Biology subjects

Li, K.

Publications and source records attributed to Li, K..

At least 181 records · Page 10Linked to original sources

bioRxiv2024

Mapping Individual Differences in the Topological Landscape of Naturalistic Brain Dynamics

Psychiatry seeks to unravel brain dysfunction and individual differences in real-world contexts. Naturalistic stimuli, like movie watching, are increasingly recognized for eliciting complex, context-dependent neural activity with high ecological validity. Yet, current methods often rely on standard paradigms that average data across time, limiting the full potential of such stimuli. Here, we present STIM, a Topological Data Analysis-based framework designed to dynamically track how individuals integrate complex contexts in real time. Applied to large-sample fMRI data from movie watching, STIM constructs a robust low-dimensional dynamical landscape that reflects group consensus while probing individual variations at both global (spanning narratives) and local (within specific narratives) levels. At the global level, individual differences emerge along a center-periphery gradient in the dynamical landscape, which significantly predicts fluid intelligence, underscoring the importance of neural adaptability and diversity. At finer scales, local geometric features correlate with context-specific psychological traits beyond cognition. STIM also captures developmental changes in the dynamical landscape and reveals abnormalities in conditions such as autism. These findings demonstrate that STIM leverages the rich information from movie stimuli and fMRI recordings as neural probes to assess individual differences in cognition and mental health.

neuroscience↗

bioRxiv2024

Human tau promotes Warburg effect-like glycolytic metabolism under acute hyperglycemia conditions through modulating the homeostasis of protein-membrane association

The neurofilaments formed by hyperphosphorylated tau is a hallmark of tau-related neurodegenerative disease, including Alzheimers disease, tau related FTDP-17, Picks disease, et al. However, the biological functions of tau and the physiological significance of its phosphorylation are still not fully understood. By using human tau (441 a.a.) transgenic (hTau) mice in which murine tau has been deleted simultaneously, murine tau knockout (Tau KO) mice and C57BL/6J (C57) mice, unexpectedly, we found that under acute hyperglycemia conditions, JNK but not previously reported GSK-3{beta} mediated tau phosphorylation. Moreover, Akt, the upstream GSK-3{beta} inhibitory kinase, was activated in a tau dependent manner. By comparing the membrane-associated proteome, we found that human tau influenced the homeostasis of protein-membrane association under acute hyperglycemia conditions. Of note, with respect to WT and Tau KO mice, the membrane-association of Krts, TFAM, TRAP1, mTOR et al, were strengthened by human tau. Whereas, the membrane-association of ribosomal proteins Rpls, proteasome proteins Psmds, and mitochondrial proteins, such as COXs, Ndufa1, Mtnt4, et al, were impeded by human tau. In vitro study showed that aerobic glycolysis was promoted in the presence of human tau, which maintained NAD+/NADH ratio. On the other hand, it restricted oxidative phosphorylation level, modulated the activity of SDH, and reduced ROS production upon challenging by high glucose. Furthermore, under acute high glucose conditions, the presence of human tau significantly augmented Akt activation, but inhibited 4EBP phosphorylation simultaneously, indicating that human tau is also involved in regulating the alternative activation of mTORC1/2. In summary, the current study revealed that human tau played an important role in regulating glycolytic metabolism under acute high hyperglycemia conditions, which is similar with the Warburg-effect, through influencing the homeostasis of protein-membrane association.

molecular biology↗

bioRxiv2024

Inhibitor of cardiolipin biosynthesis-related enzyme MoGep4 confers broad-spectrum anti-fungal activity

Plant pathogens cause devastating diseases, leading to serious losses to agriculture. Mechanistic understanding of pathogenesis of plant pathogens lays the foundation for the development of fungicides for disease control. Mitophagy, a specific form of autophagy, is important for fungal virulence. The role of cardiolipin, mitochondrial signature phospholipid, in mitophagy and pathogenesis is largely unknown in plant pathogenic fungi. The functions of enzymes involved in cardiolipin biosynthesis and relevant inhibitors were assessed using a set of assays, including genetic deletion, plant infection, lipidomics, chemical-protein interaction, chemical inhibition, and field trials. Our results showed that the cardiolipin biosynthesis-related gene MoGEP4 of the rice blast fungus Magnaporthe oryzae regulates growth, conidiation, cardiolipin biosynthesis, and virulence. Mechanistically, MoGep4 regulated mitophagy and Mps1-MAPK phosphorylation, which are required for virulence. Chemical alexidine dihydrochloride (AXD) inhibited the enzyme activity of MoGep4, cardiolipin biosynthesis and mitophagy. Importantly, AXD efficiently inhibited the growth of 10 plant pathogens and controlled rice blast and Fusarium head blight in the field. Our study demonstrated that MoGep4 regulates mitophagy, Mps1 phosphorylation and pathogenesis in M. oryzae. In addition, we found that the MoGep4 inhibitor, AXD, displays broad-spectrum antifungal activity and is a promising candidate for fungicide development.

microbiology↗

bioRxiv2024

The murine lung microbiome is dysbalanced by the human pathogenic fungus Aspergillus fumigatus resulting in enrichment of anaerobic bacteria

Here, we report significant changes in the composition of the lung microbiome and metabolome of mice under immune suppression, infection of immunosuppressed mice with virulent and avirulent strains of the clinically important human-pathogenic fungus Aspergillus fumigatus, and treatment with the clinically used antifungal drug voriconazole. Our data also indicate the important role of the gut microbiome for the lung homeostasis mediated by the plasma metabolome. In the lung microbiome, infection by A. fumigatus led to a significant increase of anaerobic bacteria, most prominently of Ligilactobacillus murinus, which was confirmed by the isolation of live bacteria from the murine lower respiratory tract. In vitro, L. murinus is tolerated and even internalized by alveolar epithelial cells. Co-cultivation of L. murinus and A. fumigatus led to a reduction in oxygen concentration accompanied by an increase of L. murinus cells suggesting that A. fumigatus establishes a microaerophilic niche, thereby promoting growth of anaerobic bacteria.

microbiology↗

bioRxiv2024

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning

BackgroundRadiotherapy resistance is a major obstacle to the long-term survival of nasopharyngeal cancer patients, as it is a primary cause of recurrence and metastasis. Identifying radiotherapy-associated biomarkers can help improve the survival prognosis of nasopharyngeal cancer patients. Consequently, discovering biomarkers associated with radiosensitization is crucial. MethodsWe evaluated 113 combinations of machine learning algorithms and ultimately selected 48 to construct a radiotherapy sensitivity score (NPC-RSS) that can predict radiosensitivity in nasopharyngeal cancer patients. Furthermore, we analyzed the relationship between NPC-RSS and the expression of genes associated with immune and radiotherapy sensitivity profiles. We employed GSEA and ssGSEA to investigate the connection between NPC-RSS and signaling pathways. ResultsWe selected the combined model glmBoost+NaiveBayes, which had the best AUC among 48 models, for our subsequent study. The NPC-RSS, built based on the 18 genes included in this model, can predict the results of the public dataset and the in-house dataset of Zhujiang Hospital, Southern Medical University, with considerable efficiency. The key genes of NPC-RSS are closely associated with immune characteristics, including chemokine and chemokine receptor families, and histocompatibility complex (MHC), and show more active immune processes. Meanwhile, these key genes were significantly associated with the expression of radiosensitization-related genes. Furthermore, GSVA and GSEA analyses demonstrated that different expression levels of key NPC-RSS genes influenced signaling pathways, such as the Wnt/{beta}-catenin signaling pathway, JAK-STAT signaling pathway,NF-kappa B signaling pathway and T cell receptor signaling pathway, which are associated with immunity and disease progression. The consistency of the expression of key genes SMARCA2 and CD9 with NPC-RSS was validated in in-house cell lines. The radiosensitive group, classified according to NPC-RSS, exhibited a more enriched and activated state of immune infiltration compared to the radioresistant group. Moreover, in single-cell samples, NPC-RSS was higher in the radiotherapy-sensitive group, with immune cells playing a predominant role. ConclusionsIn this study, we used machine learning to construct a predictive score, called NPC-RSS, associated with radiosensitivity in nasopharyngeal carcinoma patients; moreover, NPC-RSS is strongly associated with immune characteristics, expression of radiosensitivity-related genes, and signaling pathways related to disease progression. We hope that the NPC-RCC will enable more precise selection of the NPC population of potential beneficiaries of radiation therapy.

bioinformatics↗

bioRxiv2024

An organoid co-culture model for probing systemic anti-tumor immunity in lung cancer

Deciphering the interactions between tumor micro- and systemic immune macro-environment is essential for developing more effective cancer diagnosis and treatment strategies. Here, we established a gel-liquid interface (GLI) co-culture of lung cancer organoids (LCOs) and paired peripheral blood mononuclear cells (PBMCs), featuring with enhanced interactions of immune cells and tumor organoids, to mimic the in vivo systemic anti-tumor immunity induced by immune checkpoint inhibitors (ICI). The co-culture model recapitulates the in vivo ICI-induced T cell recruitment and subsequent tumor regression, predicting the clinical results precisely. We demonstrated that circulating tumor-reactive T cells, which are effector memory-like with high expression levels of GNLY, CD44 and CD9, can serve as an indicator of the immunotherapy efficacy. Interestingly, enhanced inflammatory signaling in blood T cells is accompanied with prompted exhaustion and compromised anti-tumor function, when encountering with organoids. Our findings suggest that the GLI co-culture can be used for developing diagnostic strategies for precision immunotherapies as well as understanding the underlying mechanisms. Summary figure O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/597327v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@a0829dorg.highwire.dtl.DTLVardef@1df3083org.highwire.dtl.DTLVardef@1ea1065org.highwire.dtl.DTLVardef@1da9464_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

bioinformatics↗

bioRxiv2024

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Brukers timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

bioinformatics↗

bioRxiv2024

The brassinosteroid receptor gene BRI1 safeguards cell-autonomous brassinosteroid signaling across tissues

Brassinosteroid signaling is essential for plant growth as exemplified by the dwarf phenotype of loss-of-function mutants in BRASSINOSTEROID INSENSITIVE 1 (BRI1), a ubiquitously expressed Arabidopsis brassinosteroid receptor gene. Complementation of brassinosteroid-blind receptor mutants by BRI1 expression with various tissue-specific promoters implied that local brassinosteroid signaling may instruct growth non-cell-autonomously. Here we performed such rescues with a panel of receptor variants and promoters, in combination with tissue-specific transgene knockouts. Our experiments demonstrate that brassinosteroid receptor expression in several tissues is necessary but not sufficient for rescue. Moreover, complementation with tissue-specific promoters requires the genuine BRI1 gene body sequence, which confers ubiquitous expression of trace receptor amounts that are sufficient to promote brassinosteroid-dependent root growth. Our data, therefore, argue for a largely cell-autonomous action of brassinosteroid receptors.

plant biology↗

bioRxiv2024

Splenic red pulp macrophages eliminate the liver-resistant Streptococcus pneumoniae during bloodstream infection

The spleen is well-known for defense against invasive infections of encapsulated bacteria, particularly Streptococcus pneumoniae (pneumococcus). However, the precise mechanism of the splenic anti-bacterial immunity remains elusive. Here we report that red pulp (RP) macrophages execute the splenic defense against S. pneumoniae in mice, with the help of natural antibodies (nAbs) and complement system. The spleen slowly but substantially cleared encapsulated pneumococci from the bloodstream in the early phase of blood infection, especially the serotypes that resist to liver filtration. Among splenic macrophage subpopulations, only the lack of RP macrophages led to the complete loss of the splenic immunity. Intravital microscopy detected direct capture and phagocytic killing of circulating pneumococci by RP macrophages in the absence of neutrophils and inflammatory monocytes. Likewise, plasma nAbs targeting pneumococcal cell wall phosphocholine and complement protein C3 were essential for RP macrophage-mediated immune clearance. Our findings have thus uncovered the long-sought immune pathway against invasive pneumococcal infections in the spleen. Short summaryThe spleen plays a crucial role in defending against invasive infections caused by Streptococcus pneumoniae (pneumococcus) and other encapsulated bacteria in humans, but the mechanisms underlying this organ-specific immunity remain largely elusive. This study has revealed that the spleen eliminates pneumococci through a coordinated interplay among red pulp macrophages, serum nAbs targeting phosphocholine and the complement system.

microbiology↗

bioRxiv2024

DeepSS2GO: protein function prediction from secondary structure

Predicting protein function is crucial for understanding biological life processes, preventing diseases, and developing new drug targets. In recent years, methods based on sequence, structure, and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features, while streamlining the redundant data in primary sequences and bypassing the timeconsuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-ofthe-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

bioinformatics↗

bioRxiv2024

Dual-view jointly learning improves personalized drug synergy prediction

BackgroundAccurate and robust estimation of the synergistic drug combination is important for precision medicine. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. MethodsWe have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn capture the drug synergy related features from two views. One view is the embedding of drug combination on cancer cell lines, and the other view is the combination of two drugs embeddings on cancer cell lines. Finally, the prediction net uses the features learned from the two views to predict the drug synergy of the drug combination on the cell line. In addition, we used the fine-tuning method to improve the JointSyns performance on the unseen subset within a dataset or cross dataset. ResultsJointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and make complementary contributes to the final accurate prediction of drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample only using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. ConclusionsThese results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. The source code is available on GitHub at https://github.com/LiHongCSBLab/JointSyn.

cancer biology↗

bioRxiv2024

Detection of spatial chromatin accessibility patterns with inter-cellular correlations

Recent advances in spatial sequencing technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within intact tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the first and key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here we propose an efficient and iterative model, Descartes, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking for spatially variable peaks identification, we demonstrate the superiority of Descartes in revealing cellular heterogeneity and capturing tissue structure. In terms of computational efficiency, Descartes also outperforms existing methods with spatial assumptions. Utilizing the graph of inter-cellular correlations, Descartes denoises and imputes data via the neighboring relationships, enhancing the precision of downstream analysis. We further demonstrate the ability of Descartes for peak module identification by using peak-peak correlations within the graph. When applied to spatial multi-omics data, Descartes show its potential to detect gene-peak interactions, offering valuable insights into the construction of gene regulatory networks.

bioinformatics↗

bioRxiv2024

Improved BOLD Detection with Sliced Inverse Regression

Functional magnetic resonance imaging (fMRI) has been effective in linking task-related brain responses to changes in blood oxygen level density (BOLD). However, its reliance on BOLD measurements makes it vulnerable to artifacts and false-positive signals. Commonly, researchers use many regressors in a General Linear Model to filter true signals, but this adds noise and complicates interpretation. In this paper we suggest using Sliced Inverse Regression (SIR) to simplify covariate dimensionality and identify relevant regressors. We compare a general linear model applied to both original and SIR-adjusted data, demonstrating that SIR improves signal detection, reduces noise, and yields statistically significant results even with conservative measures.

neuroscience↗

bioRxiv2024

CAMKV Kinase Signaling Is a Novel Therapeutic Avenue with Prognostic Relevance in Neuroblastoma

Neuroblastoma (NB) can be a highly aggressive malignancy in children. However, the precise mechanisms driving NB tumorigenesis remain elusive. This study revealed the critical role of CREB phosphorylation in NB cell proliferation. By employing a CRISPR-Cas9 knockout screen targeting calcium/calmodulin-dependent protein kinase (CaMK) family members, we identified the CaM kinase-like vesicle-associated (CAMKV) protein as a kinase that mediates direct phosphorylation of CREB to promote NB cell proliferation. CAMKV was found to be a transcriptional target of MYCN/MYC in NB cells. CAMKV knockout and knockdown effectively suppressed NB cell proliferation and tumor growth both in vitro and in vivo. Bioinformatic analysis revealed that high CAMKV expression is significantly correlated with poor patient survival. High-risk NB frequently had high CAMKV protein levels by Immunohistochemical staining. Integrated transcriptomic and proteomic analyses of CAMKV knockdown cells unveiled downstream targets involved in CAMKV-regulated phosphorylation and signaling pathways, many of which are linked to neural development and cancer progression. We identified small molecule inhibitors targeting CAMKV and further demonstrated the efficacy of one inhibitor in suppressing NB tumor growth and prolonging the survival of mice bearing xenografted tumors. These findings reveal a critical role for CAMKV kinase signaling in NB growth and identified CAMKV kinase as a potential therapeutic target and prognostic marker for patients with NB.

cancer biology↗

bioRxiv2024

Hexose transporters CsHT3/16 involved in post-phloem transport and affected cucumber fruit development

Hexoses are crucial for plant growth and fruit development. However, the role of hexose transporters in post-phloem sugar transport and maintenance of cellular sugar homeostasis in rapidly growing fruits, such as cucumber, is not yet fully understood. To clarify the impact of hexose transporters in cucumber fruits, we conducted systematic analyses of their tissue expression, localization, transport characteristics and physiological functions. The study revealed that CsHT3, CsHT12 and CsHT16 are the primary hexose transporter genes expressed in cucumber fruit. During the ovary and young fruit stages, CsHT3 and CsHT16 were located in the SE/CC system, but as the cucumber fruit developed and expanded, both transporters shifted to phloem parenchyma cells. The knock-out mutants of CsHT16 display shorter fruits with a larger circumference, likely due to impaired homeostasis of sugars and hormones. Simultaneously reducing the expression of CsHT3, CsHT12 and CsHT16 leads to decreased fruit size. Conversely, overexpression of CsHT3 results in increased fruit size and higher fruit sugar levels. Our data suggest that CsHT16 plays an important role in maintaining sugar homeostasis to shape the fruit, while CsHT3, CsHT12 and CsHT16 together determine the carbohydrates requirement of the enlarged cucumber fruit.

physiology↗

bioRxiv2023

Constitutive and conditional epitope-tagging of endogenous G protein coupled receptors in Drosophila

To visualize the cellular and subcellular localization of neuromodulatory G-protein coupled receptors (GPCRs) in Drosophila, we implement a molecular strategy recently used to add epitope tags to ionotropic receptors at their endogenous loci. Leveraging evolutionary conservation to identify sites more likely to permit insertion of a tag, we generated constitutive and conditional tagged alleles for Drosophila 5-HT1A, 5-HT2A, 5-HT2B, Oct{beta}1R, Oct{beta}2R, two isoforms of OAMB, and mGluR. The conditional alleles allow for the restricted expression of tagged receptor in specific cell types, an option not available for any previous reagents to label these proteins. We show that 5-HT1A and 5-HT2B localize to the mushroom bodies and central complex respectively, as predicted by their roles in sleep. By contrast, the unexpected enrichment of Oct{beta}1R in the central complex and of 5-HT1A and 5-HT2A to nerve terminals in lobular columnar cells in the visual system suggest new hypotheses about their function at these sites. Using an additional tagged allele of the serotonin transporter, a marker of serotonergic tracts, we demonstrate diverse spatial relationships between postsynaptic 5-HT receptors and presynaptic 5-HT neurons, consistent with the importance of both synaptic and volume transmission. Finally, we use the conditional allele of 5-HT1A to show that it localizes to distinct sites within the mushroom bodies as both a postsynaptic receptor in Kenyon cells and a presynaptic autoreceptor. Significance StatementIn Drosophila, despite remarkable advances in both connectomic and genomic studies, antibodies to many aminergic GPCRs are not available. We have overcome this obstacle using evolutionary conservation to identify loci in GPCRs amenable to epitope-tagging, and CRISPR/Cas9 genome editing to generated eight novel lines. This method also may be applied to other GPCRs and allows cell-specific expression of the tagged locus. We have used the tagged alleles we generated to address several questions that remain poorly understood. These include the relationship between pre- and post-synaptic sites that express the same receptor, and the use of relatively distant targets by pre-synaptic release sites that may employ volume transmission as well as standard synaptic signaling.

neuroscience↗

bioRxiv2023

Function-associated scRNA-seq on single lung cancer organoids unravels the immune landscape of tumor parenchyma

In vitro models coupled with multimodal approaches are urgently needed to decipher the local tumor immune microenvironment (TIME) owing to the heterogeneous nature of immune cells and their diverse spatial distributions. Here we generate primary lung cancer organoids (pLCOs) by isolating the tumor cell clusters, including the infiltrated immune cells, from dissected lung cancer samples. A FascRNA-seq platform allowing both phenotypic evaluation and the scRNA-seq of all the single cells in an organoid was developed to dissect the TIME in individual pLCOs. Our analysis on 171 individual pLCOs derived from 7 patients revealed that pLCOs retained the fundamental features as well as intra-tumor heterogeneity of local TIME in the parenchyma of parental tumor tissues, providing a series of models with consistent genetic background but various TIME. Linking the single cell transcriptome data of individual pLCOs with their responses to ICB allowed us to confirm the pivotal role of CD8+ Ts in ICB induced anti-tumor immunity, to identify the potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity.

cancer biology↗