Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

InstaPrism: an R package for fast implementation of BayesPrism.

SUMMARY: Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies; however, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism package which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling step with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed and memory advantage. Furthermore, the InstaPrism package is equipped with a precompiled, curated set of references tailored for a variety of cancer types, streamlining the deconvolution process. AVAILABILITY AND IMPLEMENTATION: The package InstaPrism is freely available at: https://github.com/humengying0907/InstaPrism. The source code and evaluation pipeline used in this paper can be found at: https://github.com/humengying0907/InstaPrismSourceCode.

Memory B cell subsets have divergent developmental origins that are coupled to distinct imprinted epigenetic states.

Memory B cells (MBCs) are phenotypically and functionally diverse, but their developmental origins remain undefined. Murine MBCs can be divided into subsets by expression of CD80 and PD-L2. Upon re-immunization, CD80/PD-L2 double-negative (DN) MBCs spawn germinal center B cells (GCBCs), whereas CD80/PD-L2 double-positive (DP) MBCs generate plasmablasts but not GCBCs. Using multiple approaches, including generation of an inducible GCBC-lineage reporter mouse, we demonstrate in a T cell-dependent response that DN cells formed independently of the germinal center (GC), whereas DP cells exhibited either extrafollicular (DPEX) or GCBC (DPGC) origins. Chromatin and transcriptional profiling revealed similarity of DN cells with an early memory precursor. Reciprocally, GCBC-derived DP cells shared distinct genomic features with GCBCs, while DPEX cells had hybrid features. Upon restimulation, DPEX cells were more prone to divide, while DPGC cells differentiated toward IgG1+ plasmablasts. Thus, MBC functional diversity is generated through distinct developmental histories, which imprint characteristic epigenetic patterns onto their progeny, thereby programming them for divergent functional responses.

Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction.

Motivation: Most models can be fit to data using various optimization approaches. While model choice is frequently reported in machine-learning-based research, optimizers are not often noted. We applied two different implementations of LASSO logistic regression implemented in Python's scikit-learn package, using two different optimization approaches (coordinate descent, implemented in the liblinear library, and stochastic gradient descent, or SGD), to predict mutation status and gene essentiality from gene expression across a variety of pan-cancer driver genes. For varying levels of regularization, we compared performance and model sparsity between optimizers. Results: After model selection and tuning, we found that liblinear and SGD tended to perform comparably. liblinear models required more extensive tuning of regularization strength, performing best for high model sparsities (more nonzero coefficients), but did not require selection of a learning rate parameter. SGD models required tuning of the learning rate to perform well, but generally performed more robustly across different model sparsities as regularization strength decreased. Given these tradeoffs, we believe that the choice of optimizers should be clearly reported as a part of the model selection and validation process, to allow readers and reviewers to better understand the context in which results have been generated. Availability and implementation: The code used to carry out the analyses in this study is available at https://github.com/greenelab/pancancer-evaluation/tree/master/01_stratified_classification. Performance/regularization strength curves for all genes in the Vogelstein et al. (2013) dataset are available at https://doi.org/10.6084/m9.figshare.22728644.

An intrinsically interpretable neural network architecture for sequence-to-function learning.

MOTIVATION: Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. RESULTS: We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. AVAILABILITY AND IMPLEMENTATION: The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.

IL-21R signal reprogramming cooperates with CD40 and BCR signals to select and differentiate germinal center B cells.

Both B cell receptor (BCR) and CD40 signaling are rewired in germinal center (GC) B cells (GCBCs) to synergistically induce c-MYC and phosphorylated S6 ribosomal protein (p-S6), markers of positive selection. How interleukin-21 (IL-21), a key T follicular helper (TFH)-derived cytokine, affects GCBCs is unclear. Like BCR and CD40 signals, IL-21 receptor (IL-21R) plus CD40 signals also synergize to induce c-MYC and p-S6 in GCBCs. However, IL-21R plus CD40 stimulation differentially affects GCBC fate compared with BCR plus CD40 ligation-engaging unique molecular mechanisms-as revealed by bulk RNA sequencing (RNA-seq), single-cell RNA-seq, and flow cytometry of GCBCs in vitro and in vivo. Whereas both signal pairs induced BLIMP1 in some GCBCs, only the IL-21R/CD40 combination induced IRF4hi/CD138+ cells, indicative of plasma cell differentiation, along with CCR6+/CD38+ memory B cell precursors. These findings reveal a second positive selection pathway in GCBCs, document rewired IL-21R signaling in GCBCs, and link specific TFH- and Ag-derived signals to GCBC differentiation.

An intrinsically interpretable neural network architecture for sequence to function learning.

MOTIVATION: Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called tiSFM (totally interpretable sequence to function model). tiSFM improves upon the performance of standard multi-layer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multi-layer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. RESULTS: We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. AVAILABILITY AND IMPLEMENTATION: The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.

Widespread redundancy in -omics profiles of cancer mutation states.

BACKGROUND: In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear. RESULTS: We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type. CONCLUSIONS: Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

Heterogeneity and clonality of kidney-infiltrating T cells in murine lupus nephritis.

We previously found that kidney-infiltrating T cells (KITs) in murine lupus nephritis (LN) resembled dysfunctional T cells that infiltrate tumors. This unexpected finding raised the question of how to reconcile the "exhausted" phenotype of KITs with ongoing tissue destruction in LN. To address this, we performed single-cell RNA-Seq and TCR-Seq of KITs in murine lupus models. We found that CD8+ KITs existed first in a transitional state, before clonally expanding and evolving toward exhaustion. On the other hand, CD4+ KITs did not fit into current differentiation paradigms but included both hypoxic and cytotoxic subsets with a pervasive exhaustion signature. Thus, autoimmune nephritis is unlike acute pathogen immunity; rather, the kidney microenvironment suppresses T cells by progressively inducing exhausted states. Our findings suggest that LN, a chronic condition, results from slow evolution of damage caused by dysfunctional T cells and their precursors on the way to exhaustion. These findings have implications for both autoimmunity and tumor immunology.

Treg Cells Promote the SREBP1-Dependent Metabolic Fitness of Tumor-Promoting Macrophages via Repression of CD8+ T Cell-Derived Interferon-γ.

Regulatory T (Treg) cells are crucial for immune homeostasis, but they also contribute to tumor immune evasion by promoting a suppressive tumor microenvironment (TME). Mice with Treg cell-restricted Neuropilin-1 deficiency show tumor resistance while maintaining peripheral immune homeostasis, thereby providing a controlled system to interrogate the impact of intratumoral Treg cells on the TME. Using this and other genetic models, we showed that Treg cells shaped the transcriptional landscape across multiple tumor-infiltrating immune cell types. Treg cells suppressed CD8+ T cell secretion of interferon-γ (IFNγ), which would otherwise block the activation of sterol regulatory element-binding protein 1 (SREBP1)-mediated fatty acid synthesis in immunosuppressive (M2-like) tumor-associated macrophages (TAMs). Thus, Treg cells indirectly but selectively sustained M2-like TAM metabolic fitness, mitochondrial integrity, and survival. SREBP1 inhibition augmented the efficacy of immune checkpoint blockade, suggesting that targeting Treg cells or their modulation of lipid metabolism in M2-like TAMs could improve cancer immunotherapy.

Adaptive plasticity of IL-10+ and IL-35+ Treg cells cooperatively promotes tumor T cell exhaustion.

Regulatory T cells (Treg cells) maintain host self-tolerance but are a major barrier to effective cancer immunotherapy. Treg cells subvert beneficial anti-tumor immunity by modulating inhibitory receptor expression on tumor-infiltrating lymphocytes (TILs); however, the underlying mediators and mechanisms have remained elusive. Here, we found that the cytokines IL-10 and IL-35 (Ebi3-IL-12α heterodimer) were divergently expressed by Treg cell subpopulations in the tumor microenvironment (TME) and cooperatively promoted intratumoral T cell exhaustion by modulating several inhibitory receptor expression and exhaustion-associated transcriptomic signature of CD8+ TILs. While expression of BLIMP1 (encoded by Prdm1) was a common target, IL-10 and IL-35 differentially affected effector T cell versus memory T cell fates, respectively, highlighting their differential, partially overlapping but non-redundant regulation of anti-tumor immunity. Our results reveal previously unappreciated cooperative roles for Treg cell-derived IL-10 and IL-35 in promoting BLIMP1-dependent exhaustion of CD8+ TILs that limits effective anti-tumor immunity.

Hepatocyte-Derived Lipocalin 2 Is a Potential Serum Biomarker Reflecting Tumor Burden in Hepatoblastoma.

Hepatoblastoma (HB) is the most common pediatric liver malignant tumor. Previously, we reported co-activation of ß-catenin and Yes-associated protein-1 (YAP1) in 80% of HB. Hepatic co-expression of active ß-catenin and YAP1 via sleeping beauty transposon/transposase and hydrodynamic tail vein injection led to HB development in mice. Here, we identify lipocalin 2 (Lcn2) as a target of ß-catenin and YAP1 in HB and show that serum Lcn2 values positively correlated with tumor burden. Lcn2 was strongly expressed in HB tumor cells in our mouse model. A tissue array of 62 HB cases showed highest LCN2 expression in embryonal and lowest in fetal, blastemal, and small cell undifferentiated forms of HB. Knockdown of LCN2 in HB cells had no effect on cell proliferation but reduced NF-κB reporter activity. Next, liver-specific Lcn2 knockout (KO) mice were generated. No difference in tumor burden was observed between Lcn2 KO mice and wild-type littermate controls after sleeping beauty transposon/transposase and hydrodynamic tail vein injection delivery of active YAP1 and ß-catenin, although Lcn2 KO mice with HB lacked any serum Lcn2 elevation, demonstrating that transformed hepatocytes are the source of serum Lcn2. More blastemal areas and inflammation were observed within HB in Lcn2 KO compared with wild-type tumors. In conclusion, Lcn2 expressed in hepatocytes appears to be dispensable for the pathogenesis of HB. However, transformed hepatocytes secrete serum Lcn2, making Lcn2 a valuable biomarker for HB.

Differential requirements for myeloid leukemia IFN-γ conditioning determine graft-versus-leukemia resistance and sensitivity.

The graft-versus-leukemia (GVL) effect in allogeneic hematopoietic stem cell transplantation (alloSCT) is potent against chronic phase chronic myelogenous leukemia (CP-CML), but blast crisis CML (BC-CML) and acute myeloid leukemias (AML) are GVL resistant. To understand GVL resistance, we studied GVL against mouse models of CP-CML, BC-CML, and AML generated by the transduction of mouse BM with fusion cDNAs derived from human leukemias. Prior work has shown that CD4+ T cell-mediated GVL against CP-CML and BC-CML required intact leukemia MHCII; however, stem cells from both leukemias were MHCII negative. Here, we show that CP-CML, BC-CML, and AML stem cells upregulate MHCII in alloSCT recipients. Using gene-deficient leukemias, we determined that BC-CML and AML MHC upregulation required IFN-γ stimulation, whereas CP-CML MHC upregulation was independent of both the IFN-γ receptor (IFN-γR) and the IFN-α/ß receptor IFNAR1. Importantly, IFN-γR-deficient BC-CML and AML were completely resistant to CD4- and CD8-mediated GVL, whereas IFN-γR/IFNAR1 double-deficient CP-CML was fully GVL sensitive. Mouse AML and BC-CML stem cells were MHCI+ without IFN-γ stimulation, suggesting that IFN-γ sensitizes these leukemias to T cell killing by mechanisms other than MHC upregulation. Our studies identify the requirement of IFN-γ stimulation as a mechanism for BC-CML and AML GVL resistance, whereas independence from IFN-γ renders CP-CML more GVL sensitive, even with a lower-level alloimmune response.

Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity.

Regulatory T cells (Tregs) are a barrier to anti-tumor immunity. Neuropilin-1 (Nrp1) is required to maintain intratumoral Treg stability and function but is dispensable for peripheral immune tolerance. Treg-restricted Nrp1 deletion results in profound tumor resistance due to Treg functional fragility. Thus, identifying the basis for Nrp1 dependency and the key drivers of Treg fragility could help to improve immunotherapy for human cancer. We show that a high percentage of intratumoral NRP1+ Tregs correlates with poor prognosis in melanoma and head and neck squamous cell carcinoma. Using a mouse model of melanoma where Nrp1-deficient (Nrp1-/-) and wild-type (Nrp1+/+) Tregs can be assessed in a competitive environment, we find that a high proportion of intratumoral Nrp1-/- Tregs produce interferon-γ (IFNγ), which drives the fragility of surrounding wild-type Tregs, boosts anti-tumor immunity, and facilitates tumor clearance. We also show that IFNγ-induced Treg fragility is required for response to anti-PD1, suggesting that cancer therapies promoting Treg fragility may be efficacious.

Wnt signaling regulates hepatobiliary repair following cholestatic liver injury in mice.

Hepatic repair is directed chiefly by the proliferation of resident mature epithelial cells. Furthermore, if predominant injury is to cholangiocytes, the hepatocytes can transdifferentiate to cholangiocytes to assist in the repair and vice versa, as shown by various fate-tracing studies. However, the molecular bases of reprogramming remain elusive. Using two models of biliary injury where repair occurs through cholangiocyte proliferation and hepatocyte transdifferentiation to cholangiocytes, we identify an important role of Wnt signaling. First we identify up-regulation of specific Wnt proteins in the cholangiocytes. Next, using conditional knockouts of Wntless and Wnt coreceptors low-density lipoprotein-related protein 5/6, transgenic mice expressing stable ß-catenin, and in vitro studies, we show a role of Wnt signaling through ß-catenin in hepatocyte to biliary transdifferentiation. Last, we show that specific Wnts regulate cholangiocyte proliferation, but in a ß-catenin-independent manner. CONCLUSION: Wnt signaling regulates hepatobiliary repair after cholestatic injury in both ß-catenin-dependent and -independent manners. (Hepatology 2016;64:1652-1666).

Characterization of Gonadotrope Secretoproteome Identifies Neurosecretory Protein VGF-derived Peptide Suppression of Follicle-stimulating Hormone Gene Expression.

Reproductive function is controlled by the pulsatile release of hypothalamic gonadotropin-releasing hormone (GnRH), which regulates the expression of the gonadotropins luteinizing hormone and FSH in pituitary gonadotropes. Paradoxically, Fshb gene expression is maximally induced at lower frequency GnRH pulses, which provide a very low average concentration of GnRH stimulation. We studied the role of secreted factors in modulating gonadotropin gene expression. Inhibition of secretion specifically disrupted gonadotropin subunit gene regulation but left early gene induction intact. We characterized the gonadotrope secretoproteome and global mRNA expression at baseline and after Gαs knockdown, which has been found to increase Fshb gene expression (1). We identified 1077 secreted proteins or peptides, 19 of which showed mRNA regulation by GnRH or/and Gαs knockdown. Among several novel secreted factors implicated in Fshb gene regulation, we focused on the neurosecretory protein VGF. Vgf mRNA, whose gene has been implicated in fertility (2), exhibited high induction by GnRH and depended on Gαs In contrast with Fshb induction, Vgf induction occurred preferentially at high GnRH pulse frequency. We hypothesized that a VGF-derived peptide might regulate Fshb gene induction. siRNA knockdown or extracellular immunoneutralization of VGF augmented Fshb mRNA induction by GnRH. GnRH stimulated the secretion of the VGF-derived peptide NERP1. NERP1 caused a concentration-dependent decrease in Fshb gene induction. These findings implicate a VGF-derived peptide in selective regulation of the Fshb gene. Our results support the concept that signaling specificity from the cell membrane GnRH receptor to the nuclear Fshb gene involves integration of intracellular signaling and exosignaling regulatory motifs.

Modeling a human hepatocellular carcinoma subset in mice through coexpression of met and point-mutant ß-catenin.

Hepatocellular cancer (HCC) remains a significant therapeutic challenge due to its poorly understood molecular basis. In the current study, we investigated two independent cohorts of 249 and 194 HCC cases for any combinatorial molecular aberrations. Specifically we assessed for simultaneous HMET expression or hMet activation and catenin ß1 gene (CTNNB1) mutations to address any concomitant Met and Wnt signaling. To investigate cooperation in tumorigenesis, we coexpressed hMet and ß-catenin point mutants (S33Y or S45Y) in hepatocytes using sleeping beauty transposon/transposase and hydrodynamic tail vein injection and characterized tumors for growth, signaling, gene signatures, and similarity to human HCC. Missense mutations in exon 3 of CTNNB1 were identified in subsets of HCC patients. Irrespective of amino acid affected, all exon 3 mutations induced similar changes in gene expression. Concomitant HMET overexpression or hMet activation and CTNNB1 mutations were evident in 9%-12.5% of HCCs. Coexpression of hMet and mutant-ß-catenin led to notable HCC in mice. Tumors showed active Wnt and hMet signaling with evidence of glutamine synthetase and cyclin D1 positivity and mitogen-activated protein kinase/extracellular signal-regulated kinase, AKT/Ras/mammalian target of rapamycin activation. Introduction of dominant-negative T-cell factor 4 prevented tumorigenesis. The gene expression of mouse tumors in hMet-mutant ß-catenin showed high correlation, with subsets of human HCC displaying concomitant hMet activation signature and CTNNB1 mutations. CONCLUSION: We have identified cooperation of hMet and ß-catenin activation in a subset of HCC patients and modeled this human disease in mice with a significant transcriptomic intersection; this model will provide novel insight into the biology of this tumor and allow us to evaluate novel therapies as a step toward precision medicine. (Hepatology 2016;64:1587-1605).

A Temporal Switch in the Germinal Center Determines Differential Output of Memory B and Plasma Cells.

There is little insight into or agreement about the signals that control differentiation of memory B cells (MBCs) and long-lived plasma cells (LLPCs). By performing BrdU pulse-labeling studies, we found that MBC formation preceded the formation of LLPCs in an adoptive transfer immunization system, which allowed for a synchronized Ag-specific response with homogeneous Ag-receptor, yet at natural precursor frequencies. We confirmed these observations in wild-type (WT) mice and extended them with germinal center (GC) disruption experiments and variable region gene sequencing. We thus show that the GC response undergoes a temporal switch in its output as it matures, revealing that the reaction engenders both MBC subsets with different immune effector function and, ultimately, LLPCs at largely separate points in time. These data demonstrate the kinetics of the formation of the cells that provide stable humoral immunity and therefore have implications for autoimmunity, for vaccine development, and for understanding long-term pathogen resistance.

Homer1 alternative splicing is regulated by gonadotropin-releasing hormone and modulates gonadotropin gene expression.

Hypothalamic gonadotropin-releasing hormone (GnRH) plays a critical role in reproductive physiology by regulating follicle-stimulating hormone (FSH) and luteinizing hormone (LH) gene expression in the pituitary. Analysis of gonadotrope deep-sequencing data identified a global regulation of pre-mRNA splicing by GnRH. Homer1, a gene encoding a postsynaptic density scaffolding protein, was selected for further study. Homer1 expresses a short splice form, Homer1a, and more-abundant long transcripts Homer1b/c. GnRH induced a modest increase in Homer1b/c expression and a dramatic increase in the Homer1a splice form. G protein knockdown studies suggested that the Homer1 induction, but not the regulated splicing, was Gαq/11 dependent. Phosphorylation of the splicing regulator SRp20 was found to be induced by GnRH. SRp20 depletion attenuated the GnRH-induced increase in the Homer1a-to-Homer1b/c ratio and modulated the effects of GnRH on FSHß and LHß expression. Homer1 gene knockdown resulted in increased GnRH-induced FSHß and LHß transcript levels. Furthermore, splice-form-specific reduction of Homer1b/c increased both FSHß and LHß mRNA induction, whereas reduction of Homer1a had the opposite effect on FSHß induction. These results indicate that the regulation of Homer1 splicing by GnRH contributes to gonadotropin gene control.

ß-catenin regulates GnRH-induced FSHß gene expression.

The regulation of gonadotropin synthesis by GnRH plays an essential role in the neuroendocrine control of reproduction. The known signaling mechanisms involved in gonadotropin synthesis have been expanding. For example, involvement of ß-catenin in LHß induction by GnRH has been discovered. We examined the role of ß-catenin in FSHß gene expression in LßT2 gonadotrope cells. GnRH caused a sustained increase in nuclear ß-catenin levels, which was significantly reduced by c-Jun N-terminal kinase (JNK) inhibition. Small interfering RNA-mediated knockdown of ß-catenin mRNA demonstrated that induction of FSHß mRNA by GnRH depended on ß-catenin and that regulation of FSHß by ß-catenin occurred independently of the JNK-c-jun pathway. ß-Catenin depletion had no impact on FSHß mRNA stability. In LßT2 cells transfected with FSHß promoter luciferase fusion constructs, GnRH responsiveness was conferred by the proximal promoter (-944/-1) and was markedly decreased by ß-catenin knockdown. However, none of the T-cell factor/lymphoid enhancer factor binding sites in that region were required for promoter activation by GnRH. Chromatin immunoprecipitation further corroborated the absence of direct interaction between ß-catenin and the 1.8-kb FSHß promoter. To elucidate the mechanism for the ß-catenin effect, we analyzed approximately 1 billion reads of next-generation RNA sequencing ß-catenin knockdown assays and selected the nuclear cofactor breast cancer metastasis-suppressor 1-like (Brms1L) as one candidate for further study. Subsequent experiments confirmed that Brms1L mRNA expression was decreased by ß-catenin knockdown as well as by JNK inhibition. Furthermore, knockdown of Brms1L significantly attenuated GnRH-induced FSHß expression. Thus, our findings indicate that the expression of Brms1L depends on ß-catenin activity and contributes to FSHß induction by GnRH.