Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity.

Immune responses are tightly regulated yet highly variable between individuals. To investigate human population variation of trained immunity, we immunized healthy individuals with Bacillus Calmette-Guérin (BCG). This live-attenuated vaccine induces not only an adaptive immune response against tuberculosis but also triggers innate immune activation and memory that are indicative of trained immunity. We established personal immune profiles and chromatin accessibility maps over a 90-day time course of BCG vaccination in 323 individuals. Our analysis uncovered genetic and epigenetic predictors of baseline immunity and immune response. BCG vaccination enhanced the innate immune response specifically in individuals with a dormant immune state at baseline, rather than providing a general boost of innate immunity. This study advances our understanding of BCG's heterologous immune-stimulatory effects and trained immunity in humans. Furthermore, it highlights the value of epigenetic cell states for connecting immune function with genotype and the environment.

Circadian rhythm influences induction of trained immunity by BCG vaccination.

BACKGROUNDThe antituberculosis vaccine bacillus Calmette-Guérin (BCG) reduces overall infant mortality. Induction of innate immune memory, also termed trained immunity, contributes toward protection against heterologous infections. Since immune cells display oscillations in numbers and function throughout the day, we investigated the effect of BCG administration time on the induction of trained immunity.METHODSEighteen volunteers were vaccinated with BCG at 6 pm and compared with 36 age- and sex-matched volunteers vaccinated between 8 am and 9 am. Peripheral blood mononuclear cells were stimulated with Staphylococcus aureus and Mycobacterium tuberculosis before, as well as 2 weeks and 3 months after, BCG vaccination. Cytokine production was measured to assess the induction of trained immunity and adaptive responses, respectively. Additionally, the influence of vaccination time on induction of trained immunity was studied in an independent cohort of 302 individuals vaccinated between 8 am and 12 pm with BCG.RESULTSCompared with evening vaccination, morning vaccination elicited both a stronger trained immunity and adaptive immune phenotype. In a large cohort of 302 volunteers, early morning vaccination resulted in a superior cytokine production capacity compared with later morning. A cellular, rather than soluble, substrate of the circadian effect of BCG vaccination was demonstrated by the enhanced capacity to induce trained immunity in vitro in morning- compared with evening-isolated monocytes.CONCLUSIONSBCG vaccination in the morning induces stronger trained immunity and adaptive responses compared with evening vaccination. Future studies should take vaccine administration time into account when studying specific and nonspecific effects of vaccines; early morning should be the preferred moment of BCG administration.FUNDINGThe Netherlands Organization for Scientific Research, the European Research Council, and the Danish National Research Foundation.

Assessment of predicted enzymatic activity of α-N-acetylglucosaminidase variants of unknown significance for CAGI 2016.

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α-N-acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population-scale analysis of disease epidemiology and rare variant association analysis.

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge.

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant (ΔΔGH2O) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the ΔΔGH2O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.

Assessment of methods for predicting the effects of PTEN and TPMT protein variants.

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation, we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 nonsynonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerging as top performers depending on the metric, it is nontrivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear as to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Kernelized rank learning for personalized drug recommendation.

Motivation: Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (i) medical records only contain the response of a patient to very few drugs, (ii) drugs are recommended by doctors based on their expert judgment and (iii) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties. Results: We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic. Availability and implementation: The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning. Supplementary information: Supplementary data are available at Bioinformatics online.

Investigating DNA-, RNA-, and protein-based features as a means to discriminate pathogenic synonymous variants.

Synonymous single-nucleotide variants (SNVs), although they do not alter the encoded protein sequences, have been implicated in many genetic diseases. Experimental studies indicate that synonymous SNVs can lead to changes in the secondary and tertiary structures of DNA and RNA, thereby affecting translational efficiency, cotranslational protein folding as well as the binding of DNA-/RNA-binding proteins. However, the importance of these various features in disease phenotypes is not clearly understood. Here, we have built a support vector machine (SVM) model (termed DDIG-SN) as a means to discriminate disease-causing synonymous variants. The model was trained and evaluated on nearly 900 disease-causing variants. The method achieves robust performance with the area under the receiver operating characteristic curve of 0.84 and 0.85 for protein-stratified 10-fold cross-validation and independent testing, respectively. We were able to show that the disease-causing effects in the immediate proximity to exon-intron junctions (1-3 bp) are driven by the loss of splicing motif strength, whereas the gain of splicing motif strength is the primary cause in regions further away from the splice site (4-69 bp). The method is available as a part of the DDIG server at http://sparks-lab.org/ddig.

EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models.

Protein engineering and characterisation of non-synonymous single nucleotide variants (SNVs) require accurate prediction of protein stability changes (ΔΔGu) induced by single amino acid substitutions. Here, we have developed a new prediction method called Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM), which comprises five specialised support vector machine (SVM) models and makes the final prediction from a consensus of two models selected based on the predicted secondary structure and accessible surface area of the mutated residue. The new method is applicable to single-domain monomeric proteins and can predict ΔΔGu with a protein sequence and mutation as the only inputs. EASE-MM yielded a Pearson correlation coefficient of 0.53-0.59 in 10-fold cross-validation and independent testing and was able to outperform other sequence-based methods. When compared to structure-based energy functions, EASE-MM achieved a comparable or better performance. The application to a large dataset of human germline non-synonymous SNVs showed that the disease-causing variants tend to be associated with larger magnitudes of ΔΔGu predicted with EASE-MM. The EASE-MM web-server is available at http://sparks-lab.org/server/ease.

DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels.

MOTIVATION: Frameshifting (FS) indels and nonsense (NS) variants disrupt the protein-coding sequence downstream of the mutation site by changing the reading frame or introducing a premature termination codon, respectively. Despite such drastic changes to the protein sequence, FS indels and NS variants have been discovered in healthy individuals. How to discriminate disease-causing from neutral FS indels and NS variants is an understudied problem. RESULTS: We have built a machine learning method called DDIG-in (FS) based on real human genetic variations from the Human Gene Mutation Database (inherited disease-causing) and the 1000 Genomes Project (GP) (putatively neutral). The method incorporates both sequence and predicted structural features and yields a robust performance by 10-fold cross-validation and independent tests on both FS indels and NS variants. We showed that human-derived NS variants and FS indels derived from animal orthologs can be effectively employed for independent testing of our method trained on human-derived FS indels. DDIG-in (FS) achieves a Matthews correlation coefficient (MCC) of 0.59, a sensitivity of 86%, and a specificity of 72% for FS indels. Application of DDIG-in (FS) to NS variants yields essentially the same performance (MCC of 0.43) as a method that was specifically trained for NS variants. DDIG-in (FS) was shown to make a significant improvement over existing techniques.

Feature-based multiple models improve classification of mutation-induced stability changes.

BACKGROUND: Reliable prediction of stability changes in protein variants is an important aspect of computational protein design. A number of machine learning methods that allow a classification of stability changes knowing only the sequence of the protein emerged. However, their performance on amino acid substitutions of previously unseen non-homologous proteins is rather limited. Moreover, the performance varies for different types of mutations based on the secondary structure or accessible surface area of the mutation site. RESULTS: We proposed feature-based multiple models with each model designed for a specific type of mutations. The new method is composed of five models trained for mutations in exposed, buried, helical, sheet, and coil residues. The classification of a mutation as stabilising or destabilising is made as a consensus of two models, one selected based on the predicted accessible surface area and the other based on the predicted secondary structure of the mutation site. We refer to our new method as Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM). Cross-validation results show that EASE-MM provides a notable improvement to our previous work reaching a Matthews correlation coefficient of 0.44. EASE-MM was able to correctly classify 73% and 75% of stabilising and destabilising protein variants, respectively. Using an independent test set of 238 mutations, we confirmed our results in a comparison with related work. CONCLUSIONS: EASE-MM not only outperformed other related methods but achieved more balanced results for different types of mutations based on the accessible surface area, secondary structure, or magnitude of stability changes. This can be attributed to using multiple models with the most relevant features selected for the given type of mutations. Therefore, our results support the presumption that different interactions govern stability changes in the exposed and buried residues or in residues with a different secondary structure.

Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins.

BACKGROUND: Reliable prediction of stability changes induced by a single amino acid substitution is an important aspect of computational protein design. Several machine learning methods capable of predicting stability changes from the protein sequence alone have been introduced. Prediction performance of these methods is evaluated on mutations unseen during training. Nevertheless, different mutations of the same protein, and even the same residue, as encountered during training are commonly used for evaluation. We argue that a faithful evaluation can be achieved only when a method is tested on previously unseen proteins with low sequence similarity to the training set. RESULTS: We provided experimental evidence of the limitations of the evaluation commonly used for assessing the prediction performance. Furthermore, we demonstrated that the prediction of stability changes in previously unseen non-homologous proteins is a challenging task for currently available methods. To improve the prediction performance of our previously proposed method, we identified features which led to over-fitting and further extended the model with new features. The new method employs Evolutionary And Structural Encodings with Amino Acid parameters (EASE-AA). Evaluated with an independent test set of more than 600 mutations, EASE-AA yielded a Matthews correlation coefficient of 0.36 and was able to classify correctly 66% of the stabilising and 74% of the destabilising mutations. For real-value prediction, EASE-AA achieved the correlation of predicted and experimentally measured stability changes of 0.51. CONCLUSIONS: Commonly adopted evaluation with mutations in the same protein, and even the same residue, randomly divided between the training and test sets lead to an overestimation of prediction performance. Therefore, stability changes prediction methods should be evaluated only on mutations in previously unseen non-homologous proteins. Under such an evaluation, EASE-AA predicts stability changes more reliably than currently available methods.

Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants.

BACKGROUND: Even a single amino acid substitution in a protein sequence may result in significant changes in protein stability, structure, and therefore in protein function as well. In the post-genomic era, computational methods for predicting stability changes from only the sequence of a protein are of importance. While evolutionary relationships of protein mutations can be extracted from large protein databases holding millions of protein sequences, relevant evolutionary features for the prediction of stability changes have not been proposed. Also, the use of predicted structural features in situations when a protein structure is not available has not been explored. RESULTS: We proposed a number of evolutionary and predicted structural features for the prediction of stability changes and analysed which of them capture the determinants of protein stability the best. We trained and evaluated our machine learning method on a non-redundant data set of experimentally measured stability changes. When only the direction of the stability change was predicted, we found that the best performance improvement can be achieved by the combination of the evolutionary features mutation likelihood and SIFT score in conjunction with the predicted structural feature secondary structure. The same two evolutionary features in the combination with the predicted structural feature accessible surface area achieved the lowest error when the prediction of actual values of stability changes was assessed. Compared to similar studies, our method achieved improvements in prediction performance. CONCLUSION: Although the strongest feature for the prediction of stability changes appears to be the vector of amino acid identities in the sequential neighbourhood of the mutation, the most relevant combination of evolutionary and predicted structural features further improves prediction performance. Even the predicted structural features, which did not perform well on their own, turn out to be beneficial when appropriately combined with evolutionary features. We conclude that a high prediction accuracy can be achieved knowing only the sequence of a protein when the right combination of both structural and evolutionary features is used.