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1. Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

   https://doi.org/10.1038/s41386-020-00943-x  

   Athreya, Arjun P. ; Brückl, Tanja ; Binder, Elisabeth B. ; John Rush, A. ; Biernacka, Joanna ; Frye, Mark A. ; Neavin, Drew ; Skime, Michelle ; Monrad, Ditlev ; Iyer, Ravishankar K. ; et al ( June 2021 , Neuropsychopharmacology)

   null (Ed.)

   Abstract Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% ( p  = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% ( p  = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming. 

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2. The Role of Serotonergic Gene Methylation in Regulating Anxiety-Related Personality Traits in Chimpanzees

   https://doi.org/10.3390/biology11111673  

   Staes, Nicky ; Guevara, Elaine E. ; Hopkins, William D. ; Schapiro, Steven J. ; Eens, Marcel ; Sherwood, Chet C. ; Bradley, Brenda J. ( November 2022 , Biology)

   While low serotonergic activity is often associated with psychological disorders such as depression, anxiety, mood, and personality disorders, variations in serotonin also contribute to normal personality differences. In this study, we investigated the role of blood DNA methylation levels at individual CpG sites of two key serotonergic genes (serotonin receptor gene 1A, HTR1A; serotonin transporter gene, SLC6A4) in predicting the personalities of captive chimpanzees. We found associations between methylation at 9/48 CpG sites with four personality dimensions: Dominance, Reactivity/Dependability, Agreeableness, and Openness. Directionality of effects were CpG location-dependent and confirmed a role of serotonergic methylation in reducing anxiety (Dominance) and aggression-related personality (Reactivity/Undependability) while simultaneously promoting prosocial (Agreeableness) and exploratory personalities (Openness). Although early-life adversity has been shown to impact serotonergic methylation patterns in other species, here, atypical early social rearing experiences only had a modest impact on CpG methylation levels in this chimpanzee sample. The precise environmental factors impacting serotonergic methylation in chimpanzees remain to be identified. Nevertheless, our study suggests a role in shaping natural variation in animal personalities. The results of this study offer a basis for future hypothesis-driven testing in additional populations and species to better understand the impact of ecology and evolution on complex behavioral traits.

    

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3. TCA and SSRI Antidepressants Exert Selection Pressure for Efflux-Dependent Antibiotic Resistance Mechanisms in Escherichia coli

   https://doi.org/10.1128/mbio.02191-22  

   Ou, Jin ; Elizalde, Paul ; Guo, Hao-Bo ; Qin, Hong ; Tobe, Brian T.D. ; Choy, John S. ( December 2022 , mBio)

   Bonomo, Robert A. (Ed.)

   ABSTRACT Microbial diversity is reduced in the gut microbiota of animals and humans treated with selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs). The mechanisms driving the changes in microbial composition, while largely unknown, is critical to understand considering that the gut microbiota plays important roles in drug metabolism and brain function. Using Escherichia coli , we show that the SSRI fluoxetine and the TCA amitriptyline exert strong selection pressure for enhanced efflux activity of the AcrAB-TolC pump, a member of the resistance-nodulation-cell division (RND) superfamily of transporters. Sequencing spontaneous fluoxetine- and amitriptyline-resistant mutants revealed mutations in marR and lon, negative regulators of AcrAB-TolC expression. In line with the broad specificity of AcrAB-TolC pumps these mutants conferred resistance to several classes of antibiotics. We show that the converse also occurs, as spontaneous chloramphenicol-resistant mutants displayed cross-resistance to SSRIs and TCAs. Chemical-genomic screens identified deletions in marR and lon, confirming the results observed for the spontaneous resistant mutants. In addition, deletions in 35 genes with no known role in drug resistance were identified that conferred cross-resistance to antibiotics and several displayed enhanced efflux activities. These results indicate that combinations of specific antidepressants and antibiotics may have important effects when both are used simultaneously or successively as they can impose selection for common mechanisms of resistance. Our work suggests that selection for enhanced efflux activities is an important factor to consider in understanding the microbial diversity changes associated with antidepressant treatments. IMPORTANCE Antidepressants are prescribed broadly for psychiatric conditions to alter neuronal levels of synaptic neurotransmitters such as serotonin and norepinephrine. Two categories of antidepressants are selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs); both are among the most prescribed drugs in the United States. While it is well-established that antidepressants inhibit reuptake of neurotransmitters there is evidence that they also impact microbial diversity in the gastrointestinal tract. However, the mechanisms and therefore biological and clinical effects remain obscure. We demonstrate antidepressants may influence microbial diversity through strong selection for mutant bacteria with increased AcrAB-TolC activity, an efflux pump that removes antibiotics from cells. Furthermore, we identify a new group of genes that contribute to cross-resistance between antidepressants and antibiotics, several act by regulating efflux activity, underscoring overlapping mechanisms. Overall, this work provides new insights into bacterial responses to antidepressants important for understanding antidepressant treatment effects. 

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4. Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction

   https://doi.org/10.3389/fphar.2020.614048  

   Nguyen, Thanh Thanh ; Liu, Duan ; Ho, Ming-Fen ; Athreya, Arjun P. ; Weinshilboum, Richard ( January 2021 , Frontiers in Pharmacology)

   null (Ed.)

   Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a “multiple omics” research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to “inform” genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5 , ERICH3 , DEFB1 and AHR . In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy. 

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5. Silence, Solitude, and Serotonin: Neural Mechanisms Linking Hearing Loss and Social Isolation

   https://doi.org/10.3390/brainsci10060367  

   Keesom, Sarah M. ; Hurley, Laura M. ( June 2020 , Brain Sciences)

   For social animals that communicate acoustically, hearing loss and social isolation are factors that independently influence social behavior. In human subjects, hearing loss may also contribute to objective and subjective measures of social isolation. Although the behavioral relationship between hearing loss and social isolation is evident, there is little understanding of their interdependence at the level of neural systems. Separate lines of research have shown that social isolation and hearing loss independently target the serotonergic system in the rodent brain. These two factors affect both presynaptic and postsynaptic measures of serotonergic anatomy and function, highlighting the sensitivity of serotonergic pathways to both types of insult. The effects of deficits in both acoustic and social inputs are seen not only within the auditory system, but also in other brain regions, suggesting relatively extensive effects of these deficits on serotonergic regulatory systems. Serotonin plays a much-studied role in depression and anxiety, and may also influence several aspects of auditory cognition, including auditory attention and understanding speech in challenging listening conditions. These commonalities suggest that serotonergic pathways are worthy of further exploration as potential intervening mechanisms between the related conditions of hearing loss and social isolation, and the affective and cognitive dysfunctions that follow. 

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