SLFN5-mediated chromatin dynamics sculpt higher-order DNA repair topology.

Repair of DNA double-strand breaks (DSBs) elicits three-dimensional (3D) chromatin topological changes. A recent finding reveals that 53BP1 assembles into a 3D chromatin topology pattern around DSBs. How this formation of a higher-order structure is configured and regulated remains enigmatic. Here, we report that SLFN5 is a critical factor for 53BP1 topological arrangement at DSBs. Using super-resolution imaging, we find that SLFN5 binds to 53BP1 chromatin domains to assemble a higher-order microdomain architecture by driving damaged chromatin dynamics at both DSBs and deprotected telomeres. Mechanistically, we propose that 53BP1 topology is shaped by two processes: (1) chromatin mobility driven by the SLFN5-LINC-microtubule axis and (2) the assembly of 53BP1 oligomers mediated by SLFN5. In mammals, SLFN5 deficiency disrupts the DSB repair topology and impairs non-homologous end joining, telomere fusions, class switch recombination, and sensitivity to poly (ADP-ribose) polymerase inhibitor. We establish a molecular mechanism that shapes higher-order chromatin topologies to safeguard genomic stability.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Syk-dependent homologous recombination activation promotes cancer resistance to DNA targeted therapy.

Enhanced DNA repair is an important mechanism of inherent and acquired resistance to DNA targeted therapies, including poly ADP ribose polymerase (PARP) inhibition. Spleen associated tyrosine kinase (Syk) is a non-receptor tyrosine kinase acknowledged for its regulatory roles in immune cell function, cell adhesion, and vascular development. This study presents evidence indicating that Syk expression in high-grade serous ovarian cancer and triple-negative breast cancers promotes DNA double-strand break resection, homologous recombination (HR), and subsequent therapeutic resistance. Our investigations reveal that Syk is activated by ATM following DNA damage and is recruited to DNA double-strand breaks by NBS1. Once localized to the break site, Syk phosphorylates CtIP, a pivotal mediator of resection and HR, at Thr-847 to promote repair activity, particularly in Syk-expressing cancer cells. Inhibition of Syk or its genetic deletion impedes CtIP Thr-847 phosphorylation and overcomes the resistant phenotype. Collectively, our findings suggest a model wherein Syk fosters therapeutic resistance by promoting DNA resection and HR through a hitherto uncharacterized ATM-Syk-CtIP pathway. Moreover, Syk emerges as a promising tumor-specific target to sensitize Syk-expressing tumors to PARP inhibitors, radiation and other DNA-targeted therapies.

Identification of a Notch transcriptomic signature for breast cancer.

BACKGROUND: Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would be warranted, for example to monitor the effects of future Notch-targeting therapies and to learn whether altered Notch signalling is an off-target effect of current breast cancer therapies. In this report, we have established such a classifier. METHODS: To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated or reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by γ-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort and sought to validate in independent datasets. RESULTS: An optimal 20-gene transcriptomic signature was selected. We validated the signature on two independent patient datasets (METABRIC and Oslo2), and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. CONCLUSIONS: The 20-gene transcriptional signature will be a valuable tool to evaluate the response of future Notch-targeting therapies for breast cancer, to learn about potential effects on Notch signalling from conventional breast cancer therapies and to better stratify patients for therapy considerations.

Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer.

BACKGROUND: Tumor immune infiltration and peripheral blood immune signatures have prognostic and predictive value in breast cancer. Whether distinct peripheral blood immune phenotypes are associated with response to neoadjuvant chemotherapy (NAC) remains understudied. METHODS: Peripheral blood mononuclear cells from 126 breast cancer patients enrolled in a prospective clinical trial (NCT02022202) were analyzed using Cytometry by time-of-flight with a panel of 29 immune cell surface protein markers. Kruskal-Wallis tests or Wilcoxon rank-sum tests were used to evaluate differences in immune cell subpopulations according to breast cancer subtype and response to NAC. RESULTS: There were 122 evaluable samples: 47 (38.5%) from patients with hormone receptor-positive, 39 (32%) triple-negative (TNBC), and 36 (29.5%) HER2-positive breast cancer. The relative abundances of pre-treatment peripheral blood T, B, myeloid, NK, and unclassified cells did not differ according to breast cancer subtype. In TNBC, higher pre-treatment myeloid cells were associated with lower pathologic complete response (pCR) rates. In hormone receptor-positive breast cancer, lower pre-treatment CD8 + naïve and CD4 + effector memory cells re-expressing CD45RA (TEMRA) T cells were associated with more extensive residual disease after NAC. In HER2 + breast cancer, the peripheral blood immune phenotype did not differ according to NAC response. CONCLUSIONS: Pre-treatment peripheral blood immune cell populations (myeloid in TNBC; CD8 + naïve T cells and CD4 + TEMRA cells in luminal breast cancer) were associated with response to NAC in early-stage TNBC and hormone receptor-positive breast cancers, but not in HER2 + breast cancer. TRIAL REGISTRATION: NCT02022202 . Registered 20 December 2013.

Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer.

Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present.In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

ACE2 and TMPRSS2 SARS-CoV-2 infectivity genes: deep mutational scanning and characterization of missense variants.

The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a 'landing pad' system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Deubiquitination and Activation of AMPK by USP10.

The AMP-activated protein kinase (AMPK) is the master regulator of metabolic homeostasis by sensing cellular energy status. When intracellular ATP levels decrease during energy stress, AMPK is initially activated through AMP or ADP binding and phosphorylation of a threonine residue (Thr-172) within the activation loop of its kinase domain. Here we report a key molecular mechanism by which AMPK activation is amplified under energy stress. We found that ubiquitination on AMPKα blocks AMPKα phosphorylation by LKB1. The deubiquitinase USP10 specifically removes ubiquitination on AMPKα to facilitate AMPKα phosphorylation by LKB1. Under energy stress, USP10 activity in turn is enhanced through AMPK-mediated phosphorylation of Ser76 of USP10. Thus, USP10 and AMPK form a key feedforward loop ensuring amplification of AMPK activation in response to fluctuation of cellular energy status. Disruption of this feedforward loop leads to improper AMPK activation and multiple metabolic defects.

Glucocorticoids unmask silent non-coding genetic risk variants for common diseases.

Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were 'unmasked' only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Integration of multiomics data shows down regulation of mismatch repair and tubulin pathways in triple-negative chemotherapy-resistant breast tumors.

BACKGROUND: Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence. METHODS AND RESULTS: We investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery (< 24 months) and eight who remained recurrence-free (> 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed. CONCLUSION: Analysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes.

Dynamic assessment of serum chromogranin A and treatment response with abiraterone acetate in metastatic castration-resistant prostate cancer.

OBJECTIVE: Elevated serum chromogranin A (CGA) is associated with intrinsic or treatment-related neuroendocrine differentiation (NED) in men with metastatic castration-resistant prostate cancer (mCRPC). Fluctuations in serum CGA during treatment of mCRPC have had conflicting results. We analyzed the impact of (i) rising serum CGA and (ii) baseline CGA/PSA ratio during treatment to identify associations with abiraterone acetate (AA) therapy. METHODS: Between June 2013 and August 2015, 92 men with mCRPC were enrolled in a prospective trial with uniform serum CGA processing performed before initiating abiraterone acetate/prednisone (AA/P) and serially after 12 weeks of AA/P treatments. Serum CGA was measured using a homogenous automated immunofluorescent assay. Patients receiving proton pump inhibitors or with abnormal renal function were excluded due to possible false elevations of serum CGA (n = 21 excluded), therefore 71 patients were analyzed. All patients underwent a composite response assessment at 12-weeks. Kaplan-Meier estimates and Cox Regression models were used to calculate the association with time-to-treatment failure analyses and overall survival. RESULTS: An increase in chromogranin was associated with a lower risk of treatment failure (hazard ratio [HR]: 0.52, p = 0.0181). The median CGA/PSA ratio was 7.8 (2.6-16.0) and an elevated pretreatment CGA/PSA ratio above the median was associated with a lower risk of treatment failure (HR: 0.54 p value = 0.0185). An increase in CGA was not found to be associated with OS (HR: 0.71, 95% CI: 0.42-1.21, p = 0.207). An elevated baseline CGA/PSA ratio was not associated with OS (HR: 0.62, 95% CI: 0.37-1.03, p = 0.062). An increase in PSA after 12 weeks of treatment was associated with an increased risk of treatment failure (HR: 4.14, CI: 2.21-7.73, p = < 0.0001) and worse OS (HR: 2.93, CI: 1.57-4.45, p = < 0.0001). CONCLUSIONS: We show that an increasing chromogranin on AA/P and an elevated baseline CGA/PSA in patients with mCRPC were associated with a favorable response to AA/P with no changes in survival. There may be limited clinical utility in serum CGA testing to evaluate for lethal NED as AA/P did not induce lethal NED in this cohort. This highlights that not all patients with an increasing CGA have a worse OS.

Salicylates enhance CRM1 inhibitor antitumor activity by induction of S-phase arrest and impairment of DNA-damage repair.

Chromosome region maintenance protein 1 (CRM1) mediates protein export from the nucleus and is a new target for anticancer therapeutics. Broader application of KPT-330 (selinexor), a first-in-class CRM1 inhibitor recently approved for relapsed multiple myeloma and diffuse large B-cell lymphoma, have been limited by substantial toxicity. We discovered that salicylates markedly enhance the antitumor activity of CRM1 inhibitors by extending the mechanisms of action beyond CRM1 inhibition. Using salicylates in combination enables targeting of a range of blood cancers with a much lower dose of selinexor, thereby potentially mitigating prohibitive clinical adverse effects. Choline salicylate (CS) with low-dose KPT-330 (K+CS) had potent, broad activity across high-risk hematological malignancies and solid-organ cancers ex vivo and in vivo. The K+CS combination was not toxic to nonmalignant cells as compared with malignant cells and was safe without inducing toxicity to normal organs in mice. Mechanistically, compared with KPT-330 alone, K+CS suppresses the expression of CRM1, Rad51, and thymidylate synthase proteins, leading to more efficient inhibition of CRM1-mediated nuclear export, impairment of DNA-damage repair, reduced pyrimidine synthesis, cell-cycle arrest in S-phase, and cell apoptosis. Moreover, the addition of poly (ADP-ribose) polymerase inhibitors further potentiates the K+CS antitumor effect. K+CS represents a new class of therapy for multiple types of blood cancers and will stimulate future investigations to exploit DNA-damage repair and nucleocytoplasmic transport for cancer therapy in general.

Cytochrome P450 Transcriptional Regulation by Testis-Specific Y-Encoded-Like Protein: Identification of Novel Upstream Transcription Factors.

Cytochrome P450s (CYPs) display significant inter-individual variation in expression, much of which remains unexplained by known CYP single-nucleotide polymorphisms (SNPs). Testis-specific Y-encoded-like proteins (TSPYLs) are transcriptional regulators for several drug-metabolizing CYPs including CYP3A4 However, transcription factors (TFs) that might influence CYP expression through an effect on TSPYL expression are unknown. Therefore, we studied regulators of TSPYL expression in hepatic cell lines and their possible SNP-dependent variation. Specifically, we identified candidate TFs that might influence TSPYL expression using the ENCODE ChIPseq database. Subsequently, the expression of TSPYL1/2/4 as well as that of selected CYP targets for TSPYL regulation were assayed in hepatic cell lines before and after knockdown of TFs that might influence CYP expression through TSPYL-dependent mechanisms. Those results were confirmed by studies of TF binding to TSPYL1/2/4 gene promoter regions. In hepatic cell lines, knockdown of the REST and ZBTB7A TFs resulted in decreased TSPYL1 and TSPYL4 expression and increased CYP3A4 expression, changes reversed by TSPYL1/4 overexpression. Potential binding sites for REST and ZBTB7A on the promoters of TSPYL1 and TSPYL4 were confirmed by chromatin immunoprecipitation. Finally, common SNP variants in upstream binding sites on the TSPYL1/4 promoters were identified and luciferase reporter constructs confirmed SNP-dependent modulation of TSPYL1/4 gene transcription. In summary, we identified REST and ZBTB7A as regulators of the expression of TSPYL genes which themselves can contribute to regulation of CYP expression and-potentially-of drug metabolism. SNP-dependent modulation of TSPYL transcription may contribute to individual variation in both CYP expression and-downstream-drug response phenotypes. SIGNIFICANCE STATEMENT: Testis-specific Y-encoded-like proteins (TSPYLs) are transcriptional regulators of cytochrome P450 (CYP) gene expression. Here, we report that variation in TSPYL expression as a result of the effects of genetically regulated TSPYL transcription factors is an additional factor that could result in downstream variation in CYP expression and potentially, as a result, variation in drug biotransformation.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Parkin Regulates Mitosis and Genomic Stability through Cdc20/Cdh1.

Mutations in the E3 ubiquitin ligase Parkin have been linked to familial Parkinson's disease. Parkin has also been implicated in mitosis through mechanisms that are unclear. Here we show that Parkin interacts with anaphase promoting complex/cyclosome (APC/C) coactivators Cdc20 and Cdh1 to mediate the degradation of several key mitotic regulators independent of APC/C. We demonstrate that ordered progression through mitosis is orchestrated by two distinct E3 ligases through the shared use of Cdc20 and Cdh1. Furthermore, Parkin is phosphorylated and activated by polo-like kinase 1 (Plk1) during mitosis. Parkin deficiency results in overexpression of its substrates, mitotic defects, genomic instability, and tumorigenesis. These results suggest that the Parkin-Cdc20/Cdh1 complex is an important regulator of mitosis.

WSB1 promotes tumor metastasis by inducing pVHL degradation.

The von Hippel-Lindau tumor suppressor pVHL is an E3 ligase that targets hypoxia-inducible factors (HIFs). Mutation of VHL results in HIF up-regulation and contributes to processes related to tumor progression such as invasion, metastasis, and angiogenesis. However, very little is known with regard to post-transcriptional regulation of pVHL. Here we show that WD repeat and SOCS box-containing protein 1 (WSB1) is a negative regulator of pVHL through WSB1's E3 ligase activity. Mechanistically, WSB1 promotes pVHL ubiquitination and proteasomal degradation, thereby stabilizing HIF under both normoxic and hypoxic conditions. As a consequence, WSB1 up-regulates the expression of HIF-1α's target genes and promotes cancer invasion and metastasis through its effect on pVHL. Consistent with this, WSB1 protein level negatively correlates with pVHL level and metastasis-free survival in clinical samples. This work reveals a new mechanism of pVHL's regulation by which cancer acquires invasiveness and metastatic tendency.

Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study.

PURPOSE: The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS: Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS: For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION: Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.

TCF7L2 lncRNA: a link between bipolar disorder and body mass index through glucocorticoid signaling.

Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI), which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in the BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells that highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas that are involved in metabolism. In astrocytes, knockdown of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes. We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knockdown with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.

ERICH3: vesicular association and antidepressant treatment response.

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.

BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.