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A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity.

ABSTRACT: The human genetic dissection of clinical phenotypes is complicated by genetic heterogeneity. Gene burden approaches that detect genetic signals in case-control studies are underpowered in genetically heterogeneous cohorts. We therefore developed a genome-wide computational method, network-based heterogeneity clustering (NHC), to detect physiological homogeneity in the midst of genetic heterogeneity. Simulation studies showed our method to be capable of systematically converging genes in biological proximity on the background biological interaction network, and capturing gene clusters harboring presumably deleterious variants, in an efficient and unbiased manner. We applied NHC to whole-exome sequencing data from a cohort of 122 individuals with herpes simplex encephalitis (HSE), including 13 individuals with previously published monogenic inborn errors of TLR3-dependent IFN-α/β immunity. The top gene cluster identified by our approach successfully detected and prioritized all causal variants of five TLR3 pathway genes in the 13 previously reported individuals. This approach also suggested candidate variants of three reported genes and four candidate genes from the same pathway in another ten previously unstudied individuals. TLR3 responsiveness was impaired in dermal fibroblasts from four of the five individuals tested, suggesting that the variants detected were causal for HSE. NHC is, therefore, an effective and unbiased approach for unraveling genetic heterogeneity by detecting physiological homogeneity.

SUBMITTER: Zhang P

PROVIDER: S-EPMC8206396 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Casuarina equisetifolia is an important tree of the forest, cultivated in tropical and subtropical regions, providing fuelwood, land reclamation, dune stabilization, paper production, and nitrogen fixation. We have developed a systematic in vitro propagation protocol in C. equisetifolia using nodal segments (NS). Murashige and Skoog (MS) medium augmented with BA (5.0 μM) and NAA (0.5 μM) gave rise to a maximum of 32.00 ± 0.31 shoots per explant (S/E) with shoot length (SL) of 3.94 ± 0.02 cm, and a maximum of 70% regeneration potential (RP) was recorded after 8 weeks of post inoculation. For root induction, in vitro derived shoots were transferred to the nutrient medium consisting of a half-strength (½) MS medium augmented with 2.5 μM NAA, which produced a maximum of 12.68 ± 0.33 roots/shoot (R/S) with 3.04 ± 0.50 cm root length (RL) in 60% of culture after 6 weeks. Micropropagated plants with healthy shoots and roots were successfully acclimatized in vermicompost + garden soil + sand (1:2:1) and a maximum survival percentage of 95.1% was recorded. NS was taken from a 6-weeks-old in vitro derived plant of C. equisetifolia for synthetic seed production, and it was reported that CaCl2 · 2H2O (100 mM) + Na2-alginate (4%) resulted in clear and uniform beads. Furthermore, the maximum conversion of synthetic seeds into plantlets occurred over a period of 4 weeks of storage at 4°C. Scanning Electron Microscopy (SEM) revealed the formation of direct shoot buds without any intermediate callus formation. In addition, the chlorophyll and carotenoid contents of the direct regenerated and mother plant were compared. Similarly, RAPD and ISSR primers were used for genetic homogeneity assessment of the direct regenerated plants, where a total of 18 and 19, respectively, clear and reproducible bands with 100% monomorphism were recorded. The developed micropropagation protocol can certainly be used for large-scale multiplication and germplasm preservation of C. equisetifolia. It will also help in meeting the growing demands of C. equisetifolia in the forest industry.

Project description:BackgroundComputational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.ObjectiveThis scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.MethodsFive databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.ResultsOf the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.ConclusionsThe review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

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A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity.

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