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Abstract Background The associations of different obesity and metabolic phenotypes during midlife with the risk of incident dementia remain unclear. This study aimed to investigate the associations between metabolic heterogeneity of obesity and long-term risk of dementia. Methods We conducted prospective analyses from three cohorts, including the UK Biobank (UKB), Atherosclerosis Risk in Communities (ARIC) study, and Framingham Offspring Study (FOS). Eligible participants were those aged 45–65 years with valid assessments of body mass index (BMI) and metabolic status at the study baseline. Obesity was defined as a BMI of ≥ 30.0 kg/m2, while metabolic abnormality was defined as meeting ≥ 2 of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria. Metabolic heterogeneity of obesity was evaluated based on obesity and metabolic phenotypes and grouped as metabolically normal non-obesity (MNNO), metabolically abnormal non-obesity (MANO), metabolically normal obesity (MNO), and metabolically abnormal obesity (MAO). Results Included in this study were 295,823 participants aged 56.3 ± 5.9 years from the UKB, 12,547 participants aged 54.0 ± 5.7 years from the ARIC, and 2,004 participants aged 53.9 ± 5.9 years from the FOS. Over 4,348,208 person-years, a total of 6,190 participants (3,601 in the UKB, 2,405 in the ARIC, and 184 in the FOS) developed incident dementia. In the pooled analysis of three cohorts, metabolic abnormality was associated with a hazard ratio (HR) of 1.41 (95% confidence interval [CI]: 1.10–1.80) for dementia, while obesity was associated with an HR of 1.20 (1.03–1.41). Compared with MNNO, individuals with MANO and MAO had increased risks of dementia (pooled HR: 1.33, 95% CI: 1.04–1.71 for MANO and 1.48, 1.16–1.89 for MAO). However, there was no significant difference in the risk of dementia among MNO (pooled HR: 1.10, 95% CI: 0.98–1.24). In addition, participants who recovered from MANO to MNNO had a lower risk of dementia (pooled HR: 0.79, 95% CI: 0.64–0.97), as compared with stable MANO. Conclusions Metabolic abnormality has a stronger association with dementia than obesity. Metabolically abnormal non-obesity and obesity, but not metabolically normal obesity, are associated with higher risks of incident dementia as compared with metabolically normal non-obesity. Recovering from an abnormal metabolic status to normal reduces the risk of dementia in populations without obesity. Our findings highlight the important role of metabolic status in the development of dementia and recommend the stratified management of obesity based on metabolic status.

Abstract Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.

BackgroundDespite growing interest in the clinical translation of polygenic risk scores (PRSs), it remains uncertain to what extent genomic information can enhance the prediction of psychiatric outcomes beyond the data collected during clinical visits alone. ObjectiveThis study aimed to assess the clinical utility of incorporating PRSs into a suicide risk prediction model trained on electronic health records (EHRs) and patient-reported surveys among patients admitted to the emergency department. MethodsStudy participants were recruited from the psychiatric emergency department at Massachusetts General Hospital. There were 333 adult patients of European ancestry who had high-quality genotype data available through their participation in the Mass General Brigham Biobank. Multiple neuropsychiatric PRSs were added to a previously validated suicide prediction model in a prospective cohort enrolled between February 4, 2015, and March 13, 2017. Data analysis was performed from July 11, 2022, to August 31, 2023. Suicide attempt was defined using diagnostic codes from longitudinal EHRs combined with 6-month follow-up surveys. The clinical risk score for suicide attempt was calculated from an ensemble model trained using an EHR-based suicide risk score and a brief survey, and it was subsequently used to define the baseline model. We generated PRSs for depression, bipolar disorder, schizophrenia, suicide attempt, and externalizing traits using a Bayesian polygenic scoring method for European ancestry participants. Model performance was evaluated using area under the receiver operator curve (AUC), area under the precision-recall curve, and positive predictive values. ResultsOf the 333 patients (n=178, 53.5% male; mean age 36.8, SD 13.6 years; n=333, 100% non-Hispanic and n=324, 97.3% self-reported White), 28 (8.4%) had a suicide attempt within 6 months. Adding either the schizophrenia PRS or all PRSs to the baseline model resulted in the numerically highest discrimination (AUC 0.86, 95% CI 0.73-0.99) compared to the baseline model (AUC 0.84, 95% Cl 0.70-0.98). However, the improvement in model performance was not statistically significant. ConclusionsIn this study, incorporating genomic information into clinical prediction models for suicide attempt did not improve patient risk stratification. Larger studies that include more diverse participants are required to validate whether the inclusion of psychiatric PRSs in clinical prediction models can enhance the stratification of patients at risk of suicide attempts.

Purpose: To evaluate regional changes in the posterior corneal elevation after three laser refractive surgeries for correction of myopia of different degrees. Settings and Design: Retrospective, comparative, and non-randomized study. Methods: Two hundred patients (200 eyes) who underwent laser epithelial keratoplasty (LASEK), femtosecond-assisted laser in-situ keratomileusis (FS-LASIK), and small-incision lenticule extraction (SMILE) were included in this study. According to preoperative spherical equivalent (SE), each surgical group was divided into two refractive subgroups: low-to-moderate myopia (LM group) and high myopia (H group). The posterior corneal elevation from Pentacam Scheimpflug tomography was analyzed preoperatively and at 1 month, 3 months, 6 months, and 12 months postoperatively. Three subregions of the posterior cornea were divided in this study as the central, paracentral, and peripheral regions. Statistical Analysis Used: Generalized Estimating Equations (GEE). Results: For all three surgical groups, similar changing trends were seen in the two refractive subgroups. H group presented a larger changing magnitude than the LM group in FS-LASIK over time (P < 0.05), whereas no significant difference was noted in the two refractive subgroups of LASEK or SMILE (P > 0.05). At 12 months postoperatively, the central posterior corneal elevation returned to the preoperative level in LASEK (P > 0.05) but shifted forward significantly in FS-LASIK and SMILE (P < 0.05). Conclusion: Different posterior corneal regions respond differently to corneal refractive surgeries. LASEK, FS-LASIK, and SMILE demonstrate different trends in the regional changes in posterior corneal elevation. The corneal shape seems more stable in LASEK than in FS-LASIK and SMILE.

Abstract Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

As a common foodborne pathogen， Escherichia coli （E. coli） O157：H7 can cause serious diseases in human body at low infectious doses. Lateral flow chromatographic assay （LFCA） can meet the requirement of detecting E. coli O157：H7 in food samples rapidly due to its efficient separation property. At present， the widely-used LFCA methods generate weak signal strength and possess low detection sensitivity， which can hardly detect low concentration of E. coli O157：H7. Therefore， this review focuses on the newly published LFCA literature， and a systematic induction is performed concerning the detection efficiency and sensitivity of the above studies. The advantages and disadvantages of each method are compared in this review， which will provide important conclusive guidance for the development of LFCA in E. coli O157：H7 detection.

Abstract Many of the Alzheimer’s disease (AD) risk genes are specifically expressed in microglia and astrocytes, but how and when the genetic risk localizing to these cell types contributes to AD pathophysiology remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets and uncover the impact of cell-type-specific genetic risk on AD endophenotypes. In an autopsy dataset spanning all stages of AD (n = 1457), the astrocytic ADPRS affected diffuse and neuritic plaques (amyloid-β), while microglial ADPRS affected neuritic plaques, microglial activation, neurofibrillary tangles (tau), and cognitive decline. In an independent neuroimaging dataset of cognitively unimpaired elderly (n = 2921), astrocytic ADPRS was associated with amyloid-β, and microglial ADPRS was associated with amyloid-β and tau, connecting cell-type-specific genetic risk with AD pathology even before symptom onset. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.

Abstract Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.

Summary: Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.

The current study identifies five genomic subclusters of brain regions for cortical thickness and surface area characterized by high levels of shared genetic signal. These subclusters map onto biological and functional parcellations of the cortex.

Cognitive impairments are a key feature of psychopathology. Here, authors exploit the genetic overlap between cognitive dimensions and psychopathology to parse the biology of psychiatric illness and identify “meta-loci” genome segments characterized by specific patterns of overlap.

Abstract Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Summary: Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Summary: Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Abstract Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers—the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Abstract Background Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations. Methods A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center’s (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. Results Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. Conclusions Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.

Abstract Vitamin D has been associated with a variety of human complex traits and diseases in observational studies, but a causal relationship remains unclear. To examine a putative causal effect of vitamin D across phenotypic domains and disease categories, we conducted Mendelian randomization (MR) analyses using genetic instruments associated with circulating 25-hydroxyvitamin D [25(OH)D] concentrations. We leveraged genome-wide significant 25(OH)D-associated SNPs (N = 138) from a meta-analysis combining a vitamin D GWAS conducted in 401,460 white British UK Biobank (UKBB) participants and an independent vitamin D GWAS including 42,274 samples of European ancestry, and examined 190 large-scale health-related GWAS spanning a broad spectrum of complex traits, diseases and biomarkers. We applied multiple MR methods to estimate the causal effect of vitamin D while testing and controlling for potential biases from horizontal pleiotropy. Consistent with previous findings, genetically predicted increased 25(OH)D levels significantly decreased the risk of multiple sclerosis (OR = 0.824; 95% CI 0.689–0.986). The protective effect estimate was consistent across different MR methods and four different multiple sclerosis GWAS with varying sample sizes and genotyping platforms. On the contrary, we found limited evidence in support of a causal effect of 25(OH)D on anthropometric traits, obesity, cognitive function, sleep behavior, breast and prostate cancer, and autoimmune, cardiovascular, metabolic, neurological and psychiatric traits and diseases, and blood biomarkers. Our results may inform ongoing and future randomized clinical trials of vitamin D supplementation.

Here, using pattern-learning analyses of structural, functional, and diffusion brain scans in ~40,000 UK Biobank participants, the authors provide population-scale evidence that the default network is associated with perceived social isolation.

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

The triangle barnacle Balanus trigonus Darwin, 1854, a cosmopolitan inhabitant of tropical and warm temperate seas, is a member of robust system for the study of evolutionary processes in the intertidal zone. The first mitochondrial genome of B. trigonus is presented. The complete mitochondrial genome of B. trigonus is a circular molecule of 15,560 bp, which encodes 13 protein-coding genes (PCGs), 2 rRNA genes, and 22 tRNA genes. In comparison within Sessilia, the arrangement of the mitochondrial genome of B. trigonus is more similar to Megabalanus spp. than the congener Balanus balanus, which share a same inversion of a large gene block (P-nd4L-nd4-H-nd5-F). Phylogenetic analysis based on mitochondrial PCGs reveals that B. trigonus clusters with Acasta Sulcata (BP = 100), then grouped with Megabalanus volcano and Megabalanus ajax with high support (BP = 90). In further, more data and research are needed to reveal the phylogeny within Cirripedia.

Interneuron subtypes have distinct properties and spatial distributions. Here, the authors show that the molecular-genetic basis of cortical resting-state brain function is shaped by distributions of interneuron-related transcripts and may capture individual differences in schizophrenia risk.

The complete mitochondrial genome of Tetraclita japonica (Crustacea: Maxillopoda: Sessilia) from Zhejiang (China) was presented (T. japonica ZJ). The genome is a circular molecule of 15,192 bp, all non-coding regions are 694 bp in length, and the longest one (307 bp) is located between 12S rRNA and trnK. Phylogenetic analysis based on mitochondrial PCGs shows that T. japonica ZJ clusters with T. japonica CN, and they grouped with T. japonica (BP = 100), then grouped with Tesseropora rosea, Tetraclita serrata, Tetraclita rufotincta, Epopella plicata, and Tetraclitella divisa successively . The phylogenetic tree indicated that the Balanidae and Archaeobalanidae are polyphyletic, while the Chthamalidae is monophyletic. The result can help us to understand the phylogenetic history within Sessilia.

An individual’s pattern of resting state brain connectivity, as measured with fMRI, has been shown to predict cognitive and behavioral traits. Here, the authors show that different traits are predicted by different time-scales of resting state activity (dynamic vs. static).

Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics.

The first mitochondrial genome of Tetraclita japonica from China was presented. The mitochondrial genome of T. japonica CN is a circular DNA molecule containing 15,192 bp. It contains 13 protein-coding genes, two rRNAs genes and 22 tRNAs. Four PCGs, seven tRNAs, and two rRNAs are encoded on the light strand (trnF, nd5, trnH, nd4, nd4L, trnP, nd1, trnL1, lrRNA, trnV, srRNA, trnK, and trnQ), and the other nine PCGs are located on the heavy strand. The length of all non-coding regions is 667 bp and the longest one distributed between srRNA and trnK (263 bp). Interestingly, cox1 of T. japonica CN started with CGA, which is different from most other mitochondrial PCGs whose initiation codon is ATN. In comparison with the pancrustacean ground pattern, seven tRNAs (trnQ, trnI, trnP, trnT, trnS1, trnE, and trnA) are translocated, and three tRNAs (trnK, trnC, and trnY) are inverted. Phylogenetic tree was constructed based on 13 mitochondrial PCGs. In the phylogenetic tree, T. japonica CN clustered with T. japonica JP into a branch (BP = 100). Tesseropora rosea clustered with T. japonica, and they grouped with T. rufotincta with high support (BP = 100). In the tree, T. divisa as the most distantly related species within Tetraclitidae. Moreover, phylogenetic analysis indicates that the two families (Balanidae and Archaeobalanidae) are non-monophyletic. More mitochondrial genomic data are urgently needed to reveal the deeper phylogeny within Cirripedia.

We determined the first mitochondrial genome of Tetraclita kuroshioensis from China. The mitochondrial genome of T. kuroshioensis was found to be 15,175 bp in length and consisted of 13 protein-coding genes (PCGs), 22 tRNAs, and 2 rRNAs. The longest non-coding region was 425 bp in length. Phylogenetic analysis showed that T. kuroshioensis clustered with T. serrata and then clustered with T. squamosa squamosa with high bootstrap value (BP = 100). In the future, sequencing of additional mitochondrial genomes should provide additional insights into the deep phylogeny of Cirripedia.

Cirripedia is a lower crustacean that has an invaluable place in several aspects of intertidal ecology and anti-fouling research. In this study, we present the first mitochondrial genome of Chthamalus malayensis. The complete mitochondrial genome of C. malayensis is a circular molecule of 15,230 bp. In comparison to the pancrustacean ground pattern, the mitochondrial genome of C. malayensis has a deletion of the trnC gene. Phylogenetic analysis based on mitochondrial protein-coding genes showed that C. malayensis clusters with C. antennatus (BP = 98) and is grouped with C. challengeri, Octomeris sp. BKKC-2014, and Notochthamalus scabrosus. Further studies are needed to reveal the specific phylogenic relationships within Cirripedia.

Neuroanatomical shape measurements are multidimensional geometric descriptions of brain structure. This study develops multivariate heritability analysis methods and examines structural brain MRI scans and genetic data to estimate the heritability of neuroanatomical shape.

[This corrects the article DOI: 10.1371/journal.pgen.1006711.].

The complete mitochondrial genome of Glycera chirori Izuka (Annelida: Polychaeta) was presented, which is a circular molecule of 15,930 bp nucleotides. It encodes 37 genes, including 13 PCGs, 22 tRNAs, and two rRNAs. The length of non-coding regions is 1428 bp, and the longest one (1346 bp) is speculated as the control region, which is located between trnA and trnL2 and is longer than most species in Glycera. The complete mitogenome of G. chirori Izuka consists of 31.2% A, 23.6% C, 12.9% G, and 32.2% T, which has T vs. A skew (−0.02) and C vs. G skew (−0.29), respectively. Phylogenetic analysis indicates the classification status of G. chirori Izuka and the relationship with other species in Glycera, which is closer with Glycera unicornis and Glycera fallax (bootstrap = 100). By comparisons, the gene arrangement of G. chirori Izuka and other seven species in Glycera are identical and they also cluster together in phylogenetic tree with higher support rate, which indicates the conservativeness between gene arrangement and phylogenetic analysis in Glycera. In conclusion, the complete mitochondrial genome of G. chirori Izuka can provide supportive data for further molecular and evolutionary analysis of Glycera.

Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.

Abstract Cancer is the leading cause of death worldwide. Nanomaterials and nanotechnologies could provide potential solutions. In this research, a novel biodegradable poly(lactide-co-glycolide)-d-a-tocopheryl polyethylene glycol 1000 succinate (PLGA-TPGS) random copolymer was synthesized from lactide, glycolide and d-a-tocopheryl polyethylene glycol 1000 succinate (TPGS) by ring-opening polymerization using stannous octoate as catalyst. The obtained random copolymers were characterized by 1H NMR, FTIR, GPC and TGA. The docetaxel-loaded nanoparticles made of PLGA-TPGS copolymer were prepared by a modified solvent extraction/evaporation method. The nanoparticles were then characterized by various state-of-the-art techniques. The results revealed that the size of PLGA-TPGS nanoparticles was around 250 nm. The docetaxel-loaded PLGA-TPGS nanoparticles could achieve much faster drug release in comparison with PLGA nanoparticles. In vitro cellular uptakes of such nanoparticles were investigated by CLSM, demonstrating the fluorescence PLGA-TPGS nanoparticles could be internalized by human cervix carcinoma cells (HeLa). The results also indicated that PLGA-TPGS-based nanoparticles were biocompatible, and the docetaxel-loaded PLGA-TPGS nanoparticles had significant cytotoxicity against Hela cells. The cytotoxicity against HeLa cells for PLGA-TPGS nanoparticles was in time- and concentration-dependent manner. In conclusion, PLGA-TPGS random copolymer could be acted as a novel and promising biocompatible polymeric matrix material applicable to nanoparticle-based drug delivery system for cancer chemotherapy.

Abstract Multidrug resistance (MDR) in tumor cells is a significant obstacle to the success of chemotherapy in many cancers. The purpose of this research is to test the possibility of docetaxel-loaded poly (ε-caprolactone)/Pluronic F68 (PCL/Pluronic F68) nanoparticles to overcome MDR in docetaxel-resistance human breast cancer cell line. Docetaxel-loaded nanoparticles were prepared by modified solvent displacement method using commercial PCL and self-synthesized PCL/Pluronic F68, respectively. PCL/Pluronic F68 nanoparticles were found to be of spherical shape with a rough and porous surface. The nanoparticles had an average size of around 200 nm with a narrow size distribution. The in vitro drug release profile of both nanoparticle formulations showed a biphasic release pattern. There was an increased level of uptake of PCL/Pluronic F68 nanoparticles in docetaxel-resistance human breast cancer cell line, MCF-7 TAX30, when compared with PCL nanoparticles. The cytotoxicity of PCL nanoparticles was higher than commercial Taxotere®in the MCF-7 TAX30 cell culture, but the differences were not significant (p > 0.05). However, the PCL/Pluronic F68 nanoparticles achieved significantly higher level of cytotoxicity than both of PCL nanoparticles and Taxotere®(p < 0.05), indicating docetaxel-loaded PCL/Pluronic F68 nanoparticles could overcome multidrug resistance in human breast cancer cells and therefore have considerable potential for treatment of breast cancer.

The KNOX (KNOTTED1-like homeobox) transcription factors play a pivotal role in leaf and meristem development. The majority of these proteins are characterized by the KNOX1, KNOX2, ELK and homeobox domains whereas the proteins of the KNATM family contain only the KNOX domains. We carried out an extensive inventory of these proteins and here report on a total of 394 KNOX proteins from 48 species. The land plant proteins fall into two classes (I and II) as previously shown where the class I family seems to be most closely related to the green algae homologs. The KNATM proteins are restricted to Eudicots and some species have multiple paralogs of this protein. Certain plants are characterized by a significant increase in the number of KNOX paralogs; one example is Glycine max. Through the analysis of public gene expression data we show that the class II proteins of this plant have a relatively broad expression specificity as compared to class I proteins, consistent with previous studies of other plants. In G. max, class I protein are mainly distributed in axis tissues and KNATM paralogs are overall poorly expressed; highest expression is in the early plumular axis. Overall, analysis of gene expression in G. max demonstrates clearly that the expansion in gene number is associated with functional diversification.