ALG13-Congenital Disorder of Glycosylation (ALG13-CDG): Updated clinical and molecular review and clinical management guidelines.

ALG13-Congenital Disorder of Glycosylation (CDG), is a rare X-linked CDG caused by pathogenic variants in ALG13 (OMIM 300776) that affects the N-linked glycosylation pathway. Affected individuals present with a predominantly neurological manifestation during infancy. Epileptic spasms are a common presenting symptom of ALG13-CDG. Other common phenotypes include developmental delay, seizures, intellectual disability, microcephaly, and hypotonia. Current management of ALG13-CDG is targeted to address patients' symptoms. To date, less than 100 individuals have been reported with ALG13-CDG. In this article, an international group of experts in CDG reviewed all reported individuals affected with ALG13-CDG and suggested diagnostic and management guidelines for ALG13-CDG. The guidelines are based on the best available data and expert opinion. Neurological symptoms dominate the phenotype of ALG13-CDG where epileptic spasm is confirmed to be the most common presenting symptom of ALG13-CDG in association with hypotonia and developmental delay. We propose that ACTH/prednisolone treatment should be trialed first, followed by vigabatrin, however ketogenic diet has been shown to have promising results in ALG13-CDG. In order to optimize medical management, we also suggest early cardiac, gastrointestinal, skeletal, and behavioral assessments in affected patients.

Neural and metabolic dysregulation in PMM2-deficient human in vitro neural models.

Phosphomannomutase 2-congenital disorder of glycosylation (PMM2-CDG) is a rare inborn error of metabolism caused by deficiency of the PMM2 enzyme, which leads to impaired protein glycosylation. While the disorder presents with primarily neurological symptoms, there is limited knowledge about the specific brain-related changes caused by PMM2 deficiency. Here, we demonstrate aberrant neural activity in 2D neuronal networks from PMM2-CDG individuals. Utilizing multi-omics datasets from 3D human cortical organoids (hCOs) derived from PMM2-CDG individuals, we identify widespread decreases in protein glycosylation, highlighting impaired glycosylation as a key pathological feature of PMM2-CDG, as well as impaired mitochondrial structure and abnormal glucose metabolism in PMM2-deficient hCOs, indicating disturbances in energy metabolism. Correlation between PMM2 enzymatic activity in hCOs and symptom severity suggests that the level of PMM2 enzyme function directly influences neurological manifestations. These findings enhance our understanding of specific brain-related perturbations associated with PMM2-CDG, offering insights into the underlying mechanisms and potential directions for therapeutic interventions.

Defining the phenotype of PGAP3-congenital disorder of glycosylation; a review of 65 cases.

Biallelic pathogenic variants in PGAP3 cause a rare glycosylphosphatidyl-inositol biogenesis disorder, PGAP3-CDG. This multisystem condition presents with a predominantly neurological phenotype, including developmental delay, intellectual disability, seizures, and hyperphosphatemia. Here, we summarized the phenotype of sixty-five individuals including six unreported individuals from our CDG natural history study with a confirmed PGAP3-CDG diagnosis. Common additional features found in this disorder included brain malformations, behavioral abnormalities, cleft palate, and characteristic facial features. This report aims to review the genetic and metabolic findings and characterize the disease's phenotype while highlighting the necessary clinical approach to improve the management of this rare CDG.

Long-term outcomes in ALG13-Congenital Disorder of Glycosylation.

ALG13-CDG is a rare X-linked disorder of N-linked glycosylation. Given the lack of long-term outcome data in ALG13-CDG, we collected natural history data and reviewed individuals surviving to young adulthood with confirmed pathogenic variants in ALG13 in our own cohort and in the literature. From the 14 ALG13-CDG patients enrolled into our Frontiers of Congenital Disorders of Glycosylation Consortium natural history study only two patients were older than 16 years; one of these two females is so far unreported. From the 52 patients described in the medical literature with confirmed pathogenic variants in ALG13 only five patients were older than 16 years (all females), in addition to the new, unreported patient from our natural history study. Two male patients have died due to ALG13-CDG, and there were no surviving males older than 16 years with a confirmed ALG13-CDG diagnosis. Our adolescent and young adult cohort of six patients presented with epilepsy, muscular hypotonia, speech, and developmental delay. Intellectual disability was present in all female patients with ALG13-CDG. Unreported features included ataxia, neuropathy, and severe gastrointestinal symptoms requiring G/J tube placement. In addition, two patients from our natural history study developed unilateral hearing loss. Skeletal abnormalities were found in four patients, including osteopenia and scoliosis. Major health problems included persistent seizures in three patients. Ketogenic diet was efficient for seizures in three out of four patients. Although all patients were mobile, they all had severe communication problems with mostly absent speech and were unable to function without parental support. In summary, long-term outcome in ALG13-CDG includes gastrointestinal and skeletal involvement in addition to a chronic, mostly non-progressive neurologic phenotype.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Substance Use Disorder in the COVID-19 Pandemic: A Systematic Review of Vulnerabilities and Complications.

As the world endures the coronavirus disease 2019 (COVID-19) pandemic, the conditions of 35 million vulnerable individuals struggling with substance use disorders (SUDs) worldwide have not received sufficient attention for their special health and medical needs. Many of these individuals are complicated by underlying health conditions, such as cardiovascular and lung diseases and undermined immune systems. During the pandemic, access to the healthcare systems and support groups is greatly diminished. Current research on COVID-19 has not addressed the unique challenges facing individuals with SUDs, including the heightened vulnerability and susceptibility to the disease. In this systematic review, we will discuss the pathogenesis and pathology of COVID-19, and highlight potential risk factors and complications to these individuals. We will also provide insights and considerations for COVID-19 treatment and prevention in patients with SUDs.