinvigoration by PD-1 blockade. Indeed, temporal induction of Stat5-activity in TEX using an orthogonal IL-2/IL2R{beta}-pair fostered TEXint cell accumulation and synergized with PD-L1 blockade. Constitutive Stat5a activity (STAT5CA) antagonized Tox-dependent TEX epigenetic programming to generate a durable hybrid effector/NK-like population with enhanced tumor control. Finally, enforcing Stat5-signals in established TEXprog partially rewired the TEX epigenetic landscape towards the effector/memory lineage. Together, these data highlight therapeutic opportunities of manipulating Stat5 to rewire TEX towards a durably protective hybrid program.

immunology↗

bioRxiv2022

Longitudinal single cell transcriptional and epigenetic mapping of effector, memory, and exhausted CD8 T cells reveals shared biological circuits across distinct cell fates

Naive CD8 T cells can differentiate into effector (TEFF), memory (TMEM), or exhausted (TEX) CD8 T cells. These developmental pathways are associated with distinct transcriptional and epigenetic changes that endow cells with different functional capacities and therefore therapeutic potential. The molecular circuitry underlying these developmental trajectories and the extent of heterogeneity within TEFF, TMEM, TEX populations remain poorly understood. Here, we used the lymphocytic choriomeningitis virus model of acutely-resolved and chronic infection and addressed these gaps by applying longitudinal scRNA-seq and scATAC-seq analysis. These analyses uncovered new subsets, including a subpopulation of TEX expressing NK cell-associated genes, as well as multiple distinct TCF1+ stem/progenitor-like subsets in acute and chronic infection. These data also revealed insights into the reshaping of TEX subsets following PD1 pathway blockade and identified a key role for the cell stress regulator, Btg1, in TEX differentiation. Finally, these results highlighted how the same biological circuits such as cytotoxicity or stem/progenitor pathways can be used by CD8 T cells with highly divergent underlying chromatin landscapes. Thus, this transcriptional and chromatin accessibility landscape map elucidates developmental biology and underlying mechanisms governing TEFF, TMEM, and TEX differentiation.

immunology↗

bioRxiv2022

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracy rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

biochemistry↗

bioRxiv2022

SpecDB: A Relational Database for Archiving Biomolecular NMR Spectra Data

NMR is a valuable experimental tool in the structural biologists toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Protein NMR research groups produce large amounts of data, and there is renewed interest in organizing this data to train new, sophisticated machine learning architectures to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectra Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/480212v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11a8109org.highwire.dtl.DTLVardef@10da6a6org.highwire.dtl.DTLVardef@137ffb5org.highwire.dtl.DTLVardef@174c5b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Signaling through FcγRIIA and the C5a-C5aR pathway mediates platelet hyperactivation in COVID-19

Patients with COVID-19 present with a wide variety of clinical manifestations. Thromboembolic events constitute a significant cause of morbidity and mortality in patients infected with SARS-CoV-2. Severe COVID-19 has been associated with hyperinflammation and pre-existing cardiovascular disease. Platelets are important mediators and sensors of inflammation and are directly affected by cardiovascular stressors. In this report, we found that platelets from severely ill, hospitalized COVID-19 patients exhibit higher basal levels of activation measured by P-selectin surface expression, and have a poor functional reserve upon in vitro stimulation. Correlating clinical features to the ability of plasma from COVID-19 patients to stimulate control platelets identified ferritin as a pivotal clinical marker associated with platelet hyperactivation. The COVID-19 plasma-mediated effect on control platelets was highest for patients that subsequently developed inpatient thrombotic events. Proteomic analysis of plasma from COVID-19 patients identified key mediators of inflammation and cardiovascular disease that positively correlated with in vitro platelet activation. Mechanistically, blocking the signaling of the Fc{gamma}RIIa-Syk and C5a-C5aR pathways on platelets, using antibody-mediated neutralization, IgG depletion or the Syk inhibitor fostamatinib, reversed this hyperactivity driven by COVID-19 plasma and prevented platelet aggregation in endothelial microfluidic chamber conditions, thus identifying these potentially actionable pathways as central for platelet activation and/or vascular complications in COVID-19 patients. In conclusion, we reveal a key role of platelet-mediated immunothrombosis in COVID-19 and identify distinct, clinically relevant, targetable signaling pathways that mediate this effect. These studies have implications for the role of platelet hyperactivation in complications associated with SARS-CoV-2 infection. Cover illustration O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY One-sentence summaryThe Fc{gamma}RIIA and C5a-C5aR pathways mediate platelet hyperactivation in COVID-19

immunology↗

bioRxiv2020

Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions

COVID-19 has become a global pandemic. Immune dysregulation has been implicated, but immune responses remain poorly understood. We analyzed 71 COVID-19 patients compared to recovered and healthy subjects using high dimensional cytometry. Integrated analysis of [~]200 immune and >30 clinical features revealed activation of T cell and B cell subsets, but only in some patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses could reach >30% of circulating B cells. However, another subgroup had lymphocyte activation comparable to uninfected subjects. Stable versus dynamic immunological signatures were identified and linked to trajectories of disease severity change. These analyses identified three "immunotypes" associated with poor clinical trajectories versus improving health. These immunotypes may have implications for therapeutics and vaccines.

immunology↗

bioRxiv2019

Cell non-autonomous interactions during non-immune stromal progression in the breast tumor microenvironment

SummaryThe breast tumor microenvironment of primary and metastatic sites is a complex milieu of differing cell populations, consisting of tumor cells and the surrounding stroma. Despite recent progress in delineating the immune component of the stroma, the genomic expression landscape of the non-immune stroma (NIS) population and their role in mediating cancer progression and informing effective therapies are not well understood. Here we obtained 52 cell-sorted NIS and epithelial tissue samples across 37 patients from i) normal breast, ii) normal breast adjacent to primary tumor, iii) primary tumor, and iv) metastatic tumor sites. Deep RNA-seq revealed diverging gene expression profiles as the NIS evolves from normal to metastatic tumor tissue, with intra-patient normal-primary variation comparable to inter-patient variation. Significant expression changes between normal and adjacent normal tissue support the notion of a cancer field effect, but extended out to the NIS. Most differentially expressed protein-coding genes and lncRNAs were found to be associated with pattern formation, embryogenesis, and the epithelial-mesenchymal transition. We validated the protein expression changes of a novel candidate gene, C2orf88, by immunohistochemistry staining of representative tissues. Significant mutual information between epithelial ligand and NIS receptor gene expression, across primary and metastatic tissue, suggests a unidirectional model of molecular signaling between the two tissues. Furthermore, survival analyses of 827 luminal breast tumor samples demonstrated the predictive power of the NIS gene expression to inform clinical outcomes. Together, these results highlight the evolution of NIS gene expression in breast tumors and suggest novel therapeutic strategies targeting the microenvironment.

cancer biology↗