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BackgroundClinical exome sequencing typically achieves diagnostic yields of 30%–57.5% in individuals with monogenic rare diseases. Undiagnosed diseases programmes implement strategies to improve diagnostic outcomes for these individuals.AimWe share the lessons learnt from the first 3 years of the Undiagnosed Diseases Program-Victoria, an Australian programme embedded within a clinical genetics service in the state of Victoria with a focus on paediatric rare diseases.MethodsWe enrolled families who remained without a diagnosis after clinical genomic (panel, exome or genome) sequencing between 2016 and 2018. We used family-based exome sequencing (family ES), family-based genome sequencing (family GS), RNA sequencing (RNA-seq) and high-resolution chromosomal microarray (CMA) with research-based analysis.ResultsIn 150 families, we achieved a diagnosis or strong candidate in 64 (42.7%) (37 in known genes with a consistent phenotype, 3 in known genes with a novel phenotype and 24 in novel disease genes). Fifty-four diagnoses or strong candidates were made by family ES, six by family GS with RNA-seq, two by high-resolution CMA and two by data reanalysis.ConclusionWe share our lessons learnt from the programme. Flexible implementation of multiple strategies allowed for scalability and response to the availability of new technologies. Broad implementation of family ES with research-based analysis showed promising yields post a negative clinical singleton ES. RNA-seq offered multiple benefits in family ES-negative populations. International data sharing strategies were critical in facilitating collaborations to establish novel disease–gene associations. Finally, the integrated approach of a multiskilled, multidisciplinary team was fundamental to having diverse perspectives and strategic decision-making.

Medium-chain acyl-Coenzyme A dehydrogenase (MCAD) is involved in the initial step of mitochondrial fatty acid β-oxidation (FAO). Loss of function results in MCAD deficiency, a disorder that usually presents in childhood with hypoketotic hypoglycemia, vomiting and lethargy. While the disruption of mitochondrial fatty acid metabolism is the primary metabolic defect, secondary defects in mitochondrial oxidative phosphorylation (OXPHOS) may also contribute to disease pathogenesis. Therefore, we examined OXPHOS activity and stability in MCAD-deficient patient fibroblasts that have no detectable MCAD protein. We found a deficit in mitochondrial oxygen consumption, with reduced steady-state levels of OXPHOS complexes I, III and IV, as well as the OXPHOS supercomplex. To examine the mechanisms involved, we generated an MCAD knockout (KO) using human 143B osteosarcoma cells. These cells also exhibited defects in OXPHOS complex function and steady-state levels, as well as disrupted biogenesis of newly-translated OXPHOS subunits. Overall, our findings suggest that the loss of MCAD is associated with a reduction in steady-state OXPHOS complex levels, resulting in secondary defects in OXPHOS function which may contribute to the pathology of MCAD deficiency.

Pentatricopeptide repeat domain proteins are a large family of RNA-binding proteins involved in mitochondrial RNA editing, stability, and translation. Mitochondrial translation machinery defects are an expanding group of genetic diseases in humans. We describe a patient who presented with low birth weight, mental retardation, and optic atrophy. Brain MRI showed abnormal bilateral signals at the basal ganglia and brainstem, and the patient was diagnosed as Leigh syndrome. Exome sequencing revealed two potentially loss-of-function variants [c.415-2A>G, and c.1747_1748insCT (p.Phe583Serfs*3)] in PTCD3 (also known as MRPS39). PTCD3, a member of the pentatricopeptide repeat domain protein family, is a component of the small mitoribosomal subunit. The patient had marked decreases in mitochondrial complex I and IV levels and activities, oxygen consumption and ATP biosynthesis, and generalized mitochondrial translation defects in fibroblasts. Quantitative proteomic analysis revealed decreased levels of the small mitoribosomal subunits. Complementation experiments rescued oxidative phosphorylation complex I and IV levels and activities, ATP biosynthesis, and MT-RNR1 rRNA transcript level, providing functional validation of the pathogenicity of identified variants. This is the first report of an association of PTCD3 mutations with Leigh syndrome along with combined oxidative phosphorylation deficiencies caused by defects in the mitochondrial translation machinery.

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leveraging multiple types of omics data, including prior biological knowledge from various sources, such as transcriptomics, genomics, proteomics, and metabolomics, to improve cancer classification. However, current works do not fully utilize the non-linear learning potential of GNN and lack of the integration ability to analyse high-throughput multi-omics data simultaneously with prior biological knowledge. Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. Hence, this study proposes a graph classification model called associative multi-omics graph embedding learning (AMOGEL) to effectively integrate multi-omics datasets and prior knowledge through GNN coupled with association rule mining (ARM). AMOGEL employs an early fusion technique using ARM to mine intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before the model training. Moreover, AMOGEL introduces multi-dimensional edges, with multi-omics gene associations or edges as the main contributors and prior knowledge edges as auxiliary contributors. Additionally, it uses a gene ranking technique based on attention scores, considering the relationships between neighbouring genes. Several experiments were performed on BRCA and KIPAN cancer subtypes to demonstrate the integration of multi-omics datasets (miRNA, mRNA, and DNA methylation) with prior biological knowledge of protein-protein interactions, KEGG pathways and Gene Ontology. The experimental results showed that the AMOGEL outperformed the current state-of-the-art models in terms of classification accuracy, F1 score and AUC score. The findings of this study represent a crucial step forward in advancing the effective integration of multi-omics data and prior knowledge to improve cancer subtype classification.

IntroductionSmall intestinal bacterial overgrowth (SIBO) is prevalent in irritable bowel syndrome (IBS), but its’ association with other functional gastrointestinal disorders (FGIDs) is less certain. This study aimed to explore SIBO in a multi-racial Asian population with various FGIDs compared to non-FGID controls.MethodologyConsecutive Asian adults with Rome III diagnosed common FGIDs (functional dyspepsia/FD, IBS and functional constipation/FC) and non-FGID controls were subjected to glucose breath testing, with hydrogen (H2) and methane (CH4) levels determined.ResultsA total of 244 participants (FGIDs n = 186, controls n = 58, median age 45 years, males 36%, Malay ethnicity 76%) were recruited. FGIDs had a higher prevalence trend of SIBO compared to controls (16% FGIDs vs. 10% controls, p = 0.278) with 14% in FD, 18% in IBS and 17% in FC. Compared to controls, SIBO was associated with diarrhoea-predominant IBS (IBS-D) (24% vs. 10%, P = 0.050) but not with other types of FGIDs. IBS-D remained an independent predictor of SIBO (OR = 2.864, 95% CI 1.160–7.071, p = 0.023) but not PPI usage nor history of diabetes (both p > 0.050) at multivariate analysis. Compared to controls, SIBO in IBS-D was associated with an elevated H2 level (≥ 20 ppm from baseline) (18% vs. 3%, p = 0.017), but not CH4 levels (≥ 10 ppm) (9% vs. 7%, p = 0.493). In addition, no difference was found in the prevalence of methane-positive SIBO between chronic constipation (constipation-predominant IBS and FC) compared to controls (9% vs. 7%, P = 0.466).ConclusionSIBO is prevalent amongst multi-ethnic Asian adults with and without FGIDs. Amongst various FGIDs, only IBS-D is significantly associated with SIBO.

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leveraging multiple types of omics data, including prior biological knowledge from various sources, such as transcriptomics, genomics, proteomics, and metabolomics, to improve cancer classification. However, current works do not fully utilize the non-linear learning potential of GNN and lack of the integration ability to analyse high-throughput multi-omics data simultaneously with prior biological knowledge. Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. Hence, this study proposes a graph classification model called associative multi-omics graph embedding learning (AMOGEL) to effectively integrate multi-omics datasets and prior knowledge through GNN coupled with association rule mining (ARM). AMOGEL employs an early fusion technique using ARM to mine intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before the model training. Moreover, AMOGEL introduces multi-dimensional edges, with multi-omics gene associations or edges as the main contributors and prior knowledge edges as auxiliary contributors. Additionally, it uses a gene ranking technique based on attention scores, considering the relationships between neighbouring genes. Several experiments were performed on BRCA and KIPAN cancer subtypes to demonstrate the integration of multi-omics datasets (miRNA, mRNA, and DNA methylation) with prior biological knowledge of protein-protein interactions, KEGG pathways and Gene Ontology. The experimental results showed that the AMOGEL outperformed the current state-of-the-art models in terms of classification accuracy, F1 score and AUC score. The findings of this study represent a crucial step forward in advancing the effective integration of multi-omics data and prior knowledge to improve cancer subtype classification.

Objective Psychiatric mentors are an important part of the new, seamless training program in Singapore. There is a need to assess the qualities of a good psychiatric mentor vis-a-vis those of a good psychiatrist. Method An anonymous survey was sent out to all psychiatry trainees and psychiatrists in Singapore to assess quantitatively the relative importance of 40 qualities for a good psychiatrist and a good mentor. Results The response rate was 48.7% (74/152). Factor analysis showed four themes among the qualities assessed (professional, personal values, relationship, academic-executive). A good mentor is defined by professional, relationship, and personal-values qualities. Mentors have significantly higher scores than psychiatrists for two themes (relationship and academic-executive). Conclusion Being a good mentor, in Asia, means being a good psychiatrist first and foremost but also requires additional relationship and academic-executive skills. Mentors should be formally trained in these additional skills that were not part of the psychiatric curriculum.