Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.

BACKGROUND: Non-linear relationships at the genotype level are essential in understanding the genetic interactions of complex disease traits. Genome-wide association Studies (GWAS) have revealed statistical association of the SNPs in many complex diseases. As GWAS results could not thoroughly reveal the genetic background of these disorders, Genome-Wide Interaction Studies have started to gain importance. In recent years, various statistical approaches, such as entropy-based methods, have been suggested for revealing these non-additive interactions between variants. This study presents a novel prioritization workflow integrating two-step Random Forest (RF) modeling and entropy analysis after PLINK filtering. PLINK-RF-RF workflow is followed by an entropy-based 3-way interaction information (3WII) method to capture the hidden patterns resulting from non-linear relationships between genotypes in Late-Onset Alzheimer Disease to discover early and differential diagnosis markers. RESULTS: Three models from different datasets are developed by integrating PLINK-RF-RF analysis and entropy-based three-way interaction information (3WII) calculation method, which enables the detection of the third-order interactions, which are not primarily considered in epistatic interaction studies. A reduced SNP set is selected for all three datasets by 3WII analysis by PLINK filtering and prioritization of SNP with RF-RF modeling, promising as a model minimization approach. Among SNPs revealed by 3WII, 4 SNPs out of 19 from GenADA, 1 SNP out of 27 from ADNI, and 4 SNPs out of 106 from NCRAD are mapped to genes directly associated with Alzheimer Disease. Additionally, several SNPs are associated with other neurological disorders. Also, the genes the variants mapped to in all datasets are significantly enriched in calcium ion binding, extracellular matrix, external encapsulating structure, and RUNX1 regulates estrogen receptor-mediated transcription pathways. Therefore, these functional pathways are proposed for further examination for a possible LOAD association. Besides, all 3WII variants are proposed as candidate biomarkers for the genotyping-based LOAD diagnosis. CONCLUSION: The entropy approach performed in this study reveals the complex genetic interactions that significantly contribute to LOAD risk. We benefited from the entropy-based 3WII as a model minimization step and determined the significant 3-way interactions between the prioritized SNPs by PLINK-RF-RF. This framework is a promising approach for disease association studies, which can also be modified by integrating other machine learning and entropy-based interaction methods.

Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.

Mobile element insertions (MEIs) are a known cause of genetic disease but have been underexplored due to technical limitations of genetic testing methods. Various bioinformatic tools have been developed to identify MEIs in Next Generation Sequencing data. However, most tools have been developed specifically for genome sequencing (GS) data rather than exome sequencing (ES) data, which remains more widely used for routine diagnostic testing. In this study, we benchmarked six MEI detection tools (ERVcaller, MELT, Mobster, SCRAMble, TEMP2 and xTea) on ES data and on GS data from publicly available genomic samples (HG002, NA12878). For all the tools we evaluated sensitivity and precision of different filtering strategies. Results show that there were substantial differences in tool performance between ES and GS data. MELT performed best with ES data and its combination with SCRAMble increased substantially the detection rate of MEIs. By applying both tools to 10,890 ES samples from Solve-RD and 52,624 samples from Radboudumc we were able to diagnose 10 patients who had remained undiagnosed by conventional ES analysis until now. Our study shows that MELT and SCRAMble can be used reliably to identify clinically relevant MEIs in ES data. This may lead to an additional diagnosis for 1 in 3000 to 4000 patients in routine clinical ES.

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing.

BACKGROUND: Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. RESULTS: We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. CONCLUSION: We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques.

Spatio-temporal dynamics enhance cellular diversity, neuronal function and further maturation of human cerebral organoids.

The bioengineerined and whole matured human brain organoids stand as highly valuable three-dimensional in vitro brain-mimetic models to recapitulate in vivo brain development, neurodevelopmental and neurodegenerative diseases. Various instructive signals affecting multiple biological processes including morphogenesis, developmental stages, cell fate transitions, cell migration, stem cell function and immune responses have been employed for generation of physiologically functional cerebral organoids. However, the current approaches for maturation require improvement for highly harvestable and functional cerebral organoids with reduced batch-to-batch variabilities. Here, we demonstrate two different engineering approaches, the rotating cell culture system (RCCS) microgravity bioreactor and a newly designed microfluidic platform (µ-platform) to improve harvestability, reproducibility and the survival of high-quality cerebral organoids and compare with those of traditional spinner and shaker systems. RCCS and µ-platform organoids have reached ideal sizes, approximately 95% harvestability, prolonged culture time with Ki-67 + /CD31 + /ß-catenin+ proliferative, adhesive and endothelial-like cells and exhibited enriched cellular diversity (abundant neural/glial/ endothelial cell population), structural brain morphogenesis, further functional neuronal identities (glutamate secreting glutamatergic, GABAergic and hippocampal neurons) and synaptogenesis (presynaptic-postsynaptic interaction) during whole human brain development. Both organoids expressed CD11b + /IBA1 + microglia and MBP + /OLIG2 + oligodendrocytes at high levels as of day 60. RCCS and µ-platform organoids showing high levels of physiological fidelity a high level of physiological fidelity can serve as functional preclinical models to test new therapeutic regimens for neurological diseases and benefit from multiplexing.

Essential Oil and Supercritical Carbon Dioxide Extract of Grapefruit Peels Formulated for Candida albicans Infections: Evaluation by an in Vitro Model to Study Fungal-Host Interactions.

Resistance to currently available antifungal agents raises the need to develop alternative remedies. Candida albicans is the most common opportunistic pathogenic fungus of humans, colonizing in the genital and intestinal mucosa, skin, and oral-nasal cavity and reducing quality of life. Herein, essential oil from grapefruit (Citrus paradise) peels was obtained by hydrodistillation, and the remaining plant material was sequentially subjected to supercritical carbon dioxide (SC-CO2) extraction to determine the conditions for maximizing phenolic compounds. A statistical design was used to evaluate the effect of temperature (30, 50, 70 °C), pressure (80, 150, 220 bar), and ethanol as a cosolvent (0%, 10%, and 20% v/v). Essential oil and SC-CO2 extracts were mixed at various ratios to develop an effective antifungal formulation. Subsequently, fungal infection was modeled by coculturing C. albicans with human skin keratinocytes (HaCaT) to mimic dermal mycoses, endothelial cells (HUVEC) to evaluate vascular fate, and cervical adenocarcinoma (HeLa) cells to represent additional genital mycoses. Treatment with essential oil and extract (25:75%) formulation for 8 h exhibited slight cytotoxicity toward HeLa cells, no toxicity toward HaCaT and HUVECs, whereas inhibition of C. albicans. Considering the clinical significance, such in vitro models are essential to screen potential compounds for the treatment of opportunistic fungal infections.

Decellularised extracellular matrix-based biomaterials for repair and regeneration of central nervous system.

The central nervous system (CNS), consisting of the brain and spinal cord, regulates the mind and functions of the organs. CNS diseases, leading to changes in neurological functions in corresponding sites and causing long-term disability, represent one of the major public health issues with significant clinical and economic burdens worldwide. In particular, the abnormal changes in the extracellular matrix under various disease conditions have been demonstrated as one of the main factors that can alter normal cell function and reduce the neuroregeneration potential in damaged tissue. Decellularised extracellular matrix (dECM)-based biomaterials have been recently utilised for CNS applications, closely mimicking the native tissue. dECM retains tissue-specific components, including proteoglycan as well as structural and functional proteins. Due to their unique composition, these biomaterials can stimulate sensitive repair mechanisms associated with CNS damages. Herein, we discuss the decellularisation of the brain and spinal cord as well as recellularisation of acellular matrix and the recent progress in the utilisation of brain and spinal cord dECM.

Genome-wide variant calling in reanalysis of exome sequencing data uncovered a pathogenic TUBB3 variant.

Almost half of all individuals affected by intellectual disability (ID) remain undiagnosed. In the Solve-RD project, exome sequencing (ES) datasets from unresolved individuals with (syndromic) ID (n = 1,472 probands) are systematically reanalyzed, starting from raw sequencing files, followed by genome-wide variant calling and new data interpretation. This strategy led to the identification of a disease-causing de novo missense variant in TUBB3 in a girl with severe developmental delay, secondary microcephaly, brain imaging abnormalities, high hypermetropia, strabismus and short stature. Interestingly, the TUBB3 variant could only be identified through reanalysis of ES data using a genome-wide variant calling approach, despite being located in protein coding sequence. More detailed analysis revealed that the position of the variant within exon 5 of TUBB3 was not targeted by the enrichment kit, although consistent high-quality coverage was obtained at this position, resulting from nearby targets that provide off-target coverage. In the initial analysis, variant calling was restricted to the exon targets ± 200 bases, allowing the variant to escape detection by the variant calling algorithm. This phenomenon may potentially occur more often, as we determined that 36 established ID genes have robust off-target coverage in coding sequence. Moreover, within these regions, for 17 genes (likely) pathogenic variants have been identified before. Therefore, this clinical report highlights that, although compute-intensive, performing genome-wide variant calling instead of target-based calling may lead to the detection of diagnostically relevant variants that would otherwise remain unnoticed.

Advances in microfluidic synthesis and coupling with synchrotron SAXS for continuous production and real-time structural characterization of nano-self-assemblies.

Microfluidic platforms have become highly attractive tools for synthesis of nanoparticles, including lipid nano-self-assemblies, owing to unique features and at least three important aspects inherent to miniaturized micro-devices. Firstly, the fluids flow under controlled conditions in the microchannels, providing well-defined flow profiles and shorter diffusion lengths that play important roles in enhancing the continuous production of lipid and polymer nanoparticles with relatively narrow size distributions. Secondly, various geometries adapted to microfluidic device designs can be utilized for enhancing the colloidal stability of nanoparticles and improving their drug loading. Thirdly, microfluidic devices are usually compatible with in situ characterization methods for real-time monitoring of processes occurring inside the microchannels. This is unlike conventional nanoparticle synthesis methods, where a final solution or withdrawn aliquots are separately analysed. These features inherent to microfluidic devices provide a tool-set allowing not only precise nanoparticle size control, but also real-time analyses for process optimization. In this review, we focus on recent advances and developments in the use of microfluidic devices for synthesis of lipid nanoparticles. We present different designs based on hydrodynamic flow focusing, droplet-based methods and controlled microvortices, and discuss integration of microfluidic platforms with synchrotron small-angle X ray scattering (SAXS) for in situ structural characterization of lipid nano-self-assemblies under continuous flow conditions, along with major challenges and future directions in this research area.