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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Background Vasoplegic syndrome is frequently observed during cardiac surgery and resembles a complication of high mortality and morbidity. There is a clinical need for therapy and prevention of vasoplegic syndrome during complex cardiac surgical procedures. Therefore, we investigated different strategies in a porcine model of vasoplegia.
Methods We evaluated new medical therapies and prophylaxis to avoid vasoplegic syndrome in a porcine model. After induction of anesthesia, cardiopulmonary bypass was established through median sternotomy and central cannulation. Prolonged aortic cross-clamping (120 min) simulated a complex surgical procedure. The influence of sevoflurane-guided anesthesia (sevoflurane group) and the administration of glibenclamide (glibenclamide group) were compared to a control group, which received standard anesthesia using propofol. Online hemodynamic assessment was performed using PiCCO® measurements. In addition, blood and tissue samples were taken to evaluate hemodynamic effects and the degree of inflammatory response.
Results Glibenclamide was able to break through early vasoplegic syndrome by raising the blood pressure and systemic vascular resistance as well as less need of norepinephrine doses. Sevoflurane reduced the occurrence of the vasoplegic syndrome in the mean of stable blood pressure and less need of norepinephrine doses.
Conclusion Glibenclamide could serve as a potent drug to reduce effects of vasoplegic syndrome. Sevoflurane anesthesia during cardiopulmonary bypass shows less occurrence of vasoplegic syndrome and therefore could be used to prevent it in high-risk patients.
Clinical Perspective; what is new?
* to our knowledge, this is the first randomized in vivo study evaluating the hemodynamic effects of glibenclamide after the onset of vasoplegic syndrome
* furthermore according to literature research, there is no study showing the effect of sevoflurane-guided anesthesia on the occurrence of a vasoplegic syndrome
Clinical Perspective; clinical implications?
to achieve better outcomes after complex cardiac surgery there is a need for optimized drug therapy and prevention of the vasoplegic syndrome
Wastewater-based SARS-CoV-2 epidemiology (WBE) has been established as an important tool to support individual testing strategies. Omicron sub-variants BA.4/5 have spread globally displacing the predeceasing variants. Due to the severe transmissibility and immune escape potential of BA.4/5, early monitoring was required to asses and implement countermeasures in time.
In this study, we monitored the prevalence of SARS-CoV-2 BA.4/5 at six municipal wastewater treatment plants (WWTPs) in the Federal State of North-Rhine-Westphalia (NRW, Germany) in May and June 2022. Initially, L452R-specific primers/probes originally designed for SARS-CoV-2 Delta detection were validated using inactivated authentic viruses and evaluated for their suitability to detect BA.4/5. Subsequently, the assay was used for RT-qPCR analysis of RNA purified from wastewater obtained twice a week at six WWTPs. The occurrence of L452R carrying RNA was detected in early May 2022 and the presence of BA.4/5 was confirmed by variant-specific single nucleotide polymorphism PCR (SNP-PCR) targeting E484A/F486V. Finally, the mutant fractions were quantitatively monitored by digital PCR confirming BA.4/5 as the majority variant by 5th June 2022.
In conclusions, the successive workflow using RT-qPCR, variant-specific SNP-PCR, and RT-dPCR demonstrates the strength of WBE as a versatile tool to rapidly monitor variant spreading independent of individual test capacities.
Wastewater-based SARS-CoV-2 epidemiology (WBE) has been established as an important tool to support individual testing strategies. Omicron sub-variants BA.4/5 have spread globally displacing the predeceasing variants. Due to the severe transmissibility and immune escape potential of BA.4/5, early monitoring was required to asses and implement countermeasures in time.
In this study, we monitored the prevalence of SARS-CoV-2 BA.4/5 at six municipal wastewater treatment plants (WWTPs) in the Federal State of North-Rhine-Westphalia (NRW, Germany) in May and June 2022. Initially, L452R-specific primers/probes originally designed for SARS-CoV-2 Delta detection were validated using inactivated authentic viruses and evaluated for their suitability to detect BA.4/5. Subsequently, the assay was used for RT-qPCR analysis of RNA purified from wastewater obtained twice a week at six WWTPs. The occurrence of L452R carrying RNA was detected in early May 2022 and the presence of BA.4/5 was confirmed by variant-specific single nucleotide polymorphism PCR (SNP-PCR) targeting E484A/F486V. Finally, the mutant fractions were quantitatively monitored by digital PCR confirming BA.4/5 as the majority variant by 5th June 2022.
In conclusions, the successive workflow using RT-qPCR, variant-specific SNP-PCR, and RT-dPCR demonstrates the strength of WBE as a versatile tool to rapidly monitor variant spreading independent of individual test capacities.
The gradual heterogeneity of climatic factors pose varying selection pressures across geographic distances that leave signatures of clinal variation in the genome. Separating signatures of clinal adaptation from signatures of other evolutionary forces, such as demographic processes, genetic drift, and adaptation to non-clinal conditions of the immediate local environment is a major challenge. Here, we examine climate adaptation in five natural populations of the harlequin fly Chironomus riparius sampled along a climatic gradient across Europe. Our study integrates experimental data, individual genome resequencing, Pool-Seq data, and population genetic modelling. Common-garden experiments revealed a positive correlation of population growth rates corresponding to the population origin along the climate gradient, suggesting thermal adaptation on the phenotypic level. Based on a population genomic analysis, we derived empirical estimates of historical demography and migration. We used an FST outlier approach to infer positive selection across the climate gradient, in combination with an environmental association analysis. In total we identified 162 candidate genes as genomic basis of climate adaptation. Enriched functions among these candidate genes involved the apoptotic process and molecular response to heat, as well as functions identified in other studies of climate adaptation in other insects. Our results show that local climate conditions impose strong selection pressures and lead to genomic adaptation despite strong gene flow. Moreover, these results imply that selection to different climatic conditions seems to converge on a functional level, at least between different insect species.
Salt-inducible kinases (SIKs) are key metabolic regulators. Imbalance of SIK function is associated with the development of diverse cancers, including breast, gastric and ovarian cancer. Chemical tools to clarify the roles of SIK in different diseases are, however, sparse and are generally characterized by poor kinome-wide selectivity. Here, we have adapted the pyrido[2,3-d]pyrimidin-7-one-based PAK inhibitor G-5555 for the targeting of SIK, by exploiting differences in the back-pocket region of these kinases. Optimization was supported by high-resolution crystal structures of G-5555 bound to the known off-targets MST3 and MST4, leading to a chemical probe, MRIA9, with dual SIK/PAK activity and excellent selectivity over other kinases. Furthermore, we show that MRIA9 sensitizes ovarian cancer cells to treatment with the mitotic agent paclitaxel, confirming earlier data from genetic knockdown studies and suggesting a combination therapy with SIK inhibitors and paclitaxel for the treatment of paclitaxel-resistant ovarian cancer.
Improved integration of single cell transcriptome data demonstrated on heart failure in mice and men
(2023)
Biomedical research frequently uses murine models to study disease mechanisms. However, the translation of these findings to human disease remains a significant challenge. In order to improve the comparability of mouse and human data, we present a cross-species integration pipeline for single-cell transcriptomic assays.
The pipeline merges expression matrices and assigns clear orthologous relationships. Starting from Ensembl ortholog assignments, we allocated 82% of mouse genes to unique orthologs by using additional publicly available resources such as Uniprot, and NCBI databases. For genes with multiple matches, we employed the Needleman-Wunsch global alignment based on either amino acid or nucleotide sequence to identify the ortholog with the highest degree of similarity.
The workflow was tested for its functionality and efficiency by integrating scRNA-seq datasets from heart failure patients with the corresponding mouse model. We were able to assign unique human orthologs to up to 80% of the mouse genes, utilizing the known 17,492 orthologous pairs. Curiously, the integration process enabled the identification of both common and unique regulatory pathways between species in heart failure.
In conclusion, our pipeline streamlines the integration process, enhances gene nomenclature alignment and simplifies the translation of mouse models to human disease. We have made the OrthoIntegrate R-package accessible on GitHub (https://github.com/MarianoRuzJurado/OrthoIntegrate), which includes the assignment of ortholog definitions for human and mouse, as well as the pipeline for integrating single cells.
Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.