COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model.

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model.

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N-glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N-glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

Resolution of SLC6A1 variable expressivity in a multi-generational family using deep clinical phenotyping and Drosophila models.

Purpose: Variants in SLC6A1 result in a rare neurodevelopmental disorder characterized by a variable clinical presentation of symptoms including developmental delay, epilepsy, motor dysfunction, and autism spectrum disorder. SLC6A1 haploinsufficiency has been confirmed as the predominant pathway of SLC6A1-related neurodevelopmental disorders (NDDs), however, the molecular mechanism underlying the variable clinical presentation remains unclear. Methods: Here, through work of the Undiagnosed Diseases Network, we identify an undiagnosed individual with an inherited p.(A334S) variant of uncertain significance. To resolve this case and better understand the variable expressivity with SLC6A1, we assess the phenotypes of the proband with a cohort of cases diagnosed with SLC6A1-related NDDs. We then create an allelic series in the Drosophila melanogaster to functionally characterize case variants. Results: We identify significant clinical overlap between the unsolved case and confirmed cases of SLC6A1-related NDDs and find a mild to severe clinical presentation associated with missense variants. We confirm phenotypes in flies expressing SLC6A1 variants consistent with a partial loss-of-function mechanism. Conclusion: We conclude that the p.(A334S) variant is a hypomorphic allele and begin to elucidate the underlying variability in SLC6A1-related NDDs. These insights will inform clinical diagnosis, prognosis, treatment and inform therapeutic design for those living with SLC6A1-related NDDs.

Influence of hydrological factors on bacterial community structure in a tropical monsoonal estuary in India.

In the present study, we analyzed variations in bacterial community structure along a salinity gradient in a tropical monsoonal estuary (Cochin estuary [CE]), on the southwest coast of India, using Illumina next-generation sequencing (NGS). Water samples were collected from eight different locations thrice a year to assess the variability in the bacterial community structure and to determine the physico-chemical factors influencing the bacterial diversity. Proteobacteria was the most dominant phyla in the estuary followed by Bacteroidetes, Cyanobacteria, Actinobacteria, and Firmicutes. Statistical analysis indicated significant variations in bacterial communities between freshwater and mesohaline and euryhaline regions, as well as between the monsoon (wet) and nonmonsoon (dry) periods. The abundance of Betaproteobacteria was higher in the freshwater regions, while Alphaproteobacteria and Epsilonproteobactera were more abundant in mesohaline and euryhaline regions of the estuary. Gammaproteobacteria was more abundant in regions with high nutrient concentrations. Various bacterial genera indicating the presence of fecal contamination and eutrophication were detected. Corrplot based on Pearson correlation analysis demonstrated the important physico-chemical variables (temperature, salinity, dissolved oxygen, and inorganic nutrients) that influence the distribution of dominant phyla, class, and genera. The observed spatio-temporal variations in bacterial community structure in the CE were governed by regional variations in anthropogenic inputs and seasonal variations in monsoonal rainfall and tidal influx.

Patterns of psychopathology and cognition in sex chromosome aneuploidy.

BACKGROUND: Sex chromosome aneuploidies (SCAs) are a collectively common family of genetic disorders that increase the risk for neuropsychiatric and cognitive impairment. Beyond being important medical disorders in their own right, SCAs also offer a unique naturally occurring model for studying X- and Y-chromosome influences on the human brain. However, it remains unclear if (i) different SCAs are associated with different profiles of psychopathology and (ii) the notable interindividual variation in psychopathology is related to co-occurring variation in cognitive ability. METHODS: We examined scores for 11 dimensions of psychopathology [Child/Adult Behavior Checklist (CBCL)] and general cognitive ability [full-scale IQ (FSIQ) from Wechsler tests] in 110 youth with varying SCAs (XXY = 41, XYY = 22, XXX = 27, XXYY = 20) and 131 typically developing controls (XX = 59, XY = 72). RESULTS: All SCAs were associated with elevated CBCL scores across several dimensions of psychopathology (two-sample t tests comparing the euploidic and aneuploidic groups [all |T| > 9, and p < 0.001]). Social and attentional functioning were particularly sensitive to the carriage of a supernumerary Y-chromosome. In particular, the XYY group evidenced significantly more social problems than both extra-X groups (Cohen's d effect size > 0.5, Bonferroni corrected p < .05). There was marked variability in CBCL scores within each SCA group, which generally correlated negatively with IQ, but most strongly so for social and attentional difficulties (standardized ß, - 0.3). These correlations showed subtle differences as a function of the SCA group and CBCL scale. CONCLUSIONS: There is domain-specific variation in psychopathology across SCA groups and domain-specific correlation between psychopathology and IQ within SCAs. These findings (i) help to tailor clinical assessment of this common and impactful family of genetic disorders and (ii) suggest that dosage abnormalities of X- and Y-linked genes impart somewhat distinct profiles of neuropsychiatric risk.

A Targeted Next Generation Sequencing Panel for Non-syndromic Early Onset Severe Obesity and Identification of Novel Likely -Pathogenic Variants in the MC4R and LEP Genes.

OBJECTIVES: To screen for variants in the MC4R and LEP genes in 46 patients with clinical suspicion of non-syndromic early onset severe obesity (NEOSO). METHODS: Children with early onset obesity satisfying WHO criteria of obesity were studied. The MC4R and LEP genes were sequenced using a PCR amplicon based NGS on Illumina MiSeq next generation sequencer using an in-house developed protocol. RESULTS: Of the 46 children tested, four were found to have novel pathogenic/likely-pathogenic variants (one in the MC4R gene and three in the LEP gene). In three out of the 4 families, the presence of the variants was confirmed using standard bidirectional capillary sequencing in the probands. CONCLUSIONS: Four children with novel likely pathogenic variants in the MC4R and LEP genes are reported. Genetic analysis is crucial in children with early onset obesity and should be considered.

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data.

PURPOSE: The purpose of OncoMX1 knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types. METHODS: Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database. RESULTS: OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs. CONCLUSION: OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.