Affinity Threshold Research Articles

Factors Affecting Liquid-Liquid Phase Separation of RGG Peptides with DNA G-Quadruplex.

Liquid-liquid phase separation (LLPS), mediated by G-quadruplexes (G4 s) and intrinsically disordered proteins, particularly those containing RGG domains, plays a critical role in cellular processes and diseases. However, the molecular mechanism and the role of individual amino acid residues of the protein in LLPS with G4 (G4-LLPS) are still unknown. Here, we systematically designed peptides and investigated the roles of arginine residues in G4-LLPS. It was found that the FMRP-derived RGG peptide induced LLPS with G4-forming Myc-DNA, whereas a point-mutated peptide, in which all arginine residues were replaced with lysine, was unable to undergo LLPS, indicating the importance of arginine residues. Moreover, systematically truncated peptides showed that at least five positive net charges of peptide are required to induce G4-LLPS. Furthermore, quantitative investigation demonstrated that the higher binding affinity of peptides with G4 led to a higher LLPS ability, whereas threshold of the binding affinity for undergoing LLPS was identified. These insights elucidate the pivotal role of arginine in G4-LLPS and the specific requirement for multiple arginine residues, contributing to a deeper understanding of the complex interplay between intrinsically disordered proteins and nucleic acids.

DGKα/ζ inhibition lowers the TCR affinity threshold and potentiates antitumor immunity.

Diacylglycerol kinases (DGKs) attenuate diacylglycerol (DAG) signaling by converting DAG to phosphatidic acid, thereby suppressing pathways downstream of T cell receptor signaling. Using a dual DGKα/ζ inhibitor (DGKi), tumor-specific CD8 T cells with different affinities (TRP1high and TRP1low), and altered peptide ligands, we demonstrate that inhibition of DGKα/ζ can lower the signaling threshold for T cell priming. TRP1high and TRP1low CD8 T cells produced more effector cytokines in the presence of cognate antigen and DGKi. Effector TRP1high- and TRP1low-mediated cytolysis of tumor cells with low antigen load required antigen recognition, was mediated by interferon-γ, and augmented by DGKi. Adoptive T cell transfer into mice bearing pancreatic or melanoma tumors synergized with single-agent DGKi or DGKi and antiprogrammed cell death protein 1 (PD-1), with increased expansion of low-affinity T cells and increased cytokine production observed in tumors of treated mice. Collectively, our findings highlight DGKα/ζ as therapeutic targets for augmenting tumor-specific CD8 T cell function.

Holocene vegetation dynamics of the Eastern Mediterranean region: Old controversies addressed by a new analysis

AbstractAimWe reconstruct vegetation changes since 12 ky in the Eastern Mediterranean to examine four features of the regional vegetation history that are controversial: the extent of non‐analogue vegetation assemblages in the transition from the Late Glacial to the early Holocene, the synchroneity of postglacial forest expansion, the geographical extent of temperate deciduous forest during the mid‐Holocene and the timing and trigger for the re‐establishment of drought‐tolerant vegetation during the late Holocene.LocationThe Eastern Mediterranean–Black Sea Caspian Corridor.TaxonVascular plants.MethodsWe reconstruct vegetation changes for 122 fossil pollen records using a method that accounts for within‐biome variability in pollen taxon abundance to determine the biome with which a sample has greatest affinity. Per‐biome affinity threshold values were used to identify samples that do not belong to any modern biome. We apply time series analysis and mapping to examine space and time changes.ResultsSites with non‐analogue vegetation were most common between 11.5 and 9.5 ky and mostly in the Carpathians. The transition from open vegetation to forest occurred at 10.64 ± 0.65 ky across the whole region. Temperate deciduous forest was not more extensive at 6 ky; maximum expansion occurred between 5.5 and 5 ky. Expansion of forest occurred between c. 4 and 2.8 k, followed by an abrupt decrease and a subsequent recovery. This pattern is not consistent with a systematic decline of forest towards more drought‐tolerant vegetation in the late Holocene but is consistent with centennial‐scale speleothem patterns linked to variations in moisture availability.Main ConclusionsWe show the occurrence of non‐analogue vegetation types peaked during early Holocene, forest expansion was synchronous across the region and there was an expansion of moisture‐demanding temperate trees around 5.5 to 5 ky. There is no signal of a continuous late Holocene aridification, but changes in forest cover appear to reflect climatic rather than anthropogenic influences.

Machine-learning repurposing of DrugBank compounds for opioid use disorder

Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

Continuous germinal center invasion contributes to the diversity of the immune response

Antibody responses are characterized by increasing affinity and diversity over time. Affinity maturation occurs in germinal centers by a mechanism that involves repeated cycles of somatic mutation and selection. How antibody responses diversify while also undergoing affinity maturation is not as well understood. Here, we examined germinal center (GC) dynamics by tracking B cell entry, division, somatic mutation, and specificity. Our experiments show that naive B cells continuously enter GCs where they compete for Tcell help and undergo clonal expansion. Consistent with late entry, invaders carry fewer mutations but can contribute up to 30% or more of the cells in late-stage germinal centers. Notably, cells entering the germinal center at later stages of the reaction diversify the immune response by expressing receptors that show low affinity to the immunogen. Paradoxically, the affinity threshold for late GC entry is lowered in the presence of high-affinity antibodies.

Mutational patterns along different evolution paths of follicular lymphoma.

Follicular lymphoma (FL) is an indolent disease, characterized by a median life expectancy of 18-20 years and by intermittent periods of relapse and remission. FL frequently transforms into the more aggressive diffuse large B cell lymphoma (t-FL). In previous studies, the analysis of immunoglobulin heavy chain variable region (IgHV) genes in sequential biopsies from the same patient revealed two different patterns of tumor clonal evolution: direct evolution, through acquisition of additional IgHV mutations over time, or divergent evolution, in which lymphoma clones from serial biopsies independently develop from a less-mutated common progenitor cell (CPC). Our goal in this study was to characterize the somatic hypermutation (SHM) patterns of IgHV genes in sequential FL samples from the same patients, and address the question of whether the mutation mechanisms (SHM targeting, DNA repair or both), or selection forces acting on the tumor clones, were different in FL samples compared to healthy control samples, or in late relapsed/transformed FL samples compared to earlier ones. Our analysis revealed differences in the distribution of mutations from each of the nucleotides when tumor and non-tumor clones were compared, while FL and transformed FL (t-FL) tumor clones displayed similar mutation distributions. Lineage tree measurements suggested that either initial clone affinity or selection thresholds were lower in FL samples compared to controls, but similar between FL and t-FL samples. Finally, we observed that both FL and t-FL tumor clones tend to accumulate larger numbers of potential N-glycosylation sites due to the introduction of new SHM. Taken together, these results suggest that transformation into t-FL, in contrast to initial FL development, is not associated with any major changes in DNA targeting or repair, or the selection threshold of the tumor clone.

An opinion dynamics model based on affinity and social noise

Most previous works have studied the evolution of opinions based on the Hegselmann–Krause model, the Deffuant–Weisbuch model, and the Sznajd model. However, the influence of social influence on opinions is discussed less. Based on the social influence theory and the Hegselmann–Krause model of opinion dynamics, we introduce the affinity and social noise in the Hegselmann–Krause model of opinion dynamics and propose an affinity and social noise Hegselmann–Krause model (ASNHK). The influence of affinity degree, affinity threshold, social noise, and personnel heterogeneity on opinion evolution is discussed in experimental analysis. Experimental results show that the affinity between people can improve opinions to form a consensus positively, but the affinity threshold has a negative role contrarily. Moreover, when the social noise increases, the opinions will form a consensus. When it increases to a certain value, the opinion will be decentralized. Furthermore, personnel heterogeneity has different effects on opinion evolution. Open-minded individuals are more likely to form a unified opinion, while closed-minded individuals have difficulty unifying their opinions. Overall, this research provides a clearer explanation of the group opinion evolution from social influence.

Membrane-bound mRNA immunogens lower the threshold to activate HIV Env V2 apex-directed broadly neutralizing B cell precursors in humanized mice.

Eliciting broadly neutralizing antibodies (bnAbs) is the core of HIV vaccine design. bnAbs specific to the V2-apex region of the HIV envelope acquire breadth and potency with modest somatic hypermutation, making them attractive vaccination targets. To evaluate Apex germline-targeting (ApexGT) vaccine candidates, we engineered knockin (KI) mouse models expressing the germline B cell receptor (BCR) of the bnAb PCT64. We found that high affinity of the ApexGT immunogen for PCT64-germline BCRs was necessary to specifically activate KI B cells at human physiological frequencies, recruit them to germinal centers, and select for mature bnAb mutations. Relative to protein, mRNA-encoded membrane-bound ApexGT immunization significantly increased activation and recruitment of PCT64 precursors to germinal centers and lowered their affinity threshold. We have thus developed additional models for HIV vaccine research, validated ApexGT immunogens for priming V2-apex bnAb precursors, and identified mRNA-LNP as a suitable approach to substantially improve the B cell response.

Long-lived plasma cells accumulate in the bone marrow at a constant rate from early in an immune response.

Vaccines work largely by generating long-lived plasma cells (LLPCs), but knowledge of how such cells are recruited is sparse. Although it is clear that LLPCs preferentially originate in germinal centers (GCs) and relocate to survival niches in bone marrow where they can persist for decades, the issues of the timing of LLPC recruitment and the basis of their retention remain uncertain. Here, using a genetic timestamping system in mice, we show that persistent PCs accrue in bone marrow at an approximately constant rate of one cell per hour over a period spanning several weeks after a single immunization with a model antigen. Affinity-based selection was evident in persisting PCs, reflecting a relative and dynamic rather than absolute affinity threshold as evidenced by the changing pattern of VH gene somatic mutations conveying increased affinity for antigen. We conclude that the life span of persistent, antigen-specific PCs is in part intrinsic, preprogrammed, and varied and that their final number is related to the duration of the response in a predictable way. This implies that modulating vaccines to extend the duration of the GC reaction will enhance antibody-mediated protective immunity.

An affinity threshold for maximum efficacy in anti-PD-1 immunotherapy

ABSTRACT Monoclonal antibodies targeting the programmed cell death protein 1 (PD-1) remain the most prevalent cancer immunotherapy both as a monotherapy and in combination with additional therapies. Despite the extensive success of anti-PD-1 monoclonal antibodies in the clinic, the experimental relationship between binding affinity and functional potency for anti-PD-1 antibodies in vivo has not been reported. Anti-PD-1 antibodies with higher and lower affinity than nivolumab or pembrolizumab are entering the clinic and show varied preclinical efficacy. Here, we explore the role of broad-ranging affinity variation within a single lineage in a syngeneic immunocompetent mouse model. By developing a panel of murine anti-PD-1 antibodies with varying affinity (ranging from KD = 20 pM – 15 nM), we find that there is a threshold affinity required for maximum efficacy at a given dose in the treatment of the MC38 adenocarcinoma model with anti-PD-1 immunotherapy. Physiologically based pharmacokinetic modeling complements interpretation of the experimental results and highlights the direct relationship between dose, affinity, and PD-1 target saturation in the tumor.

Susceptibility and Severity of COVID-19 Are Both Associated With Lower Overall Viral-Peptide Binding Repertoire of HLA Class I Molecules, Especially in Younger People.

An important number of studies have been conducted on the potential association between human leukocyte antigen (HLA) genes and COVID-19 susceptibility and severity since the beginning of the pandemic. However, case–control and peptide-binding prediction methods tended to provide inconsistent conclusions on risk and protective HLA alleles, whereas some researchers suggested the importance of considering the overall capacity of an individual’s HLA Class I molecules to present SARS-CoV-2-derived peptides. To close the gap between these approaches, we explored the distributions of HLA-A, -B, -C, and -DRB1 1st-field alleles in 142 Iranian patients with COVID-19 and 143 ethnically matched healthy controls, and applied in silico predictions of bound viral peptides for each individual’s HLA molecules. Frequency comparison revealed the possible predisposing roles of HLA-A*03, B*35, and DRB1*16 alleles and the protective effect of HLA-A*32, B*58, B*55, and DRB1*14 alleles in the viral infection. None of these results remained significant after multiple testing corrections, except HLA-A*03, and no allele was associated with severity, either. Compared to peptide repertoires of individual HLA molecules that are more likely population-specific, the overall coverage of virus-derived peptides by one’s HLA Class I molecules seemed to be a more prominent factor associated with both COVID-19 susceptibility and severity, which was independent of affinity index and threshold chosen, especially for people under 60 years old. Our results highlight the effect of the binding capacity of different HLA Class I molecules as a whole, and the more essential role of HLA-A compared to HLA-B and -C genes in immune responses against SARS-CoV-2 infection.

Inhibition of ammonia-oxidizing bacteria promotes the growth of ammonia-oxidizing archaea in ammonium-rich alkaline soils

Disparities in the substrate affinity and tolerance threshold for ammonia have been believed to play a key role in driving niche differentiation between ammonia-oxidizing archaea (AOA) and bacteria (AOB); however, recent surveys argue that direct competition between AOA and AOB is also important in this phenomenon. Accordingly, it is reasonable to predict that diverse AOA lineages would grow in ammonium (NH 4 + )-rich alkaline arable soils if AOB growth is suppressed. To test this hypothesis, a microcosm study was established using three different types of alkaline arable soils, in which a high NH 4 + concentration (200 μg N g -1 dry soil) was maintained by routinely replenishing urea and the activities of AOB were selectively inhibited by 1-octyne or 3,4-dimethylpyrazole phosphate (DMPP). Compared with amendment with urea alone, 1-octyne partially retarded AOB growth, while DMPP completely inhibited AOB. Both inhibitors accelerated the growth of AOA, with significantly higher ratios of abundance of AOA to AOB observed with DMPP amendment across soils. Nonmetric multidimensional scaling analysis (NMDS) indicated that different treatments significantly altered the community structures of both AOA and AOB and AOA OTUs enriched by high-NH 4 + amendment were taxonomically constrained across the soils tested and closely related to Nitrososphaera viennensis EN76 and N . garnensis . Given that these representative strains have been demonstrated to be sensitive to high ammonia concentrations, our results suggest that it is the competitiveness for ammonia, rather than disparities in substrate affinity and tolerance threshold for ammonia, that drives niche differentiation between these phylotypes and AOB in NH 4 + -rich alkaline soils.

Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system

The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%.

Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

SO-26 In-silico Lynch syndrome-related neoantigens prediction for a dendritic cell-based cancer prevention vaccine

Lynch syndrome (LS), caused by germline mutations in DNA mismatch-repair genes (MLH1, MSH2, MSH6, PMS2) predisposes patients to colorectal and endometrial cancer (CRC, EC), among other tumors. Although CCR prevention methods are effective, no preventive strategies exist for the majority of LS-related tumors. Ex-vivo generated and tumor-antigen-loaded dendritic cell (DC) vaccines are one of the recently featured antitumoral immunotherapies. However, their full therapeutic potential would likely be as a preventive approach in high-risk cancer patients. LS is a paradigmatic model for its limited and predictive mutational spectrum in repetitive DNA sequences termed microsatellites (MS). This study aims to identify the main frame-shift derived neopeptides (FSDN) that are shared among cancers from LS patients with the purpose of developing a DC-based neoepitope vaccine. A systematic search of coding MS and FSDN in LS tumors published in literature and in public databases was performed (Seltar Database; The Cancer Genome Atlas, TCGA), with application of epitope prediction pipelines and priorization of FSDN with the greatest coverage on the most frequent HLA-I and HLA-II haplotypes (pVACbind; pVACtools v2.0.1). 531 frame-shift peptide sequences derived from -1 (m1) and -2 (m2) nucleotide deletions from 269 coding microsatellites (cMS) of at least 8 bases were retrieved from SelTarbase and TCGA. Data in FASTA format was computed through the pVACbind epitope prediction pipeline with a labile HLA-binding affinity threshold (IC50 = < 5000 nM) to consider immunoediting events. We prioritized 98 epitopes, 53 HLA-I and 45 HLA-II restricted, from an original predicted number of 42828 HLA-I and 8350 HLA-II-restricted (m1 and m2) epitopes with a median coverage of 9,4 HLA-I alleles and 8,7 HLA-II alleles per epitope. Our predicted neoepitope set has an optimal coverage among LS patients in terms of HLA alleles, associated cancers and prevalence. These results are key to first perform ex vivo functional analysis to determine neoepitopes immunogenicity to later design a preventive antitumoral vaccine for LS.

Marine ammonia-oxidising archaea and bacteria occupy distinct iron and copper niches

Ammonia oxidation by archaea and bacteria (AOA and AOB), is the first step of nitrification in the oceans. As AOA have an ammonium affinity 200-fold higher than AOB isolates, the chemical niche allowing AOB to persist in the oligotrophic ocean remains unclear. Here we show that marine isolates, Nitrosopumilus maritimus strain SCM1 (AOA) and Nitrosococcus oceani strain C-107 (AOB) have contrasting physiologies in response to the trace metals iron (Fe) and copper (Cu), holding potential implications for their niche separation in the oceans. A greater affinity for unchelated Fe may allow AOB to inhabit shallower, euphotic waters where ammonium supply is high, but competition for Fe is rife. In contrast to AOB, AOA isolates have a greater affinity and toxicity threshold for unchelated Cu providing additional explanation to the greater success of AOA in the marine environment where Cu availability can be highly variable. Using comparative genomics, we predict that the proteomic and metal transport basis giving rise to contrasting physiologies in isolates is widespread across phylogenetically diverse marine AOA and AOB that are not yet available in pure culture. Our results develop the testable hypothesis that ammonia oxidation may be limited by Cu in large tracts of the open ocean and suggest a relatively earlier emergence of AOB than AOA when considered in the context of evolving trace metal availabilities over geologic time.

CD4 Inhibits Helper T Cell Activation at Lower Affinity Threshold for Full-Length T Cell Receptors Than Single Chain Signaling Constructs

CD4+ T cells are crucial for effective repression and elimination of cancer cells. Despite a paucity of CD4+ T cell receptor (TCR) clinical studies, CD4+ T cells are primed to become important therapeutics as they help circumvent tumor antigen escape and guide multifactorial immune responses. However, because CD8+ T cells directly kill tumor cells, most research has focused on the attributes of CD8+ TCRs. Less is known about how TCR affinity and CD4 expression affect CD4+ T cell activation in full length TCR (flTCR) and TCR single chain signaling (TCR-SCS) formats. Here, we generated an affinity panel of TCRs from CD4+ T cells and expressed them in flTCR and three TCR-SCS formats modeled after chimeric antigen receptors (CARs) to understand the contributions of TCR-pMHCII affinity, TCR format, and coreceptor CD4 interactions on CD4+ T cell activation. Strikingly, the coreceptor CD4 inhibited intermediate and high affinity TCR-construct activation by Lck-dependent and -independent mechanisms. These inhibition mechanisms had unique affinity thresholds dependent on the TCR format. Intracellular construct formats affected the tetramer staining for each TCR as well as IL-2 production. IL-2 production was promoted by increased TCR-pMHCII affinity and the flTCR format. Thus, CD4+ T cell therapy development should consider TCR affinity, CD4 expression, and construct format.

Human CD8+ T Cells Exhibit a Shared Antigen Threshold for Different Effector Responses.

T cells recognizing cognate pMHC Ags become activated to elicit a myriad of cellular responses, such as target cell killing and the secretion of different cytokines, that collectively contribute to adaptive immunity. These effector responses have been hypothesized to exhibit different Ag dose and affinity thresholds, suggesting that pathogen-specific information may be encoded within the nature of the Ag. In this study, using systematic experiments in a reductionist system, in which primary human CD8+ T cell blasts are stimulated by recombinant peptides presented on MHC Ag alone, we show that different inflammatory cytokines have comparable Ag dose thresholds across a 25,000-fold variation in affinity. Although costimulation by CD28, CD2, and CD27 increased cytokine production in this system, the Ag threshold remained comparable across different cytokines. When using primary human memory CD8+ T cells responding to autologous APCs, equivalent thresholds were also observed for different cytokines and killing. These findings imply a simple phenotypic model of TCR signaling in which multiple T cell responses share a common rate-limiting threshold and a conceptually simple model of CD8+ T cell Ag recognition, in which Ag dose and affinity do not provide any additional response-specific information.

How transcription factors drive choice of the T cell fate.

Recent evidence has elucidated how multipotent blood progenitors transform their identities in the thymus and undergo commitment to become T cells. Together with environmental signals, a core group of transcription factors have essential roles in this process by directly activating and repressing specific genes. Many of these transcription factors also function in later T cell development, but control different genes. Here, we review how these transcription factors work to change the activities of specific genomic loci during early intrathymic development to establish T cell lineage identity. We introduce the key regulators and highlight newly emergent insights into the rules that govern their actions. Whole-genome deep sequencing-based analysis has revealed unexpectedly rich relationships between inherited epigenetic states, transcription factor-DNA binding affinity thresholds and influences of given transcription factors on the activities of other factors in the same cells. Together, these mechanisms determine T cell identity and make the lineage choice irreversible.