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T Cell Dysfunction in Cancer Immunity and Immunotherapy

In cancer, T cells become dysfunctional owing to persistent antigen exposure. Dysfunctional T cells are characterized by reduced proliferative capacity, decreased effector function, and overexpression of multiple inhibitory receptors. Due to the presence of various inhibitory signals in the complex tumor microenvironment, tumor-specific T cells have distinct dysfunction states. Therapeutic reactivation of tumor-specific T cells has yielded good results in cancer patients. Here, we review the hallmarks of T cell dysfunction in cancer. Also, we discuss the relationship between T cell dysfunction and cancer immunotherapy.

INTRODUCTION
T cells can take part in a variety of immune responses that arise in various diseases, including infection, cancer, autoimmune diseases, and allergic diseases. In acute infections, naive T cells, upon antigen stimulation, are rapidly activated and differentiate into effector T cells (Teff). Teff differentiation involves transcriptional, epigenetic and metabolic reprogramming as well as the acquisition of effector features. After antigen clearance, most Teff die, but a small fraction of them differentiate into memory T cells, which quickly respond when the same antigen reappears (1). Memory T cells downregulate their effector program and own self-renewal ability driven by IL-7 and IL-15 in an antigen-independent manner (2). By contrast, during chronic infections and cancer, the function of T cells becomes compromised, termed T cell dysfunction, due to persistent antigen exposure (3)(4)(5)(6)(7). Previous studies have demonstrated that the severity of dysfunction is associated with the level of antigen stimulation (8,9). Furthermore, specific T cell receptor (TCR)-dependent pathways, for instance, those mediated by nuclear factor of activated T cells (NFAT) and sprouty homolog 2 (SPRY2), have been shown to be involved in T cell dysfunction, in line with the effects of ongoing TCR stimulation (10)(11)(12). In addition, chronic antigen stimulation also results in persistent expression of programmed cell death protein 1 (PD-1) by NFAT cytoplasmic 1 (NFATc1) (13). PD-1 may further regulate the level of TCR signaling (14,15). Therefore, the extent and persistence of antigenic stimulation appear to be vital factors leading to T cell dysfunction and are associated with the severity of dysfunction.
T cell exhaustion is a representative of T cell dysfunction. (16)(17)(18). Anergic T cells are induced by suboptimal stimulation, whereas senescent T cells enter a terminally differentiated state due to repeated stimulation, which involves irreversible cell cycle arrest and telomere shortening (Figure 1). In this article, we mainly discuss the exhausted T cells. T cell dysfunction was first discovered in mice infected with chronic lymphocytic choriomeningitis virus (LCMV), which is characterized by a progressive loss of function including proliferation, cytokine production and the ability to lyse target cells (3). Subsequently, T cell dysfunction was described in humans with chronic viral infections and cancer (9,(19)(20)(21). The acquired dysfunction was related to the co-expression of multiple inhibitory receptors (IRs) including PD-1, cytotoxic T lymphocyte antigen 4 (CTLA-4), T-cell immunoglobulin domain and mucin domain-3 (Tim-3), lymphocyte activation gene 3 (LAG-3), T cell immunoreceptor with Ig and ITIM domains (TIGIT), and others (4,6,9,(22)(23)(24)(25). Interestingly, studies in mice and humans have shown that dysfunctional CD8 + T cells coupregulate multiple IRs, and the type and amount of IRs are associated with the severity of the T cell dysfunction (4,9,26,27). It is worth noting that dysfunctional T cells are not completely useless. Instead, these cells retain a certain level of residual function, and this residual function may be critical in vivo, limiting persistent pathogen infection and tumor progression. However, dysfunctional T cells fail to effectively eliminate infection and cancer.

Exhausted T cells (Tex) differ from other dysfunctional T cells, including anergic T cells and senescent T cells
Reversal of T cell dysfunction is becoming more important in improving immunity to cancer. Immune checkpoint blockade, as one modality of cancer immunotherapy, is designed to enhance T cell function and thus exert an effective anti-tumor T cell response (28,29). One of the major markers of T cell dysfunction is the overexpression of PD-1. Blocking PD-1 or its ligand PD-L1 successfully reactivates T cell function, emphasizing the importance of the PD-1/PD-L1 axis in reversing T cell dysfunction (22). Encouraging results have been observed in a variety of cancer types, such as melanoma, urothelial cancer and non-small-cell lung cancer (NSCLC), with the use of antibodies targeting PD-1/PD-L1 (30)(31)(32)(33). However, most patients do not maintain sustained responses to this therapy. The lack of a longlasting response may be partly explained by the presence of other inhibitory receptors in T cells.
Here, we review the hallmarks of T cell dysfunction in cancer. Also, we discuss the relationship between T cell dysfunction and cancer immunotherapy.

Inhibitory Receptors in Dysfunctional T Cells
One of the traits of dysfunctional T cells is the increased and sustained expression of multiple inhibitory receptors, including PD-1, CTLA-4, Tim-3, and LAG-3 (Figure 3a). In general, the number of inhibitory receptors expressed by dysfunctional T cells is positively associated with T cell dysfunction. In other words, the greater the number, the more serious the dysfunction. Additionally, functional effector T cells can transiently express inhibitory receptors upon activation. For instance, PD-1 expression rapidly increases after T cell activation and may remain at moderate levels in healthy individuals (34,35). To control autoreactivity and immunopathology, inhibitory receptors play a key negative regulatory role (36). In healthy adults, for example, circulating PD-1 + CD8 + T cells are not representative of dysfunctional T cells, but instead are representative of effector memory cells (35). In addition, IR expression patterns in dysfunctional T cells also have differences between cancer and infection, albeit with many common features (5). B-and T-lymphocyte attenuator (BTLA) is upregulated in dysfunctional CD8 + T cells in cancer, but is absent in dysfunctional CD8 + T cells in LCMV (4,27). This finding shows that the molecular mechanisms that cause IR upregulation and T cell dysfunction may be somewhat different between chronic infections and cancer.
Moreover, each IR binds to its ligand, which is typically expressed by antigen presenting cells (APCs) and tumor cells in the tumor microenvironment (TME), to affect T cell survival, proliferation and function (Figure 2). In view of this, the availability and amount of ligands in the TME is critical for IR to play a negative regulatory role in CD8 + T cells. As one of the ligands for PD-1, the expression of PD-L1 in the TME relies on exposure to inflammatory cytokines (37). Conversely, CD155 and CD112, as TIGIT ligands, are continuously expressed in the TME by most melanoma cells and APCs (25). In addition, Tim-3 directly binds to Galectin-9 and CEACAM1 to inhibit T cell function (38,39). Also, Tim-3 can bind to phosphatidylserine and the DNA binding protein HMGB1 (40,41).
The molecular mechanisms of IR-mediated immunomodulation of T cells have not been fully elucidated. There are several possible mechanisms to support IR-mediated immune regulation. First, IR competes with costimulatory receptors for binding to its ligand to prevent the formation of microclusters and lipid rafts (25). For instance, CTLA-4 can compete with the costimulatory molecule CD28 to bind to CD80 and CD86 (42), while TIGIT can compete with CD226 to bind to the same ligands CD112 and CD155 (43,44). The imbalance in the expression levels of IR and costimulatory receptors affects the same ligand binding (45). In patients with metastatic melanoma, CD8 + TILs upregulate TIGIT and downregulate CD226, leading to an imbalance of TIGIT/CD226 expression (25). This imbalance may be conducive to inhibiting T cell responses to tumors in the TME, besides T cell inhibition mediated by TIGIT itself (46). Second, activation of T cells by TCR or costimulatory receptors is disrupted by negative signals mediated by PD-1 and CTLA-4 (45,47). When TIGIT/CD155 is ligated, APCs produce IL-10, which also inhibits T cell function (44). In addition, CD28 can also be expressed by PD-1 + T cells. CD28, as a costimulatory molecule, stimulates the activation of naive T cells and promotes cytokine secretion. Recent studies have revealed that the PD-1/PD-L1 axis inhibits T cell function through inactivation of CD28 signaling (48).  FIGURE 2 | Inhibitory receptors in dysfunctional T cells. Dysfunctional T cells in the tumor microenvironment (TME) express multiple inhibitory receptors, including PD-1, CTLA-4, Tim-3, LAG-3, and TIGIT. They bind to their respective ligands, which are typically expressed by antigen-presenting cells (APCs) or tumor cells in the TME. PD-1, as the major inhibitory receptor, has two ligands PD-L1 and PD-L2. CTLA-4 can compete with the costimulatory molecule CD28 to bind to CD80 and CD86. Additionally, TIGIT can compete with CD226 to bind to CD112 and CD155. In addition, Tim-3 directly binds to Galectin-9 and CEACAM1 to inhibit T cell function. These inhibitory receptors contribute to T cell dysfunction in cancer.

FIGURE 3 | Traits of T cell dysfunction in cancer.
(a) Inhibitory receptors in dysfunctional T cells. One of the traits of dysfunctional T cells is the increased and sustained expression of multiple inhibitory receptors, including PD-1, CTLA-4, Tim-3, LAG-3, and TIGIT. In general, the greater the number of inhibitory receptors coexpressed by dysfunctional T cells, the more severe the dysfunction. (b) Inhibitory cells in the TME. The TME contains various cell types involved in multiple biological processes that promote or inhibit tumor progression. Immunosuppressive cells are present in the TME, which contribute to T cell dysfunction. These inhibitory cells include Treg cells, TAMs, MDSCs, cancer-associated fibroblasts and adipocytes, and endothelial cells. (c) Suppressive soluble mediators. Some soluble molecules exist in the TME and mediate T cell dysfunction. These molecules include IL-10, type I IFNs, IDO, adenosine, VEGF-A, TGF-β, and IL-35. (d) Metabolic pathways in the TME. The activation of T cells and the exertion of antitumor immunity depend on some common metabolic pathways, such as aerobic glycolysis, amino acid metabolism, glutaminolysis, and de novo fatty acid synthesis. These metabolic pathways are also important preconditions for cancer cell proliferation and survival. Hence, within the TME, T cells compete with cancer cells to obtain adequate nutrients. In addition to nutrients, various metabolites are also involved in T cell dysfunction, such as lactic acid, low pH, and hypoxia. (e) Epigenetic imprinting of T cell dysfunction. Epigenetic imprinting of dysfunctional T cells differs from that of effector/memory T cells. Persistent PDCD1 demethylation and unique changes in chromatin accessibility occur in dysfunctional T cells. (f) Transcriptional regulation of T cell dysfunction. Transcriptional regulation of T cell dysfunction involves changes in the expression patterns and transcriptional connection of some important transcription factors, such as T-bet, Eomes, Foxo1, Blimp-1, NFAT, and TOX. TME, tumor microenvironment; Treg cells, regulatory T cells; TAMs, tumor-associated macrophages; MDSCs, myeloid-derived suppressor cells; IDO, indoleamine 2,3-dioxygenase; TGF-β, transforming growth factor-β.
In a mouse model, conditional knockout of CD28 abolishes the effects of PD-1 blockade (49). This suggests that the CD28/B7 pathway may act as an important role in the efficacy of anti-PD-1 therapy.
Accumulated evidence shows that simultaneous blockade of multiple inhibitory receptors is more effective than single IR blockade in reversing dysfunctional CD8 + T cells in vitro and in vivo. For example, blockade of PD-1 combined with blockade of CTLA-4, Tim-3, LAG-3, or TIGIT blockade, to some extent, can reverse T cell dysfunction and enhance antitumor immunity (23,25,26,50,51). These inhibitory receptor molecules are derived from different structural families and have different intracellular signaling domains. They can bind to ligands with different expression patterns.

Inhibitory Cells in the Tumor Microenvironment (TME)
The TME contains a variety of cell types that participate in various biological processes, which promote or inhibit tumor progression. Immunosuppressive cells are present in the TME and contribute to T cell dysfunction. These inhibitory cells include regulatory T cells (Treg cells), tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts and adipocytes, and endothelial cells (Figure 3b).
TAMs suppress T cell antitumor immunity and promote tumor development, involving functions such as the sustained accumulation of Treg cells and dysregulation of the vasculature due to the expression of chemokines and amino acid-degrading enzymes, such as arginase 1 and indoleamine-2,3-dioxygenase (IDO) (64)(65)(66). Similarly, MDSCs enter TME aberrantly, produce nitric oxide and reactive oxygen species, and express arginase 1 and IDO, thereby effectively promoting T cell dysfunction (67,68). In a mouse model, targeting MDSCs with monoclonal antibodies has been demonstrated to restore the antitumor immune responses and tumor killing ability of tumor-infiltrating T lymphocytes (TILs) (69).
Cancer-associated fibroblasts can secrete cytokines and chemokines, and disrupt the deposition of the extracellular matrix, which shapes the structure of the TME and thus contributes to tumorigenesis (70,71). T cell dysfunction can also be caused by cancer-associated fibroblasts via the production of TGF-β and vascular endothelial growth factor (VEGF) (72,73). Moreover, recent findings have also shown that cancerassociated adipocytes impair antitumor immunity and promote tumor malignancy in several cancers (74)(75)(76). The mechanism may be mediated by the metabolic and paracrine regulation of tumor infiltrating immune cells and cancer cells.
Endothelial cells may promote T cell dysfunction by improving the production of prostaglandin E2 (PGE2) and CD95L, while impairing T cell recruitment by reducing the expression of vascular cell adhesion molecule 1 (VCAM1) (77)(78)(79). The underlying mechanisms of these changes are mediated by hypoxia and VEGF signaling in endothelial cells. In addition, metabolic communication between cancer and endothelial cells, as well as lymphatic endothelial cells, may help impede antitumor T cells and mediate immunosuppression (80)(81)(82).

Suppressive Soluble Mediators
Some soluble molecules are present in the TME that mediate T cell dysfunction. These molecules include IL-10, type I IFNs, IDO, adenosine, VEGF-A, TGF-β, and IL-35 (Figure 3c).
IL-10 is produced by various immune cells and serves as an effective antiinflammatory molecule (83). For instance, natural killer cells, APCs, T cells, and B cells can generate IL-10 (84-87). Interestingly, the dose of IL-10 and the state of T cell activation can affect the effects of IL-10 on T cells in vivo (88). On the one hand, IL-10 impairs antitumor immunity and promotes tumor growth in mouse models (89). Simultaneous blockade of PD-1 and IL-10 results in increased survival and delays tumor growth in ovarian cancer, leading to an enhanced antitumor immune response and reduced infiltration of immunosuppressive MDSCs (90). On the other hand, high doses of IL-10 and PEGylated IL-10 hamper the progression of tumors in animals and increase the expansion and function of CD8 + TILs expressing elevated IL-10R (88,91). Thus, IL-10 may have a paradoxical effect on T cells in vivo.
Type I IFNs including IFNα and IFNβ may be typical cytokines with a dual role. As key pro-inflammatory cytokines, type I IFNs inhibit viral replication by directly inducing antiviral activity and activating innate immune cells (92). Additionally, type I IFN signaling facilitates the optimal priming of T cells and production of functional effector T cells and memory T cells (93). However, some studies have also shown that IFNα promotes the persistence of viruses in chronic infections and induces T cell dysfunction (94). During the first few days of LCMV infections, blockade of IFNα can effectively prevent the occurrence of T cell dysfunction (94). The mechanism by which IFNα promotes T cell dysfunction may be through the production of immunosuppressive molecules, such as IL-10, PD-L1, and IDO, as well as the stimulation and maintenance of PD-1 expression on T cells (95). Notably, IFNα blockade combined with PD-1 blockade effectively enhances antitumor immune responses in tumor-bearing mice (95).
IDO is expressed by tumor cells, APCs and CD8 + T cells in the TME (96)(97)(98)(99). IDO activates Tregs by producing kynurenine and inhibits T cell function by depleting the essential amino acid tryptophan (100). In addition, resistance to anti-PD-1 and anti-CTLA-4 mAbs is closely related to IDO (101). Adenosine exerts an inhibitory role and directly impedes effector T cell function by activating the adenosine receptor A2aR (102). Indirect destruction of effector T cell function is induced by increased Treg function, decreased APC activation, and MDSC induction (103). Moreover, VEGF-A is generated by tumor cells in the TME. In addition to its proangiogenic properties, VEGF-A inhibits dendritic cell maturation, increases the accumulation of MDSCs and induces Tregs, all of which contribute to the formation of an immunosuppressive TME milieu (104,105). Coexpression of various IRs is increased by VEGF-A in a VEGF-R2-and NFAT-dependent manner, including PD-1, CTLA-4, and Tim-3 (106). In addition, TGF-β is involved in T cell dysfunction in cancer (107). TGF-β inhibits antitumor immunity and shapes the TME by impeding T cell infiltration (108).
IL-35 is expressed by activated Treg cells and regulatory B cells (109)(110)(111). A recent study showed that Treg-derived IL-35 could promote T cell exhaustion (112). The underlying mechanism was direct regulation of Blimp1 expression in CD8 + T cells, increased IR expression and restricted differentiation of central memory CD8 + T cells (112). In addition, neutralization of IL-35 promotes CD8 + TIL proliferation and production of inflammatory cytokines (113). Neutralization of IL-35 also decreases tumor growth in a mouse tumor model (113). These results indicate that IL-35 favors the immunosuppressive TME.

Metabolic Pathways in the TME
The activation of T cells and exertion of antitumor immunity depend on some common metabolic pathways, such as aerobic glycolysis, amino acid metabolism, glutaminolysis and de novo fatty acid synthesis (114). These metabolic pathways are also important for cancer cell proliferation and survival. Hence, within the TME, T cells compete with cancer cells to obtain adequate nutrients (Figure 3d).
Recently, some studies have demonstrated that cancer cells compete with TILs to acquire the essential glucose, which results in less availability of glucose to T cells (115). The reduction or deprivation of glucose induces T cell dysfunction and impairs the immune response (116,117). Therefore, restraining the metabolic activity of tumor cells may have potential value in increasing the amount of glucose available to T cells, thereby improving the antitumor activity of T cells. Moreover, glycolysis is required for T cell maturation, expansion and effector function (114,118). The CD28 signaling pathway can facilitate the transition of T cell metabolism to glycolysis. Therefore, both PD-1 and CTLA-4 can restrict T cell glycolysis by interfering with CD28 signaling. Compared to CTLA-4, PD-1 can also induce the fatty acid β-oxidation (FAO) rate-limiting enzyme carnitine palmitoyl transferase (Cpt1a) to simultaneously promote FAO (119). Notably, the role of PD-1 in inducing FAO relies on the simultaneous inhibition of the PI3K/AKT and MEK/ERK signaling pathways, which regulate glucose metabolism (119). Blocking PD-1 or CTLA-4 restores the glycolytic ability of TILs in a mouse model, which promotes glycolysis in T cells (115). It is noteworthy that PD-L1 derived from tumor cells can facilitate tumor glycolysis through AKT/mTOR signaling (115). Thus, blocking the PD-1/PD-L1 axis can lead to a range of synergistic antitumor effects: restoring tumor-specific T cell function, reducing tumor glycolysis, and improving glucose availability to T cells in the TME.
Of course, certain amino acids, including arginine, glutamine and tryptophan, are also important nutrients that stimulate T cell activation and promote cancer cell proliferation and survival. For instance, arginine is a nutrient necessary for T cell activation and proliferation (120). TAMs and MDSCs express arginase 1, which decomposes and reduces arginine in the TME, and results in impaired effector T cell function (121)(122)(123). Interestingly, for T cell activation and differentiation, glutamine is essential (124). However, cancer cells exert a strong intake of glutamine, which is conducive to tumor development (125). It is well known that the rate-limiting enzyme for glutamine decomposition is glutaminase. In a mouse model, targeting glutaminase is effective in controlling tumor occurrence (126). Therefore, we can infer that excessive glutaminolysis in the TME is due to cancer cells, which restricts the utilization of glutamine by T cells and thereby promotes T cell dysfunction. Moreover, because tumor cells and suppressive immune cells secrete the tryptophan metabolism enzyme IDO, tryptophan is catabolized into kynurenine, an immunosuppressive metabolite (127). Accumulated kynurenine facilitates regulatory T cell production (128). Collectively, these results indicate that in the TME, T cells can effectively exert antitumor immunity, which is inseparable from some important nutrients. These essential nutrients may be controlled by cancer cells and immune regulatory cells.
In addition to nutrients, various metabolites are also involved in T cell dysfunction, such as lactic acid, low pH, and hypoxia (Figure 3d). Tumor-derived lactic acid can induce apoptosis in naive T cells, which may support tumor immune escape, due to the loss of the 200 kDa FAK family interacting protein (129). Lactic acid induces pH alterations and a loss of cytosolic NAD + regeneration, which further limits T cell activity and cytokine production (130). Thus, neutralizing acidic components in the TME may contribute to enhancing T cell antitumor immunity (131). Notably, hypoxia is another metabolic parameter in the TME due to rapid tumor growth. Different studies have shown that hypoxia improves or impairs T cell immune responses. In murine chronic infections and tumor models, results indicate that hypoxia can enhance the cytotoxic function of T cells as well as lead to more effective viral and tumor control (132,133). By contrast, increased expression and activity of the transcription factor hypoxia-inducible factor 1α (HIF-1α) is induced by hypoxia, which increases the expression of inhibitory receptors on T cells and reduces T cell effector function (132,134).

Epigenetic Imprinting of T Cell Dysfunction
The epigenetic imprinting of dysfunctional T cells differs from that of effector/memory T cells (Figure 3e). Normally, PDCD1 (encoding PD-1) is temporarily demethylated in activated T cells, after which normal methylation levels are restored. In chronic viral infections, results indicate that the PDCD1 demethylation levels persist in activated T cells, leading to long-term expression of PD-1 and T cell dysfunction (135)(136)(137). Even after the virus is cleared to levels that cannot be detected by current technologies, this demethylation pattern of the PDCD1 locus is still sustained, suggesting that this epigenetic program has been fixed. Interestingly, one study reported the presence of two T cell clones with different affinities in Melan-A-specific TILs in human melanoma (138). Low avidity T cell clones, due to the sustained methylation of the PDCD1 promoter, do not express PD-1 even when stimulated by TCR. Conversely, antigen-specific T cells with high functional avidity express PD-1. These phenomena suggest a relationship among expression of PD-1, modification of the PDCD1 locus and intensity of TCR signaling (138). Aside from the continued demethylation of the PDCD1 locus, dysfunctional T cells gradually undergo de novo methylation of genes involved in effector functions during persistent infections and tumor progression, limiting T cell expansion and clonal diversity (139). This cannot be reversed by PD-1 blockade. However, during PD-1 blockade, blocking dysfunction-related methylation imprinting has a synergistic effect in a murine tumor model, as manifested by increased T cell immune responses and tumor control (139).
In addition, unique changes in chromatin accessibility occur in dysfunctional T cells. For example, in chronic LCMV infection, additional accessible regions of PDCD1 appeared in dysfunctional T cells (140). However, some open regions of the IFNG locus were present in functional T cells and were absent in dysfunctional T cells. It is worth noting that after anti-PD-1 treatment, these accessible areas specific to the dysfunctional state did not significantly change, showing that the epigenetic program was only minimally remodeled (141). Similarly, tumor antigen-specific and bystander T cells were adoptively transferred in a murine tumor model (142). Gene expression and chromatin accessibility were then compared. The results showed that only tumor-specific T cells were characterized by dysfunction and exhibited increased effector functions after anti-PD-L1 therapy, with little change in chromatin accessibility (142).
Moreover, epigenetic imprinting begins in the early stages of dysfunction. Many changes in dysfunctional T cells in advanced tumors of mouse liver cancer models are also present in T cells from precancerous lesions (7). Two different chromatin states can mirror the process of moving from mild dysfunction in early tumor lesions to more severe dysfunction in late-stage tumors. On the one hand, the chromatin state of early dysfunctional TILs shows partial plasticity and can be reprogrammed by anti-PD-1 therapy. On the other hand, the advanced state is fixed and cannot be changed by anti-PD-1 therapy (143). It is noteworthy that most of the overlap exists between the chromatin accessibility state of PD-1 hi CD8 + TILs from human non-smallcell lung cancer and the chromatin accessibility state of late TILs in mice. Thus, PD-1/PD-L1 blockade in human tumors may only cause transcriptional rewiring rather than altering the chromatin accessibility pattern itself.

Transcriptional Regulation of T Cell Dysfunction
Transcriptional regulation of T cell dysfunction involves changes in the expression patterns and transcriptional connection of some important transcription factors. Some transcription factors are expressed in both functional Teff and dysfunctional T cells (Figure 3f). However, in dysfunctional T cells, these transcription factors are linked to different genes and transcriptional loops, showing activity in specific contexts (144,145).
T-bet and Eomes, as T-box family transcription factors, play a crucial role in the development of effector and memory CD8 + T cells (146,147). In acute infection, T-bet promotes the formation of Teff cells and the development of KLRG-1 + terminal Teff cells, while Eomes promotes the expression of IL-15Rβ and the development of memory T cells in a homeostatic manner (2,144,(147)(148)(149). However, in chronic infection, the role of Tbet and Eomes is different from their function in Teff and Tmem cells (145). For example, the formation of a dysfunctional T cell population requires the participation of both T-bet and Eomes. And if either transcription factor is genetically deleted, the dysfunctional T cell population cannot form (144). Furthermore, due to its potential ability to inhibit the transcription of the IR genes (e.g., Pdcd1 encoding PD-1), upregulated expression of T-bet favors the formation of progenitor PD-1 int Eomes lo Tex subpopulation (144). As for Eomes, its high expression in the terminal Tex subpopulation differs from that in the selfrenewal Tmem cells in acute infection (144). In view of this, transcriptional network analysis is performed, and it has been shown that Eomes participates in almost completely different transcription networks in functional Teff and Tmem cells compared with Tex cells (145). Therefore, the functions of T-bet and Eomes are rewiring in Tex cells.
The expression level of these transcription factors directly affects their capability of driving distinct transcriptional programs. For instance, dysfunctional T cells upregulate Foxo1 and Blimp-1, which act as positive regulators of T cell dysfunction (150,152,155). Also, metabolic-related transcription factors, including hypoxia-inducible factor (HIFs) and von Hippel-Lindau (VHL) complexes, may promote T cell dysfunction in some cases (156). In addition, recent research has found that TOX, as a new transcription factor, promotes T cell dysfunction in cancer (157)(158)(159).

HETEROGENEITY IN DYSFUNCTIONAL T CELLS
Dysfunctional T cells are heterogeneous. Firstly, PD-1 and CD44 are utilized to distinguish subpopulations of dysfunctional T cells with various biological functions. Studies have revealed that PD-1 int CD44 hi T cells had a lower degree of dysfunction than PD-1 hi CD44 int T cells, which may be partially explained by lower coexpression of IRs (9). Furthermore, the PD-1 int T cells can produce a therapeutic response to PD-1 blockade. Conversely, PD-1 hi CD44 int T cells are in a terminally dysfunctional state and have no therapeutic response to immune checkpoint blockade (9). Subsequent studies have also shown that the PD-1 int CD44 hi T cell subpopulation is mainly composed of T-bet hi and Eomes lo cells, and as a progenitor cell population, can persistently generate PD-1 hi CD44 int Eomes hi T cell population with terminal dysfunction state (144).
In addition, the understanding of dysfunctional T cell heterogeneity is further improved due to the recognized role of CXCR5 and the transcription factor Tcf-1. The CXCR5 + Tcf-1 + Tim-3 − T cell subpopulation can respond to PD-1 blockade and produce a more terminal CXCR5 − Tcf-1 − Tim-3 + T cell subpopulation (160). Interestingly, there is no difference in the expression of the transcription factors T-bet and Eomes between the two subpopulations (160). These results may reveal the heterogeneity in the original PD-1 int progenitor cell populations.
Therefore, the heterogeneity of dysfunctional T cells is complex. Identifying progenitor populations that respond to checkpoint blockade may be more beneficial to the development of cancer immunotherapy and to cancer patients.

Single or Combined Immune Checkpoint Blockade
Immunotherapy for treating tumor-induced T cell dysfunction has provided hope to patients with cancer. Currently, immunotherapy has focused on blocking immune checkpoints in patients with various solid and hematological tumors (161)(162)(163)(164). The most commonly targeted immune checkpoints are PD-1 and CTLA-4. Generally, PD-1 impedes T cell function by interfering with T cell receptor (TCR) signaling, whereas CTLA-4 impedes T cell function by competing with the costimulatory molecule CD28 to bind to CD80/CD86 (165,166). Since Treg cells also express CTLA-4, antibodies targeting CTLA-4 can inhibit Treg cell function or enhance antitumor immunity by selectively eliminating Treg cells in a mouse model (62). The CTLA-4 targeting agent, ipilimumab, was the first immune checkpoint inhibitor. With the use of ipilimumab, overall survival was significantly increased in patients with advanced melanoma (167,168). Of note, in patients with melanoma treated with anti-CTLA-4, the melanoma-reactive CD8 + T cell response in peripheral blood was found to be significantly increased (169). This result suggests that enhanced T cell priming also plays a role. Also, PD-1 pathway blockade can effectively reverse dysfunctional T cells and increase antitumor activity in patients with various cancers, especially in viral infection-driven cancers or carcinogen-induced cancers (170)(171)(172)(173)(174). In addition, as mentioned above, recent studies have revealed that the PD-1/PD-L1 axis inhibits T cell function through the inactivation of CD28 signaling instead of TCR signaling (48). In a mouse model, conditional knockout of CD28 abolished the effects of PD-1 blockade (49), suggesting that the CD28/B7 pathway may act as an important role in the efficacy of anti-PD-1 therapy. Currently, five anti-PD-1 or anti-PD-L1 antibodies have been approved by the FDA for the treatment of a variety of cancers, including hepatocellular carcinoma, renal cell carcinoma and Hodgkin's disease (175).
Moreover, compared with monotherapy, the combined use of anti-CTLA-4 and anti-PD-1 has demonstrated prolonged progression-free survival and significant tumor regression in clinical trials of cancer patients (176,177). Also, PD-1 blockade combined with other checkpoint blockades, such as Tim-3, LAG-3, or TIGIT, significantly reverses T cell dysfunction and enhances antitumor immunity in patients with cancer, compared to individual checkpoint blockade (25,26,178,179). At present, humanized anti-TIM3 (TSR-022), anti-LAG3 (MK-4280), and anti-TIGIT (BMS-986207) antibodies against various cancers are under clinical trials. However, the underlying precise mechanisms to explain the efficacy of immune checkpoint blockade have not been fully described.

Immune Checkpoint Blockade in Combination With Other Immunotherapies
The combination of inhibitory receptor blockade and costimulatory molecule targeting exerts a synergistic effect in re-activating tumor-specific T cells. CD137 (or 4-1BB), a member of the TNFR family, is expressed on activated T cells (180). In a murine model, the combination of agonistic anti-CD137 antibodies and PD-1 blockade effectively controls tumor growth (181). This combination therapy also improves the T cell response to tumor antigens and promotes effector/memory CD8 + T cell formation (182). Another member of the TNFR family, OX40, is primarily expressed on activated CD4 + T cells. And its expression on CD8 + T cells is at a lower level upon TCR triggering (183). The research results show that OX40 agonists play an antitumor immunity-promoting role in immunogenic mouse tumor models, but do not play an effective role in tumor control in a poor immunogenic tumor model (184,185). Strikingly, in a vaccination setting, the addition of PD-1 blockade to OX40 agonists virtually eliminates the antitumor effect of OX40 monotherapy due to the reduced TIL infiltration and enhanced cell death of tumor-reactive CD8 + T cells (186). This effect deserves close attention because it is uncertain whether a similar harmful effect will occur in human cancers. Also, this phenomenon underscores the importance of the timing of different immunotherapeutic interventions to avoid the negative effects of T cell overstimulation.
Furthermore, anti-CTLA-4 therapy in combination with anti-VEGF antibodies has been shown to increase antitumor immune responses and achieve encouraging clinical results in patients with metastatic melanoma (187). Moreover, in mice with melanoma, researchers have demonstrated that the use of PPARα agonists can promote fatty acid catabolism in vaccine-induced CD8 + TILs. The addition of anti-PD-1 antibodies obviously enhanced the antitumor efficacy (188).
Therefore, future research should focus on how combination therapies should be applied and which dysfunctional states may be best for them.

CAR-T Cell Therapy
A CAR-T cell is genetically modified to express an antigenspecific, non-MHC restricted receptor, which consists of the single-chain variable fragment (scFv) of an antibody, a transmembrane domain and an intracellular signaling domain (189,190). Thus far, CAR-T cell therapy has achieved great success in treating hematological malignancies (191,192). However, some patients still lack response and relapse after receiving CAR-T treatment, possibly due to the poor proliferative capacity and the short duration of T cells. Therefore, a transcriptomic analysis was conducted to compare T cells from chronic lymphocytic leukemia (CLL) responders and nonresponders after CAR-T treatment (193). The results showed that T cells from non-responders upregulated pathways associated with exhaustion and apoptosis compared to T cells from responders (193). Inhibitory receptors were also upregulated on these CAR-T cells, indicating that these inhibitory molecules may result in dysfunction and poor persistence of CAR-T cells (194,195).
Recently, CRISPR-Cas9 technology has been applied to knock out IR itself, and studies have been conducted on PD-1 and LAG-3 in CD19-BBζ CAR-T cells (196,197). In mouse xenograft models, knockout of IR-derived CAR-T cells effectively eradicated tumors (196,197). This method has also been applied to solid tumors. CRISPR-Cas9 was utilized to knock out PD-1 in CD133-specific CAR-T cells. In a mouse glioma model, CD133-specific CAR-T cells with PD-1 knockout enhanced the control of tumor growth compared to control CD133-CAR-T cells (198). Based on the encouraging results obtained above, CAR-T cells edited by CRISPR-Cas9 have entered clinical trials. For example, CD19-specific CAR-T cells with PD-1 knockout are being investigated in PhaseIclinical trials (NCT03298828).
Additionally, CAR-T cells are designed to secrete immune checkpoint antibodies themselves. In CD19 + lung cancer xenograft models, CAR-T cells have been engineered to secrete anti-PD-1 antibodies and significantly improved antitumor activity, manifested by enhanced T cell proliferation, increased cytotoxicity, and prolonged overall survival (199). Compared with standard CAR-T cells, anti-CAIX CAR-T cells secreting anti-PD-L1 antibodies significantly increase antitumor activity, as evidenced by enhanced cytokine production and immune cell recruitment and the significant reduction in tumor size in the humanized ccRCC mouse model (200). Whether these CARs are likely to succeed in the body is still unknown. Currently, CAR-T cells, which are designed to secrete PD-1, CTLA-4 or PD-L1 antibodies, have entered clinical trials for cancers expressing MUC1, EGFR, EGFRVIII, and mesothelin (201).
In addition, other strategies have been used to enhance CAR-T cell function in TME, including inhibition of inhibitory soluble molecules such as IDO, adenosine and VEGF, and protection from immunosuppression of non-tumor cells, such as MDSCs and TAMs. Interestingly, the combination of EGFRVIIICAR-T cells with the blockade of IDO1 significantly reduces tumor growth in a xenograft colon cancer model (202). In HER2 CAR-T cells, blockade of adenosine 2A receptor enhances CAR-T cell activation and cytokine production, which contributes to the improved antitumor efficacy of CAR-T cells (203). The addition of PD-1 blockade further enhanced T cell immunity (203). Also, VEGF blockade has achieved success in solid tumors including melanoma, and VEGF-targeted CAR-T cells have achieved good results in a variety of preclinical solid tumor models (187,204,205).

CHALLENGES AND POSSIBLE SOLUTIONS
Resistance to therapy is a major challenge in cancer immunotherapy. Defects involved in IFNγ signaling and antigen presentation pathways, as tumor-intrinsic alterations, can lead to resistance to cancer immunotherapy, which explains why immunotherapy is not effective in some cancer patients (206,207). In melanoma patients treated with PD-1 blockade, for example, the acquired resistance is attributed to the inactivation of IFNγ signaling and defects in the antigen presentation mechanism (206).
Moreover, alterations in the functional state of TILs are also among the reasons for resistance to immunotherapy (208). Interestingly, after adoptive transfer of MART-1 TCR-transduced T cells in advanced melanoma patients, the T cell functional phenotype was altered during relapse, which was characterized by a complete loss of initial cytotoxic activity or an acquired inflammatory cytokine secretion but lack of cytotoxicity (209). Therefore, to assess whether changes in the dysfunctional state are also critical in mediating the immunotherapeutic resistance of cancer patients, it will be of value to compare monotherapy and combination therapy at the beginning of treatment and at the moment of resistance.
In addition, reactivation of treatment-induced tumor-specific T cells may unexpectedly promote the further progression of T cell dysfunction. By comparing longitudinal samples of colon cancer models in both immunocompetent and immunodeficient mice, we found two important tumor escape mechanisms involving the accumulation of immunosuppressive cell populations (e.g., Tregs) and enhanced expression of multiple inhibitory receptors on T cells, which contribute to the deterioration of T cell function (210). Given this, Chauvin et al. discovered that, after PD-1 blockade in vitro, TIGIT expression was increased on TA-specific CD8 + T cells (25). Dual TIGIT and PD-1 blockade could promote the effector function of CD8 + T cells. Similarly, Tim-3 was upregulated on TILs in a PI3K/AKTdependent manner upon PD-1 blockade. Notably, the combined blockade of the two receptors significantly improved antitumor immunity (211). In a mouse lung cancer model, several other immune checkpoints, such as CTLA-4, Tim-3, and LAG-3, were also found to be upregulated upon resistance to PD-1 blockade (212). Therefore, upregulation of other immune checkpoints also provides a potential resistance mechanism after anti-PD-1 treatment.

CONCLUSIONS
Dysfunctional T cells have various cellular states and different characteristics in cancer. Both persistent TCR triggering and T-cell internal and external factors can affect the fate of dysfunctional T cells. Although the characteristics of T cell dysfunction in cancer are similar to those of T cell dysfunction in chronic viral infection, there are various factors in the complicated TME that promote the process of immunosuppression, which can further shape T cell dysfunction in cancer. Gradually, it is conceivable that dysfunctional T cells in cancer differ from those found in chronic infection sites. However, heterogeneous dysfunctional T cell states still exist among different tumors and even within tumors. To reverse dysfunctional T cells and restore antitumor immunity, immune checkpoint blockade and related combination therapies have been applied. However, some challenges still remain to be solved, such as how to maintain long-lasting efficacy and how to choose the best combination therapy. Moreover, it is essential to understand the processes that drive and maintain these different dysfunctional T cell states. Continued advances in technology will help achieve this goal, supporting the development of personalized strategies for targeting dysfunctional T cells to achieve precise treatment for cancer patients.

AUTHOR CONTRIBUTIONS
AX conceived and wrote the manuscript and prepared the figures. YZ and JX contributed to performing the literature collection. TY and X-JL directed and approved the manuscript. All authors declare no conflict of interest and gave the final approval of the manuscript submission.


extfieldsofstudy: Medicine Chemistry

text:
LmbU, a Cluster-Situated Regulator for Lincomycin, Consists of a DNA-Binding Domain, an Auto-Inhibitory Domain, and Forms Homodimer

Few studies were reported about the regulatory mechanism of lincomycin biosynthesis since it was found in 1962. Although we have proved that a cluster-situated regulator (CSR) LmbU (GenBank Accession No. ABX00623.1) positively modulates lincomycin biosynthesis in Streptomyces lincolnensis NRRL 2936, the molecular mechanism of LmbU regulation is still unclear. In this study, we demonstrated that LmbU binds to the target lmbAp by a central DNA-binding domain (DBD), which interacts with the binding sites through the helix-turn-helix (HTH) motif. N-terminal of LmbU includes an auto-inhibitory domain (AID), inhibiting the DNA-binding activity of LmbU. Without the AID, LmbU variant can bind to its own promoter. Interestingly, compared to other LmbU homologs, the homologs within the biosynthetic gene clusters (BGCs) of known antibiotics generally contain N-terminal AIDs, which offer them the abilities to play complex regulatory functions. In addition, cysteine 12 (C12) has been proved to be mainly responsible for LmbU homodimer formation in vitro. In conclusion, LmbU homologs naturally exist in hundreds of actinomycetes, and belong to a new regulatory family, LmbU family. The present study reveals the DBD, AID and dimerization of LmbU, and sheds new light on the regulatory mechanism of LmbU and its homologs.

INTRODUCTION
Streptomycetes are well known as prolific producers of bioactive secondary metabolites including more than half of antibiotics as well as antitumor agents, antifungal compounds and vitamins, which have remarkable pharmacological, and industrial importance. Biosynthetic genes for antibiotics and other secondary metabolites are typically clustered together on the chromosomes (Cundliffe, 2006;Liu et al., 2013) or the plasmids (O'Rourke et al., 2009), designated as BGC, and are subject to multi-level and complex regulation cascades. Among them, CSRs provide direct contributions to the biosynthesis of antibiotics by responding to pleiotropic regulators (Ohnishi et al., 2005), global regulators (Uguru et al., 2005;Higo et al., 2011;Iqbal et al., 2012), and different kinds of regulatory small molecules (Wang et al., 2009;Zhang et al., 2013), subsequently regulating expression of the other biosynthetic genes within their cognate clusters. However, not all the BGCs share a same regulatory scheme. Some of them, such as jadomycin BGC, harbor multiple CSRs (Zou et al., 2014), some of them, such as sansanmycin BGC, contain a single CSR (Li et al., 2013), while the others, such as lincomycin BGC, lack any distinct CSRs (Hou et al., 2017). Generally, CSRs belong to a variety of regulatory protein families, which are divided by sequence or structural similarities, including SARP (Streptomyces antibiotic regulatory protein) family, LAL (large ATP-binding regulators of the LuxR) family, TetR family, and so on.
The most common and best studied CSRs are those of the SARP family in Streptomyces, including ActII-ORF4 within actinorhodine BGC as well as RedD within undecylprodigiosin BGC in Streptomyces coelicolor (Takano et al., 1992;Arias et al., 1999), DnrI within daunorubicin BGC in Streptomyces peucetius (Sheldon et al., 2002), and CcaR within cephamycin-clavulanic acid BGC in Streptomyces clavuligerus (Santamarta et al., 2011). These members are classified by having an N-terminal HTH DBD subject to OmpR-type and a transcriptional activation domain (Wietzorrek and Bibb, 1997), which generally positively regulate the biosynthesis of secondary metabolites (Sheldon et al., 2002;Tanaka et al., 2007). The target promoters of these members usually contain direct repeats, for instance, direct heptameric repeats (5'-TCGAGXX-3') with 4 bp spacers are conserved upstream the -10 regions of the promoters targeted by ActII-ORF4 and DnrI (Tanaka et al., 2007). The LAL family members usually function as activators in Streptomyces too, and comprise an N-terminal ATP-binding domain with a C-terminal LuxR-type DBD, including PikD within pikromycin BGC in Streptomyces venezuelae (Wilson et al., 2001), RapH within rapamycin BGC in Streptomyces hygroscopicus (Kuscer et al., 2007), and AveR within avermectin BGC in Streptomyces avermitilis (Guo et al., 2010). Compared to the SARP and LAL families, TetR family members are widely distributed in various bacteria, including ActR within actinorhodin BGC in S. coelicolor (Tahlan et al., 2008), TetR in Escherichia coli (Kisker et al., 1995), RolR in Corynebacterium glutamicum (Li et al., 2012), LplR in Rhodococcus erythropolis (Si et al., 2012), and VtpR in Vibrio tubiashii (Hasegawa and Häse, 2009). The TetR family members usually function as transcriptional repressors and consist of an N-terminal DBD and a C-terminal functional domain, which can bind to one or more ligands, subsequently losing the DNA-binding activity and turning on transcription of the target genes (Yu et al., 2010;Cuthbertson and Nodwell, 2013).
Previously, we have reported that LmbU functions as an activator belonging to a novel regulatory family, LmbU family (Hou et al., 2017;van der Heul et al., 2018), and promotes lincomycin biosynthesis by directly regulating transcription of the biosynthetic genes (Hou et al., 2017). The conserved binding site of LmbU is a palindromic sequence 5'-CGCCGGCG-3' , which was found in the promoter regions of the lmbA and lmbW genes. While, the regulatory mechanism of LmbU to other genes lack of the conserved motif is still unknown. In addition, because LmbU and its homologs have no significant sequence and structural similarities to other known CSRs, the binding pattern and functional domains of LmbU are also unclear. In the present study, we characterized the DBD (HTH motif) of LmbU and demonstrated that N-terminal of LmbU contains an AID, which was found in LmbU homologs within the BGCs of antibiotics, but not in that outside BGC of antibiotics. LmbU inhibits transcription of its own gene in vivo, and LmbU variant without AID can bind to lmbUp promoter. In addition, we revealed that LmbU can form homodimer by a disulfide bond in vitro.

MATERIALS AND METHODS
Homology Modeling, Sequence Alignment, and Phylogenetic Tree Analysis Secondary structure analysis of LmbU was performed by using an online software PredictProtein 1 . Homology modeling of LmbU was performed by using an online software SwissModel 2 . The templates used for LmbU modeling were chosen by ranking, including TtSpo0J derived from Thermus thermophiles (GenBank Accession No. WP_011173975.1), HpSpo0J derived from Helicobacter pylori (GenBank Accession No. ACJ08256.1), AtaR derived from E. coli (GenBank accession no. 6AJN_F), ParG derived from a multidrug resistance plasmid TP228 from E. coli (GenBank Accession No. ACV89876.1), AmrZ derived from Pseudomonas aeruginosa (GenBank Accession No. APJ53923.1), ω repressor derived from a plasmid Psm19035 from Streptococcus pyogenes (GenBank accession no. AAR27202.1), and Arc repressor derived from Salmonella bacteriophage P22 (GenBank Accession No. AAM81381.1). Sequence alignment of LmbU with its homologs and the targets of LmbU were carried out by using DNAMAN (Hou et al., 2017). Phylogenetic tree analysis was inferred by using MEGA v7.0.14 with the maximum likelihood method, the LmbU homologs were chosen by ranking (Bown et al., 2017;Hou et al., 2018).

Bacterial Strains, Plasmids, and Growth Conditions
The bacterial strains and plasmids used in this study are listed in Table 1. E. coli JM83, BL21 (DE3) and ET12567/pUZ8002 strains were used for plasmids construction, protein overexpression and E. coli-Streptomyces conjugation, respectively. Streptomyces lincolnensis wild-type strain NRRL 2936 and lmbU disruption strain JLUa2 were used for xylTE reporter assays in vivo (Mao et al., 2015;Hou et al., 2018). The pET-28a (+) plasmid was used for overexpression of LmbU and its variants, and the pIB139 plasmid was used for xylTE reporter analysis. E. coli strains were grown in liquid or on solid Luria-Bertani media at 37 • C. S. lincolnensis NRRL 2936 and mutants were grown in liquid YEME medium or on solid SMA and ISP4 media at 28 • C as described previously (Hou et al., 2017). The media were added with 50 µg/ml kanamycin, 50 µg/ml apramycin, and 30 µg/ml chloramphenicol as appropriate.
To construct LmbU point-mutant variants, DNA fragments covering different upstream or downstream within lmbU genes were, respectively amplified by PCR using the primer pairs listed in Supplementary Table S1. Among them, primer pairs U-P1/U-RR-P2 with U-RR-P3/U-P4 were used for combined mutation of R101 and R102, primer pairs U-P1/U-R101-P2 with U-R101-P3/U-P4 were used for mutation of R101, primer pairs U-P1/U-R102-P2 with U-R102-P3/U-P4 were used for mutation of R102, primer pairs U-P1/U-C63-P2 with U-C63-P3/U-P4 were used for mutation of C63. The mutations were introduced by primers P2 and P3, R was replaced with A, and C was replaced with G. The corresponding DNA fragments of upstream and downstream of lmbU were inserted into the NdeI/EcoRI restriction sites of the pET-28a (+) vector by using Super Efficiency Fast Seamless Cloning kits (DoGene, China), resulting in various expression plasmids pLU-08, pLU-16, pLU-17, and pLU-15, which were used for expression of LmbU RR , LmbU R101A , LmbU R102A , and LmbU C63G . In addition, to construct LmbU point-mutant variant LmbU C63G , a DNA fragment was amplified by PCR using primer pairs U-C12-P1/U-R28a, and inserted into the NdeI/EcoRI restriction sites of the pET-28a (+) vector, resulting in expression plasmids pLU-14.

Electrophoretic Mobility Shift Assay (EMSA)
Electrophoretic mobility shift assay were carried out as described previously (Hou et al., 2017). Briefly, biotin-labeled probe lmbAp (5 ng) was incubated with His 6 -LmbU or variants (different concentrations) in the binding reaction mixture contained 10 mM Tris-HCl (pH 8.0), 1 mM EDTA, 0.2 mM dithiothreitol, 20 g/mL bovine serum albumin, 1.2% glycerol, and 50 g/mL poly (dI-C). After incubation at 28 • C for 20 min, the samples were separated on 6% non-denaturing polyacrylamide gels in 0.5 × TBE buffer (54 g/L Tris, 1.86 g/L EDTA and 27.5 g/L boric acid, pH 8.0) in ice-water bath at 100 V, and transferred to the positively charged nylon membrane. The biotin-labeled probes were detected by streptavidin-horseradish-peroxidase (HRP) conjugate and BeyoECL Plus (Beyotime Biotechnology, China). Each experiment was at least repeated two times, and the representative images are shown.

xylTE Reporter Assays
To analyze the function of LmbU and its variants in vivo, we performed xylTE reporter assays. DNA fragments covering LmbU expression cassettes (ermE * p promoter plus lmbU gene or lmbU variants) were amplified by PCR using primer pairs E * p-lmbU-F/R with pLU-1, pLU-03, and pLU-06 as templates, and using primer pairs E * p-lmbU-F/lmbU4-R with pLU-2 and pLU-7 as templates. The amplified fragments were inserted into the NheI restriction sites of the pATE152 plasmid by using T4 DNA ligase (TAKARA, Japan), resulting in pAU01, pAU02, pAU03, pAU06, and pAU07 plasmids. The obtained plasmids were then introduced into the lmbU disruption strain JLUa2 and integrated into the attB site of the chromosome to generate reporter strains JAU01, JAU02, JAU03, JAU06, and JAU07. The reporter plasmid pUTE152 was constructed as pATE152 described previously (Hou et al., 2018). The region upstream (relative to the translation start codon) of the lmbU gene (-329 -1 bp) was amplified using primer pairs pUxyl-1/pUxyl-2, and the xylTE gene was amplified by PCR using primer pair pAxyl-3/pAxyl-4. Two fragments were inserted into the PvuII site of the plasmid pSET152 using Super Efficiency Fast Seamless Cloning kits (Do Gene, China). The obtained plasmid was introduced into wild-type strain NRRL 2936 and lmbU disruption strain JLUa2, and integrated into the attB site of the chromosome to generate reporter strains LNU and LUU.
The analysis of catechol dioxygenase activity was performed as described previously (Hou et al., 2018). Streptomyces strains were grown in YEME medium at 28 • C for 1 day, cells were washed in 20 mM potassium phosphate buffer, and suspended in 1 ml sample buffer (100 mM potassium phosphate, pH 7.5, 20 mM EDTA, 10% acetone). Total proteins were harvested by sonication, and quantified using the Bradford method (Bradford, 1976). 20 µl total proteins were added to 180 µl assay buffer (100 mM potassium phosphate, pH 7.5, 1 mM catechol), and were detected at 375 nm at 1, 2, 3, 4, 5, and 6 min, respectively. The activity was calculated as the rate of change per minute per milligram of protein and converted to milliunits per milligram. Data represent means ± standard deviations of results from three independent experiments. Statistical significance is indicated vs. the results of wild-type LmbU by using T test (Kim, 2015), ns, not significant; * * P < 0.01; * * * P < 0.001.

Dimerization Analysis of LmbU
The purified LmbU protein and variants were dealt with different loading buffers, which contained or did not contain SDS or DTT. The total loading buffer consists of 50 mM Tris-HCl (pH 6.8), 2% SDS (m/v), 0.1% bromophenol blue (m/v), 10% glycerin (m/v), and 100 mM DTT. The samples were analyzed by 12% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) (Supplementary Table S2) and stained with Coomassie brilliant blue R-250. The electrophoresis buffer consists of 3 g/L Tris, 19 g/L glycine and 1 g/L SDS. The molecular weights of LmbU dimer (56 kDa) and LmbU monomer (28 kDa) were standardized by the protein marker (TAKARA, Japan).

Bioinformatics Analysis of the Structure of LmbU
In our previous study, we have characterized LmbU as a DNA-binding protein involved in lincomycin biosynthesis, and identified the target genes and the binding site of LmbU (Hou et al., 2017). To further investigate the regulatory mechanism of LmbU, we performed bioinformatics analysis of the structure of LmbU. Secondary structure analysis showed that LmbU protein contains 9 α-helices and 2 β-strands. In addition, 14 protein binding regions, one RNA-binding region and 3 DNA-binding regions were predicted in LmbU (Supplementary Figure S1). Structure modeling demonstrated that two potential DBDs, a putative HTH motif including amino acid (aa) 80-102, and a putative ribbon-helix-helix (RHH) motif including aa 167-206, were predicted in LmbU (Figure 1). However, all the templates used for LmbU modeling are not derived from Streptomyces, indicating the regulatory pattern of LmbU may be complex and novel compared to other CSRs.
In addition, LmbU homologs naturally exist in hundreds of actinomycetes (Supplementary Figure S2), indicating LmbU homologs might play important roles in metabolism of natural products. Sequence alignment of LmbU with eight selected homologs revealed that the HTH motifs are highly conserved, 11 out of 23 amino acids, especially 10 out of 13 amino acids in the latter helix, are totally identical. In contrast, only 7 out of 40 amino acids in the RHH motif are totally identical (Supplementary Figure S3). These data indicated that the FIGURE 1 | Bioinformatics analysis of the structure of LmbU. (A) Structure based alignment of LmbU with partial structural homologs from various bacteria. Putative HTH and RHH motifs are indicated by blue and green boxes, respectively. TtSpo0J is derived from Thermus thermophiles (GenBank accession no. WP_011173975.1), HpSpo0J is derived from Helicobacter pylori (GenBank accession no. ACJ08256.1), ParG is derived from a multidrug resistance plasmid TP228 from E. coli (GenBank accession no. ACV89876.1), AmrZ is derived from Pseudomonas aeruginosa (GenBank accession no. APJ53923.1), ω repressor is derived from a plasmid Psm19035 from Streptococcus pyogenes (GenBank accession no. AAR27202.1), and Arc repressor is derived from Salmonella bacteriophage P22 (GenBank accession no. AAM81381.1). (B) Structural modeling of HTH motif. (C) Structural modeling of RHH motif. The results were generated by using a online software SwissModel (https://www.swissmodel.expasy.org/interactive).
Frontiers in Microbiology | www.frontiersin.org HTH motif might be more important for DNA-binding than the RHH motif.

Identification of the DNA-Binding Function of HTH and RHH Motifs
To identify whether the HTH or/and RHH motifs were relative to the DNA-binding of LmbU, two deletion variants were constructed and expressed, one deleted the HTH motif (designed as LmbU DH ) and the other deleted the RHH motif (designed as LmbU DR ) (Supplementary Figure S4A). Unfortunately, His 6 -LmbU DH failed to express in E. coli BL21 (DE3). EMSA analysis revealed that His 6 -LmbU DR had the DNA-binding activity (Supplementary Figure S4B), suggesting the RHH motif is not critical for DNA-binding of LmbU. To further verify the DNAbinding activities of RHH and HTH motifs, we expressed and purified the His 6 -LmbU 1−161 (aa 1-161) and His 6 -LmbU 86−225 (aa 86 − 225) variants, which contained the intact HTH motif, and the intact RHH motif, respectively (Figure 2A). EMSA analysis demonstrated that His 6 -LmbU 1−161 could bind to the lmbAp probe, while His 6 -LmbU 86−225 could not (Figure 2B), which also indicated that the RHH motif is not a critical DBD, and the DBD may exist in LmbU 1−161 . Subsequently, xylTE reporter assay was carried out to identify the function of the LmbU variants in vivo. The reporter plasmid pATE152, where xylTE gene was controlled by lmbAp promoter, was introduced into wild-type strain NRRL 2936 and lmbU disruption strain JLUa2, resulting in reporter strains LNA and LUA, respectively. The data showed that LmbU activates lmbAp promoter (Supplementary Figure S5), which is available and coincident with that of neo r reporter assay (Hou et al., 2017). In addition, the enzyme activities of total proteins extracted from the cells cultured for 1 day were observed higher than that from the cells cultured for 2 days (Supplementary Figure S5), thus, in the following study, we just detected the enzyme activities at day 1. Reporter plasmids pAUTE1 and pAUTE2 were constructed and introduced into JLUa2 strain, respectively, where the xylTE gene was controlled by the lmbAp promoter and the lmbU mutant genes were controlled by ermE * p. The data showed that LmbU 1−161 rather than LmbU 86−225 could activate the lmbAp promoter ( Figure 2C), which is consistent with the results of EMSA, showing that LmbU 1−161 variant contains a pivotal DBD.

Verification of the DBD and AID of LmbU
To narrow down the region of potential DBD of LmbU, we further truncated LmbU 1−161 to the LmbU 1−141 and LmbU 1−131 variants. However, both His 6 -LmbU 1−141 and His 6 -LmbU 1−131 were failed to express in E. coli BL21 (DE3). Therefore, semiquantitative reverse transcription and polymerase chain reaction (sqRT-PCR) and Western blotting were performed to check the RNA levels and the protein levels of LmbU variants, respectively. The data demonstrated that RNA levels of lmbU 1−141 and lmbU 1−131 had no differences with that of lmbU, but protein levels of them were severely reduced compared to that of LmbU (Supplementary Figure S6). Then, we extended LmbU 86−225 to LmbU 58−225 ( Figure 3A) and performed EMSA. The data revealed that His 6 -LmbU 58−225 could recover the DNA-binding activity, and the affinity to the target seemed enhanced compared to LmbU. Further EMSA analysis showed that the complex bands were observed when 0.2 µM His 6 -LmbU 58−225 was added ( Figure 3B), but that was observed when 3.2 µM His 6 -LmbU was added, indicating N-terminal of LmbU contains an AID against DNA-binding.
Given that both His 6 -LmbU 1−161 and His 6 -LmbU 58−225 have DNA-binding activities, we speculated that the DBD of LmbU was located in the overlapping region of the two variants. Thus, we constructed and expressed the His 6 -LmbU 58−161 variant ( Figure 3A) and performed EMSA. As expected, His 6 -LmbU 58−161 was found to bind to the lmbAp probe as well. In addition, the complex bands were observed when 0.1 µM His 6 -LmbU 58−161 was added, and 0.2 µM protein could completely impede the migration of the lmbAp probe (Figure 3B), indicating that LmbU 58−161 has a better affinity to the target compared to LmbU and LmbU 58−225 . Thus, we demonstrate that LmbU 58−161 has a DNA-binding activity, and the HTH motif is located in this region, suggesting the HTH motif is possibly a crucial DBD of LmbU. Furthermore, xylTE reporter assays showed that LmbU 58−25 could activate lmbAp promoter, but not like the result of EMSA, the activity of LmbU 58−225 for lmbAp promoter was not enhanced compared to that of LmbU ( Figure 3C). While, LmbU 58−161 could not activate lmbAp promoter (Figure 3C), indicating that C-terminal of LmbU performed a certain function to regulate the activity of lmbAp promoter in vivo.
It has been reported that polar and positively charged amino acids are usually important for DNA-binding of regulators, such as arginine (Davis et al., 2013;Bhukya et al., 2014). To further verify whether the HTH motif is responsible for DNA-binding, two arginines in the motif, R101 and R102, were, respectively substituted with either an alanine or a similarly charged lysine, resulting in LmbU R101A , LmbU R102A , LmbU R101K , and LmbU R102K . EMSA analysis revealed that His 6 -LmbU R102A and His 6 -LmbU R102K could bind to the lmbAp probe ( Figure 4A) while His 6 -LmbU R101A and His 6 -LmbU R101K could not (Figure 4B), indicating that the HTH motif is a critical DBD and R101 plays a key role in DNA-binding. These data also demonstrated that the HTH motif, not the RHH motif is the DBD of LmbU.

Insight Into Regulation of LmbU to lmbUp
In our previous study, we found that LmbU regulates the lmbC, lmbK and lmbU genes, but does not bind to their promoters (Hou et al., 2017). Considering the DNA-binding activities of LmbU 58−225 and LmbU 58−161 were enhanced compared to that of LmbU, we performed EMSA using LmbU 58−225 and LmbU 58−161 with lmbCp, lmbKp and lmbUp probes, the P V−W 3 probe was used as a positive control. The data showed that both His 6 -LmbU 58−225 and His 6 -LmbU 58−161 could not bind to the lmbCp and lmbKp probes, but seemed to bind to the lmbUp probe (Supplementary Figure S7). Subsequently, further EMSA with competition analysis were carried out using His 6 -LmbU 58−225 and LmbU 58−161 with the lmbUp probe. The results showed that both of the two variants can bind to lmbUp directly and Biotin-labeled lmbAp (263 bp, 5 ng) was incubated with increasing concentrations (0, 3.2, 6.4, and 12.8 µM) of His 6 -LmbU, His 6 -LmbU 1-161 , and His 6 -LmbU 86-225 , respectively. The DNA-protein complexes and the free probes are indicated by arrows. (C) XylTE reporter assay of LmbU and variants to lmbAp in vivo. The reporter plasmids were constructed with xylTE reporter gene controlled by lmbAp and lmbU or lmbU variants controlled by ermE * p. The reporter plasmids were integrated into the attB site of the chromosome of JLUa2 to generate reporter strains. In addition, LUA was used as a negative control, which was derived from JLUa2 harboring xylTE reporter gene controlled by lmbAp. Data represent means ± standard deviations of results from three independent experiments. Statistical significance is indicated vs. the results of wild-type LmbU by using T test (Kim, 2015), ns, not significant, * * P < 0.01. specifically with a concentration-dependent manner ( Figure 5A and Supplementary Figure S8). In addition, xylTE reporter assay showed that LmbU represses the activity of the lmbUp promoter in vivo (Figure 5B), indicating that LmbU might regulate the activity of lmbUp promoter by binding to lmbUp in a different pattern compared to lmbAp and lmbWp.

Identification of Dimerization of LmbU
Generally, regulatory proteins perform their functions by forming homodimers (Bhukya et al., 2014;Hayashi et al., 2014). To investigate the polymeric form of LmbU, we performed SDS-PAGE by using purified LmbU and variants from E. coli BL21 (DE3), which were dealt with different loading buffers (containing DTT/SDS or not). The data showed that LmbU could form a homodimer, which was affected by DTT, but not by SDS (Figure 6A), indicating that the homodimer is likely to be formed by disulfide bond among cysteines. Sequence analysis showed that LmbU contains three cysteines, C12, C63, and C196, the first two of which are included in LmbU 1−161 and the last one is included in LmbU 113−225 . To figure this out, we firstly carried out SDS-PAGE using LmbU 1−161 and LmbU 113−225 , respectively, and found that the former could form homodimer, but the latter could not (Figure 6B), suggesting the crucial cysteines for dimerization were located in aa 1-161. Then, the two cysteines C12 and C63 were mutated to glycines, resulting in the LmbU C12G , LmbU C63G and LmbU C12G/C63G variants. SDS-PAGE analysis revealed that LmbU C12G and LmbU C12G/C63G could not form homodimer, but LmbU C63G could form homodimer partly ( Figure 6C), indicating C12 plays a key role in forming LmbU homodimer, and C63 plays a supporting role.

DISCUSSION
In our previous study, we have demonstrated that a global regulator BldA (Hou et al., 2018) and a CSR LmbU (Hou et al., 2017) are involved in lincomycin biosynthesis. Recently, a TetRtype regulator SLCG_2919 has been proved to negatively regulate lincomycin biosynthesis (Xu et al., 2018). However, the regulatory mechanism of lincomycin biosynthesis is still unclear at present. Importantly, we found that hundreds of LmbU homologs exist in or outside the BGCs of different natural products derived from a variety of actinomycetes (Supplementary Figure S2), indicating LmbU homologs might play important roles in metabolism and do not only act as CSRs of natural products. Although there are so much LmbU homologs, few studies have been reported. SACE_5599, a homolog of LmbU outside the BGCs of natural products, can regulate not only erythromycin production, but also morphological differentiation in Saccharopolyspora erythraea (Kirm et al., 2013), which has been shown to bind to the promoter regions of lmbAp, and lmbWp within S. lincolnensis as well in our previous study (Hou et al., 2017). HmtD, a homolog of LmbU in the BGC of himastatin, positively regulates the biosynthesis of himastatin in Streptomyces hygroscopicus, however, the relevant mechanism is still unknown (Xie et al., 2019). In addition, structural prediction of LmbU demonstrated that LmbU protein does not include Data represent means ± standard deviations of results from three independent experiments. Statistical significance is indicated vs. the results of wild-type LmbU by using T test (Kim, 2015), ns, not significant; * * P < 0.01. a known domain similar to that of other CSRs, indicating the regulatory pattern of LmbU and its homologs was novel and complex compared to other CSRs. In the present study, we illuminate the functional domains of LmbU, including DBD and AID, and insight into the regulatory pattern of LmbU.
We demonstrated that LmbU consists of three functional domains, including a N-terminal AID (aa 1-57), a central DBD (aa 80-102), and a C-terminal unknown domain (aa 162-225) (Figure 7). To our knowledge, HTH motif is the best known and widely used DBD, although LmbU has been shown to bind to the targets by HTH motif as well, the sequence, and structure of HTH motif within LmbU is unlike the most of the regulators in Streptomyces (Natsume et al., 2004;Guo et al., 2010;Hayashi et al., 2013), indicating LmbU and its homologs function in a novel regulatory mechanism.
Furthermore, we found that the N-terminal AID represses the DNA-binding of LmbU, and without this domain, LmbU variants can bind to its own promoter region and inhibit transcription of itself, forming negative feedback regulation (Figure 5). Moreover, we found that LmbU homologs within the BGCs of the known antibiotics all contain the N-terminal AID (Supplementary Figure S3), such as HrmB (GenBank Accession No. AEH41782.1) for hormaomycin (Hofer et al., 2011), HmtD (GenBank accession no. CBZ42138.1) for himastatin (Ma et al., 2011), and AcmO (GenBank accession no. ADG27350.1) for actinomycin (Keller et al., 2010). Thus, we speculate that whether LmbU and its homologs within the BGCs of antibiotics function in a more complicated manner, by forming different conformations or different variants. These kinds of cases are not common in the known CSRs, but were found in global regulators. For instance, in Bacillus subtilis, the C-terminal of GlnR acts as an auto-inhibitory domain (AID) repressing dimer formation and DNA-binding, when interacting with DNA, GlnR changes its conformational and oligomeric state, resulting in a stable complex Wray and Fisher, 2008;Schumacher et al., 2015). In S. coelicolor, BldD undergoes degradation of the C-terminal domain, resulting in two forms, which may play roles at vegetative stage or at the late stage of life cycle, respectively (Lee et al., 2007). Interestingly, two PmbA (TldE)-TldD family proteins, LmbIH and LmbQ, are found in the lincomycin BGC. As reported, in E. coli, TldD and TldE participate in the cleavage of the modified MccB17 precursor peptide to mature antibiotic by forming heterodimer (Allali et al., 2002;Rodriguez-Sainz et al., 1990;Ghilarov et al., 2017). These studies promote us to speculate that LmbU may undergo accurate post-translational modification by LmbIH and LmbQ in the late growth stage,   6.4,12.8,and 25.6 µM). EMSAs with 200-fold excess of unlabeled specific DNA or non-specific DNA were added as controls, to confirm specificity of the band shifts. The DNA-protein complexes and the free probes are indicated by arrows. (B) XylTE reporter analysis of the effect of LmbU to lmbUp in vivo. LNU, wild-type strain NRRL 2936 harboring the reporter plasmid pUTE152; LUU, lmbU disruption strain JLUa2 harboring the reporter plasmid pUTE152. Data represent means ± standard deviations of results from three independent experiments. Statistical significance is indicated vs. the results of wild-type LmbU by using T test (Kim, 2015), * * P < 0.01. resulting in functional variant, which binds to its own promoter, and inhibits the biosynthesis of lincomycin. However, further studies are needed to confirm these hypotheses.
In addition, we demonstrated that compared to the functional variant LmbU 58−225 , LmbU 58−161 can bind to the target DNA in vitro with a better affinity, but can not activate transcription  of the reporter gene in vivo. Considering LmbU 1−161 without aa 162-225 has a similar DNA-binding affinity to LmbU, thus we thought there is no inhibitory domain within the C-terminal, the better affinity of LmbU 58−161 might be due to the exposure of the DBD domain. In addition, these data showed that the C-terminal amino acids play important roles as an unknown domain, either in structural stability or in interaction with ligands. However, we also found that LmbU 1−161 has activity to lmbAp promoter both in vitro and in vivo, indicating the C-terminal domain is not necessary, which appeared to be different from the hypothesis mentioned above. In consideration of the unclear regulatory mechanism of LmbU to the target lmbCp and lmbKp with no identified binding sites, the function of the C-terminal domain needs to be further illuminated.
Generally, transcriptional regulators bind to the target DNA by forming homodimers. For instance, in Streptomyces griseus, a global regulator AdpA either binds to two sites with different lengths in the target DNA (type I or type I'), or binds to a single site in the target DNA with one subunit of the homodimer (type II) (Yamazaki et al., 2004); in S. coelicolor A3 (2), a γ-butyrolactone receptor CprB interacts with the target DNA through two individual CprB homodimers (Bhukya et al., 2014). Here, we showed that LmbU can form homodimer mainly via C12. And the DNA-binding mode of LmbU need to be further investigated.
In summary, we have demonstrated the functional domains of LmbU, which is a representative of the LmbU family transcriptional regulators. LmbU consists of an N-terminal AID, a central DBD and a C-terminal unknown domain. In addition, LmbU forms homodimer mainly via the C12 in vitro. By applying this knowledge, we speculate that the unusual properties of LmbU will be exploited for future applications in the realization of highyield of lincomycin, and in the functional research of LmbU family proteins.

AUTHOR CONTRIBUTIONS
BH, HZ, and HW designed the experiments. BH, XZ, and YK carried out the experiments. BH, JY, HW, and HZ analyzed the data. BH and HW wrote the manuscript. RW discussed the experimental design and contributed to the manuscript. All authors assisted with critical reading of the manuscript.

FUNDING
This study was supported by the National Natural Science Foundation of China (NSFC) (3120026).


extfieldsofstudy: Medicine Biology

text:
Development of a cost‐effective single nucleotide polymorphism genotyping array for management of greater yam germplasm collections

Abstract Using genome‐wide single nucleotide polymorphism (SNP) discovery in greater yam (Discorea alata L.), 4,593 good quality SNPs were identified in 40 accessions. One hundred ninety six of these SNPs were selected to represent the overall dataset and used to design a competitive allele specific PCR array (KASPar). This array was validated on 141 accessions from the Tropical Plants Biological Resources Centre (CRB‐PT) and CIRAD collections that encompass worldwide D. alata diversity. Overall, 129 SNPs were successfully converted as cost‐effective genotyping tools. The results showed that the ploidy levels of accessions could be accurately estimated using this array. The rate of redundant accessions within the collections was high in agreement with the low genetic diversity of D. alata and its diversification by somatic clone selection. The overall diversity resulting from these 129 polymorphic SNPs was consistent with the findings of previously published studies. This KASPar array will be useful in collection management, ploidy level inference, while complementing accurate agro‐morphological descriptions.

| INTRODUC TI ON
Greater yam (Discorea alata L.) is one of the major cultivated yam species (Discorea spp.) and the most widely spread among tropical and subtropical regions. The high importance of D. alata for food security has prompted the establishment of several international and national ex situ collections. Due to the limited shelf-life of stored tuber, yam genetic resources are conserved in vitro or/and in the field. All of these repeated manipulations are time-consuming and may affect long-term conservation. Quality control of genotype purity and general collection management is mainly based on morphological descriptors (IPGRI/IITA, 1997; Mahalakshmi et al., 2007). However, these descriptors are not reliable enough to rationalize ex situ D. alata collection. Indeed, several studies have revealed that morphological variations are not necessarily linked to geographic origin or genetic lineage (Arnau et al., 2017;Lebot, Trilles, Noyer, & Modesto, 1998;Vandenbroucke et al., 2016). Complementary characterization tools are thus required for the conservation and dynamic management of ex situ collections related to germplasm exchange, the development of core collection or identification of future parents for breeding programs. D. alata is also a polyploid species with ploidy levels of 2n = 2x, 3x, or 4x and a basic chromosome number of x = 20 (Arnau, Némorin, Maledon, & Abraham, 2009). Ploidy levels detection is consequently a prerequisite for the identification of possible parents as crosses between the different ploidy levels can fail (Nemorin et al., 2013).
These studies generated essential information on the diversity and representativity of the germplasm collections. However, these tools were not tailored for routine collection management. They were found to be either poorly discriminating within D. alata species or they were complex and not cost-effective to use. Besides the development of high-throughput methods for genome-wide variant detection, such as genotyping-by-sequencing (Davey et al., 2011) paired with cost-effective SNP assay (Broccanello et al., 2018) as KASPar can lead to the development of appropriate markers for collection management. This approach has been successfully implemented in maize (Semagn et al., 2012), chickpea (Hiremath et al., 2012), Citrus (Garcia-Lor, Ancillo, Navarro, & Ollitrault, 2013), pigeon pea (Saxena et al., 2014), and Brassica rapa (Su et al., 2018). Regarding the recent release of yam (Dioscorea spp.) genomic resources (Saski, Bhattacharjee, Scheffler, & Asiedu, 2015;Tamiru et al., 2017), the design of such markers for D. alata collection management would be worthwhile. Indeed, once developed they do not require any specific bioinformatics or wet chemistry skills. The results contain few erroneous and missing data and can be easily analyzed and interpreted.
The main objectives of this study were (a) to identify genomewide polymorphic SNP markers, (b) to develop a cost-effective SNP genotyping array using KASPar technology and (c) to test its use as a tool in managing yam ex situ collections.

| Materials
Based on a previous microsatellite markers study (Arnau et al., 2017), a set of 48 accessions representing worldwide D. alata diversity was selected and genotyped to identify polymorphic SNPs and design KASPar markers. Then, for the purpose of validating these markers, 141 landraces from the Tropical Plants Biological Resources Centre (CRB-PT) and CIRAD ex situ collections maintained in the West French Indies (Guadeloupe) were used.
First, DNA extractions were performed with dried leaves from the 48 accessions as described by Risterucci et al. (2009). The genomic DNA quality was checked using agarose gel electrophoresis, and the quantity was estimated using a Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Wilmington, USA). For GBS, a genomic library was prepared using the PstI-MseI restriction enzymes (New England Biolabs, Hitchin, UK) with a DNA normalized quantity of 200 ng per sample. The procedures published by Elshire et al. (2011) were adapted as described in Cormier et al. (2019).

| KASPar genotyping and allele calling
Polymorphic SNP flanking sequences (60 bp upstream and 60 bp downstream around the variant position) were selected using SNiPlay3 (Dereeper et al., 2011). In order to assess their putative physical positions, these sequences were then blasted to the D. rotundata reference genome (TDr96_F1 Pseudo_Chromosome: BDMI01000001-BDMI01000021; Tamiru et al., 2017). The physical position of each SNP was defined using their flanking sequences best hit using a BLAST E-value threshold of 1e−30 (Basic Local Alignment Search Tool). Finally, 192 SNPs were selected by forming 192 k-means cluster based on their relative physical distance (Euclidean distance) and selecting the SNP nearest to the centroid of each cluster using R 3.4.0 (R core team, 2017).
The 192 SNPs were converted into a KASPar assay at LGC genomics where the primer design and wet chemistry was conducted (Middlesex, UK) on a validation panel of 141 landraces from the CRB-PT and CIRAD ex situ collections. From raw fluorescence data, allele calling was performed using LGC Kluster Caller software by defining fluorescence clusters. Some accessions with known ploidy level were used as reference to identify fluorescence clusters and assess allelic dosage.

| Diversity analysis
To identify duplicate accessions and compare accessions with different ploidy levels, a matrix of dissimilarity between each accession pair was computed as the percentage of shared alleles based on the allele presence/absence. Then, to refine the kinship assessment, similarities between accessions with the same ploidy level were computed in the same way but using the allelic dosage. For diploid accessions, genotypes were coded as 0, 1, and 2 where the number represents the number of nonreference allele. Heterozygous genotypes assessed as polyploid during allele calling were converted to 1. Moreover, for triploid accessions, genotypes were coded as 0, 1, 2, and 3 with allelic dosage score as 1:1 during allele call converted to 1.5. For tetraploid accessions, genotypes were thus coded as 0, 1, 2, 3, or 4 and no correction was needed.
Diversity analysis was conducted in two steps. During the first step, groups of duplicate accessions (redundancy groups) were defined by grouping accessions having up to one allele mismatch. Then, in the second step, the diversity analysis focused on the similarity between those groups. Clustering based on allele frequencies within redundancy groups followed by a bootstrap approach (pvclust R package, ward.D2, 10,000 boots, AU threshold = 0.95; Suzuki & Shimodaira, 2006) was used to identify gene pools. A diversity network between redundancy groups was also drawn using significant kinship detected through genotype permutations (1,000), with a significance threshold of 0.05.

| KASPar assay development and validation
Genotyping-by-sequencing (GBS) produced more than 344 million reads resulting in 521,918 sequence tags out of which 207,810 (39.82%) aligned exactly once on D. alata contigs. The remaining reads aligned at multiple locations (25.18%) or did not align to any contig (35%). From these sequence tags, SNP calling produced a raw vcf file of 158,695 SNPs. This raw vcf file was then filtered resulting in a dataset of 40 accessions (Appendix A), and 4,593 good quality SNPs out of which 3,879 (84%) SNPs were mapped by BLAST on the D. rotundata reference genome. The KASPar assay was then developed by selecting 192 SNPs representative of SNPs mapped along the D. rotundata reference sequence, and they were tested on 141 accessions.
Among the 192 SNPs, 26 (13%) SNPs failed as they did not produce any amplification signal. From the remaining 166 SNPs (87%), 129 SNPs (Appendix C) with less than 20% missing data and a minor allele frequency of over 5% were retained as high-quality SNPs. This final dataset (129 SNPs × 141 accessions) contained an overall missing data rate of only 0.5% with a maximum of 3% missing data per accession.
The 129 validated KASPar SNPs were distributed on all linkage groups used to construct the D. rotundata reference genome ( Figure 1). Their distribution was not homogeneous along chromosomes as their position was planned to be representative of that of the initial set of 3,879 mapped SNPs and not equally spaced.

| Assessment of ploidy levels
In our D. alata validation panel, three ploidy levels (2x, 3x and 4x) coexisted (Appendix B). Thus, the KASPar assay could theoretically produce a maximum of seven types of fluorescence signal (Table 1) corresponding to two types of fluorescence signal in homozygous states (2:0 = 3:0 = 4:0; 0:2 = 0:3 = 0:4), the fluorescence signal of mixed and balanced allelic dosages (1:1 for diploids or 2:2 for tetraploids) and the four types of fluorescence signal corresponding to the different possible unbalanced allelic dosages at heterozygotic loci ("polyploid-like" in Table 1) of triploids and tetraploids (1:3; 1:2; 2:1; F I G U R E 1 Location of KASPar SNPs on the D. rotundata reference genome (Tamiru et al., 2017). The 21 linkage group are aligned from left to right. Black dots, failed or bad quality SNPs; red dots, the 129 validated SNPs 3:1). In our case, due to insufficient fluorescence resolution, it was not possible to distinguish fluorescence signals of the 1:3 tetraploid allelic dosage from the 1:2 triploid allelic dosage, or the 2:1 triploid allelic dosage from the 3:1 tetraploid allelic dosage. Consequently, a maximum of five types of fluorescence signals were identified.
However, the overall allele call and allelic dosage assessment quality were good. Indeed, the ratio of genotypes scored as "polyploid-like" on overall heterozygous genotypes by accession was low (0.09 ± 0.05) for diploids and high for triploids (0.83 ± 0.05). In addition, the three distributions of this ratio corresponding to the three ploidy levels did almost not overlap ( Figure 2).
We were thus not able to differentiate all allelic dosage from each other when looking at one SNP. However, ploidy level could be deduced when taking all the KASPar array into account and considering the proportion of genotypes scored as "polyploid-like" per accession. This KASPar assay thus differentiated the accession ploidy level and allowed us to assign it for 12 accessions originally of unknown ploidy. Nine were set as diploid and three as triploid.

| Diversity analysis
Overall, 141 accessions from CRB-PT and CIRAD ex situ collections in Guadeloupe were used to validate the KASPar assay (96 diploids, 36 triploids, and nine tetraploids including accessions with known and deduced ploidy level).
The allele presence and/or absence was used to assess the similarity between accessions and thus to identify duplicate accessions More generally, redundancy groups only consisted of accessions with the same ploidy level ( Figure 4). Moreover, similarities within triploids or within tetraploids were higher than within diploids.
The diversity analysis was based on these 43 redundancy groups to avoid bias. After clustering, the bootstrap procedure detected five significant gene pools, named "cluster" here, represented in the kinship network ( Figure 5). Only one (cluster C,

| Assessment of allelic dosage and detection of ploidy levels
KASPar technology is based on competitive allele-specific amplification followed by allele-specific fluorescence assessment (Semagn, Babu, Hearne, & Olsen, 2014). Detection of allelic dosage in polyploid species is thus possible (Cuenca, Aleza, Navarro, & Ollitrault, 2013). However, several parameters may influence the fluorescence, such as the DNA quality or primer specificity, and consequently the ability to discriminate fluorescence signals and the allelic dosage. In our case, we were able to discriminate five types of fluorescence signal. At heterozygous loci, fluorescence signals were a mixture of two types of allelic-specific fluorescence.
Fluorescence signals should also be balanced for diploids which have a balanced allelic dosage (1:1) at heterozygous loci. Diploids should therefore theoretically have no genotypes assessed as "polyploid-like." Conversely, triploids should theoretically have only genotypes assessed as "polyploid-like" at heterozygous loci.
A balanced allelic dosage is impossible for triploids. Our results showed that 91 ± 5% and 83 ± 5% of heterozygous genotypes were correctly called for diploids and triploids, respectively.
Regarding the recent explosion of genotyping related to next-generation sequencing, bioinformatics tools have been developed to accurately determine dosages (e.g., GBS2ploidy; Gompert & Mock, 2017). However, this requires deep sequencing and usually an assumption of ploidy levels present in the dataset (Bourke, Voorrips, Visser, & Maliepaard, 2018).
Application in collection management may nevertheless not require allelic dosage assessment at each locus. Our aim was thus to develop a tool for estimating ploidy levels and not variations in copy number. Moreover, the results showed that ploidy levels for each accession can be accurately deduced from the percentage of "polypoid-like" genotypes on overall heterozygous genotypes. Regarding the overlapping distributions of this ratio (Figure 2), the only risk is to confuse triploids and tetraploids estimated at 3%. Consequently, ploidy level assessment is possible and fairly accurate for D. alata using the KASPar assay developed in this study.
F I G U R E 5 Network of kinship for the 43 D. alata redundancy groups based on significant similarity (p < 0.05, edge-weighted springembedded layout). Nodes shape and letter, cluster of diversity identified by a bootstrap procedure; red nodes, diploids; green nodes, triploids; blue nodes, tetraploids; edge colors, similarity from gray (0.64) to black (1) with previous genetic diversity studies that already pooled these accessions together and highlighted this intragroup variability in tubers (Arnau et al., 2017;Malapa et al., 2005).

| Diversity and collection management
The CRB-PT collection has been shown to be representative of worldwide D. alata diversity (Arnau et al., 2017). A subset of this ex situ collection has been genotyped in this study. However, all diversity groups identified by Arnau et al. (2017)

| CON CLUS ION
This is the first SNP array designed for D. alata and validated on a subset of accessions representative of worldwide D. alata diversity. This tool will allow users to estimate accession ploidy levels and genetic lineages. The results showed a good correlation between the diversity assessed by this KASPar array and the findings of previous studies. This KASPar array is a robust and cost-effective tool for diversity assessment and collections management. Regarding the importance of vegetative reproduction and somaclonal selection in D. alata, it is a good tool to complement agro-morphological description in collections.

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest.   Pos. Pos. Pos. Pos. Pos.