Analysis of gene expression and DNA methylation in a cognitive phenotype model of schizophrenia

<p>The underpinning hypothesis of this study is that the environmental insults implicated in schizophrenia cause epigenetic changes that trigger deleterious gene expression, resulting in deviations from normal neurodevelopment. The behavioural abnormalities in schizophrenia can be grouped into the three common classes of symptoms: positive, negative, and cognitive. Cognitive symptoms are symptoms that impair cognitive processing and have detrimental effects on individuals with schizophrenia. Maternal immune activation refers to a rat model that stimulates a maternal immune system with an infection or infectious-like stimulus resulting in adverse phenotypes. A cognitive phenotype, maternal immune activation (MIA) model of schizophrenia was employed to use epigenetic markers to discover what deleterious genes drive the cognitive deficits phenotype. Previous work has discerned many changes in gene expression that are implicated in schizophrenia. A hypothesis-driven approach was utilized to determine whether previously studied candidate genes are relevant in the cognitive symptoms of schizophrenia in this cognitive-phenotype model. It was found that prenatal treatment of lipopolysaccharide (LPS) (which is the major outer membrane component of gram-negative bacteria and mimics bacterial infection) on prenatal day 10 and 11 led to changes in mRNA levels in the prefrontal cortex of adolescent rats. Typically, an increase in the amount of transcript in the LPS condition compared to the saline condition, or a greater variability in the amount of transcript between replicates in the LPS condition than the saline condition, was observed. Statistical analysis revealed that these changes did not met statistical significance. To build towards a whole genome DNA methylation analysis, two discrete approaches were used. The first utilized bisulfite modification and investigated changes in candidate genes as a precursor to genome-wide BS-sequencing. DNA methylation was measured across CpG rich regions and an absence of DNA methylation was detected in these regions in both the LPS and saline conditions in the candidate genes. The second approach utilized a long-read sequencing platform to establish the feasibility of a bisulfite conversion-free method for whole-genome DNA methylation approach within our lab. Through the establishment of this method factors that affect the reliability, quality, and accuracy of the final sequencing product were explored. Many of which were in the downstream-from-sequencing, data analysis component of the process. Discoveries were also made regarding how much data would be needed to make direct DNA methylation detection feasible. The data presented here demonstrated that the cognitive-phenotype MIA model had altered gene expression correlating with previously measured behavioural cognitive deficits in the prefrontal cortex in genes that were known to be associated with schizophrenia. To extend this further, a whole genome approach would be needed to discover novel drivers of the phenotype. In the current study, headway was made towards the development and establishment of a whole genome DNA methylation detection method to further this continued aim.</p>

Analysis of gene expression and DNA methylation in a cognitive phenotype model of schizophrenia

<p>The underpinning hypothesis of this study is that the environmental insults implicated in schizophrenia cause epigenetic changes that trigger deleterious gene expression, resulting in deviations from normal neurodevelopment. The behavioural abnormalities in schizophrenia can be grouped into the three common classes of symptoms: positive, negative, and cognitive. Cognitive symptoms are symptoms that impair cognitive processing and have detrimental effects on individuals with schizophrenia. Maternal immune activation refers to a rat model that stimulates a maternal immune system with an infection or infectious-like stimulus resulting in adverse phenotypes. A cognitive phenotype, maternal immune activation (MIA) model of schizophrenia was employed to use epigenetic markers to discover what deleterious genes drive the cognitive deficits phenotype. Previous work has discerned many changes in gene expression that are implicated in schizophrenia. A hypothesis-driven approach was utilized to determine whether previously studied candidate genes are relevant in the cognitive symptoms of schizophrenia in this cognitive-phenotype model. It was found that prenatal treatment of lipopolysaccharide (LPS) (which is the major outer membrane component of gram-negative bacteria and mimics bacterial infection) on prenatal day 10 and 11 led to changes in mRNA levels in the prefrontal cortex of adolescent rats. Typically, an increase in the amount of transcript in the LPS condition compared to the saline condition, or a greater variability in the amount of transcript between replicates in the LPS condition than the saline condition, was observed. Statistical analysis revealed that these changes did not met statistical significance. To build towards a whole genome DNA methylation analysis, two discrete approaches were used. The first utilized bisulfite modification and investigated changes in candidate genes as a precursor to genome-wide BS-sequencing. DNA methylation was measured across CpG rich regions and an absence of DNA methylation was detected in these regions in both the LPS and saline conditions in the candidate genes. The second approach utilized a long-read sequencing platform to establish the feasibility of a bisulfite conversion-free method for whole-genome DNA methylation approach within our lab. Through the establishment of this method factors that affect the reliability, quality, and accuracy of the final sequencing product were explored. Many of which were in the downstream-from-sequencing, data analysis component of the process. Discoveries were also made regarding how much data would be needed to make direct DNA methylation detection feasible. The data presented here demonstrated that the cognitive-phenotype MIA model had altered gene expression correlating with previously measured behavioural cognitive deficits in the prefrontal cortex in genes that were known to be associated with schizophrenia. To extend this further, a whole genome approach would be needed to discover novel drivers of the phenotype. In the current study, headway was made towards the development and establishment of a whole genome DNA methylation detection method to further this continued aim.</p>

Sarcopenic obesity is associated with telomere shortening: findings from the NHANES 1999–2002

Sarcopenic obesity (SO) is characterised by the concurrent presence of sarcopenia and excess adiposity. Telomere shortening has been associated with sarcopenia and obesity alone but the association between SO and telomere length (TL) has not been investigated. This study aimed to investigate SO and TL in an adult population. Data were from 5397 individuals (mean age = 44.7 years, 51.3% male) enrolled in the National Health and Nutrition Examination Survey. Body composition (BC) was assessed by Dual Energy X-Ray Absorptiometry. Two models were used to assess SO: a BC model including four phenotypes derived from the combination of high or low adiposity and muscle mass; and, a truncal fat mass to appendicular skeletal mass ratio (TrFM/ASM). TL was assessed using quantitative polymerase chain reaction and expressed as base pairs. The mean TL, relative to the reference DNA, was calculated and expressed as the mean T/S ratio. A General Linear Model was applied to determine associations between TL for SO. In adjusted analysis, only individuals with SO, defined as the presence of high adiposity-low muscle mass (four-phenotype model), had significantly shorter telomeres (p = 0.05) than the reference group (i.e. low adiposity-high muscle mass), with a mean T/S ratio of 1.02 (95%CI: 0.98–1.05) compared to 1.05 (95%CI: 1.01–1.09), respectively. TrFM/ASM was not associated with TL. Preliminary findings suggest that sarcopenia and obesity may act synergistically to shorten telomeres.

A Genotype-to-Phenotype Modeling Framework to Predict Human Pathogenicity of Novel Coronaviruses

Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a phenotype-of-concern has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone has previously been considered un-tenable with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. Building from our prior work developing a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks, and leveraging a taxonomic group-shuffle-split paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level capable of accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (SADS-CoV) genome sequences as non-human pathogens. LASSO feature selection identified several degenerate nucleotide predictor motifs with high model coefficients for the human pathogen class that were present across widely disparate classes of coronaviruses. However, these motifs differed in which genes they were present in, what specific codons were used to encode them, and what the translated amino acid motif was. This emphasizes the importance of a phenetic view of emerging pathogenic RNA viruses, as opposed to the canonical phylogenetic interpretations most-commonly used to track and manage viral zoonoses. Applying our model to more recent Orthocoronavirinae genomes deposited since October 2018 yields a novel contextual view of pathogen-potential across bat-related, canine-related, porcine-related, and rodent-related coronaviruses and critical adaptations which may have contributed to the emergence of the pandemic SARS-CoV-2 virus. Finally, we discuss the utility of these predictive models (and their associated predictor motifs) to novel biosurveillance protocols that substantially increase the pound-for-pound information content of field-collected sequencing data and make a strong argument for the necessity of routine collection and sequencing of zoonotic viruses.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the “phenotype to genotype” and “genotype to phenotype” concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Description and clinical validation of a real-time AI diagnostic companion for fetal ultrasound examination

Objective: To describe a real-time decision support system (DSS), named SONIO, to assist ultrasound-based prenatal diagnosis and to assess its performance using a clinical database of precisely phenotyped postmortem examinations. Population and Methods: This DSS is knowledge-based and comprises a dedicated thesaurus of 294 syndromes and diseases. It operates by suggesting, at each step of the ultrasound examination, the best next symptom to check for in order to optimize the diagnostic pathway to the smallest number of possible diagnoses. This assistant was tested on a single-center database of 251 cases of postmortem phenotypes with a definite diagnosis. Adjudication of discordant diagnoses was made by a panel of external experts. The primary outcome was a target concordance rate >90% between the postmortem diagnosis and the top-7 diagnoses given by SONIO when providing the full phenotype as input. Secondary outcomes included concordance for the top-5 and top-3 diagnoses; We also assessed a '1-by-1' model, providing only the anomalies sequentially prompted by the system, mimicking the use of the software in a real-life clinical setting. Results: The validation database covered 96 of the 294 (32.65%) syndromes and 79% of their overall prevalence in the SONIO thesaurus. The adjudicators discarded 42/251 cases as they were not amenable to ultrasound based diagnosis. SONIO failed to make the diagnosis on 7/209 cases. On average, each case displayed 6 anomalies, 3 of which were considered atypical for the condition. Using the 'full-phenotype' model, the success rate of the top-7 output of Sonio was 96.7% (202/209). This was 91.9% and 87.1% for the top-5 and top-3 outputs respectively. Using the '1-by-1' model, the correct diagnosis was within the top-7, top-5 and top-3 of SONIO's output in 72.4%, 69.3% and 63.1%. Conclusion: Sonio is a robust DSS with a success-rate >95% for top-7 ranking diagnoses when the full phenotype is provided, using a large database of noisy real data. The success rate over 70% using the '1-by-1' model was understandably lower, given that SONIO's sequential queries may not systematically cover the full phenotype.

23 Frailty, Older Age-Associated Conditions, and Multimorbidity Amongst People Experiencing Homelessness in A Hostel in London

Introduction People experiencing homelessness (PEH) face poor health outcomes and extreme health inequity, and evidence suggests earlier onset of older age-associated conditions and signs of premature ageing. This is the first UK study to assess frailty in this population. The objective was to assess frailty, age-associated conditions, and multimorbidity in PEH residing in hostel accommodation, drawing comparisons with population data. Methods Participants were drawn from a hostel in London for PEH aged over 30. Age-associated conditions were identified using validated tools and a questionnaire modelled on comprehensive geriatric assessments. Participants’ keyworkers completed questionnaires to provide collateral information. Frailty was defined according to five criteria in Fried’s phenotype model: participants with three or more criteria are classified as frail, one or two criteria as vulnerable, and no criteria as not frail. Multimorbidity was defined as the presence of two or more long-term conditions in one person. Comparisons were made with population data from The English Longitudinal Study of Ageing and Health Survey for England. Results Thirty-three people participated (83% of eligible residents), with a mean age of 55.7 years (range 38–74). Frailty was identified in 18/33 participants (55%), with 13/33 (39%) classified as vulnerable, and 2/33 (6%) as not frail. Participants met an average of 2.6/5 frailty phenotype criteria, comparable to 90-year-olds in the general population. The most common age-associated conditions identified were: falls (in 61%), visual impairment (61%), low grip strength (61%), mobility impairment (52%), and cognitive impairment (45%). Multimorbidity was present in all thirty-three participants. Conclusions A wide range of unmet health needs was identified. The high prevalence of frailty and age-associated conditions support evidence of premature ageing, indicating a need to include holistic older-age assessments in PEH at a younger age. Involvement of health professionals with experience of working with older people could contribute to improving health outcomes for homeless patients.

Hierarchical Modelling of Haplotype Effects on a Phylogeny

We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.

Leigh Syndrome as a Phenotype of Near-Homoplasmic m.8344 A>G Variant in Children

In the field of mitochondrial medicine, correlation of clinical phenotype with mutation heteroplasmy remains an outstanding question with few, if any, clear thresholds corresponding to a given phenotype. The m.8344A>G mutation is most commonly associated with myoclonus epilepsy and ragged red fiber syndrome (MERRF) at varying levels of heteroplasmy. However, a handful of cases been previously reported in which individuals homoplasmic or nearly homoplasmic for this mutation in the blood have presented with multiple bulbar palsies, respiratory failure, and progressive neurologic decline almost uniformly following a respiratory illness. MRI brain in all affected individuals revealed symmetric T2 hyperintense lesions of subcortical gray matter structures, consistent with Leigh syndrome. Here, we present 3 cases with clinical, biochemical, and neuro-imaging findings with the additional reporting of spinal lesions. This new phenotype supports a heteroplasmy-dependent phenotype model for this mutation and recognition of this can help clinicians with diagnosis and anticipatory clinical guidance.