An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases

Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) has become a powerful tool for WWTP, and is a challenge due to more complex multidimensional pattern recognition. Herein, weighted average ensemble strategy was conducted to assemble multiple diverse basic models to obtain better prediction capability to optimize carbon source addition (Model-1) and further control surplus sludge yield (Model-2). The ensemble models significantly outperformed all single models with MAE of 5.82 g/m3, MSE of 60.59 and R2 value of 0.98 in Model-1 and MAE of 15.09 g/m3, MSE of 449.01 and R2 value of 0.93 in Model-2. The optimal input feature subset was explored to reduce model complexity, indicating that the final ensemble models can predict with high precision using relatively few features with MAE of 6.41 g/m3, MSE of 78.49 and R2 value of 0.97 in Model-1 and MAE of 12.82 g/m3, MSE of 232.71 and R2 value of 0.95 in Model-2. Furthermore, the final models were deployed into an offline web application to facilitate their utility in real-world settings, demonstrating 47.25 % savings in carbon source addition and 15.89 % reductions in surplus sludge yield for an extra month of running. This work offers an efficient approach for the WWTP to optimize carbon source addition and provides new insights into controlling surplus sludge yield.

VUStruct: a compute pipeline for high throughput and personalized structural biology

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Probable digenic inheritance of Diamond-Blackfan anemia

A 26-year-old female proband with a clinical diagnosis and consistent phenotype of Diamond-Blackfan anemia (DBA, OMIM 105650) without an identified genotype was referred to the Undiagnosed Diseases Network. DBA is classically associated with monoallelic variants that have an autosomal-dominant or -recessive mode of inheritance. Intriguingly, her case was solved by a detection of a digenic interaction between non-allelic RPS19 and RPL27 variants. This was confirmed with a machine learning structural model, co-segregation analysis, and RNA sequencing. This is the first report of DBA caused by a digenic effect of two non-allelic variants demonstrated by machine learning structural model. This case suggests that atypical phenotypic presentations of DBA may be caused by digenic inheritance in some individuals. We also conclude that a machine learning structural model can be useful in detecting digenic models of possible interactions between products encoded by alleles of different genes inherited from non-affected carrier parents that can result in DBA with an unrealized 25% recurrence risk.

Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease

Albinism is characterized by a variable degree of hypopigmentation affecting the skin and the hair, and causing ophthalmologic abnormalities. Its oculocutaneous, ocular and syndromic forms follow an autosomal or X-linked recessive mode of inheritance, and 22 disease-causing genes are implicated in their development. Our aim was to clarify the genetic background of a Hungarian albinism cohort. Using a 22-gene albinism panel, the genetic background of 11 of the 17 Hungarian patients was elucidated. In patients with unidentified genetic backgrounds (n = 6), whole exome sequencing was performed. Our investigations revealed a novel, previously unreported rare variant (N687S) of the two-pore channel two gene (TPCN2). The N687S variant of the encoded TPC2 protein is carried by a 15-year-old Hungarian male albinism patient and his clinically unaffected mother. Our segregational analysis and in vitro functional experiments suggest that the detected novel rare TPCN2 variant alone is not a disease-causing variant in albinism. Deep genetic analyses of the family revealed that the patient also carries a phenotype-modifying R305W variant of the OCA2 protein, and he is the only family member harboring this genotype. Our results raise the possibility that this digenic combination might contribute to the observed differences between the patient and the mother, and found the genetic background of the disease in his case.

Faster and more accurate pathogenic combination predictions with VarCoPP2.0

BACKGROUND

The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture.

RESULTS

We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database ( https://olida.ibsquare.be ). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels.

CONCLUSIONS

Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform ( https://orval.ibsquare.be ) to apply VarCoPP2.0 on their data.