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The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Objective: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. Methods: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. Results: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. Conclusion: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions. Prev of MS in the US-E-Appendix-Feb-19-2018

Web Supplementary Files Web Supplementary File 1 - FASTA files containing full-length reconstruction input sequences: full_length_reconstruction_input_sequence_fastas.zip Web Supplementary File 2 - FASTA files containing Muscle alignments of the full-length reconstruction input sequences. full_length_reconstruction_input_sequence_alns.zip Web Supplementary File 3 - FASTA file of full-length reconstructed sequences: full_length_reconstructions.fa Web Supplementary File 4 - Table of full-length reconstruction statistics: full_length_reconstruction_stats.csv Web Supplementary File 5 - FASTA files containing ORF reconstruction input sequences: orf_fastas.zip Web Supplementary File 6 - FASTA files containing Macse alignments of the ORF reconstruction input sequences: ORF_reconstruction_input_sequence_alns.zip Web Supplementary File 7 - Table of ORF reconstruction statistics: ORF_reconstructions.fa Web Supplementary File 8 - Table of ORF reconstruction statistics: ORF_reconstruction_stats.csv Web Supplementary File 9 - Table of Composite Sequences: bestfl_selection_fixed_CS_seqs.csv Web Supplementary File 10 - Database of gold standards: L1_goldstandards.csv Data Underlying Figures RepeatMasker scans of hg38 and ancestral genomes: anc_gen_RM_out_files.zip Figure 4 4A Source alignment of 54 composite sequences: 220121_dropped12+L1ME3A_muscle.nt.afa Tree produced using the alignment and FastTree: 220121_dropped12+L1ME3A.tree 4B Source alignment of 67 Dfam L1 subfamily 3’ end models: 200123_dfam_3ends.fa.muscle.aln Tree produced using the alignment: 200123_dfam_3ends.fa.muscle.aln.tree Figure 5 KZFP-TE enrichment p-values (from Barazandeh et al 2018): TE_KZFP_enrichment_pvals.xlsx KZFP-TE top 500 peak overlap (from Barazandeh et al 2018): top500_peak_overlap.xlsx Figure 6 RepeatMasker .out file for the Composite Sequence custom library queried against hg38: CS_RM_hg38.fa.out.gz Figure S2 RepeatMasker scan .out file of hg38 (CG corrected Kimura Divergence values are in last column): hg38+KimDiv_RM.out RepeatMasker scan .out file of the Progressive Cactus eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): Progressive_Cactus_Euth+KimDiv_RM.out RepeatMasker scan .out file of the Ancestors 1.1 eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): Ancestors_Euth+KimDiv_RM.out Figure S5 RepeatMasker scan .out files for Progressive Cactus simian and primate reconstructed ancestral genomes: progCactus_RM_outfiles.zip S5A FASTA files containing Cactus genome-derived reconstructed sequences equivalent to the L1MA2, L1MA4, and L1MD1-3 best full-length sequences: progCactus_reconstruction_bestFL_equivalents.zip S5B FASTA files containing Muscle alignments of Cactus genome-derived full-length reconstruction input sequences: progCactus_reconstruction_input_sequence_alns.zip Figure S6 S6A Results of Conserved Domain scans of Cactus genome-derived full-length reconstructed sequences: CD_search_results_short_nms.txt S6B-D Character posterior probabilities of “best” full-length reconstructed sequences: best_fl_post_probs.zip Figure S7 S7B-C Results of Conserved Domain scans of translated initial full-length reconstructed sequences: initial_recons_all_3frametrans_CD-search.txt Results of Conserved Domain scans of translated reconstructed ORFs: recons_ORF1-2_all_3frametrans_CD-search.csv Figure S15 S15A Source alignment of 67 composite sequences: bestfl_selection_fixed_CS_seqs_muscle.nt.afa Tree produced using the alignment: bestfl_selection_fixed_CS_seqs_muscle.nt.afa.tree S15B-E Source Muscle alignments for phylogenetic trees of reconstructed sequence components: ORF2: ORF2_keep54_muscle.nt.afa 5’ UTR: 5utr_keep54_muscle.nt.afa ORF1: ORF1_keep54_muscle.nt.afa 3’ UTR: 3utr_keep54_muscle.nt.afa Trees produced using above alignments: ORF2: ORF2_keep54_muscle.nt.afa.tree 5’ UTR: 5utr_keep54_muscle.nt.afa.tree ORF1: ORF1_keep54_muscle.nt.afa.tree 3’ UTR: 3utr_keep54_muscle.nt.afa.tree Figure S17 Unfiltered BLAST results of Composite Sequences queried against hg38: CS_hg38_blastn.csv.zip BED file of L1 instances annotated using BLAST pipeline: BLAST_L1_hits.bed

AbstractLeptin influences eating behavior. Exposure to early adversity is associated with eating behaviour disorders and metabolic syndrome, but the role of the leptin receptor on this relationship is poorly explored. We investigated whether individual differences in brain region specific leptin receptor (LepR) gene networks could moderate the effects of early adversity on eating behavior and metabolism. We created an expression-based polygenic risk score (ePRS) reflecting variations in the function of LepR gene network in prefrontal cortex and hypothalamus to investigate the interactions between a cumulative index of postnatal adversity on eating behavior in two independent birth cohorts (MAVAN and GUSTO). To explore whether variations in the prefrontal cortex or hypothalamic genetic scores could be associated with metabolic measurements, we also assessed the relationship between LepR-ePRS and fasting blood glucose and leptin levels in a third independent cohort (ALSPAC). We identified significant interaction effects between postnatal adversity and prefrontal-based LepR-ePRS on the Child Eating Behavior Questionnaire scores. In MAVAN, we observed a significant interaction effect on food enjoyment at 48 months (β = 61.58, p = 0.015) and 72 months (β = 97.78, p = 0.001); food responsiveness at 48 months (β = 83.79, p = 0.009) satiety at 48 months (β = −43.63, p = 0.047). Similar results were observed in the GUSTO cohort, with a significant interaction effect on food enjoyment (β = 30.48, p = 0.006) food fussiness score (β = −24.07, p = 0.02) and satiety score at 60 months (β = −17.00, p = 0.037). No effects were found when focusing on the hypothalamus-based LepR-ePRS on eating behavior in MAVAN and GUSTO cohorts, and there was no effect of hypothalamus and prefrontal cortex based ePRSs on metabolic measures in ALSPAC. Our study indicated that exposure to postnatal adversity interacts with prefrontal cortex LepR-ePRS to moderate eating behavior, suggesting a neurobiological mechanism associated with the development of eating behavior problems in response to early adversity. The knowledge of these mechanisms may guide the understanding of eating patterns associated with risk for obesity in response to fluctuations in stress exposure early in life.

OBJECTIVE. To assess whether HS severity is mirrored at the level of large-scale networks. METHODS. We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 TLE patients with histopathological diagnosis of HS (n=25; TLE-HS) and isolated gliosis (n=19; TLE-G), and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm which simulates functional dynamics from structural network data. RESULTS. Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from grey matter atrophy. CONCLUSIONS. Severe HS is associated with marked remodeling of connectome topology and structurally-governed functional dynamics in TLE, as opposed to isolated gliosis which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales. Data_phen_conn_dryadPhenotypic information and mean connectome feature data for Bernhardt et al. (2019) Temporal lobe epilepsy: hippocampal pathology modulates white matter connectome topology and controllability. Neurology

Abstract Biofilms that form on implanted medical devices cause recalcitrant infections. The early events enabling contaminating bacteria to evade immune clearance, before a mature biofilm is established, are poorly understood. Live imaging in vitro demonstrated that Staphylococcus aureus sparsely inoculated on an abiotic surface can go undiscovered by human neutrophils , grow, and form aggregates. Small (~50 μm 2) aggregates of attached bacteria resisted killing by human neutrophils, resulting in neutrophil lysis and bacterial persistence. In vivo, neutrophil recruitment to a peritoneal implant was spatially heterogenous, with some bacterial aggregates remaining undiscovered by neutrophils after 24 h. Intravital imaging in mouse skin revealed that attached S. aureus aggregates grew and remained undiscovered by neutrophils for up to 3 h. These results suggest a model in which delayed recruitment of neutrophils to an abiotic implant presents a critical window in which bacteria establish a nascent biofilm and acquire tolerance to neutrophil killing.

The discovery that the protist Monocercomonoides exilis completely lacks mitochondria demonstrates that these organelles are not absolutely essential to eukaryotic cells. However, the degree to which the metabolism and cellular systems of this organism have adapted to the loss of mitochondria is unknown. Here, we report an extensive analysis of the M. exilis genome to address this question. Unexpectedly, we find that M. exilis genome structure and content is similar in complexity to other eukaryotes and less “reduced” than genomes of some other protists from the Metamonada group 30 to which it belongs. Furthermore, the predicted cytoskeletal systems, the organization of endomembrane systems, and biosynthetic pathways also display canonical eukaryotic complexity. The only apparent preadaptation that permitted the loss of mitochondria was the acquisition of the SUF system for Fe–S cluster assembly. Changes in other systems, including in amino acid metabolism and oxidative stress response, were coincident with the loss of mitochondria but are likely adaptations to the microaerophilic and endobiotic niche rather than the mitochondrial loss per se. Apart from the lack of 35 mitochondria and peroxisomes, we show that M. exilis is a fully elaborated eukaryotic cell that is a promising model system in which eukaryotic cell biology can be investigated in the absence of mitochondria. Key words: amitochondrial eukaryote, cell biology, Monocercomonoides, oxymonads, protist genomics.

AbstractSocial experiences are an important predictor of disease susceptibility and survival in humans and other social mammals. Chronic social stress is thought to generate a pro-inflammatory state characterized by elevated antibacterial defenses and reduced investment in antiviral defense. Here, we manipulated long-term social status in female rhesus macaques to show that social subordination alters the gene expression response to ex vivo bacterial and viral challenge. As predicted by current models, bacterial lipopolysaccharide polarizes the immune response such that low status corresponds to higher expression of genes in NF-κB-dependent pro-inflammatory pathways and lower expression of genes involved in the antiviral response and type I interferon (IFN) signaling. Counter to predictions, however, low status drives more exaggerated expression of both NF-κB and IFN-associated genes after cells are exposed to the viral mimic Gardiquimod. Status-driven gene expression patterns are not only linked to social status at the time of sampling, but also to social history (i.e., past social status), especially in unstimulated cells. However, for a subset of genes, we observed interaction effects in which females who fell in rank were more strongly affected by current social status than those who climbed the social hierarchy. Together, our results indicate that the effects of social status on immune cell gene expression depend on pathogen exposure, pathogen type, and social history – in support of social experience-mediated biological embedding in adulthood, even in the conventionally memory-less innate immune system.

Living in permanent social groups forces animals to make decisions about when, how and with whom to interact, requiring decisions to be made that integrate multiple sources of information. Changing social environments can influence this decision-making process by constraining choice or altering the likelihood of a positive outcome. Here, we conceptualised grooming as a choice situation where an individual chooses one of a number of potential partners. Studying two wild populations of sympatric primate species, sooty mangabeys (Cercocebus atys atys) and Western chimpanzees (Pan troglodytes verus), we tested what properties of potential partners influenced grooming decisions, including their relative value based on available alternatives and the social relationships of potential partners with bystanders who could observe the outcome of the decision. Across 1,529 decision events, multiple partner attributes (e.g. dominance ranks, social relationship quality, reproductive state, partner sex) influenced choice. Individuals preferred to initiate grooming with partners of similar global rank, but this effect was driven by a bias towards partners with a high rank compared to other locally available options. Individuals also avoided grooming partners who had strong social relationships with at least one bystander. Results indicated flexible decision-making in grooming interactions in both species, based on a partner’s value given the local social environment. Viewing partner choice as a value-based decision-making process allows researchers to compare how different species solve similar social problems. Data Model1Data for Models 1-1 and 1-2Data Model2Data for Models 2-1 and 2-2Script Model 1 and 2Scripts necessary to analyse Models 1 and 2

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net2), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer’s disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.