Yannis Trakadis

Phenylalanine hydroxylase deficiency

Phenylalanine hydroxylase deficiency is an autosomal recessive disorder that results in intolerance to the dietary intake of the essential amino acid phenylalanine. It occurs in approximately 1:15,000 individuals. Deficiency of this enzyme produces a spectrum of disorders including classic phenylketonuria, mild phenylketonuria, and mild hyperphenylalaninemia. Classic phenylketonuria is caused by a complete or near-complete deficiency of phenylalanine hydroxylase activity and without dietary restriction of phenylalanine most children will develop profound and irreversible intellectual disability. Mild phenylketonuria and mild hyperphenylalaninemia are associated with lower risk of impaired cognitive development in the absence of treatment. Phenylalanine hydroxylase deficiency can be diagnosed by newborn screening based on detection of the presence of hyperphenylalaninemia using the Guthrie microbial inhibition assay or other assays on a blood spot obtained from a heel prick. Since the introduction of newborn screening, the major neurologic consequences of hyperphenylalaninemia have been largely eradicated. Affected individuals can lead normal lives. However, recent data suggest that homeostasis is not fully restored with current therapy. Treated individuals have a higher incidence of neuropsychological problems. The mainstay of treatment for hyperphenylalaninemia involves a low-protein diet and use of a phenylalanine-free medical formula. This treatment must commence as soon as possible after birth and should continue for life. Regular monitoring of plasma phenylalanine and tyrosine concentrations is necessary. Targets of plasma phenylalanine of 120–360 μmol/L (2–6 mg/dL) in the first decade of life are essential for optimal outcome. Phenylalanine targets in adolescence and adulthood are less clear. A significant proportion of patients with phenylketonuria may benefit from adjuvant therapy with 6R-tetrahydrobiopterin stereoisomer. Special consideration must be given to adult women with hyperphenylalaninemia because of the teratogenic effects of phenylalanine. Women with phenylalanine hydroxylase deficiency considering pregnancy should follow special guidelines and assure adequate energy intake with the proper proportion of protein, fat, and carbohydrates to minimize risks to the developing fetus. Molecular genetic testing of the phenylalanine hydroxylase gene is available for genetic counseling purposes to determine carrier status of at-risk relatives and for prenatal testing.

PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes

BackgroundWe propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test.Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient’s phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score.ResultsWhen assigning the same weight to all variants in the exome, PhenoVar predicted the correct diagnosis in 10/20 patients, while in 15/20 the correct diagnosis was among the 4 highest ranked diagnoses. When assigning a higher weight to variants known, or bioinformatically predicted, to cause disease, PhenoVar’s yield increased to 14/20 (18/20 in top 4). No incidental findings were identified using our cut-off phenotypic threshold.ConclusionThe phenotype-driven approach described could render widespread use of ES more practical, ethical and clinically useful. The implications about novel disease identification, advancement of complex diseases and personalized medicine are discussed.

Child and family experiences with inborn errors of metabolism: a qualitative interview study with representatives of patient groups

BackgroundPatient-centered health care for children with inborn errors of metabolism (IEM) and their families is important and requires an understanding of patient experiences, needs, and priorities. IEM-specific patient groups have emerged as important voices within these rare disease communities and are uniquely positioned to contribute to this understanding. We conducted qualitative interviews with IEM patient group representatives to increase understanding of patient and family experiences, needs, and priorities and inform patient-centered research and care.MethodsWe developed a sampling frame of patient groups representing IEM disease communities from Canada, the United States, and United Kingdom. With consent, we interviewed participants to explore their views on experiences, needs, and outcomes that are most important to children with IEM and their families. We analyzed the data using a qualitative descriptive approach to identify key themes and sub-themes.ResultsWe interviewed 18 organizational representatives between February 28 and September 17, 2014, representing 16 IEMs and/or disease categories. Twelve participants voluntarily self-identified as parents and/or were themselves patients. Three key themes emerged from the coded data: managing the uncertainty associated with raising and caring for a child with a rare disease; challenges associated with the affected child’s life transitions, and; the collective struggle for improved outcomes and interventions that rare disease communities navigate.ConclusionHealth care providers can support children with IEM and their families by acknowledging and reducing uncertainty, supporting families through children’s life transitions, and contributing to rare disease communities’ progress toward improved interventions, experiences, and outcomes.

Patient-controlled encrypted genomic data: an approach to advance clinical genomics

BackgroundThe revolution in DNA sequencing technologies over the past decade has made it feasible to sequence an individual’s whole genome at a relatively low cost. The potential value of the information generated by genomic technologies for medicine and society is enormous. However, in order for exome sequencing, and eventually whole genome sequencing, to be implemented clinically, a number of major challenges need to be overcome. For instance, obtaining meaningful informed-consent, managing incidental findings and the great volume of data generated (including multiple findings with uncertain clinical significance), re-interpreting the genomic data and providing additional counselling to patients as genetic knowledge evolves are issues that need to be addressed. It appears that medical genetics is shifting from the present “phenotype-first” medical model to a “data-first” model which leads to multiple complexities.DiscussionThis manuscript discusses the different challenges associated with integrating genomic technologies into clinical practice and describes a “phenotype-first” approach, namely, “Individualized Mutation-weighed Phenotype Search”, and its benefits. The proposed approach allows for a more efficient prioritization of the genes to be tested in a clinical lab based on both the patient’s phenotype and his/her entire genomic data. It simplifies “informed-consent” for clinical use of genomic technologies and helps to protect the patient’s autonomy and privacy. Overall, this approach could potentially render widespread use of genomic technologies, in the immediate future, practical, ethical and clinically useful.SummaryThe “Individualized Mutation-weighed Phenotype Search” approach allows for an incremental integration of genomic technologies into clinical practice. It ensures that we do not over-medicalize genomic data but, rather, continue our current medical model which is based on serving the patient’s concerns. Service should not be solely driven by technology but rather by the medical needs and the extent to which a technology can be safely and effectively utilized.

Scoping review of patient- and family-oriented outcomes and measures for chronic pediatric disease

BackgroundImprovements in health care for children with chronic diseases must be informed by research that emphasizes outcomes of importance to patients and families. To support a program of research in the field of rare inborn errors of metabolism (IEM), we conducted a broad scoping review of primary studies that: (i) focused on chronic pediatric diseases similar to IEM in etiology or manifestations and in complexity of management; (ii) reported patient- and/or family-oriented outcomes; and (iii) measured these outcomes using self-administered tools.MethodsWe developed a comprehensive review protocol and implemented an electronic search strategy to identify relevant citations in Medline, EMBASE, DARE and Cochrane. Two reviewers applied pre-specified criteria to titles/abstracts using a liberal accelerated approach. Articles eligible for full-text review were screened by two independent reviewers with discrepancies resolved by consensus. One researcher abstracted data on study characteristics, patient- and family-oriented outcomes, and self-administered measures. Data were validated by a second researcher.Results4,118 citations were screened with 304 articles included. Across all included reports, the most-represented diseases were diabetes (35%), cerebral palsy (23%) and epilepsy (18%). We identified 43 unique patient- and family-oriented outcomes from among five emergent domains, with mental health outcomes appearing most frequently. The studies reported the use of 405 independent self-administered measures of these outcomes.ConclusionsPatient- and family-oriented research investigating chronic pediatric diseases emphasizes mental health and appears to be relatively well-developed in the diabetes literature. Future research can build on this foundation while identifying additional outcomes that are priorities for patients and families.

Incontinentia Pigmenti in an XY Boy: Case Report and Review of the Literature

Background: Incontinentia pigmenti (IP) is a rare genetic skin disorder with X-linked dominant inheritance and a characteristic sequence of cutaneous manifestations, which is regarded as lethal in XY males. Objective: To report a case of a surviving XY male with the common IKBKG (NEMO) gene deletion confirming IP. Methods and Results: A newborn XY male with suspected IP underwent a skin biopsy on affected tissue for histopathology. Molecular genetic testing was also performed on the specimen and revealed the common IKBKG gene deletion with a pattern suggestive of somatic mosaicism. Our findings are aligned with a PubMed literature review for XY males with IP and documented IKBKG mutation. We determined that only 10 such genetically proven cases have been reported, including our case. Conclusion: Although relatively rare, cases of IP in XY males with the common NEMO mutation have likely been underreported due to the unavailability of appropriate testing in the past. Karyotype and molecular testing should be considered when clinical suspicion of IP arises for a male patient.

Inborn errors of metabolism associated with psychosis: literature review and case–control study using exome data from 5090 adult individuals

A literature review was conducted, using the computerized “Online Mendelian Inheritance in Man” (OMIM) and PubMed, to identify inborn errors of metabolism (IEM) in which psychosis may be a predominant feature or the initial presenting symptom. Different combinations of the following keywords were searched using OMIM: “psychosis”, “schizophrenia”, or “hallucinations” and “metabolic”, “inborn error of metabolism”, “inborn errors of metabolism”, “biochemical genetics”, or “metabolic genetics”. The OMIM search generated 126 OMIM entries, 40 of which were well known IEM. After removing IEM lacking evidence in PubMed for an association with psychosis, 29 OMIM entries were identified. Several of these IEM are treatable. They involve different small organelles (lysosomes, peroxisomes, mitochondria), iron or copper accumulation, as well as defects in other met-abolic pathways (e.g., defects leading to hyperammonemia or homocystinemia). A clinical checklist summarizing the key features of these conditions and a guide to clinical approach are provided. The genes corresponding to each of these con-ditions were identified. Whole exome data from 2545 adult cases with schizophrenia and 2545 unrelated controls, accessed via the Database of Genotypes and Phenotypes (dbGaP), were analyzed for rare functional variants in these genes. The odds ratio of having a rare functional variant in cases versus controls was calculated for each gene. Eight genes are significantly associated with schizophrenia (p < 0.05, OR >1) using an unselected group of adult patients with schizophrenia. Increased awareness of clinical clues for these IEM will optimize referrals and timely metabolic interventions.

Dyssegmental dysplasia, Silverman-Handmaker type: A challenging antenatal diagnosis in a dizygotic twin pregnancy

Dyssegmental dysplasia Silverman‐Handmaker (DDSH; MIM 224410) type is an extremely rare skeletal dysplasia caused by functional null mutations in the perlecan gene. Less than forty cases are reported in the literature, of which only four were prenatally detected.

Metabolomics in patients with psychosis: A systematic review: Metabolomics in patients with psychosis: A systematic review

The purpose of this article is to provide a comprehensive review of metabolomics studies for psychosis, as a means of biomarker discovery. Manuscripts were selected for review if they involved discovery of metabolites using high‐throughput analysis in human subjects and were published in the last decade. The metabolites identified were searched in Human Metabolome Data Base (HMDB) for a link to psychosis. Metabolites associated with psychosis based on evidence in HMBD were then searched using PubMed to explore the availability of further evidence. Almost all of the studies which underwent full review involved patients with schizophrenia. Ten biomarkers were identified. Six of them were reported in two or more independent metabolomics studies: N‐acetyl aspartate, lactate, tryptophan, kynurenine, glutamate, and creatine. Four additional metabolites were encountered in a single metabolomics study but had significant evidence (two supporting articles or more) for a link to psychosis based on PubMed: linoleic acid, D‐serine, glutathione, and 3‐hydroxybutyrate. The pathways affected are discussed as they may be relevant to the pathophysiology of psychosis, and specifically of schizophrenia, as well as, constitute new drug targets for treatment of related conditions. Based on the biomarkers identified, early diagnosis of schizophrenia and/or monitoring may be possible.

Machine learning in schizophrenia genomics, a case-control study using 5,090 exomes

Our hypothesis is that machine learning (ML) analysis of whole exome sequencing (WES) data can be used to identify individuals at high risk for schizophrenia (SCZ). This study applies ML to WES data from 2,545 individuals with SCZ and 2,545 unaffected individuals, accessed via the database of genotypes and phenotypes (dbGaP). Single nucleotide variants and small insertions and deletions were annotated by ANNOVAR using the reference genome hg19/GRCh37. Rare (predicted functional) variants with a minor allele frequency ≤1% and genotype quality ≥90 including missense, frameshift, stop gain, stop loss, intronic, and exonic splicing variants were selected. A file containing all cases and controls, the names of genes with variants meeting our criteria, and the number of variants per gene for each individual, was used for ML analysis. The supervised machine‐learning algorithm used the patterns of variants observed in the different genes to determine which subset of genes can best predict that an individual is affected. Seventy percent of the data was used to train the algorithm and the remaining 30% of data (n = 1,526) was used to evaluate its efficiency. The supervised ML algorithm, gradient boosted trees with regularization (eXtreme Gradient Boosting implementation) was the best performing algorithm yielding promising results (accuracy: 85.7%, specificity: 86.6%, sensitivity: 84.9%, area under the receiver‐operator characteristic curve: 0.95). The top 50 features (genes) of the algorithm were analyzed using bioinformatics resources for new insights about the pathophysiology of SCZ. This manuscript presents a novel predictor which could potentially enable studies exploring disease‐modifying intervention in the early stages of the disease.