Structural basis of NINJ1-mediated plasma membrane rupture in cell death.

Eukaryotic cells can undergo different forms of programmed cell death, many of which culminate in plasma membrane rupture as the defining terminal event1-7. Plasma membrane rupture was long thought to be driven by osmotic pressure, but it has recently been shown to be in many cases an active process, mediated by the protein ninjurin-18 (NINJ1). Here we resolve the structure of NINJ1 and the mechanism by which it ruptures membranes. Super-resolution microscopy reveals that NINJ1 clusters into structurally diverse assemblies in the membranes of dying cells, in particular large, filamentous assemblies with branched morphology. A cryo-electron microscopy structure of NINJ1 filaments shows a tightly packed fence-like array of transmembrane α-helices. Filament directionality and stability is defined by two amphipathic α-helices that interlink adjacent filament subunits. The NINJ1 filament features a hydrophilic side and a hydrophobic side, and molecular dynamics simulations show that it can stably cap membrane edges. The function of the resulting supramolecular arrangement was validated by site-directed mutagenesis. Our data thus suggest that, during lytic cell death, the extracellular α-helices of NINJ1 insert into the plasma membrane to polymerize NINJ1 monomers into amphipathic filaments that rupture the plasma membrane. The membrane protein NINJ1 is therefore an interactive component of the eukaryotic cell membrane that functions as an in-built breaking point in response to activation of cell death.

The CUL4B-based E3 ubiquitin ligase regulates mitosis and brain development by recruiting phospho-specific DCAFs.

The paralogs CUL4A and CUL4B assemble cullin-RING E3 ubiquitin ligase (CRL) complexes regulating multiple chromatin-associated cellular functions. Although they are structurally similar, we found that the unique N-terminal extension of CUL4B is heavily phosphorylated during mitosis, and the phosphorylation pattern is perturbed in the CUL4B-P50L mutation causing X-linked intellectual disability (XLID). Phenotypic characterization and mutational analysis revealed that CUL4B phosphorylation is required for efficient progression through mitosis, controlling spindle positioning and cortical tension. While CUL4B phosphorylation triggers chromatin exclusion, it promotes binding to actin regulators and to two previously unrecognized CUL4B-specific substrate receptors (DCAFs), LIS1 and WDR1. Indeed, co-immunoprecipitation experiments and biochemical analysis revealed that LIS1 and WDR1 interact with DDB1, and their binding is enhanced by the phosphorylated N-terminal domain of CUL4B. Finally, a human forebrain organoid model demonstrated that CUL4B is required to develop stable ventricular structures that correlate with onset of forebrain differentiation. Together, our study uncovers previously unrecognized DCAFs relevant for mitosis and brain development that specifically bind CUL4B, but not the CUL4B-P50L patient mutant, by a phosphorylation-dependent mechanism.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Publisher Correction: Hydrostatic pressure and the actomyosin cortex drive mitotic cell rounding.

Change history: In Fig. 1b and c of this Letter, the inset times in the DIC and GFP microscopy images should be in minutes ('min') instead of seconds ('s'). This has not been corrected online.

Synthetic Biology: Bottom-Up Assembly of Molecular Systems.

The bottom-up assembly of biological and chemical components opens exciting opportunities to engineer artificial vesicular systems for applications with previously unmet requirements. The modular combination of scaffolds and functional building blocks enables the engineering of complex systems with biomimetic or new-to-nature functionalities. Inspired by the compartmentalized organization of cells and organelles, lipid or polymer vesicles are widely used as model membrane systems to investigate the translocation of solutes and the transduction of signals by membrane proteins. The bottom-up assembly and functionalization of such artificial compartments enables full control over their composition and can thus provide specifically optimized environments for synthetic biological processes. This review aims to inspire future endeavors by providing a diverse toolbox of molecular modules, engineering methodologies, and different approaches to assemble artificial vesicular systems. Important technical and practical aspects are addressed and selected applications are presented, highlighting particular achievements and limitations of the bottom-up approach. Complementing the cutting-edge technological achievements, fundamental aspects are also discussed to cater to the inherently diverse background of the target audience, which results from the interdisciplinary nature of synthetic biology. The engineering of proteins as functional modules and the use of lipids and block copolymers as scaffold modules for the assembly of functionalized vesicular systems are explored in detail. Particular emphasis is placed on ensuring the controlled assembly of these components into increasingly complex vesicular systems. Finally, all descriptions are presented in the greater context of engineering valuable synthetic biological systems for applications in biocatalysis, biosensing, bioremediation, or targeted drug delivery.

Integrative Genetic Variation, DNA Methylation, and Gene Expression Analysis of Escitalopram and Aripiprazole Treatment Outcomes in Depression: A CAN-BIND-1 Study.

INTRODUCTION: Little is known about the interplay between genetics and epigenetics on antidepressant treatment (1) response and remission, (2) side effects, and (3) serum levels. This study explored the relationship among single nucleotide polymorphisms (SNPs), DNA methylation (DNAm), and mRNA levels of four pharmacokinetic genes, CYP2C19, CYP2D6, CYP3A4, and ABCB1, and its effect on these outcomes. METHODS: The Canadian Biomarker Integration Network for Depression-1 dataset consisted of 177 individuals with major depressive disorder treated for 8 weeks with escitalopram (ESC) followed by 8 weeks with ESC monotherapy or augmentation with aripiprazole. DNAm quantitative trait loci (mQTL), identified by SNP-CpG associations between 20 SNPs and 60 CpG sites in whole blood, were tested for associations with our outcomes, followed by causal inference tests (CITs) to identify methylation-mediated genetic effects. RESULTS: Eleven cis-SNP-CpG pairs (q<0.05) constituting four unique SNPs were identified. Although no significant associations were observed between mQTLs and response/remission, CYP2C19 rs4244285 was associated with treatment-related weight gain (q=0.027) and serum concentrations of ESCadj (q<0.001). Between weeks 2-4, 6.7% and 14.9% of those with *1/*1 (normal metabolizers) and *1/*2 (intermediate metabolizers) genotypes, respectively, reported ≥2 lbs of weight gain. In contrast, the *2/*2 genotype (poor metabolizers) did not report weight gain during this period and demonstrated the highest ESCadj concentrations. CITs did not indicate that these effects were epigenetically mediated. DISCUSSION: These results elucidate functional mechanisms underlying the established associations between CYP2C19 rs4244285 and ESC pharmacokinetics. This mQTL SNP as a marker for antidepressant-related weight gain needs to be further explored.

Influence of CYP2C19, CYP2D6, and ABCB1 Gene Variants and Serum Levels of Escitalopram and Aripiprazole on Treatment-Emergent Sexual Dysfunction: A Canadian Biomarker Integration Network in Depression 1 (CAN-BIND 1) Study.

OBJECTIVES: Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample. METHODS: A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models. RESULTS: In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052). CONCLUSIONS: CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 Update on Clinical Guidelines for Management of Major Depressive Disorder in Adults: Réseau canadien pour les traitements de l'humeur et de l'anxiété (CANMAT) 2023 : Mise à jour des lignes directrices cliniques pour la prise en charge du trouble dépressif majeur chez les adultes.

BACKGROUND: The Canadian Network for Mood and Anxiety Treatments (CANMAT) last published clinical guidelines for the management of major depressive disorder (MDD) in 2016. Owing to advances in the field, an update was needed to incorporate new evidence and provide new and revised recommendations for the assessment and management of MDD in adults. METHODS: CANMAT convened a guidelines editorial group comprised of academic clinicians and patient partners. A systematic literature review was conducted, focusing on systematic reviews and meta-analyses published since the 2016 guidelines. Recommendations were organized by lines of treatment, which were informed by CANMAT-defined levels of evidence and supplemented by clinical support (consisting of expert consensus on safety, tolerability, and feasibility). Drafts were revised based on review by patient partners, expert peer review, and a defined expert consensus process. RESULTS: The updated guidelines comprise eight primary topics, in a question-and-answer format, that map a patient care journey from assessment to selection of evidence-based treatments, prevention of recurrence, and strategies for inadequate response. The guidelines adopt a personalized care approach that emphasizes shared decision-making that reflects the values, preferences, and treatment history of the patient with MDD. Tables provide new and updated recommendations for psychological, pharmacological, lifestyle, complementary and alternative medicine, digital health, and neuromodulation treatments. Caveats and limitations of the evidence are highlighted. CONCLUSIONS: The CANMAT 2023 updated guidelines provide evidence-informed recommendations for the management of MDD, in a clinician-friendly format. These updated guidelines emphasize a collaborative, personalized, and systematic management approach that will help optimize outcomes for adults with MDD.

Tailoring the Sensitivity of Microcantilevers To Monitor the Mass of Single Adherent Living Cells.

Microcantilevers are widely employed as mass sensors for biological samples, from single molecules to single cells. However, the accurate mass quantification of living adherent cells is impaired by the microcantilever's mass sensitivity and cell migration, both of which can lead to detect masses mismatching by ≫50%. Here, we design photothermally actuated microcantilevers to optimize the accuracy of cell mass measurements. By reducing the inertial mass of the microcantilever using a focused ion beam, we considerably increase its mass sensitivity, which is validated by finite element analysis and experimentally by gelatin microbeads. The improved microcantilevers allow us to instantly monitor at much improved accuracy the mass of both living HeLa cells and mouse fibroblasts adhering to different substrates. Finally, we show that the improved cantilever design favorably restricts cell migration and thus reduces the large measurement errors associated with this effect.

Biphasic reinforcement of nascent adhesions by vinculin.

Vinculin is an integral component of integrin adhesions, where it functions as a molecular clutch coupling intracellular contraction to the extracellular matrix. Quantitating its contribution to the reinforcement of newly forming adhesions, however, requires ultrasensitive cell force assays covering short time and low force ranges. Here, we have combined atomic force microscopy-based single-cell force spectroscopy (SCFS) and optical tweezers force spectroscopy to investigate the role of vinculin in reinforcement of individual nascent adhesions during the first 5 min of cell contact with fibronectin or vitronectin. At minimal adhesion times (5-10 s), mouse embryonic fibroblast (MEF) wildtype (wt) and vinculin knock-out (vin(-/-) ) cells develop comparable adhesion forces on the scale of several individual integrin-ligand bonds, confirming that vinculin is dispensable for adhesion initiation. In contrast, after 60 to 120 s, adhesion strength and traction reinforce quickly in wt cells, while remaining low in vin(-/-) cells. Re-expression of full-length vinculin or a constitutively active vinculin mutant (vinT12) in MEF vin(-/-) cells restored adhesion and traction with the same efficiency, while vinculin with a mutated talin-binding head region (vinA50I) or missing the actin-binding tail-domain (vin880) was ineffective. Integrating total internal reflection fluorescence imaging into the SCFS setup furthermore enabled us to correlate vinculin-green fluorescent protein (GFP) recruitment to nascent adhesion sites with the built-up of vinculin-dependent adhesion forces directly. Vinculin recruitment and cell adhesion reinforcement followed synchronous biphasic patterns, suggesting vinculin recruitment, but not activation, as the rate-limiting step for adhesion reinforcement. Combining sensitive SCFS with fluorescence microscopy thus provides insight into the temporal sequence of vinculin-dependent mechanical reinforcement in nascent integrin adhesions.

Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study.

Given the polygenic nature of antipsychotic-induced weight gain (AIWG), we investigated whether polygenic risk scores (PRS) for various psychiatric and metabolic traits were associated with AIWG. We included individuals with schizophrenia (SCZ) of European ancestry from two cohorts (N = 151, age = 40.3 ± 11.8 and N = 138, age = 36.5 ± 10.8). We investigated associations of AIWG defined as binary and continuous variables with PRS calculated from genome-wide association studies of body mass index (BMI), coronary artery disease (CAD), fasting glucose, fasting insulin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, type 1 and 2 diabetes mellitus, and SCZ, using regression models. We observed nominal associations (uncorrected p < 0.05) between PRSs for BMI, CAD, and LDL-C, type 1 diabetes, and SCZ with AIWG. While results became non-significant after correction for multiple testing, these preliminary results suggest that PRS analyses might contribute to identifying risk factors of AIWG and might help to elucidate mechanisms at play in AIWG.

The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review.

Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.

Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report.

BACKGROUND: Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers. METHODS: In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively. RESULTS: A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction. CONCLUSIONS: A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Gene-drug pairings for antidepressants and antipsychotics: level of evidence and clinical application.

Substantial inter-individual discrepancies exist in both therapeutic effectiveness and adverse effects of antidepressant and antipsychotic medications, which can, in part, be explained by genetic variation. Here, we searched the Pharmacogenomics Knowledge Base for gene-antidepressant and gene-antipsychotic pairs with the highest level of evidence. We then extracted and compared the associated prescribing recommendations for these pairs developed by the Clinical Pharmacogenomics Implementation Consortium, the Dutch Pharmacogenetics Working Group or approved product labels in the US, Canada, Europe, and Asia. Finally, we highlight key economical, educational, regulatory, and ethical issues that, if not appropriately considered, can hinder the implementation of these recommendations in clinical practice. Our review indicates that evidence-based guidelines are available to assist with the implementation of pharmacogenetic-guided antidepressant and antipsychotic prescribing, although the maximum impact of these guidelines on patient care will not be realized until key barriers are minimized or eliminated.

Dopaminergic dysfunction and excitatory/inhibitory imbalance in treatment-resistant schizophrenia and novel neuromodulatory treatment.

Antipsychotic drugs are the mainstay in the treatment of schizophrenia. However, one-third of patients do not show adequate improvement in positive symptoms with non-clozapine antipsychotics. Additionally, approximately half of them show poor response to clozapine, electroconvulsive therapy, or other augmentation strategies. However, the development of novel treatment for these conditions is difficult due to the complex and heterogenous pathophysiology of treatment-resistant schizophrenia (TRS). Therefore, this review provides key findings, potential treatments, and a roadmap for future research in this area. First, we review the neurobiological pathophysiology of TRS, particularly the dopaminergic, glutamatergic, and GABAergic pathways. Next, the limitations of existing and promising treatments are presented. Specifically, this article focuses on the therapeutic potential of neuromodulation, including electroconvulsive therapy, repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and deep brain stimulation. Finally, we propose multivariate analyses that integrate various perspectives of the pathogenesis, such as dopaminergic dysfunction and excitatory/inhibitory imbalance, thereby elucidating the heterogeneity of TRS that could not be obtained by conventional statistics. These analyses can in turn lead to a precision medicine approach with closed-loop neuromodulation targeting the detected pathophysiology of TRS.

Genomic Investigation of Remission and Relapse of Psychotic Depression Treated with Sertraline plus Olanzapine: The STOP-PD II Study.

INTRODUCTION: Little is known regarding genetic factors associated with treatment outcome of psychotic depression. We explored genomic associations of remission and relapse of psychotic depression treated with pharmacotherapy. METHODS: Genomic analyses were performed in 171 men and women aged 18-85 years with an episode of psychotic depression who participated in the Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II). Participants were treated with open-label sertraline plus olanzapine for up to 12 weeks; those who achieved remission or near-remission and maintained it following 8 weeks of stabilization were eligible to participate in a 36-week randomized controlled trial that compared sertraline plus olanzapine with sertraline plus placebo in preventing relapse. RESULTS: There were no genome-wide significant associations with either remission or relapse. However, at a suggestive threshold, SNP rs1026501 (31 kb from SYNPO2) in the whole sample and rs6844137 (within the intronic region of SYNPO2) in the European ancestry subsample were associated with a decreased likelihood of remission. In polygenic risk analyses, participants who had greater improvement after antidepressant treatments showed a higher likelihood of reaching remission. Those who achieved remission and had a higher polygenic risk for Alzheimer's disease had a significantly decreased likelihood of relapse. CONCLUSION: Our analyses provide preliminary insights into the genetic architecture of remission and relapse in a well-characterized group of patients with psychotic depression.

Atomic Force Microscopy-Based Force Spectroscopy and Multiparametric Imaging of Biomolecular and Cellular Systems.

During the last three decades, a series of key technological improvements turned atomic force microscopy (AFM) into a nanoscopic laboratory to directly observe and chemically characterize molecular and cell biological systems under physiological conditions. Here, we review key technological improvements that have established AFM as an analytical tool to observe and quantify native biological systems from the micro- to the nanoscale. Native biological systems include living tissues, cells, and cellular components such as single or complexed proteins, nucleic acids, lipids, or sugars. We showcase the procedures to customize nanoscopic chemical laboratories by functionalizing AFM tips and outline the advantages and limitations in applying different AFM modes to chemically image, sense, and manipulate biosystems at (sub)nanometer spatial and millisecond temporal resolution. We further discuss theoretical approaches to extract the kinetic and thermodynamic parameters of specific biomolecular interactions detected by AFM for single bonds and extend the discussion to multiple bonds. Finally, we highlight the potential of combining AFM with optical microscopy and spectroscopy to address the full complexity of biological systems and to tackle fundamental challenges in life sciences.

Inertial picobalance reveals fast mass fluctuations in mammalian cells.

The regulation of size, volume and mass in living cells is physiologically important, and dysregulation of these parameters gives rise to many diseases. Cell mass is largely determined by the amount of water, proteins, lipids, carbohydrates and nucleic acids present in a cell, and is tightly linked to metabolism, proliferation and gene expression. Technologies have emerged in recent years that make it possible to track the masses of single suspended cells and adherent cells. However, it has not been possible to track individual adherent cells in physiological conditions at the mass and time resolutions required to observe fast cellular dynamics. Here we introduce a cell balance (a 'picobalance'), based on an optically excited microresonator, that measures the total mass of single or multiple adherent cells in culture conditions over days with millisecond time resolution and picogram mass sensitivity. Using our technique, we observe that the mass of living mammalian cells fluctuates intrinsically by around one to four per cent over timescales of seconds throughout the cell cycle. Perturbation experiments link these mass fluctuations to the basic cellular processes of ATP synthesis and water transport. Furthermore, we show that growth and cell cycle progression are arrested in cells infected with vaccinia virus, but mass fluctuations continue until cell death. Our measurements suggest that all living cells show fast and subtle mass fluctuations throughout the cell cycle. As our cell balance is easy to handle and compatible with fluorescence microscopy, we anticipate that our approach will contribute to the understanding of cell mass regulation in various cell states and across timescales, which is important in areas including physiology, cancer research, stem-cell differentiation and drug discovery.

Baseline Functional Connectivity in Resting State Networks Associated with Depression and Remission Status after 16 Weeks of Pharmacotherapy: A CAN-BIND Report.

Understanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. So far, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity associated with MDD and pharmacotherapy outcomes in a large, multisite sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior cingulate cortex, insula and dorsolateral prefrontal cortex) and all other brain voxels. Partial least squares was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission (MADRS ≤ 10) early (within 8 weeks), late (within 16 weeks), or not at all. We replicated previous findings of altered connectivity in patients. In addition, baseline connectivity of the anterior/posterior cingulate and insula seeds differentiated patients with different treatment outcomes. The stability of these differences was established in the largest single-site subsample. Our replication and extension of altered connectivity highlighted previously reported and new differences between patients and controls, and revealed features that might predict remission prior to pharmacotherapy. Trial registration:ClinicalTrials.gov: NCT01655706.

The Use of Next-Generation Sequencing in Pharmacogenomics.

BACKGROUND: Next-generation sequencing (NGS) methods have become more commonly performed in clinical and research laboratories. METHODS: This review summarizes the current laboratory NGS-based diagnostic approaches in pharmacogenomics including targeted multi-gene panel sequencing, whole-exome sequencing (WES), and whole-genome sequencing (WGS). RESULTS: Clinical laboratories perform multiple non-uniform types of pharmacogenetic panels, which can reduce the overall number of single-gene tests to be more cost-efficient. Compared to the targeted multi-gene panels, which are not typically designed to detect novel variants, WES and WGS have a greater potential to identify secondary pharmacogenomic findings, which might be predictive for the pharmacotherapy outcome of different patient settings. WGS overcomes the limitations of WES enabling a more accurate exome-sequencing at appropriate coverage and the sequencing of non-coding regions. Different NGS-based study designs with different test strategies and study populations, varying sample sizes, and distinct analytical and interpretation procedures lead to different identification results of pharmacogenomic variants. CONCLUSIONS: The rapid progress in gene sequencing technologies will overcome the clinical and laboratory challenges of WES and WGS. Further high throughput NGS-based pharmacogenomics studies in different populations and patient settings are necessary to expand knowledge about rare functional variants and to enhance translation in clinical practice.