Biology subjects

Holmes, A.

Publications and source records attributed to Holmes, A..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Mining underutilized whole-genome sequencing projects to improve 16S rRNA databases

Current approaches to interpreting 16S rDNA amplicon data are hampered by several factors. Among these are database inaccuracy or incompleteness, sequencing error, and biased DNA/RNA extraction. Existing 16S rRNA databases source the majority of sequences from deposited amplicon sequences, draft genomes, and complete genomes. Most of the draft genomes available are assembled from short reads. However, repeated ribosomal regions are notoriously difficult to assemble well from short reads, and as a consequence the short-read-assembled 16S rDNA region may be an amalgamation of different loci within the genome. This complicates high-resolution community analysis, as a draft genomes 16S rDNA sequence may be a chimera of multiple loci; in such cases, the draft-derived sequences in a database may not represent a 16S rRNA sequence as it occurs in biology. We present Focus16, a pipeline for improving 16S rRNA databases by mining NCBIs Sequence Read Archive for whole-genome sequencing runs that could be reassembled to yield additional 16S rRNA sequences. Using riboSeed (a genome assembly tool for correcting rDNA misassembly), Focus16 provides a way to augment 16S rRNA databases with high-quality re-assembled sequences. In this study, we augmented the widely-used SILVA 16S rRNA database with the novel sequences disclosed by Focus16 and re-processed amplicon sequences from several benchmarking datasets with DADA2. Using this augmented SILVA database increased the number of amplicon sequence variants that could be assigned taxonomic annotations. Further, fine-scale classification was improved by revealing ambiguities. We observed, for example, that amplicon sequence variants (ASVs) may be assigned to a specific genus where Focus16-correction would indicate that the ASV is represented in two or more genera. Thus, we demonstrate that improvements can be made to taxonomic classification by incorporating these carefully re-assembled 16S rRNA sequences, and we invite the community to expand our work to augment existing 16S rRNA reference databases such as SILVA, GreenGenes, and RDP.

bioinformatics↗

bioRxiv2020

Local and distributed cortical markers of effort expenditure during sustained goal pursuit

The adaptive adjustment of behavior in pursuit of desired goals is critical for survival. To accomplish this complex feat, individuals must weigh the potential benefits of a given course of action against time, energy, and resource costs. Prior research in this domain has greatly advanced understanding of the cortico-striatal circuits that support the anticipation and receipt of desired outcomes, characterizing core aspects of subjective valuation at discrete points in time. However, motivated goal pursuit is not a static or cost neutral process and the brain mechanisms that underlie individual differences in the dynamic updating of effort expenditure across time remain unclear. Here, 38 healthy right-handed participants underwent functional MRI (fMRI) while completing a novel paradigm to examine their willingness to exert physical effort over a prolonged trial, either to obtain monetary rewards or avoid punishments. During sustained goal pursuit, medial prefrontal cortex (mPFC) response scaled with trial-to-trial differences in effort expenditure as a function of both monetary condition and eventual task earnings. Multivariate pattern analysis (MVPA) searchlights were used to examine relations linking prior trial-level effort expenditure to subsequent brain responses to feedback. At reward feedback, whole-brain searchlights identified signals reflecting past effort expenditure in dorsal and ventral mPFC, encompassing broad swaths of frontoparietal and dorsal attention networks. These results suggest a core role for mPFC in scaling effort expenditure during sustained goal pursuit, with the subsequent tracking of effort costs following successful goal attainment extending to incorporate distributed brain networks that support executive functioning and externally oriented attention. Significance StatementHistorically, much of the research on subjective valuation has focused on discrete points in time. Here, we examine brain responses associated with willingness to exert physical effort during the sustained pursuit of desired goals. Our analyses reveal a distributed pattern of brain activity encompassing aspects of ventral mPFC that tracks with trial-level variability in effort expenditure. Indicating that the brain represents echoes of effort at the point of feedback, searchlight analyses revealed signals associated with past effort expenditure in broad swaths of dorsal and medial PFC. These data have important implications for the study of how the brains valuation mechanisms contend with the complexity of real-world dynamic environments with relevance for the study of behavior across health and disease.

neuroscience↗

bioRxiv2020

Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence

The transition from childhood to adolescence is marked by pronounced shifts in brain structure and function that coincide with the development of physical, cognitive, and social abilities. Prior work in adult populations has characterized the topographical organization of cortex, revealing macroscale functional gradients that extend from unimodal (somato/motor and visual) regions through the cortical association areas that underpin complex cognition in humans. However, the presence of these core functional gradients across development as well as their maturational course have yet to be established. Here, leveraging 378 resting-state fMRI scans from 190 healthy individuals aged 6-17 years, we demonstrate that the transition from childhood to adolescence is reflected in the gradual maturation of gradient patterns across the cortical sheet. In children, the overarching organizational gradient is anchored within unimodal cortex, between somato/motor and visual territories. Conversely, in adolescence the principal gradient of connectivity transitions into an adult-like spatial framework, with the default network at the opposite end of a spectrum from primary sensory and motor regions. The observed gradient transitions are gradually refined with age, reaching a sharp inflection point in 13- and 14-year-olds. Functional maturation was nonuniformly distributed across cortical networks. Unimodal networks reached their mature positions early in development, while association regions, in particular medial prefrontal cortex, reached a later peak during adolescence. These data reveal age-dependent changes in the macroscale organization of cortex and suggest the scheduled maturation of functional gradient patterns may be critically important for understanding how cognitive and behavioral capabilities are refined across development. SignificanceHuman abilities and behavior change dramatically across development, emerging from a cascade of hierarchical changes in brain circuitry. Here, we describe age-dependent shifts in the macroscale functional organization of cortex in childhood and adolescence. The characterization of functional connectivity patterns in children revealed an overarching organizational framework anchored within unimodal cortex, between somato/motor and visual regions. Conversely, in adolescents we observed a transition into an adult-like gradient that situates the default network at the opposite end of a spectrum from primary sensory and motor regions. This spatial framework emerged gradually with age, reaching a sharp inflection point at the transition from childhood to adolescence. These data reveal the presence of a developmental change from a functional motif first dominated by the distinction between sensory and motor systems, and then balanced through interactions with later-maturing aspects of association cortex that support more abstract cognitive functions.

neuroscience↗

bioRxiv2020

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. SignificanceThe widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

neuroscience↗

bioRxiv2020

A high-throughput genomic screen identifies a role for the plasmid-borne Type II secretion system of Escherichia coli O157:H7 (Sakai) in plant-microbe interactions

Food-borne illness arising from Shiga-toxigenic Escherichia coli (STEC) is often linked to consumption of fruit and vegetables as the bacteria have the ability to interact with plants and use them as alternative or secondary hosts. The initial stages of the interaction involve chemotaxis, attachment and potentially, responding to the early stages of microbe perception by the plant host. We used a high-throughput positive-selection approach to identify early interaction factors of E. coli O157:H7 isolate Sakai to spinach. A bacterial artificial chromosome (BAC) clone library was quantified by microarray hybridisation, and gene loci enrichment measured using a Bayesian hierarchical model. The screen of four successive rounds of short-term (2 hour) interaction with spinach roots produced in 115 CDS credible candidates, comprising seven contiguous genomic regions. Two candidate regions were selected for functional assessment: a chaperone-usher fimbrial gene cluster (loc6) and the pO157 plasmid-encoded type two secretion system (T2SS). Interaction of bacteria with spinach tissue was reduced in the absence of the pO157 plasmid, which was appeared to involve the T2SS EtpD secretin protein, whereas loss of loc6 did not impact interactions. The T2SS genes, etpD and etpC, were expressed at a plant-relevant temperature of 18 {degrees}C, and etpD expressed in planta by E. coli Sakai on spinach plants. Thus, a whole genome screening approach using a combination of computational modelling and functional assays has identified a novel function for STEC T2SS in interactions with plant tissue.

microbiology↗

bioRxiv2019

Phasic signaling in the bed nucleus of the stria terminalis during fear learning predicts within- and across-session cued fear expression

While results from many past studies have implicated the bed nucleus of the stria terminalis (BNST) in mediating the expression of sustained negative affect, recent studies have highlighted a more complex role for BNST that includes aspects of fear learning in addition to defensive responding. As BNST is thought to encode ambiguous or unpredictable threat, it seems plausible that it may be involved in encoding early cued fear learning, especially immediately following a first tone-shock pairing when the CS-US contingency is not fully apparent. To investigate this, we conducted in vivo electrophysiological recording studies to examine neural dynamics of BNST units during cued fear acquisition and recall. We identified two functionally distinct subpopulations of BNST neurons that encode the intertrial interval (ITI) and seem to contribute to within- and across-session fear learning. \"Ramping\" cell activity during cued fear acquisition parallels the increase in freezing expression as mice learn the CS-US contingency, while \"Phasic\" cells encode post-shock (USpost) periods (30 s following encounter with footshock) only during early trials. Importantly, the magnitude of Phasic unit responsivity to the first USpost period predicted not only freezing expression in response to the subsequent CS during acquisition, but also CS freezing evoked 24 hr later during CS retrieval. These findings suggest for the first time that BNST activity may serve as an instructive signal during cued fear learning.

neuroscience↗

bioRxiv2019

Generating Templates and Growth Charts for School-Aged Brain Development

Standard brain templates and growth charts provide an invaluable resource for basic science research, with the eventual goal of contributing to the clinical care of neuropsychiatric conditions. Here, we report on a protocol to generate MRI brain templates in children and adolescents at one-year intervals from 6-to-18 years of age, with their corresponding growth charts, using a large-scale neuroimaging data resource (948 brain images from China and United States). To assure that the brain templates and growth charts are reliable and accurate, we developed a refined pipeline consisting of template construction, image registration, brain area labeling and growth chart modeling. The pipeline comprises multiple modular workflows that can be used for multiple applications. In our approach, population- and age-specific templates were first constructed to avoid systemic bias in registration. Brain areas were then labeled based on the appropriate templates, and their morphological metrics were extracted for modeling associated growth curves. We implemented warp cost as a function of age differences between individual brains and template brains. A strong U-shaped cost function was revealed, indicating larger age differences are associated with greater registration errors. This validates the necessity of age-specific reference templates in pediatric brain imaging studies. Growth chart analyses revealed preferential shape differences between Chinese and US samples in lateral frontal and parietal areas, aspects of cortex which are most variable across individuals with regard to structure and function as well as associated behavioral performance. This growth distinction is largely driven by neurodevelopmental differences between Chinese and US age-specific brain templates. The pipeline together with the brain templates and charts are publicly available and integrated into the Connectome Computation System.

neuroscience↗

bioRxiv2019

Predicting transfer RNA gene activity from genome variation data

Transfer RNA (tRNA) genes are among the most highly transcribed genes in the genome due to their central role in protein synthesis. However, there is evidence for a broad range of gene expression across tRNA loci. This complexity, combined with difficulty in measuring transcript abundance and high sequence identity across transcripts, has severely limited our collective understanding of tRNA gene expression regulation and evolution. We establish sequence-based correlates to tRNA gene expression and develop a tRNA gene classification method that does not require, but benefits from comparative genomic information, and achieves accuracy comparable to molecular assays. We observe that guanine+cytosine (G+C) content and CpG density surrounding tRNA loci is exceptionally well correlated with tRNA gene activity, supporting a prominent regulatory role of the local genomic context in combination with internal sequence features. We use our tRNA gene activity predictions in conjunction with a comprehensive tRNA gene ortholog set spanning 29 placental mammals to infer the frequency of changes to tRNA gene expression among orthologs. Our method adds an important new dimension to tRNA annotation and will help focus the study of natural tRNA variants. Its simplicity and robustness enables facile application to other clades and timescales, as well as exploration of functional diversification of tRNAs and other large gene families.

bioinformatics↗

bioRxiv2019

Evaluating a digital sepsis alert in a London multi-site hospital network: a natural experiment using electronic health record data

ObjectiveTo determine the impact of a digital sepsis alert on patient outcomes in a UK multi-site hospital network.\n\nMethodsA natural experiment utlising the phased introduction of a digital sepsis alert into a multi-site hospital network. Sepsis alerts were either visible to clinicans (the intervention group) or running silently and not visible (the control group). Inverse probability of treatment weighted multivariable logistic regression was used to estimate the effect of the intervention on patient outcomes.\n\nOutcomes: In-hospital 30-day mortality (all inpatients), prolonged hospital stay ([≥]7 days) and timely antibiotics ([≤]60 minutes of the alert) for patients who alerted in the Emergency Department.\n\nResultsThe introduction of the alert was associated with lower odds of death (OR:0.76; 95%CI:(0.70, 0.84) n=21,183); lower odds of prolonged hospital stay [≥]7 days (OR:0.93; 95%CI:(0.88, 0.99) n=9988); and in patients who required antibiotics, an increased odds of receiving timely antibiotics (OR:1.71; 95%CI:(1.57,1.87) n=4622).\n\nDiscussionCurrent evidence that digital sepsis alerts are effective is mixed. In this large UK study a digital sepsis alert has been shown to be associated with improved outcomes, including timely antibiotics, which may suggest a causal pathway. It is not known whether the presence of alerting is responsible for improved outcomes, or whether the alert acted as a useful driver for quality improvement initiatives.\n\nConclusionsThese findings strongly suggest that the the introduction of a network-wide digital sepsis alert is associated with improvements in patient outcomes, demonstrating that digital based interventions can be successfully introduced and readily evaluated.\n\nFundingImperial NIHR Biomedical Research Centre: NIHR-BRC-P68711.

epidemiology↗

bioRxiv2019

The hypoxic ventilatory response is facilitated by the activation of Lkb1-AMPK signalling pathways downstream of the carotid bodies

We recently demonstrated that the role of the AMP-activated protein kinase (AMPK), a ubiquitously expressed enzyme that governs cell-autonomous metabolic homeostasis, has been extended to system-level control of breathing and thus oxygen and energy (ATP) supply to the body. Here we assess the contribution to the hypoxic ventilatory response (HVR) of two upstream kinases that govern the activities of AMPK. Lkb1, which activates AMPK in response to metabolic stress and CaMKK2 which mediates the alternative Ca2+-dependent mechanism of AMPK activation. HVRs remained unaffected in mice with global deletion of the CaMKK2 gene. By contrast, HVRs were markedly attenuated in mice with conditional deletion of LKB1 in catecholaminergic cells, including carotid body type I cells and brainstem respiratory networks. In these mice hypoxia evoked hypoventilation, apnoea and Cheyne-Stokes-like breathing, rather than hyperventilation. Attenuation of HVRs, albeit less severe, was also conferred in mice carrying [~]90% knockdown of Lkb1 expression. Carotid body afferent input responses were retained following either [~]90% knockdown of Lkb1 or AMPK deletion. In marked contrast, LKB1 deletion virtually abolished carotid body afferent discharge during normoxia, hypoxia and hypercapnia. We conclude that Lkb1 and AMPK, but not CaMKK2, facilitate HVRs at a site downstream of the carotid bodies.

physiology↗

bioRxiv2018

Functional connectomics of affective and psychotic pathology

Converging evidence indicates that groups of patients with nominally distinct psychiatric diagnoses are not separated by sharp or discontinuous neurobiological boundaries. In healthy populations, individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of transdiagnostic symptom profiles observed in psychiatric patients may map onto detectable patterns of network function. To examine the manner through which neurobiological variation might underlie clinical presentation we obtained functional magnetic resonance imaging (fMRI) data from over 1,000 individuals, including 210 diagnosed with a primary psychotic disorder or affective psychosis (bipolar disorder with psychosis and schizophrenia or schizoaffective disorder), 192 presenting with a primary affective disorder without psychosis (unipolar depression, bipolar disorder without psychosis), and 608 demographically and data-quality matched healthy comparison participants recruited through a large-scale study of brain imaging and genetics. Here, we examine variation in functional connectomes across psychiatric diagnoses, finding striking evidence for disease connectomic \"fingerprints\" that are commonly disrupted across distinct forms of pathology and appear to scale as a function of illness severity. Conversely, other properties of network connectivity were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. This work allows us to establish key biological and clinical features of the functional connectomes of severe mental disease.\n\nSIGNIFICANCE STATEMENTHistorically, most research on the biological origins of psychiatric illness has focused on individual diagnostic categories, studied in isolation. Mounting evidence suggests nominally distinct psychiatric diagnoses are not separated by clear neurobiological boundaries. Here, we derive functional connectomic signatures in over 1,000 individuals including patients presenting with specific categories of impairment (psychosis), clinical diagnoses, or severity of illness as reflected in treatment seeking. Our analyses revealed features of connectome functioning that are commonly disrupted across distinct forms of pathology, scaling with clinical severity. Conversely, other aspects of network connectivity were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. These data have important implications for the establishment of functional connectome fingerprints of severe mental disease.

neuroscience↗

bioRxiv2018

Do Deep Neural Networks Outperform Kernel Regression for Functional Connectivity Prediction of Behavior?

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.

neuroscience↗

bioRxiv2018

Simultaneous single-channel multiplex and quantification of carbapenem-resistant genes using multidimensional standard curves

Multiplexing and absolute quantification of nucleic acids, both have, in their own right, significant and extensive use in biomedical related fields, especially in point-of-care applications. Currently, the ability to detect several nucleic acid targets in a single-reaction scales linearly with the number of targets; an expensive and time-consuming feat. Here, we propose a new methodology based on multidimensional standard curves that extends the use of real-time PCR data obtained by common qPCR instruments. By applying this novel methodology, we achieve simultaneous single-channel multiplexing and enhanced quantification of multiple targets using only real-time amplification data. This is obtained without the need of fluorescent probes, agarose gels, melting curves or sequencing analysis. Given the importance and demand for tackling challenges in antimicrobial resistance, the proposed method is applied to the four most prominent carbapenem-resistant genes: blaOXA-48, blaNDM, blaVIM and blaKPC, which account for 97% of the UKs reported carbapenemase-producing Enterobacteriaceae.

microbiology↗

bioRxiv2018

Molecular mimicry in deoxy-nucleotide catalysis: the structure of Escherichia coli dGTPase reveals the molecular basis of dGTP selectivity

Deoxynucleotide triphosphate triphosphyohydrolyases (dNTPases) play a critical role in cellular survival and DNA replication through the proper maintenance of cellular dNTP pools by hydrolyzing dNTPs into deoxynucleosides and inorganic triphosphate (PPPi). While the vast majority of these enzymes display broad activity towards canonical dNTPs, exemplified by Sterile Alpha Motif (SAM) and Histidine-aspartate (HD) domain-containing protein 1 (SAMHD1), which blocks reverse transcription of retroviruses in macrophages by maintaining dNTP pools at low levels, Escherichia coli (Ec)-dGTPase is the only known enzyme that specifically hydrolyzes dGTP. However, the mechanism behind dGTP selectivity is unclear. Here we present the free-, ligand (dGTP)- and inhibitor (GTP)-bound structures of hexameric E. coli dGTPase. To obtain these structures, we applied UV-fluorescence microscopy, video analysis and highly automated goniometer-based instrumentation to map and rapidly position individual crystals randomly-located on fixed target holders, resulting in the highest indexing-rates observed for a serial femtosecond crystallography (SFX) experiment. The structure features a highly dynamic active site where conformational changes are coupled to substrate (dGTP), but not inhibitor binding, since GTP locks dGTPase in its apo form. Moreover, despite no sequence homology, dGTPase and SAMHD1 share similar active site and HD motif architectures; however, dGTPase residues at the end of the substrate-binding pocket mimic Watson Crick interactions providing Guanine base specificity, while a 7 [A] cleft separates SAMHD1 residues from dNTP bases, abolishing nucleotide-type discrimination. Furthermore, the structures sheds light into the mechanism by which long distance binding (25 [A]) of single stranded DNA in an allosteric site primes the active site by conformationally \"opening\" a tyrosine gate allowing enhanced substrate binding.\n\nSignificance StatementdNTPases play a critical role in cellular survival through maintenance of cellular dNTP. While dNTPases display activity towards dNTPs, such as SAMHD1 -which blocks reverse transcription of HIV-1 in macrophages- Escherichia coli (Ec)-dGTPase is the only known enzyme that specifically hydrolyzes dGTP. Here we use novel free electron laser data collection to shed light into the mechanisms of (Ec)-dGTPase selectivity. The structure features a dynamic active site where conformational changes are coupled to dGTP binding. Moreover, despite no sequence homology between (Ec)-dGTPase and SAMHD1, both enzymes share similar active site architectures; however, dGTPase residues at the end of the substrate-binding pocket provide dGTP specificity, while a 7 [A] cleft separates SAMHD1 residues from dNTP.

biophysics↗

bioRxiv2018

Easily phylotyping E. coli via the EzClermont web app and command-line tool.

The Clermont PCR method of phylotyping Escherichia coli has remained a useful classification scheme despite the proliferation of higher-resolution sequence typing schemes. We have implemented an in silico Clermont PCR method as both a web app and as a command-line tool to allow researchers to easily apply this phylotyping scheme to genome assemblies easily. Availability and ImplementationEzClermont is available as a web app at http://www.ezclermont.org. For local use, EzClermont can be installed with pip or installed from the source code at https://github.com/nickp60/ezclermont. All analysis was done with version 0.4.0. Contactn.waters4@nuigalway.ie, leighton.pritchard@strath.ac.uk Supplementary informationTable S1: test dataset; S2: validation dataset; S3: results.

bioinformatics↗

bioRxiv2018

A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

neuroscience↗

bioRxiv2017

The NSIGHT1 Randomized Controlled Trial: Rapid Whole Genome Sequencing for Accelerated Etiologic Diagnosis in Critically Ill Infants

ImportanceGenetic disorders, including congenital anomalies, are a leading cause of morbidity and mortality in infants, especially in neonatal and pediatric intensive care units (NICU and PICU). While genomic sequencing is useful for diagnosis of genetic diseases, results are usually reported too late to guide inpatient management.\n\nObjectiveTo test the hypothesis that rapid whole genome sequencing (rWGS) increases the proportion of infants in NICUs and PICUs receiving a genetic diagnosis within 28 days.\n\nDesignAn investigator-initiated, partially blinded, pragmatic, randomized controlled study with enrollment from October 2014 - June 2016, and follow up until December 2016.\n\nSettingA regional neonatal and pediatric intensive care unit in a tertiary referral childrens hospital.\n\nParticipantsSixty five of 129 screened families with infants aged less than four months, in neonatal and pediatric intensive care units, and with illnesses of unknown etiology, completed the study.\n\nInterventionParent and infant trio rWGS.\n\nMain Outcome and MeasureThe hypothesis and end-points were formulated a priori. The primary end-point was rate of genetic diagnosis within 28 days of enrollment or first standard test order.\n\nResultsTwenty six female proband infants, 37 male infants, and two infants of undetermined sex were randomized to receive rWGS plus standard tests (n=32, cases) or standard tests alone (n=33, controls). The study was terminated early due to loss of equipoise: 63% (21) controls received genomic sequencing as standard tests. Nevertheless, intention to treat analysis showed the rate of genetic diagnosis within 28 days to be higher in cases (31%, ten of 32) than controls (3%, one of 33; difference, 28% [95% CI, 10% to 46%]; p=0.003). Among infants enrolled in the first 25 days of life, the rate of neonatal diagnosis was higher in cases (32%, seven of 22) than controls (0%, zero of 23; difference, 32% [95% CI, 11% to 53%]; p=0.004). Age at diagnosis (median in cases 25 days, range 14-90 days vs median in controls 130 days, range 37-451) and time to diagnosis (median in cases thirteen days, range 1-84 days vs median in controls 107 days, range 21-429 days) were significantly less in cases than controls (p=0.04).\n\nCONCLUSIONSrWGS increased the proportion of infants in a regional NICU and PICU who received a timely diagnosis of a genetic disease. Additional, adequately powered studies are needed to determine whether accelerated diagnosis is associated with improved outcomes in this setting. ClinicalTrials.gov Identifier: NCT02225522.

clinical trials↗

bioRxiv2017

Controlling for Intra-Subject and Inter-Subject Variability in Individual-Specific Cortical Network Parcellations

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 minutes) showed comparable generalizability as parcellations estimated by two state-of-the-art methods using five sessions (50 minutes). We also showed that behavioral phenotypes across cognition, personality and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

neuroscience↗