Machine Learning Methods for the Discovery and Analysis of MicroRNAs

MicroRNAs are a highly conserved class of small endogenous RNA, about ~22nt in length, involved in post-transcriptional gene silencing and have prominent roles in disease and development. Though the process of microRNA discovery was once an arduous task, the advent of high throughput sequencing technology has resulted in novel microRNAs being discovered at a rapid rate. Several data-driven pipelines and machine learning-based methods have been devised so that the beginning stages of microRNA discovery can be performed in silico. Despite these efforts, several challenges have persisted in the computational prediction of microRNAs. These challenges include the identification of microRNAs with low expression, proper determination of the precursor span, and the precise labeling of the cleavage sites involved in their biogenesis. This thesis addresses these challenges with two new machine learning-based approaches. MiRWoods improves precursor detection and uses stacked random forests for the sensitive detection of microRNAs. We report that miRWoods has a 10% higher recall of annotated microRNAs when compared with other software. We applied this method to the genomes of human, mouse, Felis catus (cat) and Bos Taurus (cow) and identified hundreds of novel microRNAs in small RNA sequencing datasets. Our novel predictions include a microRNA in an intron of tyrosine kinase 2 (TYK2), that is present in both cat and cow, as well as a family of mirtrons with two instances in the human genome. Our predictions support a more expanded miR-2284 family in the bovine genome, a larger mir-548 family in the human genome, and a larger let-7 family in the feline genome. DeepMirCut is a deep learning approach for identifying cleavage sites within microRNAs. This approach is inspired by site-labeling methods for natural language processing, and can accurately predict how the microRNA processing enzymes Dicer and Drosha cleave the microRNA precursor

Decisional Informatics for Psychosocial Rehabilitation: A Feasibility Pilot on Tailored and Fluid Treatment Algorithms for Serious Mental Illness

This study introduces a computerized clinical decision-support tool, the Fluid Outpatient Rehabilitation Treatment (FORT), that incorporates individual and ever-evolving patient needs to guide clinicians in developing and updating treatment decisions in real-time. In this proof-of-concept feasibility pilot, FORT was compared against traditional treatment planning using similar behavioral therapies in 52 adults with severe mental illness attending community-based day treatment. At posttreatment and follow-up, group differences and moderate-to-large effect sizes favoring FORT were detected in social function, work readiness, self-esteem, working memory, processing speed, and mental flexibility. Of participants who identified obtaining a General Education Diploma as their goal, 73% in FORT passed the examination compared with 18% in traditional treatment planning. FORT was also associated with higher agency cost-effectiveness and a better average benefit-cost ratio, even when considering diagnosis, baseline symptoms, and education. Although the comparison groups were not completely equivalent, the findings suggest computerized decision support systems that collaborate with human decision-makers to personalize psychiatric rehabilitation and address critical decisions may have a role in improving treatment effectiveness and efficiency

Estimating the Effect of Liver and Pancreas Volume and Fat Content on Risk of Diabetes: A Mendelian Randomization Study

Fat content and volume of liver and pancreas are associated with risk of diabetes in observational studies; whether these associations are causal is unknown. We conducted a Mendelian randomization (MR) study to examine causality of such associations. We used genetic variants associated (P < 5 × 10-8) with the exposures (liver and pancreas volume and fat content) using MRI scans of UK Biobank participants (n = 32,859). We obtained summary-level data for risk of type 1 (9,358 cases) and type 2 (55,005 cases) diabetes from the largest available genome-wide association studies. We performed inverse-variance weighted MR as main analysis and several sensitivity analyses to assess pleiotropy and to exclude variants with potential pleiotropic effects. Observationally, liver fat and volume were associated with type 2 diabetes (odds ratio per 1 SD higher exposure 2.16 [2.02, 2.31] and 2.11 [1.96, 2.27], respectively). Pancreatic fat was associated with type 2 diabetes (1.42 [1.34, 1.51]) but not type 1 diabetes, and pancreas volume was negatively associated with type 1 diabetes (0.42 [0.36, 0.48]) and type 2 diabetes (0.73 [0.68, 0.78]). MR analysis provided evidence only for a causal role of liver fat and pancreas volume in risk of type 2 diabetes (1.27 [1.08, 1.49] or 27% increased risk and 0.76 [0.62, 0.94] or 24% decreased risk per 1SD, respectively) and no causal associations with type 1 diabetes. Our findings assist in understanding the causal role of ectopic fat in the liver and pancreas and of organ volume in the pathophysiology of type 1 and 2 diabetes. [Abstract copyright: © 2022 by the American Diabetes Association.

Adiposity and hepatic lipid in healthy full-term, breastfed, and formula-fed human infants: a prospective short-term longitudinal cohort study

Background: The effect of mode of infant feeding on adiposity deposition is not fully understood. Objective: The objective was to test the hypothesis that differences in total and regional adipose tissue content and intrahepatocellular lipid (IHCL) arise in early infancy between breast- and formula-fed infants and to describe longitudinal changes. Design: This prospective longitudinal cohort study was performed in 2 hospitals in the United Kingdom. Healthy, full-term, appropriate weight-for-gestational age infants were recruited; adipose tissue volume and distribution were directly quantified by using whole-body magnetic resonance imaging; IHCL was assessed by in vivo proton magnetic resonance spectroscopy. Measurements were performed after birth (median age: 13 d) and at 6–12 wk of age. Method of infant feeding was recorded prospectively by using maternally completed feeding diaries. Breastfed was defined as >80% of feeds consisting of breast milk at both points; formula-fed was defined as >80% of feeds consisting of formula milk at both points. Results: Longitudinal results were obtained from 70 infants (36 breastfed, 9 mixed-fed, and 25 formula-fed). No differences were found in total or regional adipose tissue or IHCL between breastfed and formula-fed infants. In pooled analyses including all feeding groups, IHCL and total adipose tissue approximately doubled between birth and 6–12 wk: IHCL after birth (median: 0.949; IQR: 0.521–1.711) and at 6–12 wk (1.828; 1.376–2.697; P < 0.001) and total adipose tissue after birth (0.749 L; 0.620–0.928 L) and at 6–12 wk (1.547 L; 1.332–1.790 L; P < 0.001). Increasing adiposity was characterized by greater relative increases in subcutaneous than in internal adipose tissue depots. Conclusions: No differences were detectable in adipose tissue or IHCL accretion between breastfed and formula-fed infants up to 2 mo. The substantial increase in IHCL seen over this period in both breastfed and formula-fed infants is a novel observation, which suggests that hepatic storage of lipids may be physiologic up to 2 mo. This trial was registered at www.clinicaltrials.gov as NCT02033005

Dissociation between exercise-induced reduction in liver fat and changes in hepatic and peripheral glucose homoeostasis in obese patients with non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is associated with multi-organ (hepatic, skeletal muscle, adipose tissue) insulin resistance (IR). Exercise is an effective treatment for lowering liver fat but its effect on IR in NAFLD is unknown. We aimed to determine whether supervised exercise in NAFLD would reduce liver fat and improve hepatic and peripheral (skeletal muscle and adipose tissue) insulin sensitivity. Sixty nine NAFLD patients were randomized to 16 weeks exercise supervision (n=38) or counselling (n=31) without dietary modification. All participants underwent MRI/spectroscopy to assess changes in body fat and in liver and skeletal muscle triglyceride, before and following exercise/counselling. To quantify changes in hepatic and peripheral insulin sensitivity, a pre-determined subset (n=12 per group) underwent a two-stage hyperinsulinaemic euglycaemic clamp pre- and post-intervention. Results are shown as mean [95% confidence interval (CI)]. Fifty participants (30 exercise, 20 counselling), 51 years (IQR 40, 56), body mass index (BMI) 31 kg/m(2) (IQR 29, 35) with baseline liver fat/water % of 18.8% (IQR 10.7, 34.6) completed the study (12/12 exercise and 7/12 counselling completed the clamp studies). Supervised exercise mediated a greater reduction in liver fat/water percentage than counselling [Δ mean change 4.7% (0.01, 9.4); P<0.05], which correlated with the change in cardiorespiratory fitness (r=-0.34, P=0.0173). With exercise, peripheral insulin sensitivity significantly increased (following high-dose insulin) despite no significant change in hepatic glucose production (HGP; following low-dose insulin); no changes were observed in the control group. Although supervised exercise effectively reduced liver fat, improving peripheral IR in NAFLD, the reduction in liver fat was insufficient to improve hepatic IR

Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies

Introduction Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects. Materials and Methods 3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics. Results Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view. Discussion and Conclusions In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource

Understanding Social Situations (USS): A proof-of-concept social–cognitive intervention targeting theory of mind and attributional bias in individuals with psychosis.

In this proof-of-concept trial, we examined the feasibility and preliminary efficacy of Understanding Social Situations (USS), a new social cognitive intervention that targets higher-level social cognitive skills using methods common to neurocognitive remediation, including drill and practice and hierarchically structured training, which may compensate for the negative effects of cognitive impairment on learning

Differing genetic variants associated with liver fat and their contrasting relationships with cardiovascular diseases and cancer.

The underlying mechanisms for the link between steatotic liver disease and cardiovascular and cancer outcomes are poorly understood. We aimed to use MRI-derived measures of liver fat and genetics to investigate causal mechanisms that link higher liver fat to various health outcomes. We conducted a genome-wide association study on 37,358 UK Biobank participants to identify genetic variants associated with liver fat measured from MRI scans. We used Mendelian randomization approach to investigate the causal effect of liver fat on health outcomes independent of BMI, alcohol consumption and lipids using data from published GWAS and FinnGen. We identified 13 genetic variants associated with liver fat that showed differing risks to health outcomes. Genetic variants associated with impaired hepatic triglyceride export showed liver fat-increasing alleles to be correlated with a reduced risk of coronary artery disease and myocardial infarction but an elevated risk of type 2 diabetes; and variants associated with enhanced de novo lipogenesis showed liver fat-increasing alleles to be linked to a higher risk of myocardial infarction and coronary artery disease. Genetically higher liver fat content increased the risk of non-alcohol liver cirrhosis, hepatocellular and Intrahepatic bile ducts and gallbladder cancers, exhibiting a dose-dependent relationship, irrespective of the mechanism. This study provides fresh insight into the heterogeneous effect of liver fat on health outcomes. It challenges the notion that liver fat per se is an independent risk factor for cardiovascular disease, underscoring the dependency of this association on the specific mechanisms that drive fat accumulation in the liver. However, excess liver fat, regardless of how achieved, appears to be causally linked to liver cirrhosis and cancers in a dose dependent manner. This research advances our understanding of the heterogeneity in mechanisms influencing liver fat accumulation, providing new insights into how liver fat accumulation may impact various health outcomes. The findings challenge the notion that liver fat is an independent risk factor for cardiovascular disease and highlight the mechanistic effect of some genetic variants on fat accumulation and the development of cardiovascular diseases. This study is of particular importance for healthcare professionals including physicians and researchers as well as patients as it allows for more targeted and personalised treatment by understanding the relationship between liver fat and various health outcomes. The findings emphasise the need for a personalised management approach and a reshaping of risk assessment criteria. It also provides room for prioritising a clinical intervention aimed at reducing liver fat content (likely by intentional weight loss, however, achieved) that could help protect against liver related fibrosis and cancer

Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Background & Aims: Excess liver iron content is common and is linked to hepatic and extrahepatic disease risk. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals in UK Biobank with MRI quantified liver iron, and validated our findings in an independent cohort (n=1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 29 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 anthropometric traits and diseases. Results: We identified three independent genetic variants (rs1800562 (C282Y) and rs1799945 (H63D) in HFE and rs855791 (V736A) in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p<5x10-8). The two HFE variants account for ~85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases