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Journal: Human genomics


There is a growing support for the stance that patients and research participants should have better and easier access to their raw (uninterpreted) genomic sequence data in both clinical and research contexts.


Fifty random genetically unstudied families (limb-girdle muscular dystrophy (LGMD)/myopathy) were screened with a gene panel incorporating 759 OMIM genes associated with neurological disorders. Average coverage of the CDS and 10 bp flanking regions of genes was 99 %. All families were referred to the Neurosciences Clinic of King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Patients presented with muscle weakness affecting the pelvic and shoulder girdle. Muscle biopsy in all cases showed dystrophic or myopathic changes. Our main objective was to evaluate a neurological gene panel as a first-line diagnostic test for LGMD/myopathies.

Concepts: Biology, Neurology, Riyadh, Muscular system, Muscular dystrophy, Iraq, Jeddah, King Faisal Specialist Hospital


The X chromosome and X-linked variants have largely been ignored in genome-wide and candidate association studies of infectious diseases due to the complexity of statistical analysis of the X chromosome. This exclusion is significant, since the X chromosome contains a high density of immune-related genes and regulatory elements that are extensively involved in both the innate and adaptive immune responses. Many diseases present with a clear sex bias, and apart from the influence of sex hormones and socioeconomic and behavioural factors, the X chromosome, X-linked genes and X chromosome inactivation mechanisms contribute to this difference. Females are functional mosaics for X-linked genes due to X chromosome inactivation and this, combined with other X chromosome inactivation mechanisms such as genes that escape silencing and skewed inactivation, could contribute to an immunological advantage for females in many infections. In this review, we discuss the involvement of the X chromosome and X inactivation in immunity and address its role in sexual dimorphism of infectious diseases using tuberculosis susceptibility as an example, in which male sex bias is clear, yet not fully explored.


Five affected individuals with syndromic tremulous dystonia, spasticity, and white matter disease from a consanguineous extended family covering a period of over 24 years are presented. A positional cloning approach utilizing genome-wide linkage, homozygozity mapping and whole exome sequencing was used for genetic characterization. The impact of a calmodulin-binding transcription activator 2, (CAMTA2) isoform 2, hypomorphic mutation on mRNA and protein abundance was studied using fluorescent reporter expression cassettes. Human brain sub-region cDNA libraries were used to study the expression pattern of CAMTA2 transcript variants.

Concepts: DNA, Gene, Genetics, Gene expression, Transcription, Molecular biology, Messenger RNA, Transcription factor


MicroRNAs (miRNAs) are a class of non-coding RNA, which have recently been shown to have a wide variety of regulatory functions in relation to gene expression. Since their identification nearly 20 years ago, miRNAs have been found to play an important role in cancer, including in neurofibromatosis type 1 (NF1)-associated tumours. NF1 is the most commonly inherited tumour predisposition syndrome and can lead to malignancy via the development of malignant peripheral nerve sheath tumours (MPNSTs). Although the mechanisms by which benign neurofibromas develop into MPNSTs still remain to be elucidated, it is becoming increasingly clear that miRNAs play a key role in this process and have the potential to be used as both diagnostic and prognostic markers of tumorigenesis.

Concepts: DNA, Gene, Gene expression, Cancer, Oncology, RNA, Malignant peripheral nerve sheath tumor, Nerve sheath tumor


Chronic alcohol consumption is a significant cause of liver disease worldwide. Several biochemical mechanisms have been linked to the initiation and progression of alcoholic liver disease (ALD) such as oxidative stress, inflammation, and metabolic dysregulation, including the disruption of NAD+/NADH. Indeed, an ethanol-mediated reduction in hepatic NAD+ levels is thought to be one factor underlying ethanol-induced steatosis, oxidative stress, steatohepatitis, insulin resistance, and inhibition of gluconeogenesis. Therefore, we applied a NAD+ boosting supplement to investigate alterations in the pathogenesis of early-stage ALD.


Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.

Concepts: DNA, Gene, Genetics, Epidemiology, Human Genome Project, Genome, Genomics, Natural environment


Alternative splicing is a major contributor to cellular diversity. Therefore the identification and quantification of differentially spliced transcripts in genome-wide transcript analysis is an important consideration. Here, I review the software available for analysis of RNA-Seq data for differential splicing and discuss intrinsic challenges for differential splicing analyses. Three approaches to differential splicing analysis are described, along with their associated software implementations, their strengths, limitations, and caveats. Suggestions for future work include more extensive experimental validation to assess accuracy of the software predictions and consensus formats for outputs that would facilitate visualizations, data exchange, and downstream analyses.

Concepts: Scientific method, Gene expression, Critical thinking, Intron, Mathematical analysis, Computer program, Logic, Microsoft


The recent coronavirus disease (COVID-19), caused by SARS-CoV-2, is inarguably the most challenging coronavirus outbreak relative to the previous outbreaks involving SARS-CoV and MERS-CoV. With the number of COVID-19 cases now exceeding 2 million worldwide, it is apparent that (i) transmission of SARS-CoV-2 is very high and (ii) there are large variations in disease severity, one component of which may be genetic variability in the response to the virus. Controlling current rates of infection and combating future waves require a better understanding of the routes of exposure to SARS-CoV-2 and the underlying genomic susceptibility to this disease. In this mini-review, we highlight possible genetic determinants of COVID-19 and the contribution of aerosol exposure as a potentially important transmission route of SARS-CoV-2.


In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.