The relationship of trait-like compassion with epigenetic aging: The population-based prospective Young Finns Study

Introduction: Helping others within and beyond the family has been related to living a healthy and long life. Compassion is a prosocial personality trait characterized by concern for another person who is suffering and the motivation to help. The current study examines whether epigenetic aging is a potential biological mechanism that explains the link between prosociality and longevity. Methods: We used data from the Young Finns Study that follows six birth-cohorts from age 3–18 to 19–49. Trait-like compassion for others was measured with the Temperament and Character Inventory in the years 1997 and 2001. Epigenetic age acceleration and telomere length were measured with five DNA methylation (DNAm) indicators (DNAmAgeHorvath, IEAA_Hannum, EEAA_Hannum, DNAmPhenoAge, and DNAmTL) based on blood drawn in 2011. We controlled for sex, socioeconomic status in childhood and adulthood, and body-mass index. Results and discussion: An association between higher compassion in 1997 and a less accelerated DNAmPhenoAge, which builds on previous work on phenotypic aging, approached statistical significance in a sex-adjusted model (n = 1,030; b = −0.34; p = 0.050). Compassion in 1997 predicted less accelerated epigenetic aging over and above the control variables (n = 843; b = −0.47; p = 0.016). There was no relationship between compassion in 2001 (n = 1108/910) and any of the other four studied epigenetic aging indicators. High compassion for others might indeed influence whether an individual’s biological age is lower than their chronological age. The conducted robustness checks partially support this conclusion, yet cannot rule out that there might be a broader prosocial trait behind the findings. The observed associations are interesting but should be interpreted as weak requiring replication.Academy of Finland 286284 134309 126925 121584 124282 129378 117787 41071 322098Social Insurance Institution of FinlandCompetitive State Research Financing of the Expert Responsibility area of Kuopio, TampereCompetitive State Research Financing of the Expert Responsibility area of Kuopio, TampereJuho Vainio FoundationPaavo Nurmi FoundationFinnish Foundation for Cardiovascular ResearchFinnish Cultural Foundation Finnish IT center for scienceSigrid Juselius FoundationTampere Tuberculosis FoundationYrjoe Jahnsson FoundationEmil Aaltonen FoundationSigne and Ane Gyllenberg FoundationDiabetes Research Foundation of the Finnish Diabetes AssociationEuropean CommissionEuropean Research Council (ERC) European CommissionTampere University Hospital Supporting FoundationFinnish Society of Clinical Chemistry 755320 848146 74292

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30–45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidomegenotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.Research Council of FinlandSocial Insurance Institution of FinlandCompetitive State Research Financing of Expert Responsibility area of Kuopio, Tampere and Turku University HospitalsJuho Vainio FoundationPaavo Nurmi FoundationFinnish Foundation for Cardiovascular ResearchFinnish Cultural Foundation Finnish IT center for scienceSigrid Juselius FoundationTampere Tuberculosis FoundationEmil Aaltonen FoundationYrjo Jahnsson FoundationSigne and Ane Gyllenberg FoundationDiabetes Research Foundation of Finnish Diabetes Association 322098 286284 134309 126925 121584 124282 255381 256474 283115 319060 320297 314389 338395 330809 104821 129378 117797 141071 INFRAIA-2016-1-730897Horizon 2020European Research Council (ERC) European Commission 349708Tampere University Hospital Supporting FoundationFinnish Society of Clinical ChemistrySpanish Government RTI2018-098983-B-100Laboratoriolaaketieteen Edistamissaatio~SrIda Montinin saatioKalle Kaiharin saatioAarne Koskelon saatioFaculty of Medicine and Health Technology, Tampere UniversityProject HPC-EUROPA3 X51001 50191928EC Research Innovation Action under H2020 Programme 75532

Optimization of multi-classifiers for computational biology: application to gene finding and expression

Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods’ aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further.Ministry of Science and Innovation, Spain (MICINN) Spanish Government TIN-2006-12879Junta de Andalucia TIC-02788Howard Hughes Medical InstituteEuropean Commission Junta de Andaluci

Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis

The online version contains supplementary material available at https://doi.org/10.1038/s41380-023-02039-6The human brain’s resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person’s rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).EU FEDER grants through the Spanish Ministry of Science and Technology PID2021-125017OB-I00, RTI2018-098983-B-I00, D43 TW011793-06A1, PID2021-125017OB-I00, RTI2018-098983-B-I00, D43 TW011793-06A1United States Department of Health & Human Services National Institutes of Health (NIH) - USA R01-MH124060Psychosis-Risk Outcomes Network U01 MH12463

Evolution of genetic networks for human creativity

The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequencing the genomes of chimpanzees and Neanderthals, our closest hominid relatives. Data-driven methods allowed us to uncover networks of genes distinguishing the three major systems of modern human personality and adaptability: emotional reactivity, self-control, and self-awareness. Now we have identified which of these genes are present in chimpanzees and Neanderthals. We replicated our findings in separate analyses of three high-coverage genomes of Neanderthals. We found that Neanderthals had nearly the same genes for emotional reactivity as chimpanzees, and they were intermediate between modern humans and chimpanzees in their numbers of genes for both self-control and self-awareness. 95% of the 267 genes we found only in modern humans were not protein-coding, including many long-non-coding RNAs in the self-awareness network. These genes may have arisen by positive selection for the characteristics of human well-being and behavioral modernity, including creativity, prosocial behavior, and healthy longevity. The genes that cluster in association with those found only in modern humans are over-expressed in brain regions involved in human self-awareness and creativity, including late-myelinating and phylogenetically recent regions of neocortex for autobiographical memory in frontal, parietal, and temporal regions, as well as related components of cortico-thalamo-ponto-cerebellar-cortical and cortico-striato-cortical loops. We conclude that modern humans have more than 200 unique non-protein-coding genes regulating co-expression of many more proteincoding genes in coordinated networks that underlie their capacities for self-awareness, creativity, prosocial behavior, and healthy longevity, which are not found in chimpanzees or Neanderthals

Identification of novel prostate cancer genes in patients stratified by Gleason classification: Role of antitumoral genes

Spanish Ministry of Science and Innovation, Grant/Award Number: PRE2019-089807; Spanish Ministry of Science and Technology, Grant/Award Numbers: PI15/00914, RTI2018-098983-B-100; Universidad de Granada/CBUAProstate cancer (PCa) is a tumor with a great heterogeneity, both at a molecular and clinical level. Despite its global good prognosis, cases can vary from indolent to lethal metastatic and scientific efforts are aimed to discern those with worse outcomes. Current prognostic markers, as Gleason score, fall short when it comes to distinguishing these cases. Identification of new early biomarkers to enable a better PCa distinction and classification remains a challenge. In order to identify new genes implicated in PCa progression we conducted several differential gene expression analyses over paired samples comparing primary PCa tissue against healthy prostatic tissue of PCa patients. The results obtained show that this approach is a serious alternative to overcome patient heterogeneity. We were able to identify 250 genes whose expression varies along with tissue differentiation—healthy to tumor tissue, 161 of these genes are described here for the first time to be related to PCa. The further manual curation of these genes allowed to annotate 39 genes with antitumoral activity, 22 of them described for the first time to be related to PCa proliferation and metastasis. These findings could be replicated in different cohorts for most genes. Results obtained considering paired differential expression, functional annotation and replication results point to: CGREF1, UNC5A, C16orf74, LGR6, IGSF1, QPRT and CA14 as possible new early markers in PCa. These genes may prevent the progression of the disease and their expression should be studied in patients with different outcomes.Spanish Government PRE2019-089807 PI15/00914 RTI2018-098983-B-100Universidad de Granada/CBU

Uncovering Tumour Heterogeneity through PKR and nc886 Analysis in Metastatic Colon Cancer Patients Treated with 5-FU-Based Chemotherapy

Colorectal cancer treatment has advanced over the past decade. The drug 5-fluorouracil is still used with a wide percentage of patients who do not respond. Therefore, a challenge is the identification of predictive biomarkers. The protein kinase R (PKR also called EIF2AK2) and its regulator, the non-coding pre-mir-nc886, have multiple e ects on cells in response to numerous types of stress, including chemotherapy. In this work, we performed an ambispective study with 197 metastatic colon cancer patients with unresectable metastases to determine the relative expression levels of both nc886 and PKR by qPCR, as well as the location of PKR by immunohistochemistry in tumour samples and healthy tissues (plasma and colon epithelium). As primary end point, the expression levels were related to the objective response to first-line chemotherapy following the response evaluation criteria in solid tumours (RECIST) and, as the second end point, with survival at 18 and 36 months. Hierarchical agglomerative clustering was performed to accommodate the heterogeneity and complexity of oncological patients’ data. High expression levels of nc886 were related to the response to treatment and allowed to identify clusters of patients. Although the PKR mRNA expression was not associated with chemotherapy response, the absence of PKR location in the nucleolus was correlated with first-line chemotherapy response. Moreover, a relationship between survival and the expression of both PKR and nc886 in healthy tissues was found. Therefore, this work evaluated the best way to analyse the potential biomarkers PKR and nc886 in order to establish clusters of patients depending on the cancer outcomes using algorithms for complex and heterogeneous data.This research was funded by the Instituto de Salud Carlos III (DTS15/00174; PIE16-00045), by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía and European Regional Development Fund (ERDF), references SOMM17/6109/UGR (UCE-PP2017-3) and (PI-0441-2014), and by the Chair “Doctors Galera-Requena in cancer stem cell research” (CMC-CTS963). This research was also funded partially by RTI2018-098983-B-I00

Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity (“atomic” activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.United States Department of Health & Human Services 90BISA0015United States Department of Health & Human Services National Institutes of Health (NIH) - USA K01HD09538

Three genetic–environmental networks for human personality

The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 322098, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), 41071 (Skidi), and 308676; the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjo Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association: and EU Horizon 2020 (grant 755320 for TAXINOMISIS); and Tampere University Hospital Supporting Foundation. The American Foundation for Suicide Prevention supported the study of healthy Germans. The national Healthy Twin Family Register of Korea supported the study of healthy Koreans. The Anthropedia Foundation and the Spanish Ministry of Science and Technology TIN2012-38805 and DPI201569585-R supported this collaboration.Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.Academy of Finland European Commission 286284 322098 134309 126925 121584 124282 129378 117787 41071 308676Social Insurance Institution of FinlandCompetitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals X51001Juho Vainio FoundationPaavo Nurmi FoundationFinnish Foundation for Cardiovascular ResearchFinnish Foundation for Cardiovascular ResearchFinnish Cultural FoundationFinnish IT center for scienceTampere Tuberculosis FoundationEmil Aaltonen FoundationYrjo Jahnsson FoundationDiabetes Research Foundation of Finnish Diabetes AssociationEU Horizon 2020 755320Tampere University Hospital Supporting FoundationAmerican Foundation for Suicide Preventionnational Healthy Twin Family Register of KoreaAnthropedia FoundationSpanish Government TIN2012-38805 DPI201569585-RSigne and Ane Gyllenberg Foundatio