The infant gut microbiome and cognitive development in malnutrition.

Malnutrition affects 195 million children under the age of five worldwide with long term effects that include impaired cognitive development. Brain development occurs rapidly over the first 36 months of life. Whilst seemingly independent, changes to the brain and gut microbiome are linked by metabolites, hormones, and neurotransmitters as part of the gut-brain axis. In the context of severe malnutrition, the composition of the gut microbiome and the repertoire of biochemicals exchanged via the gut-brain axis vary when compared to healthy individuals. These effects are primarily due to the recognized interacting determinants, macro- and micronutrient deficiencies, infection, infestations and toxins related to poor sanitation, and a dearth of psycho-social stimulation. The standard of care for the treatment of severe acute malnutrition is focused on nutritional repletion and weight restoration through the provision of macro- and micronutrients, the latter usually in excess of recommended dietary allowances (RDA). However, existing formulations and supplements have not been designed to specifically address key recovery requirements for brain and gut microbiome development. Animal model studies indicate that treatments targeting the gut microbiome could improve brain development. Despite this, research on humans targeting the gut microbiome with the aim of restoring brain functionality are scarce. We conclude that there is a need for assessment of cognition and the use of various tools that permit visualization of the brain anatomy and function (e.g., Magnetic resonance imaging (MRI), functional near-infrared spectroscopy (fNIRS), electroencephalogram (EEG)) to understand how interventions targeting the gut microbiome impact brain development.

Multidimensional evaluation of the early emergence of executive function and development in Bangladeshi children using nutritional and psychosocial intervention: A randomized controlled trial protocol.

INTRODUCTION: Reversing malnutrition-induced impairment of cognition and emotional regulation is a critical global gap. We hypothesize that brain-targeted micronutrient supplemented nutritional rehabilitation in children with moderate acute malnutrition, followed by 2 years micronutrient supplementation will impact on the cognition and emotion regulation of these children. METHODS: The primary outcome of this prospective, randomized controlled trial is to study the development of executive functions (EFs) and emotion regulation (ER) in this cohort. Moderate acute malnourished (MAM; WLZ/WHZ <-2 and ≥-3 z-score, and/or 11.5 cm ≤ MUAC < 12.5cm; n = 140)children aged around one year (11m-13m) in Mirpur, Dhaka, Bangladesh will be randomized (1:1) to receive either locally produced Ready to Use Supplementary Food (RUSF) or Enhanced Ready to Use Supplementary Food (E-RUSF) until anthropometric recovery (WLZ/WHZ > -1SD), or for 3 months after enrollment (whichever is earlier). The randomized MAMs groups will be given either Small Quantity Lipid Based Nutrient Supplement (SQLNS) or Enhanced Small Quantity Lipid Based Nutrient Supplement (E-SQLNS), respectively until the end of the 2-year follow up period. Standard psychosocial stimulation will be provided to the MAMs intervention groups. Biological samples will be collected, anthropometric and neurocognitive assessments will be performed at 2 (22m-26m) and 3 (34m-38m) years of age. Two control groups will be recruited: 1), non-malnourished one-year (11m-13m) old children (WLZ/WHZ score>-1SD; n = 70); and 2) three-year (34m-38m) old children (n = 70) with untreated MAM (WHZ <-2 and ≥-3 z-score, and/or 11.5≤MUAC<12.5 cm). The 3-year-old MAM reference group will be assessed once and provided with 2 months of nutritional rehabilitation support (RUSF Nutriset's Plumpy'Sup™).

Horizontal gene transfer after faecal microbiota transplantation in adolescents with obesity.

BACKGROUND: Horizontal gene transfer (HGT) describes the transmission of DNA outside of direct ancestral lineages. The process is best characterised within the bacterial kingdom and can enable the acquisition of genetic traits that support bacterial adaptation to novel niches. The adaptation of bacteria to novel niches has particular relevance for faecal microbiota transplantation (FMT), a therapeutic procedure which aims to resolve gut-related health conditions of individuals, through transplanted gut microbiota from healthy donors. RESULTS: Three hundred eighty-one stool metagenomic samples from a placebo-controlled FMT trial for obese adolescents (the Gut Bugs Trial) were analysed for HGT, using two complementary methodologies. First, all putative HGT events, including historical HGT signatures, were quantified using the bioinformatics application WAAFLE. Second, metagenomic assembly and gene clustering were used to assess and quantify donor-specific genes transferred to recipients following the intervention. Both methodologies found no difference between the level of putative HGT events in the gut microbiomes of FMT and placebo recipients, post-intervention. HGT events facilitated by engrafted donor species in the FMT recipient gut at 6 weeks post-intervention were identified and characterised. Bacterial strains contributing to this subset of HGT events predominantly belonged to the phylum Bacteroidetes. Engraftment-dependent horizontally transferred genes were retained within recipient microbiomes at 12 and 26 weeks post-intervention. CONCLUSION: Our study suggests that novel microorganisms introduced into the recipient gut following FMT have no impact on the basal rate of HGT within the human gut microbiome. Analyses of further FMT studies are required to assess the generalisability of this conclusion across different FMT study designs and for the treatment of different gut-related conditions. Video Abstract.

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis.

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

Identification of 27 allele-specific regulatory variants in Parkinson's disease using a massively parallel reporter assay.

Genome wide association studies (GWAS) have identified a number of genomic loci that are associated with Parkinson's disease (PD) risk. However, the majority of these variants lie in non-coding regions, and thus the mechanisms by which they influence disease development, and/or potential subtypes, remain largely elusive. To address this, we used a massively parallel reporter assay (MPRA) to screen the regulatory function of 5254 variants that have a known or putative connection to PD. We identified 138 loci with enhancer activity, of which 27 exhibited allele-specific regulatory activity in HEK293 cells. The identified regulatory variant(s) typically did not match the original tag variant within the PD associated locus, supporting the need for deeper exploration of these loci. The existence of allele specific transcriptional impacts within HEK293 cells, confirms that at least a subset of the PD associated regions mark functional gene regulatory elements. Future functional studies that confirm the putative targets of the empirically verified regulatory variants will be crucial for gaining a greater understanding of how gene regulatory network(s) modulate PD risk.

Protocol for the Gut Bugs in Autism Trial: a double-blind randomised placebo-controlled trial of faecal microbiome transfer for the treatment of gastrointestinal symptoms in autistic adolescents and adults.

INTRODUCTION: Autism (formally autism spectrum disorder) encompasses a group of complex neurodevelopmental conditions, characterised by differences in communication and social interactions. Co-occurring chronic gastrointestinal symptoms are common among autistic individuals and can adversely affect their quality of life. This study aims to evaluate the efficacy of oral encapsulated faecal microbiome transfer (FMT) in improving gastrointestinal symptoms and well-being among autistic adolescents and adults. METHODS AND ANALYSIS: This double-blind, randomised, placebo-controlled trial will recruit 100 autistic adolescents and adults aged 16-45 years, who have mild to severe gastrointestinal symptoms (Gastrointestinal Symptoms Rating Scale (GSRS) score ≥2.0). We will also recruit eight healthy donors aged 18-32 years, who will undergo extensive clinical screening. Recipients will be randomised 1:1 to receive FMT or placebo, stratified by biological sex. Capsules will be administered over two consecutive days following an overnight bowel cleanse with follow-up assessments at 6, 12 and 26 weeks post-treatment. The primary outcome is GSRS score at 6 weeks. Other assessments include anthropometry, body composition, hair cortisol concentration, gut microbiome profile, urine/plasma gut-derived metabolites, plasma markers of gut inflammation/permeability and questionnaires on general well-being, sleep quality, physical activity, food diversity and treatment tolerability. Adverse events will be recorded and reviewed by an independent data monitoring committee. ETHICS AND DISSEMINATION: Ethics approval for the study was granted by the Central Health and Disability Ethics Committee on 24 August 2021 (reference number: 21/CEN/211). Results will be published in peer-reviewed journals and presented to both scientific and consumer group audiences. TRIAL REGISTRATION NUMBER: ACTRN12622000015741.

Discovering genetic mechanisms underlying the co-occurrence of Parkinson's disease and non-motor traits.

Understanding the biological mechanisms that underlie the non-motor symptoms of Parkinson's disease (PD) requires comprehensive frameworks that unravel the complex interplay of genetic risk factors. Here, we used a disease-agnostic brain cortex gene regulatory network integrated with Mendelian Randomization analyses that identified 19 genes whose changes in expression were causally linked to PD. We further used the network to identify genes that are regulated by PD-associated genome-wide association study (GWAS) SNPs. Extended protein interaction networks derived from PD-risk genes and PD-associated SNPs identified convergent impacts on biological pathways and phenotypes, connecting PD with established co-occurring traits, including non-motor symptoms. These findings hold promise for therapeutic development. In conclusion, while distinct sets of genes likely influence PD risk and outcomes, the existence of genes in common and intersecting pathways associated with other traits suggests that they may contribute to both increased PD risk and symptom heterogeneity observed in people with Parkinson's.

De novo identification of complex traits associated with asthma.

Introduction: Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Methods: Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. Results: We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Discussion: Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.

De novo network analysis reveals autism causal genes and developmental links to co-occurring traits.

Autism is a complex neurodevelopmental condition that manifests in various ways. Autism is often accompanied by other conditions, such as attention-deficit/hyperactivity disorder and schizophrenia, which can complicate diagnosis and management. Although research has investigated the role of specific genes in autism, their relationship with co-occurring traits is not fully understood. To address this, we conducted a two-sample Mendelian randomisation analysis and identified four genes located at the 17q21.31 locus that are putatively causal for autism in fetal cortical tissue (LINC02210, LRRC37A4P, RP11-259G18.1, and RP11-798G7.6). LINC02210 was also identified as putatively causal for autism in adult cortical tissue. By integrating data from expression quantitative trait loci, genes and protein interactions, we identified that the 17q21.31 locus contributes to the intersection between autism and other neurological traits in fetal cortical tissue. We also identified a distinct cluster of co-occurring traits, including cognition and worry, linked to the genetic loci at 3p21.1. Our findings provide insights into the relationship between autism and co-occurring traits, which could be used to develop predictive models for more accurate diagnosis and better clinical management.

An open-label pilot trial of faecal microbiome transfer to restore the gut microbiome in anorexia nervosa: protocol.

INTRODUCTION: Individuals with anorexia nervosa (AN) harbour distinct gut microbiomes compared with healthy individuals, which are sufficient to induce weight loss and anxiety-like behaviours when transplanted into germ-free mice. We hypothesise that faecal microbiome transfer (FMT) from healthy donors would help restore the gut microbiome of individuals with AN, which in turn, may aid patient recovery. METHODS: We aim to conduct an open-label pilot study in 20 females aged 16-32 years in Auckland, New Zealand who meet the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criteria for AN and have a body mass index 13-19 kg/m2. We will recruit four healthy, lean, female donors, aged 18-32 years, who will undergo extensive clinical screening prior to stool donation. Faecal microbiota will be harvested from donors and double encapsulated in delayed release, acid-resistant capsules. All participants will receive a single course of 20 FMT capsules (five from each donor) which they can choose to take over two or four consecutive days. Stool and blood samples will be collected from participants over a period of 3 months to assess their gut microbiome profile, metabolome, levels of intestinal inflammation and nutritional status. Our primary outcome is a shift in the gut microbiome composition at 3 weeks post-FMT (Bray-Curtis dissimilarity). We will also monitor participants' body composition (whole-body dual-energy X-ray absorptiometry scans), eating disorder psychopathology, mental health and assess their views on, and tolerability of, treatment. All adverse events will be recorded and reviewed by an independent data monitoring committee. ETHICS AND DISSEMINATION: Ethics approval was provided by the Central Health and Disability Ethics Committee (Ministry of Health, New Zealand, 21/CEN/212). Results will be published in peer-reviewed journals and presented to both scientific and consumer group audiences. TRIAL REGISTRATION NUMBER: ACTRN12621001504808.

Comorbidity genetic risk and pathways impact SARS-CoV-2 infection outcomes.

Understanding the genetic risk and mechanisms through which SARS-CoV-2 infection outcomes and comorbidities interact to impact acute and long-term sequelae is essential if we are to reduce the ongoing health burdens of the COVID-19 pandemic. Here we use a de novo protein diffusion network analysis coupled with tissue-specific gene regulatory networks, to examine putative mechanisms for associations between SARS-CoV-2 infection outcomes and comorbidities. Our approach identifies a shared genetic aetiology and molecular mechanisms for known and previously unknown comorbidities of SARS-CoV-2 infection outcomes. Additionally, genomic variants, genes and biological pathways that provide putative causal mechanisms connecting inherited risk factors for SARS-CoV-2 infection and coronary artery disease and Parkinson's disease are identified for the first time. Our findings provide an in depth understanding of genetic impacts on traits that collectively alter an individual's predisposition to acute and post-acute SARS-CoV-2 infection outcomes. The existence of complex inter-relationships between the comorbidities we identify raises the possibility of a much greater post-acute burden arising from SARS-CoV-2 infection if this genetic predisposition is realised.

The growth hormone receptor interacts with transcriptional regulator HMGN1 upon GH-induced nuclear translocation.

Growth hormone (GH) actions are mediated through binding to its cell-surface receptor, the GH receptor (GHR), with consequent activation of downstream signalling. However, nuclear GHR localisation has also been observed and is associated with increased cancer cell proliferation. Here we investigated the functional implications of nuclear translocation of the GHR in the human endometrial cancer cell-line, RL95-2, and human mammary epithelial cell-line, MCF-10A. We found that following GH treatment, the GHR rapidly translocates to the nucleus, with maximal localisation at 5-10 min. Combined immunoprecipitation-mass spectrometry analysis of RL95-2 whole cell lysates identified 40 novel GHR binding partners, including the transcriptional regulator, HMGN1. Moreover, microarray analysis demonstrated that the gene targets of HMGN1 were differentially expressed following GH treatment, and co-immunoprecipitation showed that HMGN1 associates with the GHR in the nucleus. Therefore, our results suggest that GHR nuclear translocation might mediate GH actions via interaction with chromatin factors that then drive changes in specific downstream transcriptional programs.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Socioeconomic disadvantage and polygenic risk for high BMI magnify obesity risk across childhood: a longitudinal, population, cohort study.

BACKGROUND: Across the life course, socioeconomic disadvantage disproportionately afflicts those with genetic predispositions to inflammatory diseases. We describe how socioeconomic disadvantage and polygenic risk for high BMI magnify the risk of obesity across childhood, and using causal analyses, explore the hypothetical impact of intervening on socioeconomic disadvantage to reduce adolescent obesity. METHODS: Data were drawn from a nationally representative Australian birth cohort, with biennial data collection between 2004 and 2018 (research and ethics committee approved). We generated a polygenic risk score for BMI using published genome-wide association studies. We measured early-childhood disadvantage (age 2-3 years) with a neighbourhood census-based measure and a family-level composite of parent income, occupation, and education. We used generalised linear regression (Poisson-log link) to estimate the risk of overweight or obesity (BMI ≥85th percentile) at age 14-15 years for children with early-childhood disadvantage (quintiles 4-5) versus average (quintile 3) and least disadvantage (quintiles 1-2), for those with high and low polygenic risk separately. FINDINGS: For 1607 children (n=796 female, n=811 male; 31% of the original cohort [N=5107]), polygenic risk and disadvantage were both associated with overweight or obesity; effects of disadvantage were more marked as polygenic risk increased. Of children with polygenic risk higher than the median (n=805), 37% of children living in disadvantage at age 2-3 years had an overweight or obese BMI by adolescence, compared with 26% of those with least disadvantage. For genetically vulnerable children, causal analyses indicated that early neighbourhood intervention to lessen disadvantage (to quintile 1-2) would reduce risk of adolescent overweight or obesity by 23% (risk ratio 0·77; 95% CI 0·57-1·04); estimates for improving family environments were similar (0·59; 0·43-0·80). INTERPRETATION: Actions addressing socioeconomic disadvantage could mitigate polygenic risk for developing obesity. This study benefits from population-representative longitudinal data but is limited by sample size. FUNDING: Australian National Health and Medical Research Council.

Spatially constrained gene regulation identifies key genetic contributions of preeclampsia, hypertension, and proteinuria†.

Preeclampsia (PE) is a relatively common but severe pregnancy disorder (with very limited effective treatments) characterized by hypertension (HTN) and usually proteinuria (PRO) or other organ damage. Genome-wide association studies (GWAS) of PE, HTN, and PRO have mostly identified risk loci single nucleotide polymorphisms (SNPs) located in noncoding genomic regions, likely impacting the regulation of distal gene expression. The latest GWAS associated (P < 1 × 10-6) SNPs to PE (n = 25), HTN (n = 1926), and PRO (n = 170). Our algorithmic analysis (CoDeS3D) used chromatin connection data (Hi-C) derived from 70 cell lines followed by analysis of two expression quantitative trail loci (eQTL) cohorts: GTEx (838 donors, 54 tissues, totaling 15 253 samples) and DICE (91 donors, 13 blood tissue types). We identified spatially constrained eQTLs which implicate gene targets in PE (n = 16), HTN (n = 3561), and PRO (n = 335). By overlapping these target genes and their molecular pathways (protein-protein interaction networks), we identified shared functional impacts between PE and HTN, which are significantly enriched for regulatory interactions which target genes intolerant to loss-of-function mutations. While the disease-associated SNP loci mostly do not overlap, the regulatory signals (target genes and pathways) overlap, informing on PE risk mechanisms. This demonstrates a model in which genetic predisposition to HTN and PRO lays a molecular groundwork toward risk for PE pathogenesis. This overlap at the gene regulatory network level identifies possible shared therapeutic targets for future study.

Fish oil supplementation during pregnancy and postpartum in mothers with overweight and obesity to improve body composition and metabolic health during infancy: A double-blind randomized controlled trial.

BACKGROUND: Maternal obesity during pregnancy is associated with an increased risk of obesity and metabolic disease in the offspring. Supplementation with fish oil (FO), which is insulin sensitizing, during pregnancy in mothers with overweight or obesity may prevent the development of greater adiposity and metabolic dysfunction in their children. OBJECTIVES: To determine the effects of FO supplementation throughout the second half of pregnancy and lactation in mothers with overweight or obesity on infant body composition and metabolism. METHODS: A double-blind randomized controlled trial of 6 g FO (3.55 g/d of n-3 PUFAs) compared with olive oil (control) from mid-pregnancy until 3 mo postpartum. Eligible women had singleton pregnancies at 12-20 wk of gestation, and BMI ≥ 25 kg/m2. The primary outcome was the infant body fat percentage (DXA scans) at 2 wk of age. Secondary outcomes included maternal metabolic markers during pregnancy, infant anthropometry at 2 wk and 3 mo of age, and metabolic markers at 3 mo. RESULTS: A total of 129 mothers were randomized, and 98 infants had a DXA scan at 2 wk. PRIMARY OUTCOME: Imputed and nonimputed analyses showed no effects of FO supplementation on infant body fat percentage at age 2 wk. SECONDARY OUTCOMES: There were no treatment effects on infant outcomes at 2 wk, but FO infants had a higher BMI z-score (P = 0.025) and ponderal index (P = 0.017) at age 3 mo. FO supplementation lowered maternal triglycerides by 17% at 30 wk of pregnancy (P = 0.0002) and infant triglycerides by 21% at 3 mo of age (P = 0.016) but did not affect maternal or infant insulin resistance. The rate of emergency cesarean section was lower with FO supplementation [aRR = 0.38 (95%CI 0.16, 0.90); P = 0.027]. CONCLUSIONS: FO supplementation of mothers with overweight or obesity during pregnancy did not impact infant body composition. There is a need to follow up the offspring to determine whether the observed metabolic effects persist. CLINICAL TRIAL REGISTRY NUMBER: This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12617001078347p). In addition, the Universal Trial Number, WHO, was obtained (U1111-1199-5860).

De novo discovery of traits co-occurring with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of chronic lung conditions. Genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) associated with COPD and the co-occurring conditions, suggesting common biological mechanisms underlying COPD and these co-occurring conditions. To identify them, we have integrated information across different biological levels (i.e., genetic variants, lung-specific 3D genome structure, gene expression and protein-protein interactions) to build lung-specific gene regulatory and protein-protein interaction networks. We have queried these networks using disease-associated SNPs for COPD, unipolar depression and coronary artery disease. COPD-associated SNPs can control genes involved in the regulation of lung or pulmonary function, asthma, brain region volumes, cortical surface area, depressed affect, neuroticism, Parkinson's disease, white matter microstructure and smoking behaviour. We describe the regulatory connections, genes and biochemical pathways that underlay these co-occurring trait-SNP-gene associations. Collectively, our findings provide new avenues for the investigation of the underlying biology and diverse clinical presentations of COPD. In so doing, we identify a collection of genetic variants and genes that may aid COPD patient stratification and treatment.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics.

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.

Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called "curse of dimensionality" (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most "informative" features and remove noisy "non-informative," irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis.

BACKGROUND: There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression. MAIN BODY: We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data. CONCLUSION: We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.