Biology subjects

Zheng, W.

Publications and source records attributed to Zheng, W..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Resolving Conformational Heterogeneity in IntrinsicallyDisordered Proteins via Experimentally Guided Multi-Replica Simulations

The conformational landscapes of intrinsically disordered proteins (IDPs) remain obscured by the ensemble averaging inherent to experimental observables. Here, we present Multi-replica Averaged Restraint Simulation (MARS), a data-driven modeling framework that reconstructs these landscapes by enforcing ensemble-averaged restraints across multiple replicas without imposing prior structural knowledge. Using the N-terminal domain of estrogen receptor alpha (ER-NTD) as a model system, MARS simultaneously integrates small-angle X-ray scattering (SAXS) and six paramagnetic relaxation enhancement (PRE) profiles, comprising over 600 pairwise restraints, to generate ensembles that quantitatively fit all input structural restraints. The resulting ensemble is consistent with independent backbone relaxation measurements and reveals two major conformational states: a dominant extended state and a low-populated yet functionally relevant compact state whose structural features align with prior mutagenesis studies. Systematic benchmarking demonstrates that SAXS and PRE provide orthogonal global and local constraints, that each PRE profile contributes non-redundant structural information, and that multi-replica sampling is essential for preserving conformational heterogeneity. MARS offers a scalable framework for integrating orthogonal biophysical measurements to resolve both dominant and rare functional states in IDPs.

biophysics↗

bioRxiv2026

Quantitative horizon scanning identifies high-priority wood-boring beetle pests for regulatory review in Canada

Invasive wood-boring beetles are a significant threat to forests, imposing high economic and environmental costs. With their high global biodiversity, it is difficult to assess all wood-boring beetles potential to invade a particular region. Horizon scanning can create short-lists of high priority pests for risk assessment by National Plant Protection Organizations; however, it is often resource-intensive, creating potential blind spots due to resource constraints. Here, we use a multi-layered horizon scan, including self-organizing maps and climate suitability modelling combined with rapid risk assessment, to produce a short-list of wood-boring beetles for regulatory review in Canada. This method relies on open-source data and could be applicable to any country. From an initial list of 10,824 species with available georeferenced observations, our method yielded a short-list of 24 species. The method led to the regulation of two additional wood-boring beetles in Canada, demonstrating its real-world applicability in accelerating the regulation of quarantine pests. Several already-regulated species were also identified, suggesting consistency with existing risk identification and assessment procedures. The limitations on the method are the availability of biologically meaningful species occurrence data, relevance of climate norms for species establishment, and availability of data for rapid risk assessment.

ecology↗

bioRxiv2025

Residue-Specific Modulation of Aggregation-Associated Interactions bySpermine in Tau, α-Synuclein, and Aβ40

Preventing neurodegenerative diseases associated with intrinsically disordered proteins (IDPs) remains a major challenge due to the lack of a detailed, sequence-level picture of disease-relevant structures formation and the influence of cellular factors that modulate these transitions. Here, we probe spermine (Spm), a +4 charged polyamine abundant in cells, to determine how it reshapes the conformational ensembles and fibril-associated contact propensities of three disease-linked IDPs: the K18 domain of Tau, -synuclein (S) and amyloid-{beta}40 (A{beta}40). Using long all-atom molecular dynamics simulations across a range of Spm concentrations, we quantify residue-level changes in intra-chain contacts relative to native contacts observed in fibrils, and corroborate computational predictions with ThT fluorescence assays for Tau constructs. Spm acts in a sequence-and region-specific manner, not simply through overall net charge. In K18, Spm binds near the fourth microtubule binding repeat, disrupting intra-chain contacts associated with Alzheimers fibril structures and thereby inhibiting aggregation. In S, Spm binds mainly to acidic residues in the C-terminal half of the sequence and redistributes intramolecular contacts in a way that increases contact propensity in the central aggregation-prone region and therefore aggregation, in line with previous studies showing Spm-enhanced S aggregation. For A{beta}40, Spm neutralizes acidic residues near positions 22-24 and shifts the balance of intra-chain interactions toward its aggregation-prone core, resulting in a net promotion of fibril-like conformations. These divergent effects show that net charge alone cannot predict polyamine influence on IDPs. Instead, residue-specific binding hotspots and local reweighting of aggregation-linked contacts determine whether Spm promotes or suppresses fibril-like conformations. This combined simulation-experimental framework provides a mechanistic basis for how small molecules reprogram IDP conformational ensembles and suggests principles for designing ligands that exploit similar residue-level modulation.

biophysics↗

bioRxiv2025

Structural Basis of TACO1-Mediated Efficient Mitochondrial Translation

Translation elongation is a universally conserved step in protein synthesis, relying on elongation factors that engage the ribosomal L7/L12 stalk to mediate aminoacyl-tRNA delivery, accommodation, and ribosomal translocation. Using in organello cryo-electron microscopy, we reveal how the mitochondrial translation accelerator TACO1 promotes efficient elongation on human mitoribosomes. TACO1 binds the mitoribosomal region typically bound by elongation factor Tu (mtEF-Tu), bridging the large and small subunits via contacts with 16S rRNA, bL12m, A-site tRNA, and uS12m. While active throughout elongation, TACO1 is especially critical when translating polyproline motifs. Its absence prolongs mtEF-Tu residence in A/T states, causes persistent mitoribosomal stalling and premature subunit dissociation. Structural analyses indicate that TACO1 competes with mtEF-Tu for mitoribosome binding, stabilizes A-site tRNA, and enhances peptidyl transfer through a mechanism distinct from EF-P and eIF5A. These findings suggest that bacterial TACO1 orthologs may serve analogous roles, highlighting an evolutionarily conserved strategy for maintaining elongation efficiency during challenging translation events.

biophysics↗

bioRxiv2025

Nuclear export governs TDP-43 phase transitions and cytoplasmic aggregation

RNA-binding protein TAR DNA-binding protein 43 (TDP-43) can form liquid-like, nuclear assemblies whose phase behavior is thought to influence its aggregation propensity and neurotoxic activity. The mechanism(s) that governs the liquid-to-solid phase transition of TDP-43 is poorly defined. Here we combined chemical and genome-wide genetic screenings to identify cellular factors that modulate the phase behavior of an RNA-binding defective TDP-43 mutant. Our screens uncovered multiple cellular processes including RNA splicing, protein translation, proteostasis imbalance and nuclear export as TDP-43 phase regulators. We also developed a semi-permeabilized cell system that partially recapitulates the TDP-43 phase transition in vitro, and showed that nuclear export inhibition reshapes the nuclear environment to favor RNA-dependent liquid-liquid phase separation (LLPS) of TDP-43, which mitigates its aggregation. Nuclear export inhibition in a brain organoid model bearing an ALS-associated mutation reduces pathogenic phospho-TDP-43 accumulation. These findings identify multiple modulators of TDP-43 phase transitions in a sensitized model system and establish a framework for further dissecting the link between nuclear transport and TDP-43 phase dynamics.

cell biology↗

bioRxiv2025

From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells

Drug repurposing offers a promising approach for identifying novel treatments, especially for rare cancers like inflammatory breast cancer (IBC), an aggressive type with limited therapeutic options. Here, we present a comprehensive validation and verification study of compounds identified through two computational approaches: Literature Wide Association Studies (LWAS) and Gene Reversal Rate (GRR), using orthogonal cell viability assays in 2D models across IBC and non-IBC cell lines. In the SUM149 IBC cell line, repurposed compounds predicted from LWAS achieved a 70% success rate, with several showing nanomolar potency, while those predicted from GRR showed a 38% success rate. Through systematic combination screening in both 2D and 3D-spheroid models, we identified novel synergistic compound pairs targeting crosstalk between IGF1-R, EGFR and PI3K/Akt/mTOR pathways, with high synergy scores across multiple reference models. Using these combinations, western blotting analysis revealed significant suppression in the phosphorylation of key signaling proteins and downstream effectors, while wound healing assays demonstrated reduced cell migration for some combinations, suggesting effective pathway inhibition. To further validate these findings at the transcriptional level, RNA-Seq analysis in SUM149 cells confirmed that the GRR drug combinations significantly reversed the IBC gene expression signature (IBC-GES). These findings not only validated our computational predictions but also identified promising combination strategies that could potentially overcome drug resistance in IBC. Our integrated computational-experimental approach establishes a framework for systematic drug repurposing and highlights novel therapeutic combinations warranting further investigation.

cancer biology↗

bioRxiv2025

Pseudodynamics+: Reconstructing Population Dynamics from Time-Resolved Single Cell Landscapes with Physics Informed Neural Networks

Single-cell profiling provides snapshots of the heterogeneous states that characterise developmental processes, organ regeneration and progression towards disease in a complex landscape. The underlying trajectories are of pivotal interest, but existing methods for reconstructing cell state trajectories commonly neglect population sizes. However, snapshot experiments make it difficult to interpret cell flux because the observed trajectories are confounded by changes in overall population size. This ambiguity can lead to misinterpreting changes in proliferation or death rates as changes in cellular migration. We introduce pseudodynamics+, a physics-informed neural network framework that solves high-dimensional flow equations on complex, branching landscapes. By integrating single-cell genomics with population dynamics, pseudodynamics+ estimates state- and time-dependent parameters of growth, differentiation, and diffusion. The model recapitulates proliferation bursts during T-cell maturation and, when applied to LARRY-barcoded data, predicts differentiation rates consistent with clonal behaviour. When applied to time-resolved persistent-labelling datasets of in vivo mouse bone marrow haematopoiesis, pseudodynamics+ reconstructs continuous tissue flows with dynamic parameters aligned with known molecular signatures. Notably, simulations revealed a previously unrecognised shift from megakaryocyte-biased to balanced progenitor output, explained by evolving fate preferences of progenitor states, as revealed by simulations leveraging our estimated dynamic parameters. Pseudodynamics+ therefore establishes a population-aware framework for reconstructing single-cell population dynamics and is available at https://github.com/Gottgens-lab/pseudodynamics_plus.

bioinformatics↗

bioRxiv2025

An inherent T cell-activating mRNA delivery platform for in vivo CAR T generation

The clinical success of CAR T cell therapy has underscored the need for scalable, non-invasive strategies for in vivo T cell engineering. Although mRNA delivery offers a promising alternative, current lipid-based vectors lack intrinsic T cell tropism and often rely on antibody conjugation, complicating manufacturing and raising safety concerns. Here we present an inherent T cell-activating polymer-lipid nanoparticle (ERTLNPs) that enable ligand-free, efficient mRNA transfection and activation of T cells in vivo. ERTLNPs, composed of p-toluenesulfonyl arginine (RT)-functionalized polyethylenimine (denoted as PEI-RT) and helper lipids, preferentially accumulate in the spleen following systemic administration. Without exogenous stimulation, ERTLNPs intrinsically activate T cells, triggering robust mRNA expression and proliferation. Mechanistically, ERTLNPs engage the PI3K/AKT/mTOR signaling axis to reprogram T cell metabolism, promoting expansion while restraining exhaustion. Systemic delivery of mRNA encoding fibroblast activation protein chimeric antigen receptor (mFAP CAR) via ERTLNPs leads to in situ generation of functional CAR T cells, which efficiently eliminate pathological fibroblasts in models of cancer and fibrosis, with minimal off-target effects. This ligand-free, metabolically reprogramming mRNA delivery platform provides a clinically translatable approach for in vivo CAR T cell generation and broadens the landscape of non-viral immunotherapeutic engineering.

bioengineering↗

bioRxiv2025

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Decoding cellular systems requires integrating diverse omics data, yet most models are trained from scratch on a single modality, restricting generalization. Here we present CellHermes, a biological language model that leverages pretrained large language models (LLMs) to integrate multimodal forms of omics data, such as transcriptomic profile and PPI network, through natural language for better omics understanding. By reformulating these datasets into question-answer pairs, CellHermes emulates multiple self-supervised learning paradigms within a single, universal, language-based framework with LORA fine-tuning on existing natural language-LLM, achieving comparable or even better performance compared to current single-cell foundation models. Within this framework, CellHermes functions as an encoder, a predictor and an explainer for supporting a range of downstream tasks. For encoder, CellHermes encodes biological entities with embeddings that enable accurate network discovery and cross-dataset generalization; For predictor, CellHermes unifies heterogeneous downstream tasks by translating them into natural language question-answers pairs, allowing a single model to perform multi-task prediction by instruction fine-tuning; For explainer, CellHermes explains molecular mechanisms by combining attention analysis with text-based reasoning, leveraging the interpretability and reasoning capabilities of LLMs. We evaluated the ability of CellHermes as an encoder on representing genes and cells using 5 gene-level downstream tasks and 5 diverse single-cell datasets across different tissues compared with other single cell foundation models. We also evaluated CellHermes as a predictor on BioUniBench, a new benchmark of 10 tasks across 7 databases for LLMs, including perturbation response, cell fitness estimation, gene-disease association, and cell type identification. All benchmarks suggest CellHermes can unify multiple biological tasks within one model without compromising performance. By applying CellHermes as an explainer for a melanoma patients dataset, it can uncover potential key genes on tumor-reactive T cells. Together, CellHermes establishes natural language as a unifying medium for omics, offering a foundation that, in the future, potentially enables a more integrated and interpretable research loop of biological representation, prediction, and interpretability.

bioinformatics↗

bioRxiv2025

A human forebrain organoid model phenocopies dysregulated RNA and protein homeostasis in ALS/FTD-associated TDP-43 proteinopathies

TAR DNA-binding protein 43 (TDP-43) proteinopathy is a defining pathological feature of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), yet the downstream molecular events linking TDP-43 dysfunction to neurodegeneration remain incompletely understood. Here, we model TDP-43 proteinopathy by introducing the ALS/FTD-associated TARDBP K181E mutation into human induced pluripotent stem cells and differentiating them into three-dimensional forebrain organoids. Organoids carrying the K181E mutation exhibit spontaneous TDP-43 hyperphosphorylation, cytoplasmic p-TDP43 accumulation, RNA dysregulation, pro-inflammatory signaling, and activation of apoptotic pathways without TDP-43 overexpression or external stress. Single-cell transcriptomics and enhanced crosslinking immunoprecipitation reveal that the K181E mutation alters TDP-43 RNA-binding specificity, causing widespread RNA mis-splicing including cryptic exon inclusion. Among affected targets, we identify cryptic exon inclusion in the transcription factor PRDM2. PRDM2 cryptic exon-derived peptide immunoreactivity was detected in ALS spinal motor neurons and associated with p-TDP-43 pathology. These findings identify PRDM2 mis-splicing as a candidate downstream event linking TDP-43 dysfunction relevant to ALS and FTD. One sentence summaryHuman brain organoids reveal how mutant TDP-43 disrupts RNA processing and causes neuronal stress.

neuroscience↗

bioRxiv2025

Chicken IRF10 suppresses the cGAS-STING-IFN antiviral signaling pathway by targeting IRF7

The chicken (ch) innate immune cGAS-STING-IFN signaling pathway plays a crucial role in host antiviral defense, wherein chIRF7 serves as the key downstream transcription factor of chSTING responsible for initiating type I IFN transcription. While chicken IRF family encompasses 8 members, the potential involvement of other IRF members in chicken cGAS-STING-IFN signaling transduction and/or regulation remains poorly characterized. In this study, we identified chIRF10 as a potent suppressor of chicken cGAS-STING-IFN signaling against several viruses, by screening of chIRF family members. Mechanistically, the inhibitory activity of chIRF10 depended on its IRF associated domain (IAD) but not its DNA binding domain (DBD). Further, chIRF10 suppressed both cGAS-STING mediated IFN signaling and TBK1/IKK{varepsilon}/IRF7 activated IFN signaling. Importantly, chIRF10 could interact with chIRF7, and inhibit its dimerization and activation. In summary, our findings unveiled a novel function of chIRF10 as well as a novel regulatory mechanism governing chicken cGAS-STING-IFN antiviral signaling, thereby providing a scientific foundation for developing strategies against chicken viral diseases.

immunology↗

bioRxiv2025

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗

bioRxiv2025

Bio-synthesis of 15N-Labeled G-Quadruplexes to Investigate the Structure and Interactions in the Cell Lysate using Nuclear Magnetic Resonance

G-quadruplexes (G4) play key roles in biology, making it critical to understand their structure and ligand-binding behavior in cellular environments for advancing G4-targeted therapeutics. While in-cell nuclear magnetic resonance (NMR) is a powerful technique for studying G4 in situ, its application is limited by the challenge of producing isotope-labeled single-stranded DNA (ssDNA). Here, we introduce Restriction Endonuclease Digestion (RED), a simple and cost-effective method to generate 15N-labeled ssDNA. This approach combines molecular cloning and enzymatic design processing by propagating plasmids in E. coli cultured with 15NH4Cl, followed by double restriction digestion and isolation target 15N-ssDNA. Using RED, we produced milligram-scale quantities of 96%-enriched 15N-labeled human telomeric G4 ssDNA (wtTel23c, CTAGGG(TTAGGG)3), ideal for NMR analysis. The NMR spectra revealed that wtTel23c adopts G4 topology and undergoes multiple conformations of wtTel23c in potassium-containing solutions and in Xenopus laevis cell lysate. Interaction studies with the ligand TMPyP4 showed distinct binding profiles in cellular and dilute environments. In dilute solution, TMPyP4 binds to the top tetrad of wtTel23c, while it binds to the loop in cellular environments. The RED method offers an efficient strategy for producing stable isotope-labeled ssDNA, opening new avenues for studying G4 structures and their ligand interactions in complex biological contexts.

biochemistry↗

bioRxiv2025

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations (cis-pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

bioinformatics↗

bioRxiv2025

StoPred: Accurate Stoichiometry Prediction for ProteinComplexes Using Protein Language Models and GraphAttention

Proteins often function as part of complexes, and the specific stoichiometry of these assemblies is critical for their biological roles, but experimental determination of assembly composition remains challenging and existing computational methods for stoichiometry prediction are limited. Existing approaches rely on template-based searches or require predefined stoichiometry for structure prediction, hampering their applicability to proteins without close homologs or known assembly states. Recent advances using protein language models (pLM) have enabled sequence-based prediction of homo-oligomer stoichiometry, but these methods are not applicable to hetero-oligomeric complexes and do not fully leverage inter-subunit relationships. Here, we present StoPred, a method that predicts the stoichiometry of protein complexes by integrating pLM embeddings with a graph attention network to model subunit-level interactions. StoPred infers stoichiometry directly from sequence or structure features for both homo-and hetero-oligomers, without requiring template assemblies or predefined composition. We benchmark StoPred against deep learning-based and template-based methods, and show that it achieves improved accuracy and efficiency across curated and blind datasets, with up to 16% and 41% higher top-1 accuracy for homomeric and heteromeric complexes, respectively, compared to the strongest prior method on our held-out test dataset. More importantly, StoPred is the first deep learning-based method capable of accurately predicting the stoichiometry of hetero-oligomeric complexes.

bioinformatics↗

bioRxiv2025

Machine learning-based definition of cellular senescence reveals pro-senescence potential implication in lung adenocarcinoma

Despite growing evidence implicating cellular senescence in tumor progression, methodological challenges in objectively quantifying senescent cell burden across cancer types continue to limit mechanistic studies and clinical translation. To address this, we developed the Predictive Cellular Senescence Model (PreCSenM), a machine learning-based tool that assigns a CS score by integrating senescence-associated features across 888 samples. Benchmarking analyses confirmed that PreCSenM markedly outperforms existing methodologies in reliably predicting senescent states within both normal and cancerous transcriptomic datasets. In lung adenocarcinoma (LUAD), PreCSenM identified the CS score as a robust predictor of clinical outcomes. Multi-omics analyses further revealed that lower CS levels correlate with decreased genomic stability and enhanced immune responses, aligning with clinical observations. Notably, drug discovery using PreCSenM identified histone deacetylase inhibitors (HDACis) as potent inducers of CS in LUAD. Transcriptional and epigenetic profiling of HDACi-treated cells pinpointed FOSB as a core transcription factor driving CS, and FOSB knockdown blocked HDACi-induced senescence. Overall, our study establishes PreCSenM as a multidimensional senescence quantification tool, bridging computational prediction with clinical relevance and mechanistic validation to enable senescence-directed therapies in precision oncology.

bioinformatics↗

bioRxiv2025

Agro-STARR-seq enables in planta genome-wide enhancer discovery and AI-empowerd enhancer design

Comprehensive enhancer screening is essential for dissecting gene regulatory networks. However, methods for such screening remain limited in plants. We present Agro-STARR-seq, an in planta enhancer screening platform using Agrobacterium-mediated delivery of STARR-seq libraries into Nicotiana benthamiana leaves. In the Arabidopsis genome, we identified 14,715 sequences with enhancer activity, of which 91.59% were located outside of open chromatin regions. This largest in planta enhancer dataset to date enabled AI-assisted design of highly active synthetic enhancers. For the large N. benthamiana genome, we pre-processed the genomic DNA with an Assay for Transposase Accessible Chromatin (ATAC) procedure before the Agro-STARR-seq protocol,and found that only one-third of open chromatin exhibited enhancer activity, 76.00% of which showed no canonical H3K27ac histone marks. Footprinting analysis further enables systematic linkage of transcription factor (TF) binding to enhancer function. Overall, Agro-STARR-seq provides a novel platform for dissecting and engineering plant enhancers.

genomics↗

bioRxiv2025

Impaired IFNγ responsiveness of lung monocyte-derived cells limits immunity to Mycobacterium tuberculosis

Lung mononuclear phagocyte (MNP) subsets differ in their ability to restrict Mycobacterium tuberculosis (Mtb) during chronic infection, yet the mechanisms underlying this difference are not well defined. Here, we show that CD11clo monocyte-derived cells (MNC1), the subset of lung cells that is most permissive for Mtb viability during chronic infection, express lower levels of interferon-gamma (IFN{gamma}) signaling proteins, resulting in reduced responses to IFN{gamma} compared to alveolar macrophages (AM) and CD11chi MNC2. Moreover, type I IFN signaling suppresses IFN{gamma}-mediated MHC-II expression, impairing antigen-specific CD4 T cell activation by MNC1 cells. Importantly, prior immunity conferred by contained Mtb infection enhances IFN{gamma} responsiveness of monocyte-derived cells, reducing bacterial burdens in lungs and within MNC subsets. Our findings indicate that heterogeneous IFN{gamma} responsiveness is exploited by Mtb for persistence in vivo. Overcoming or bypassing impaired IFN{gamma} responsiveness may guide the development of more effective TB vaccines and host-directed therapies.

immunology↗