Biology subjects

Brown, S.

Publications and source records attributed to Brown, S..

10 recordsLinked to original sources

bioRxiv2018

Predicting One-Year Outcome in First Episode Psychosis using Machine Learning

Lay SummaryO_ST_ABSEvidence before this studyC_ST_ABSOur knowledge of factors which predict outcome in first episode psychosis (FEP) is incomplete. Poor premorbid adjustment, history of developmental disorder, symptom severity at baseline and duration of untreated psychosis are the most replicated predictors of poor clinical, functional, cognitive, and biological outcomes. Yet, such group level differences are not always replicated in individuals, nor can observational results be clearly equated with causation. Advanced machine learning techniques have potential to revolutionise medicine by looking at causation and the prediction of individual patient outcome. Within psychiatry, Koutsouleris et al employed machine learning to predict 4- and 52-week functional outcome in FEP to a 75% and 73.8% test-fold balanced accuracy on repeated nested internal cross-validation. The authors suggest that before employing a machine learning model \"into real-world care, replication is needed in external first episode samples\".\n\nAdded value of this studyWe believe our study to be the first externally validated evidence, in a temporally and geographically independent cohort, for predictive modelling in FEP at an individual patient level. Our results demonstrate the ability to predict both symptom remission and functioning (in employment, education or training (EET)) at one-year. The performance of our EET model was particularly robust, with an ability to accurately predict the one-year EET outcome in more than 85% of patients. Regularised regression results in sparse models which are uniquely interpretable and identify meaningful predictors of recovery including specific individual PANSS items, and social support. This builds on existing studies of group-level differences and the elegant work of Koutsouleris et al.\n\nImplications of all the available evidenceWe have demonstrated the externally validated ability to accurately predict one-year symptomatic and functional status in individual patients with FEP. External validation in a plausibly related temporally and geographically distinct population assesses model transportability to an untested situation rather than simply reproducibility alone. We propose that our results represent important and exciting progress in unlocking the potential of predictive modelling in psychiatric illness. The next step prior to implementation into routine clinical practice would be to establish whether, by the accurate identification of individuals who will have poor outcomes, we can meaningful intervene to improve their prognosis.\n\nAbstractO_ST_ABSBackgroundC_ST_ABSEarly illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes.\n\nWe use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at 1 year.\n\nMethods83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009.\n\nElastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at 1 year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation.\n\nOutcomesAfter excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS.\n\nInterpretationUsing advanced statistical machine learning techniques, we provide the first externally validated evidence for the ability to predict 1-year EET status and symptom remission in FEP patients.\n\nFundingThe authors acknowledge financial support from NHS Research Scotland, the Chief Scientist Office, the Wellcome Trust, and the Scottish Mental Health Research Network.

neuroscience↗

bioRxiv2018

ITPase Deficiency Causes Martsolf Syndrome With a Lethal Infantile Dilated Cardiomyopathy

Martsolf syndrome is characterized by congenital cataracts, postnatal microcephaly, developmental delay, hypotonia, short stature and biallelic hypomorphic mutations in either RAB3GAP1 or RAB3GAP2. Through genetic analysis of 85 unrelated \"mutation negative\" probands referred with Martsolf syndrome we identified two individuals with different homozygous null mutations in ITPA, the gene encoding inosine triphosphate pyrophosphatase (ITPase). The probands reported here each presented with a lethal and highly distinctive disorder; Martsolf syndrome with infantile-onset dilated cardiomyopathy. Severe ITPase-deficiency has been previously reported with infantile epileptic encephalopathy (MIM 616647). ITPase acts to prevent incorporation of inosine bases (rl/dl) into RNA and DNA. In Itpa-null cells, dI was undetectable in genomic DNA. dI could be identified at a low level in mtDNA but this was not associated with detectable mitochondrial genome instability, mtDNA depletion or biochemical dysfunction of the mitochondria. rI accumulation was detectable in lymphoblastoid RNA from an affected individual. In Itpa-null mouse embryos rI was detectable in the brain and kidney with the highest level seen in the embryonic heart (rI at 1 in 385 bases). Transcriptome and proteome analysis in mutant cells revealed no major differences with controls. The rate of transcription and the total amount of cellular RNA also appeared normal. rI accumulation in RNA - and by implication rI production - correlates with the severity of organ dysfunction in ITPase deficiency but the basis of the cellulopathy remains cryptic. While we cannot exclude cumulative minor effects, there are no major anomalies in the production, processing, stability and/or translation of mRNA.\n\nAuthor SummaryNucleotide triphosphate bases containing inosine, ITP and dITP, are continually produced within the cell as a consequence of various essential biosynthetic reactions. The enzyme inosine triphosphate pyrophosphatase (ITPase) scavenges ITP and dITP to prevent their incorporation into RNA and DNA. Here we describe two unrelated families with complete loss of ITPase function as a consequence of disruptive mutations affecting both alleles of ITPA, the gene that encodes this protein. Both of the families have a very distinctive and severe combination of clinical problems, most notably a failure of heart muscle that was lethal in infancy or early childhood. They also have features of another rare genetic disorder affecting the brain and the eyes called Martsolf syndrome. We could not detect any evidence of dITP accumulation in genomic DNA from the affected individuals. A low but detectable level of inosine was present in the mitochondrial DNA but this did not have any obvious detrimental effect. The inosine accumulation in RNA was detectable in the patient cells. We made both cellular and animal models that were completely deficient in ITPase. Using these reagents we could show that the highest level of inosine accumulation into RNA was seen in the embryonic mouse heart. In this tissue more than 1 in 400 bases in all RNA in the cell was inosine. In normal tissues inosine is almost undetectable using very sensitive assays. The inosine accumulation did not seem to be having a global effect on the balance of RNA molecules or proteins.

genetics↗

bioRxiv2018

Imputation of Behavioral Candidate Gene Repeat Polymorphisms in 486,551 Publicly-Available UK Biobank Individuals.

Some of the most widely studied polymorphisms in psychiatric genetics include variable number tandem repeat polymorphisms (VNTRs) in SLC6A3, DRD4, SLC6A4, and MAOA. While initial findings suggested large effects, their importance with respect to psychiatric phenotypes is the subject of much debate with broadly conflicting results. Despite broad interest, these loci remain absent from the largest available samples, such as the UK Biobank, limiting researchers ability to test these contentious hypotheses rigorously in large samples. Here, using two independent reference datasets, we report out-of-sample imputation accuracy estimates of >0.96 for all four VNTR polymorphisms and one modifying SNP, depending on the reference and target dataset. We describe the imputation procedures of these candidate polymorphisms in 486,551 UK Biobank individuals, and have made the imputed polymorphism data available to UK Biobank researchers. This resource, provided to the community, will allow the most rigorous tests to-date of the roles of these polymorphisms in behavioral and psychiatric phenotypes.

genetics↗

bioRxiv2018

Identification of biomarker and HPV strain for Cervical Cancer from pre-existing RNAseq data

Human papillomavirus (HPV) increased the risk of afflicting cervical cancer. Among over a hundred strains, HPV-16 and HPV-18 caused 70% of cervical cancers and precancerous cervical lesions (WHO 2018). To reveal the profile of HPV strains, HPViewer is designed by Hao et al. (2018) for metagenomic or human genomic shotgun sequencing data analysis. The application of HPViewer in detecting HPV strains in RNA sequencing data was assessed and results were communicated in the table. The performance of HPViewer in analyzing RNA sequencing data from multiple sources, demonstrated the potential of enlarging the application of HPViewer to RNA sequencing data. Furthermore, we attempted to verify the capability of a potential biomarker p16INK4a in detecting cervical cancer from precancerous lesions. Considering the protein nature of this biomarker, the experiment was designed to detect the differentially expressed gene, associating with this protein function group, in RNA-seq data from two articles. Compare to the findings from Royse et al. (2014), confirmatory result was reproduced that comparisons between both groups yielded insignificant outcome. Since data from single article was insufficient to provide meaningful clue, final dataset was collected from multiple sources. The results were compromised by batch effect, but they supported p16INK4a to be a prospective biomarker for cervical cancer diagnosis.

bioinformatics↗

bioRxiv2018

Selection of horizontal gene transfer through public good production

In bacteria, cooperative genes encoding public good molecules are preferentially located on mobile genetic elements (MGEs), and horizontal transfer of MGEs favours the maintenance of public good cooperation. The rate of horizontal transfer itself can evolve in response to selective pressures acting on both MGEs and bacterial hosts: benefits and costs of infectious spread, but also indirect effects of MGE genes to the host. We show here that carriage of public good genes on MGEs can generate another indirect selection for MGE transfer. Transfer increases public good production and, when relatedness is sufficiently high, public goods benefit preferentially genotypes with high transfer ability. Both our simulations and experiments indicate that transfer is not required to occur among kin, provided that public goods still benefit kin. Public good gene mobility thus aligns the interests of chromosomes and MGEs concerning transfer, promoting gene exchange among bacteria.

evolutionary biology↗

bioRxiv2018

Parkinsons disease and bacteriophages as its overlooked contributors

Recent studies suggest that alterations in the gut phagobiota may contribute to pathophysiological processes in mammals; however, the association of bacteriophage community structure with Parkinsons disease (PD) has not been yet characterized. Towards this end, we used a published dataset to analyse bacteriophage composition and determine the phage/bacteria ratio in faecal samples from drug-naive PD patients and healthy participants. Our analyses revealed significant alterations in the representation of certain bacteriophages in the phagobiota of PD patients. We identified shifts of the phage/bacteria ratio in lactic acid bacteria known to produce dopamine and regulate intestinal permeability, which are major factors implicated in PD pathogenesis. Furthermore, we observed the depletion of Lactococcus spp. in the PD group, which was most likely due to the increase of lytic c2-like and 936-like lactococcal phages frequently present in dairy products. Our findings add bacteriophages to the list of possible factors associated with the development of PD, suggesting that gut phagobiota composition may serve as a diagnostic tool as well as a target for therapeutic intervention, which should be confirmed in further studies. Our results open a discussion on the role of environmental phages and phagobiota composition in health and disease.

microbiology↗

bioRxiv2018

Haemolymph removal by the parasite Varroa destructor can trigger the proliferation of the Deformed Wing Virus in mite infested bees (Apis mellifera), contributing to enhanced pathogen virulence

The association between the Deformed Wing Virus and the parasitic mite Varroa destructor has been identified as a major cause of worldwide honey bee colony losses. The mite acts as a vector of the viral pathogen and can trigger its replication in infected bees. However, the mechanistic details underlying this tripartite interaction are still poorly defined, and, in particular, the causes of viral proliferation in mite infested bees.\n\nHere we develop and test a novel hypothesis - grounded in ecological predator-prey theory - that mite feeding destabilizes viral immune control through the removal of both viral prey and immune predators, triggering uncontrolled viral replication. Consistent with this hypothesis, we show that experimental removal of increasing volumes of haemolymph from individual bees results in increasing viral densities. In contrast, we find no support for alternative proposed mechanisms of viral expansion via mite immune-suppression or within-host viral evolution.\n\nOverall, these results provide a new model for the mechanisms driving pathogen-parasite interactions in bees, which ultimately underpin honey bee health decline and colony losses.

ecology↗

bioRxiv2018

Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs. Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelialmesenchymal transition through modulation of TGF{beta} signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment, and 12 lncRNAs related to cancer-associated fibroblasts. At least one member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype. Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.\n\nAuthor SummaryThe established view of protein production is that genomic DNA is transcribed into RNA, which is then translated into protein. Proteins play a critical role in shaping the function of each individual cell in the human body yet they represent less than 2% of human genomic sequence whilst up to 90% of the genome is transcribed. To explain this disparity, the existence of thousands of long non-coding RNAs (lncRNAs) has emerged that do not encode proteins but perform function as an RNA molecule. Most lncRNAs have yet to be assigned a specific biological role, so to address this we apply a novel computational approach to characterise the function of >800 lncRNAs through consistent association with protein coding genes across multiple cancer types. By doing so, we discover 16 \"modules\" of closely related lncRNAs that share broad functional themes, the most compelling of which consists of 12 lncRNAs that could regulate activation of specific cells neighbouring the tumour, leading to accelerated tumour progression and invasion. Overall, the study provides the most robust view of the lncRNA-protein coding gene landscape to date, adding to growing evidence that lncRNAs are key regulators of cancer, and have therapeutic potential comparable to proteins.

bioinformatics↗

bioRxiv2017

A genome-wide RNAi screen identifies MASK as a positive regulator of cytokine receptor stability

In order for cells to sense and thus respond to their environment, they require transmembrane receptors, which bind extracellular ligands and then transduce this signal within the cell. A subset of receptors, with single-pass transmembrane domains are known as cytokine receptors and act via the Janus Kinase and Signal Transducer and Activator of Transcription (JAK/STAT) pathway. These receptors are essential for processes such as haematopoiesis, immune responses and tissue homeostasis. In order to transduce ligand activation, cytokine receptors must dimerise. However, mechanisms regulating their dimerisation are largely unknown. In order to better understand the processes regulating cytokine receptor levels, activity and dimerisation, we used the highly conserved JAK/STAT pathway in Drosophila, which acts via a single receptor, known as Domeless. We have performed a genome-wide RNAi screen in Drosophila cells, identifying MASK as a positive regulator of Domeless dimerisation and protein levels. We show that MASK is able to regulate JAK/STAT signalling both in vitro and in vivo. We go on to show that MASK is able to bind to Domeless via its Ankyrin repeat domains and alters the stability of the receptor. Finally, we extend our observations to the human homologue, ANKHD1, and demonstrate functional conservation, with ANKHD1 able to regulate JAK/STAT signalling and the levels of a subset of pathway receptors in human cells. Taken together, we have identified MASK as a conserved regulator of cytokine receptor levels, which may have implications for human health.

cell biology↗

bioRxiv2017

A lineage-related reciprocal inhibition circuitry for sensory-motor action selection

The insect central complex and vertebrate basal ganglia are forebrain centres involved in selection and maintenance of behavioural actions. However, little is known about the formation of the underlying circuits, or how they integrate sensory information for motor actions. Here, we show that paired embryonic neuroblasts generate central complex ring neurons that mediate sensory-motor transformation and action selection in Drosophila. Lineage analysis resolves four ring neuron subtypes, R1-R4, that form GABAergic inhibition circuitry among inhibitory sister cells. Genetic manipulations, together with functional imaging, demonstrate subtype-specific R neurons mediate the selection and maintenance of behavioural activity. A computational model substantiates genetic and behavioural observations suggesting that R neuron circuitry functions as salience detector using competitive inhibition to amplify, maintain or switch between activity states. The resultant gating mechanism translates facilitation, inhibition and disinhibition of behavioural activity as R neuron functions into selection of motor actions and their organisation into action sequences.

neuroscience↗