Biology subjects

Vihinen, M.

Publications and source records attributed to Vihinen, M..

4 recordsLinked to original sources

bioRxiv2024

Optimizing clinical interpretability of functional evidence in epilepsy-related ion channel variants

Variants in genes encoding the voltage-gated ion channels are among the most common monogenic causes of epilepsy and neurodevelopmental disorders. Functional effects of a variant are increasingly important for diagnosis and therapeutic decisions. To incorporate knowledge regarding functional consequences in formal clinical variant interpretation, we developed an approach for evaluating multiple functional measurements within the Bayesian framework of the modified ACMG/AMP guidelines. We analyzed 216 functional assessments of 191 variants in SCN1A (n=74), SCN2A (n=66), SCN3A (n=18), and SCN8A (n=33). Of 20 commonly measured biophysical parameters, the most frequent drivers of overall functional consequence were persistent current (f=0.54), voltage dependence of activation (f=0.51), and voltage dependence of fast inactivation (f=0.40) for gain-of-function and peak current (f=0.87) for loss-of-function. By comparing measurements of 23 benign variants, we determined thresholds by which published data on these four parameters confer Strong evidence of variant pathogenicity (likelihood ratio > 18.7) under the ACMG/AMP rubric. Similarly, we delineated evidence weights for the most common epilepsy-related potassium channel gene, KCNQ2, through reports of 80 pathogenic and 24 benign variants, accounting for heterozygous and homozygous experimental conditions. We collected the resulting categorization of functional data into FENICS, a biomedical ontology of 152 standardized terms for coherent annotation of electrophysiological results. Across 271 variants in SCN1A/2A/3A/8A and KCNQ2, 1,731 annotations are available in ClinVar, facilitating use of this evidence in variant classification. In summary, we introduce and apply an ACMG/AMP-calibrated framework for electrophysiological studies in epilepsy-related channelopathies to delineate the impact of functional evidence on clinical variant interpretation.

genetics↗

bioRxiv2023

TNFa hinders FGF4 efficacy to mitigate ALS astrocyte dysfunction and cGAS-STING pathway-induced innate immune reactivity

Astrocytes play an important role in the onset and progression of amyotrophic lateral sclerosis (ALS), a fatal disorder characterized by the relentless degeneration of motor neurons (MNs) in the central nervous system. Despite evidence showing that ALS astrocytes are toxic to MNs, little is understood about the earliest pathological changes that lead to their neurotoxic phenotype. In this study, we generated human astrocytes from induced pluripotent stem cells (iPSCs) harboring the ALS-associated A4V mutation in superoxide dismutase 1 (SOD1), to examine cellular pathways and network changes similar to early stages of the disease. By using proteomics as a molecular indicator, we observed significant alterations in the levels of proteins linked to ALS pathology and the cGAS-STING pathway-induced innate immunity. Interestingly, we found that the protein profile of reactive ALS astrocytes differed from that of wildtype astrocytes treated with the pro-inflammatory cytokine TNF. Notably, we showed that fibroblast growth factor 4 (FGF4) reversed ALS astrocyte dysfunction and reactivity, but failed to provide protection to MNs when expressed in the spinal cord of the SOD1G93A mouse model of ALS. Further analysis showed that ALS astrocyte reactivity which was rescued by FGF4 was abrogated by TNF. The latter is capable of exacerbating the dysfunction and reactivity of ALS astrocytes compared to control. Our data show that iPSC-derived ALS astrocytes are dysfunctional and spontaneously exhibit a reactive phenotype when generated from iPSCs. This suggests that this phenotype may resemble the early stages of the disease. Our data also demonstrate that reducing mutant astrocyte reactivity in vivo using FGF4 is not sufficient to prevent MN death in a mouse model of ALS. To mitigate ALS, future studies should investigate whether dual therapies that both lower astrocyte reactivity and reverse disease-associated cellular dysfunction could prevent MN death. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/566131v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@9490b5org.highwire.dtl.DTLVardef@1a897e5org.highwire.dtl.DTLVardef@18c4578org.highwire.dtl.DTLVardef@24da7d_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- ALS astrocytes are dysfunctional and reactive compared to wildtype astrocytes - FGF4 reverses ALS astrocyte dysfunction and reactivity - FGF4 lowers ALS astrocyte reactivity in vivo but fails to protect ALS motor neurons from death - ALS astrocyte reactivity rescued by FGF4 is attenuated by TNF

neuroscience↗

bioRxiv2019

Variation benchmark datasets: update, criteria, quality and applications

Development of new computational methods and testing their performance has to be done on experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets. They have been used for training and benchmarking predictors for various types of variations and their effects. There are 419 new datasets from 109 papers containing altogether 329003373 variants; however there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property predictions for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performance to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and showed that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.\n\nAUTHOR SUMMARYA prediction method performance can only be assessed in comparison to existing knowledge. For that purpose benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. We collected variation datasets from literature, website and databases. There are 419 separate new datasets, which however contain plenty of redundancy. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property predictions for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. The updated VariBench facilitates development and testing of new methods and comparison of obtained performance to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies and showed that such comparisons are possible and useful when the details of studies and the datasets are shared.

bioinformatics↗

bioRxiv2018

How good are pathogenicity predictors in detecting benign variants?

Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of computational tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency [≥]1% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations. PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%). While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing. The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.\n\nAUTHOR SUMMARYIn precision/personalized medicine of many conditions it is essential to investigate individuals genome. Interpretation of the observed variation (mutation) sets is feasible only with computational approaches. We assessed the performance of variant pathogenicity/tolerance prediction programs on benign variants. Variants were obtained from high-quality ExAC database and selected to have minor allele frequency between 1 and 25%. We obtained 63,160 such cases and investigated 10 widely used predictors. Specificities of the methods showed large differences, from 0.64 to 0.96%, thus users of these methods have to be careful when choosing the one(s) they will use. We investigated further the performances on different populations, allele frequencies, separately for males and females, chromosome wise and for population unique and non-unique variants. The ranking of the tools remained the same in all these scenarios, i.e. the best methods were the best irrespective on how the data is filtered and grouped. This is to our knowledge the first evaluation of method performance on benign variants.

bioinformatics↗