Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts.A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein explicitly and implicitly mentioned biological molecule terms that pertain functional categories in biomedical literature. That is, PL-PPF employs a combination of statistical-based explicit term extraction techniques and logic-based implicit term extraction techniques. The statistical component of PL-PPF predicts some of the functions of a protein by extracting the explicitly mentioned functional terms that directly describe the functions of the protein from the biomedical texts associated with the protein. The logic-based component of PL-PPF predicts additional functions of the protein by inferring the functional terms that co-occur implicitly with the protein in the biomedical texts associated with it. First, the system employs its statistical-based component to extract the explicitly mentioned functional terms. Then, it employs its logic-based component to infer additional functions of the protein. Our hypothesis is that important biological molecule terms pertaining functional categories of proteins are likely to co-occur implicitly with the proteins in biomedical texts. We evaluated PL-PPF experimentally and compared it with five systems. Results revealed better prediction performance.To overcome the limitations of methods that rely solely on explicitly mentioned terms in texts to predict protein functions, we propose in this paper an Information Extraction system called PL-PPF. The proposed system employs techniques for predicting the functions of proteins based on their co-occurrences with  and the other adopting only the explicit terms co-occurrence extraction techniques . The experimental results showed that the complete version outperformed significantly the other version. This is attributed to the effectiveness of the rules of predicate logic to infer functional terms that co-occur implicitly with proteins in biomedical texts. A demo application of PL-PPF can be accessed through the following link: http://ecesrvr.kustar.ac.ae:8080/plppf/The experimental results showed that PL-PPF outperformed the other five systems. This is an indication of the effectiveness and practical viability of PL-PPF’s combination of explicit and implicit techniques. We also evaluated two versions of PL-PPF: one adopting the complete techniques The online version of this article (10.1186/s12859-019-2594-y) contains supplementary material, which is available to authorized users. Determining protein functions has been one of the central objectives for bioinformaticians, especially after the post-genomic era. This is because proteins have key roles in many biological processes. Identifying protein functions using experimental approaches is laborious and time consuming. Therefore, computational methods have been used extensively as alternatives. The underlying techniques adopted by most of these approaches revolve around computing protein functions from already annotated proteins. Most of them reference already annotated proteins using their structures , sequencimplicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts. Towards this, we propose in this paper an Information Extraction system called PL-PPF (Predicate Logic for Predicting Protein Functions) that employs techniques for predicting the functions of proteins based on their co-occurrences in texts with explicitly and implicitly mentioned biological molecule terms pertaining functional categories. PL-PPF infers the implicit terms using the rules of predicate logic. It does so by triggering protein specification rules recursively in the form of predicate logic’s premises [semantic relationships among the biological terms in sentences.We observe that some important biological molecule terms pertaining functional categories may premises . It extrinference rules for predicate logic. It does so to deduce functional relationships between proteins. Ultimately, these deduced relationships enable PL-PPF to predict the functions of unannotated proteins. Let Pu be an unannotated protein. Let Lc be a list of known protein characteristics represented in the form of predicate logic’s premises [Pu based on their co-occurrences in biomedical texts. It extracts the semantically related biological molecule terms to Pu in the sentences of the texts by employing linguistic computational techniques. It would then utilize these extracted terms as identifiers to serve as triggers for the appropriate premises from the list Lc using the standard rules of inferences [Pu in the texts.Using known protein and biological characteristics, PL-PPF composes rule-based protein specifications. These specifications are known protein characteristics in literature. PL-PPF composes these specifications in a pattern similar to predicate logic’s premises . It trigpremises . PL-PPF ferences , 16. TheSimilar to our approach, a number of studies employed logic-based approaches as complementary to statistical approaches to perform some biological-related tasks. For example,  demonstrLogic-based approaches have been successfully applied to solve complex problems in bioinformatics by viewing these problems as binary classification tasks. For example,  achievedUsing known biological characteristics, PL-PPF composes rule-based protein specifications. It composes these specifications in a pattern similar to predicate logic’s premises . “Repressemantically related to an unannotated protein pu based on their explicit co-occurrences in texts. If an extracted term denotes a functional category f, PL-PPF will assign pu the function f. PL-PPF will also use the extracted term to serve as a given premise and apply it as a trigger identifier for the appropriate protein specification rules to identify additional functions of pu. “.PL-PPF employs computational linguistic techniques to extract the biological molecule terms that are pu the functional terms that co-occur implicitly with pu in the texts by recursively triggering the appropriate premises constructed in step 1 and the given premises extracted in step 2 using the standard rules of inference for predicate logic. The conclusion will be a functional category that co-occurs implicitly with pu in the texts. “PL-PPF will assign PL-PPF infers the functions of an unannotated protein by going through the following sequential steps:sample of protein specification rules in the form of predicate logic’s premises. It includes only the rules used in the examples presented in the paper to illustrate the proposed concepts. We constructed the premises in Table 1 is constructed based on the following protein characteristics: (1) the folding of a protein takes place after a sequence of structural changes  [Premise Rprotein) , and (2)protein) .2 and R3 are constructed based on the following protein characteristic: each protein’s sequence is unique and defines the structure and function of the protein [Premises R protein .4 is constructed based on the following protein characteristics: (1) the covalent bonds of a protein contribute to its structure [Premise Rtructure , and (2)ristics:  the cova5 is constructed based on the following protein characteristic: a protein’s non-covalent interaction folding and dimensional structure can define the protein’s biological function [Premise R6 is constructed based on the following protein characteristic: protein-protein interactions form complexes by interacting with one another [Premises R another .7 and R8 are constructed based on the following protein characteristics: (1) a complex assembly can result in a new function that neither protein can provide alone  [ [Premises Runction) , and (2)unction) .9 is constructed based on the following protein characteristics: (1) proteins can be classified based on the similarities of their structural domains [p can be inferred from the functions of proteins that fall under the same structural classification as p [Premise Rristics:  proteins domains , and (3)ristics:  proteins10 is constructed based on the following protein characteristics: (1) proteins can be classified based on the similarities of their amino acid sequences [p can be inferred from the structures of the proteins that fall under the same amino acid sequence classification as p [Premise Requences , and (2)ion as p .11 is constructed based on the following protein characteristic: the sequence of a protein’s amino acids is inferred from the combination of the protein’s covalent interactions with ligands and the protein’s function [Premise R12 is constructed based on the following protein characteristic: non-covalent bonds between proteins during their transient interactions lead to Protein-Protein Interactions [Premise Rractions .13 is constructed based on the following protein characteristic: the structure of a protein can reveal an insight into its amino acid sequence [Premise Rsequence .A predicate is a statement of one or more predicate variables. It can be transformed to a proposition by assigning values to the variables. These values determine whether the statements are true or false. The propositions are constructed by connecting the statements using logical connectives. PL-PPF composes protein specifications in a similar fashion. Using known protein and biological characteristics, PL-PPF composes the protein specifications from these known characteristics. It represents the specifications in a pattern similar to predicate logic’s premises . It usesexplicitly with an unannotated protein pu in the sentences of biomedical texts. If an extracted term denotes a functional category f, PL-PPF will assign pu the function f. PL-PPF will also use the extracted term to serve as a given premise and apply it as a trigger identifier for the appropriate protein specification rules to infer the functional category that co-occurs implicitly with pu in texts. The co-occurrence of a biological molecule term and pu in a sentence does not guarantee that this term and pu are associated. To be associated, the term and pu have to be semantically related in the sentence. We consider a term as semantically related to an unannotated protein, if their co-occurrence probability of being related is significantly larger than their co-occurrence probability of being unrelated in texts. PL-PPF computes the occurrence probabilities of terms using Z-score [PL-PPF extracts the biological molecule terms that co-occur  Z-score . For twoWe use the term “training dataset” to differentiate between the following: (1) the set of biomedical texts stored in PL-PPF’s database, and (2) the set of biomedical texts associated with an unannotated protein, whose functions need to be annotated. To differentiate between the two, we call the texts stored in PL-PPF’s database a “training dataset”. In order for two molecule terms in texts associated with an unannotated protein to be semantically related, they have to be semantically related in the texts stored in the database .Based on linguistics, two nouns are considered related within a sentence, if they are connected by a pronoun  . PL-PPF Based on linguistics, two nouns are considered unrelated within a sentence, if they are connected by a preposition modifier  , 24. PL-We present below two of the key computational linguistic techniques adopted by PL-PPF to extract the molecule terms that are semantically related to an unannotated protein based on their explicit co-occurrences in the sentences:p implicitly using the following: (1) the protein specification rules  described in “ that co-occur explicitly with p in biomedical literature and described in “inference rules for predicate logic. PL-PPF can infer the functions of p by recursively triggering the protein specification rules using the premises  and the standard inference rules for predicate logic. At each recursion, an inference rule is triggered and applied to the premises that have been proven previously. This will lead to a newly proven premise. The final conclusion will be a protein function, which will be considered as the function of p. The conclusion is valid, if it has been deducted from all previous premises [PL-PPF computes the functions of an unannotated protein premises . Table 2We now present case studies in Examples 1 to 4 to show the effectiveness of the deductive inferencing methodology presented in this section. The examples use various biological molecule terms as given premises for inferring the functions of unannotated proteins.u in biomedical texts after applying the techniques presented in “x) and ST(Px)  and ST(Px) in texts can be indicative of an implicit mentioning of the function of Px ). Therefore, the co-occurrences of FD(Px), ST(Px), and Pu can be indicative of an implicit co-occurrences of F(Px) and Pu. Accordingly, the functions of Pu is likely to be similar to F(Px). Table x) and ST(Px) are indicative of F(Px).Consider that PL-PPF extracted the following terms based on their co-occurrences with an unannotated protein Pu in biomedical texts: AAS(Px) and AAS(Py)  and AAS(Py) in texts can be indicative of implicit mentioning of the functions of Px and Py  and F(Py)). Therefore, the co-occurrences of AAS(Px), AAS(Py), and Pu can be indicatives of implicit co-occurrences of F(Px), F(Py), and Pu. Accordingly, the functions of Pu is likely to be similar to F(Px) and F(Py). Table x) and AAS(Py) are indicative of F(Px) and F(Py).Consider that PL-PPF extracted the following terms based on their explicit co-occurrences with an unannotated protein Pu in biomedical texts: ST(Px)  in texts can be indicative of implicit mentioning of the function of Px ). Therefore, the co-occurrences of ST(Px) and Pu can be indicatives of implicit co-occurrences of F(Px) and Pu. Accordingly, the functions of Pu is likely to be similar to F(Px). Table x) is indicative of F(Px).Consider that PL-PPF extracted the following term based on its explicit co-occurrences with an unannotated protein Pu in biomedical texts: NCBND and F(Px)  and F(Px) in texts can be indicative of implicit mentioning of the function of Py ). Therefore, the co-occurrences of NCBND, F(Px), and Pu can be indicative of implicit co-occurrences of F(Py), and Pu. Accordingly, the functions of Pu is likely to be similar to F(Py). Table x, Py) and F(Px) are indicative of F(Py).Consider that PL-PPF extracted the following terms based on their explicit co-occurrences with an unannotated protein Ppu the functions of the set S of already annotated proteins, if pu and S have similar characteristic terms. The classifier employed by Text-KNN is based on k-nearest neighbour and the classifier employed by Text-SVM is based on support vector machine. In the framework of GOstruct, an unannotated protein pu is annotated with the functions of a Gene Ontology (GO) term, if this term co-occurs in close proximity with pu in biomedical texts.We implemented PL-PPF in Java and used Prolog as the logic programming language. We ran it on Intel(R) Core(TM) i7 processor and a CPU that has frequency equals 2.70 GHz. The machine has 16 GB of RAM. We ran PL-PPF using Windows 10 Pro. We compared it experimentally with the following five systems: DeepGO , IFP_IFChttp://ecesrvr.kustar.ac.ae:8080/plppf/rules.pdfThe complete list of specification rules used by PL-PPF in the experiments and the abbreviations of the terms included in the list can be accessed through the following two links, respectively:http://ecesrvr.kustar.ac.ae:8080/plppf/abbreviations.pdft to be semantically related to an unannotated protein pu, if the co-occurrence probability of t and pu using Z-score [We compared the systems using GO dataset , which c Z-score  is greatWe also compared the systems using the 6086 SGD dataset . The datp using the following standard quality metrics shown in Eqs. 1, 2 and 3:Cp: The number of correctly predicted functions for protein p.Np: The actual number of correct functions of protein p.Mp: The number of functions predicted for protein p by one of the systems.We divided each of the GO and SGD datasets to five sets. The systems were assessed five times. At each time, a different set of each of the GO and SGD datasets was used for testing and the remaining four sets were used to train the systems. We considered the testing proteins as unannotated and assessed the systems for predicting their functions accurately. We evaluated two versions of PL-PPF: one adopts all the techniques described in this paper and the other adopts only the explicit terms co-occurrence extraction techniques  of the GO ontology. The size of proteins annotated with the functional category of a GO annotation term decreases as its hierarchical level increases. We aim at investigating whether the accuracy of a system for predicting the functional categories of GO annotation terms gets better as the sizes of these terms increases. We randomly divided the proteins annotated with each functional category We ran each system ten times against the GO dataset. The number of proteins, whose associate biomedical texts are used as a training dataset, keeps accumulating at each run. At each run, we randomly selected 1000 Biological Process testing proteins and 500 Molecular Function testing proteins as unannotated and assessed the systems for predicting their functions. The first run was performed using: (1) 52,386 Biological Process proteins and 11,576 Molecular Function proteins, whose associate biomedical texts are used as a training dataset, and (2) 1000 Biological Process proteins and 500 Molecular Function proteins, whose functions are considered unannotated. At each run, thereafter, the set of proteins, whose associate biomedical texts are used as a training dataset, includes also the Biological Process and Molecular Function proteins, whose functions were annotated in the prior run. Figures The protein centric maximum F-measure, which was used in evaluating the DeepGO method.The same GO dataset used in evaluating the DeepGO method . As the figures show also that the complete version of PL-PPF  outperforms significantly the version of PL-PPF, which employs only the explicit techniques. This is attributed to the effectiveness of the rules of predicate logic in inferring the functional terms that co-occur implicitly with proteins in biomedical texts.As Figs. As Fig. As Figs. explicit term extraction component and logic-based implicit term extraction component. Our hypothesis is that important biological molecule terms pertaining functional categories are likely to co-occur implicitly with proteins in biomedical texts.In general, we attribute the performance of PL-PPF over the other five systems to the fact that PL-PPF employs a combination of statistical and logic-based approaches while the other five systems employ only statistical-based approaches. That is, PL-PPF includes a combination of statistical-based Table Some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in biomedical texts. Most current information extraction approaches do not take advantage of such implicitly inferred terms and focus solely on explicitly mentioned terms in texts. In this paper, we introduced an information extraction system called PL-PPF. The system predicts protein functions based on both explicitly and implicitly mentioned functional terms in biomedical texts. PL-PPF extracts explicitly mentioned functional terms in texts using computational linguistic techniques that identify semantically related terms in differently structured forms of sentences. It extracts implicitly mentioned functional terms by recursively triggering protein specification rules using the standard inference rules for predicate logic. We compared PL-PPF experimentally with the following five systems: DeepGO , IFP_IFCAdditional file 1:The data presented in Tables S1 and S2 is the Biological Process and Molecular Function GO annotation terms used in the experiments as well as the randomly selected sets of training and testing proteins annotated with the functions of these GO terms. (DOCX 56 kb)


The article presents tests aimed to verify the possibility of Thermomechanically Controlled Processed (TMCP) steels T-joints laser welding. The 10 mm thick high-yield-point steel S700MC obtained in an industrial manufacturing process was used for tests of laser welding. The joints made during the tests were single- and double-sided. Subsequent nondestructive tests revealed that the laser-welded joints represented quality level B in accordance with PN-EN ISO 13919-1. Single-sided welding performed at the output laser beam power of 11 kW provided the penetration depth of just 8 mm without visibly deforming of the joint. The double-sided welded joints were characterized by proper geometry and the presence of gas pores in the welds not compromising the requirements of quality level B (strict requirements). The identified weld structure was bainitic-ferritic. The weld hardness was by approximately 60 HV1 higher than that of the base material (280 HV1). The HAZ (Heat Affected Zone) area was slightly softer than the base material. The tests of thin foils performed using a high-resolution scanning transmission electron microscope revealed that, during welding, an increase in the content of the base material in the weld was accompanied by an increase in contents of alloying microagents Ti and Nb, particularly near the fusion line. The above-named alloying microagents, in the form of fine-dispersive  type precipitates, could reduce plastic properties of joints. Laser welding is one of many technologies enabling the joining of materials. Because of its unquestionable advantages, the above-named technology has proved successful when joining small-sized and simply-shaped elements. However, presently, technological progress combined with increased laser power enable the making of joints in areas previously almost entirely dependent on conventional welding techniques ,2,3. TheThe tests aimed to verify the possibility of making welded joints in TMCP steels using the laser beam without the filler metal. The tests involved 10 mm thick, high-yield-point steel S700MC obtained in the industrial manufacturing process. The chemical composition and properties of the steel are presented in Steel S700MC is characterised by the nonequilibrium, fine-grained, bainitic-ferritic structure and a relatively low carbon equivalent (0.33%) . High mechanical properties of steel S700MC are obtained through the thermomechanical control process (TMCP) combined with the use of alloying microagents such as Ti and Nb, as well as a small amount of V. The total content of alloying microagents amounts to 0.17% by weight. The above-presented value does not exceed the permissible content of 0.22% in relation to steels with microagents. Steel S700MC is characterised by a very low carbon content (0.056%). The aforesaid content can reduce the effect of austenitic transformations during welding thermal cycles on mechanical and plastic properties of welded joints.Laser beam insertion angle: α = 6°Lift-off of the laser beam in relation to the T-joint base: a = 0.5 mmTo adjust appropriate parameters enabling the obtainment of proper T-joints, it was necessary to perform bead-on-plate welding tests using various process parameters. An adopted bead-on-plate welding speed of 2 m/min was constant, whereas parameters subjected to changes included the power of the laser beam and the position of the laser beam focus. The above-presented bead-on-plate welding speed ensured that the efficiency of the laser welding of T-joints would be significantly higher than that obtained using arc methods when welding the same type of joints. It was noticed that the bead-on-plate welding process was more stable (fewer spatters formed) when the laser beam focus penetrated the material subjected to welding . Metallographic tests were used to determine the geometrical dimensions of the penetration run in relation to a given group of parameters. Afterwards, a graphic software GIMP2.10.4  programme was used to outline the fusion lines of the plate penetration run and to plot the above-named shape of the weld on the schematic diagram of the T-joint . The forcol = 200 mmcollimator lens focal length: ffoc = 400 mmfocusing lens focal length: fThe bead-on-plate welding and joints welding tests were performed using a TruLaser Robot 5120 robotic laser welding station  (TRUMPF). The station included a TruDisk 12002 disc laser having a maximum power of 12 kW and provided with a working head attached to a six-axis KUKA KR30HA industrial robot . The welfoc = 0.6 (mm)Laser beam focus diameter dR = 7.5 (mm)Rayleigh length Zf = 4.6 (deg.)Focused laser beam divergence angle ΘLaser beam quality parameter BPP = 12 (mm·mrad)The above-presented optical parameters of the head and the use of the above-named optical fibre enabled the obtainment of the laser beam characterised by the following parameters :The welding tests were performed using various laser beam power values and various laser beam focus positions in relation to the laser beam propagation axis and in relation to the T-joint web surface, that is, parameter f . A negatThe initial welding tests revealed that it was difficult to perform the stable single-sided welding of 10 mm thick T-joints. The maximum penetration obtained using stable process parameters amounted to 8 mm . The forAt the same time, on the weld face side, it was possible to observe the incompletely filled groove along the entire length of the joint . BecauseMacroscopic metallographic tests involving the use of an Olympus SZX9 light stereoscopic microscope ; the test specimens were etched using Adler’s reagent ;Microscopic metallographic tests involving the use of a NIKON ECLIPSE MA100 light microscope ; the test specimens were etched using Nital ;Quantitative tests concerning contents of microagents hardening steel S700MC, performed using a JXA-8230 electron microprobe X-ray analyser (JEOL)  and wavelength dispersion spectroscopy (WDS) ;Tests of thin foils using a Titan 80–300 kV high-resolution scanning transmission electron microscope (HR S/TEM) (FEI)  equipped with an XFEG electron gun featuring Schottky field emission of enhanced brightness;Phase analysis-related X-ray tests  performed using an X’Pert PRO diffractometer and an X’Celerator strip detector;Vickers hardness test performed using a WILSON WOLPERT 430 hardness tester , following the requirements of PN-EN ISO 9015-1 ;Assessment of a welding imperfection (gas pores), following the requirements of PN-EN ISO 13919-1:2002 ;An attempt to bend the T-joint on a ZWICK/ROELL Z 330RED testing machine)  .The test welded joints were subjected to visual tests following the requirements of PN-EN ISO 17637:2011 standards . AfterwaThe research-related visual tests enabled the elimination of joints characterised by the lack of penetration or excessive penetration on the root side. The remaining, double-sided joints were characterised by the proper geometry of the welds . The microscopic tests concerning the weld area revealed the bainitic-ferritic structure. The HAZ area did not reveal any significant changes in the grain size in relation to the base material. The foregoing was related to a very low heat input to the welded joint area during the laser welding process .The microscopic tests revealed the presence of gas pores in the welded joints. The formation of the above-named gas pores could be ascribed to the fast crystallisation of the liquid metal pool triggered by very high welding rates responsible for the impeded release of gases from the weld zone. The gas pores could form as a result of the confinement of gases dissolved in the metal or as a result of the evaporation of alloying elements or impurities, if any, located on the surface of the interface of the joined sheets. The identification of the significance of the above-named imperfection required the performance of verification tests based on the PN-EN ISO 13919-1:2002 standard  Table 3Table 3. The largest welding imperfection of the T-joints satisfied the requirements of quality level B, therefore the remaining joints also satisfied the requirements of quality level B.Bending test of welded joints with one and two sides showed very high strength and plastic properties, The X-ray phase analysis revealed that the base material and the weld made using the laser beam were entirely composed of the ferritic phase .In the laser welds made without the use of the filler metal, the contents of Ti and Nb were significantly higher than those in the welds made using the filler metal. Higher contents of hardening constituents could lead to decreased toughness in the weld area in comparison with that of the base material. An increase in the contents of microalloy elements was particularly visible near the fusion line, which was confirmed by the detailed analysis of the chemical composition performed using an electron microprobe X-ray analyser . CertainThe significant content of hardening phases in the weld during cooling led to the significant precipitation hardening through fine-dispersive  type precipitates , precipitated near greater N particles (100 nm in size) , which rThe analysis of the hardness measurement results revealed that a change in the laser beam power during welding performed at a constant welding rate did not significantly affect the hardness in the weld area. The hardness of the weld was restricted within the range of 340 HV1 to 350 HV1, whereas the hardness of the base material amounted to 280 HV1 . The HAZThe tests concerning the laser welding of T-joints made in 10 mm thick steel S700MC revealed that the use of the TruDisk 12002 laser having a maximum power of 12 kW enabled the obtainment of high-quality T-joints satisfying the requirements of quality level B in accordance with the PN-EN ISO 13919-1 standard  and char


DNA methylation (DNAm) profiles in central airway epithelial cells (AECs) may play a key role in pathological processes in asthma. The goal of the current study is to compare the diagnostic performance of DNAm markers across three tissues: AECs, nasal epithelial cells (NECs), and peripheral blood mononuclear cells (PBMCs). Additionally, we focused on the results using the machine learning algorithm in the context of multi-locus effects to evaluate the diagnostic performance of the optimal subset of CpG sites. We obtained 74 subjects with asthma and 41 controls from AECs, 15 subjects with asthma and 14 controls from NECs, 697 subjects with asthma and 97 controls from PBMCs. Epigenome-wide DNA methylation levels in AECs, NECs and PBMCs were measured using the Infinium Human Methylation 450 K BeadChip. Overlap analysis across the three different sample sources at the locus and pathway levels were studied to investigate shared or unique pathophysiological processes of asthma across tissues. Using the top 100 asthma-associated methylation markers as classifiers from each dataset, we found that both AEC- and NEC-based DNAm signatures exerted a lower classification error than the PBMC-based DNAm markers . The area-under-the-curve (AUC) analysis based on out-of-bag errors using the random forest classification algorithm revealed that PBMC-, NEC-, and AEC-based methylation data yielded 31 loci (AUC: 0.87), 8 loci (AUC: 0.99), and 4 loci (AUC: 0.97) from each optimal subset of tissue-specific markers, respectively. We also discovered the locus-locus interaction of DNAm levels of the CDH6 gene and RAPGEF3 gene might interact with each other to jointly predict the risk of asthma – which suggests the pivotal role of cell-cell junction in the pathological changes of asthma. Both AECs and NECs might provide better diagnostic accuracy and efficacy levels than PBMCs. Further research is warranted to evaluate how these tissue-specific DNAm markers classify and predict asthma risk. Although the heritability estimate may reach 80%2, the rapid increment in its incidence cannot be simply explained by genetic factors alone. Epigenetics may account for the mechanisms underlying environmental effects on genetic functions3, and hence may play a role in the environment-related pathogenesis of asthma4. DNA methylation (DNAm) is the first identified epigenetic mechanism that been extensively studied in allergic disease8. Emerging evidence has suggested that DNAm can be considered as a robust epigenetic marker that could aid clinical applications, such as diagnostics9.Asthma affects 11 million children in the U.S., and has been on the rise over the past two decades10. Surrogate markers from easily accessible tissues are the markers of choice for studies involving large-scale screening and children12. Most asthma epigenetic studies have relied on easily accessible surrogate tissues such as peripheral blood mononuclear cells (PBMCs)15. However, DNAm tend to be highly tissue- or cell-type specific20, so PBMCs-based methylation patterns might not be correlated with airway epithelial cell-based (AEC-based) methylation patterns21. Another surrogate tissue is nasal epithelial cells (NECs). Accumulating evidence has suggested that NECs are biologically relevant proxies for AECs24. NECs can also be readily and easily sampled during asthma attacks, which can help to understand the pathophysiological changes in bronchial airways26 It remains unclear whether DNAm signatures from peripheral tissues, such as PBMCs or NECs can provide better surrogate markers to facilitate the diagnostics of asthma. In addition, little is known regarding the relationship among DNAm profiles of PBMCs, NECs, and AECs in asthma.One of the major challenges of DNAm research on asthma is the selection of the target tissue for DNA samples. The direct access to the lung tissue DNA which is primarily involved in asthma pathophysiology is limitedFurthermore, most previous studies have implemented the single-locus model to identify the trait-associated loci with variable DNAm levels – which might have ignored the joint effects of epigenetic modifications over multiple loci. Therefore, the overarching goal of this study is to systemically compare the diagnostic performance of different tissue-specific DNAm datasets. In addition, we have evaluated the diagnostic (or prediction) performance of these tissue-specific DNAm markers, in both single-locus and multi-locus models. We anticipated that these results would clarify which type of surrogate marker might yield a better diagnostic accuracy and help to gain some insight into tissue-specific DNAm patterns underlying the development of asthma.http://www.ncbi.nlm.nih.gov/geo/)28. We obtained DNAm data from airway epithelial cells (AECs), nasal epithelial cells (NECs), peripheral blood mononuclear cells (PBMCs), respectively31. Each individual dataset was uploaded to GEO by the original study groups32. The information of (1) GEO accession, (2) sample type, (3) platform, (4) numbers of asthmatic and control individuals, can be found in Table The data used in this study was retrieved from the publicly available Gene Expression Omnibus (GEO) database at NCBI , NECs (15 cases and 14 controls), and PBMCs (97 cases and 97 controls), using the Infinium HumanMethylation450 BeadChip. We constructed data tables containing DNA methylation values using GEO2R, a web based limma R packages from the Bioconductor project. GEOquery R package was used to parse GEO data into R data structures that can be used by other R packages. The original datasets deposited to the Gene Expression Omnibus database have gone through QC processes described elsewhere34, and the normalized M-values were used in all downstream analyses. Normalized data was used to run the generalized linear model that adjusted for age, sex, and the surrogate variable detected by R function “sva” (to reduce unwanted bias caused by batch effects). The analyses were performed in samples from PBMCs, NECs, and AECs, independently. We also used heat maps to visualize how the top 100 asthma-associated loci which were ranked by the p-value, from each dataset could distinguish cases from controls using the clustering analysis with Euclidean distance metric and Ward’s minimum variance method as the linkage algorithm. The classification errors were then calculated in each cluster. As graphical representation and visually assess the quality of each individual dataset, hierarchical cluster analysis was performed using Genesis software35.Data from the methylation array were normalized with the Subset-quantile Within Array Normalization (SWAN) method contained in the R package minfihttp://cpdb.molgen.mpg.de/) to perform over-representation analyses36. The analysis criteria included: (1) one-next neighbors for the radius with p-value < 0.01, (2) pathway-based sets at least two overlapped genes and p-value < 0.01, and (3) gene ontology level 2 categories with p-value < 0.01. The results from the second approach helped visualize the possible “hub” pathway from the networks associated with the candidate genes.In order to gain further insight into the functional significance, we used two approaches to identify pathways overlap among AECs, NECs, and PBMCs based on enriched candidate genes in each study. The overlapping frequency analysis demonstrates how often the same set of genes is selected from different samples sources and which tissues tend to select the same set of genes. We selected the top 100 asthma-associated loci from each study and then used the webtool at ConsensusPathDB  in AECs, NECs and PBMCs, and N is the total number of genes analyzed, then the concordance rate (ζ) is defined as:Concordance rate. The concordance rate measures the proportion of shared genes among the top ranked genes in AECs, NECs, and PBMCs in the tissue-stratified analysis. Genes were ranked using the p-value from the most to least significant, and the top ranked 100 genes were identified in each tissue. If xi and yi are the fold changes of the ith gene in AECs or NECs and PBMCs, respectively; r) ranges from −1 to 1, with r closer to 1 or −1 indicating a monotonically increasing or decreasing relationship and r closer to 0 signifying weak or no relationship.Correlation among AECs, NECs and PBMCs. We calculated the correlation in differentially methylated genes among tissues and determine whether readily accessible tissue could be used as a reliable surrogate marker to predict DNA methylation in less accessible tissues, which would facilitate the development of novel differential methylation-based models for assessing asthma risk and progression. We used the Pearson correlation coefficient to compare the fold change differences between AECs, NECs and PBMCs. The Pearson’s correlation coefficient between the fold change X in AECs or NECs and Y in PBMCs is defined as:37. We considered the similarity between two tissues as the number of genes (or pathways) shared divided by the total number of genes (or pathways) present in either of them. It is expressed as follows: J = C/(A + B − C), where A is the number of genes present in tissue A; B is the number of genes present in tissue B; and C is the number of genes present in both tissue A and tissue B. The number of genes present in either of the tissues is given by A + B − C. The Jaccard Index ranges from 0 to 1, where a higher value indicates a higher similarity between two tissue groups.Jaccard Index (J). The Jaccard index was used at the gene and pathway level to determine relatedness between asthma tissues/cells in terms of differentially methylated genes and pathways38. We used “mean decrease accuracy” as the primary parameter for variable-specific importance. The higher this value is, the more important this predictor could be. When the area-under-the-curve based on the OOB errors reached the peak value, we could determine which predictors might constitute the optimal set.We used random forest (RF) classification to identify two optimal sets of predictors  from each dataset. To identify the optimal set of predictors, we used the R package “AUCRF” to calculate the out-of-bag (OOB) error by implementing the backward elimination based on the initial ranking of importance of the variables. The OOB method has been used in several machine learning models utilizing bootstrap aggregation (bagging) to sub-sample data samples used for training to measure the performance of the predictort indicates the ratio of this class . The higher Gini impurity index means that the CpG site could be a better distinguisher between cases and controls.We used the random forest classification (RF) algorithm to rank the top 100 asthma-associated loci. We used the R function “randomForestExplainer” to plot the relationship between two parameter values: “Gini_increase” and “accuracy_decrease.” “Gini_increase” indicates the increase in the Gini impurity index, and “accuracy_decrease” refers to the mean decrease of prediction accuracy after the corresponding predictor was permuted. The Gini impurity index was calculated as 39.To compare the prediction accuracy in the context of locus-by-locus interactions, we further calculated the conditional minimum depth of trees using the R function “randomForestExplainer.” We selected the top 5 pairs of loci consisted of a root variable and another variable ranked by their conditional minimum depths. The interactions were visualized using the signals that underwent inverse rank-based transformation so the results from different datasets can be compared. The final set of validation analysis with generalized linear model that regresses the outcome against two different CpG methylations and their interaction was performed using the software STATA SEFigure Table To answer the question of how often the same set of probes/genes is selected from different sample sources, we studied the overlap pattern of the top 100 genes selected from different cells/tissues. Using the top 100 ranked probes/genes from each cell/tissue, we only identified one overlapped gene from the three gene sets derived from the DNA samples extracted from the PBMCs (76 genes), NECs (64 genes), and AECs (69 genes), respectively. However, we identified 101, 104, and 141 asthma-associated genetic pathways based on the genes derived from the PBMC, NEC, and AEC data, respectively. Among these three sets of pathways derived from three different tissues, 23 pathways were shared. To compare the degree of similarity between the DNAm levels at the same probes from various tissues, we used the Jaccard similarity index. The Jaccard index scores, based on KEGG pathway overlap analysis, for the PBMCs-NECs, PBMCs-AECs, and NECs-AECs, were 0.20, 0.27, and 0.30, respectively Fig. . The oveWe believe that DNAm patterns of multiple loci, at least for those with larger marginal effects, exert joint effects on the diagnosis. To further explore how the DNAm levels predict the diagnosis of asthma in the context of multi-locus effects, we used Random Forest classification algorithm (RF) to identify the optimal subset of probes from the tissue-specific top-ranked 100 probes for each dataset. Each optimal subset of predictors could be used to compute the prediction errors. The area-under-curve (AUC) of prediction errors in the absence of the association between the subsets of probes and the outcome  could then be used to infer the corresponding diagnostic ability. Figure Rank-based feature selection is one of the frequently used criteria in many feature selection methods that apply one or more ranking scores to separate the highly relevant features from the least relevant features. Here, we used the combination of two RF indexes  to rank the relative importance of the predictors that included probes and demographic factors and surrogate variables. Again, RF-based feature selection could allow us to identify top predictors in the context of the multi-locus model. The p-values were computed using the one-sided binomial test to denote the probability of split on the predictor as if it was uniformly drawn from all candidate variables for PBMC-based DNAm  gene, which can mediate the allergic airway inflammation in asthma47. The AEC data show that Rap Guanine Nucleotide Exchange Factor 3 (RAPGEF3) gene might act in concert with another candidate gene, cadherin-6 (CDH6) gene, to jointly influence the risk of asthma. The expression of the RAPGEF3 gene  has been found to play a role in neutrophil dysfunction48 and airway smooth muscle remodeling49. Both genes have been found to be involved in cell-cell junction, which may play a pivotal role in the pathophysiology of asthma. The interaction between DNAm levels of these two genes seem to lend some support to this mechanism.Our findings suggest that NECs may provide comparable diagnostic performances with AECs, both of which have better diagnostic performance than PBMCs. The PBMC data show that cytosolic acyl coenzyme A thioester hydrolase 7 (ACOT7) gene might act in concert with another candidate gene, Zinc Finger Protein 366 (ZNF366) gene, to jointly influence the risk of asthma. The role of methylations of ACOT7 gene in asthma has been reported by several genome-wide epigenomic studies on serum immunoglobin E (IgE) levels50. The asthma-associated genes derived from PBMCs and AECs have yielded completely different “hub” pathways. The candidate genes derived from the PBMC-based methylation profiles were enriched in only three pathways, primarily in the C-MYB transcription factor network. On the contrary, the candidate genes derived from the AEC-based samples were enriched in thirteen pathways, many of which are related to immune regulation, such as IL17 signaling pathway, CD40/CD40L signaling, Dectin-1 signaling, and receptor activator of nuclear factor kappa-Β ligand pathway . The role of IL17 receptor gene in asthma has been reported by several studies54. Additionally, the AEC-based candidate genes are also enriched in two integrin-related pathways, which play a role in host cell defense systems. Furthermore, AEC-based genes are also enriched in two pathways: Platelet Aggregation and Rho GTPases pathway. Rho-related C3 botulinum toxin substrate 1 in the platelet is involved in the airway smooth muscle proliferation, which is a key component in the pathophysiology of asthma55. Our DNAm analysis shows that the overlap is more remarkable at the pathway level than the gene level across three tissues – which suggests that asthma involves differentially regulated biological pathways rather than individual genes in isolation.The candidate genes from these three different tissues share a significant proportion of pathways linked to asthma. For example, the MAPK-signaling pathway and IL17 signaling pathway is involved in the pathological processes of allergic diseases56. Therefore, the two sets of markers of tissue-specific DNAm levels might predict asthma with comparable accuracy and efficiency despite the age difference. Second, the NECs came from a relatively smaller sample size compared with PBMCs or AECs. Therefore, the NEC-based data analysis results warrant replications with a larger sample to validate the current findings. Additionally, this might have led to an insufficient statistical power to detect locus-locus interactions. Third, we did not control for medication effects, which might have an impact on epigenetic patterns and hence might confound the results. Fourth, the variable timings of DNA extraction might contribute to the variation in methylations. The present study has notable strengths. We determined whether readily accessible tissue could be used as a reliable surrogate marker to predict DNA methylation in less accessible tissues, which would facilitate the development of novel differential methylation-based models for assessing asthma risk and progression. Our data suggest that both AEC- and NEC-based DNAm data had better predictive accuracy  and efficacy  levels than the PBMC-based DNAm data. Furthermore, we obtained sets of the tissue-specific candidate loci from EWAS, which might have generated some false positive results in spite of the adjustment of confounders, especially heterogeneity in cellular composition. However, the purpose of our study is not to definitively identify genes that influence the risk of asthma through epigenetic modifications. Instead, we aimed to evaluate DNAm markers as diagnostic biomarkers. Therefore, it may not be necessary to remove the effects of all confounders. Notably, the ranks of the top five probes with DNAm levels associated with the diagnosis remained the same after we adjusted for batch effect and cellular heterogeneity using the surrogate variable analysis. Finally, our study might be subject to model overfitting since we did not evaluate the prediction accuracy in independent samples. However, the purpose of our study is to compare diagnostic performance across different tissue-specific sets of probes, so the concern of model overfitting might not substantially impact the validity of the conclusions57.The current study has several limitations. First, AEC-based DNAm data came from an adult population, while the other two datasets came from pediatric populations. The age-dependent DNAm levels might lead to biased associations. In our current study, we have adjusted for the age effect in each tissue-specific EWAS, and hence the top 100 probes from each EWAS were associated with asthma regardless of the age variation in each tissue-specific dataset. Note that NEC-based and AEC-based sets of optimal subsets of predictors were found to yield similar diagnostic performance. Previous evidence has shown that NECs and AECs have similar responses to cytokine stimulation and comparable expression of relevant surface receptorsIt is important to note that our analyses, and hence interpretations, are subject to additional limitations. The study is based on DNA methylation and associated clinical outcome data available in the public domain. Due to lack of available clinical data, the number of covariates is restricted to existing data. Associations that stemmed from un-adjusted covariates or confounding factors, such as non-biologically-related experimental variations, might still be present. Nevertheless, the present study focused on the predictive value of epigenetic markers. Therefore, our findings of the optimal subset of predicting CpG sites may not be affected by unknown confounders. If un-adjusted confounders were involved in the optimal subset of predictors, they might attenuate the predictive value of these CpG sites. To clarify whether un-adjusted confounders exerted an impact on the selection of predictors, we have also compared the results of the RF analysis that incorporated one of the potential confounders, cellular heterogeneity with the results of the RF analysis without taking cellular heterogeneity into account. These findings suggest that machine learning algorithm with a focus on prediction may be less susceptible to confounders. Nevertheless, such predictors may need further verification to clarify whether they have unbiasedly predicted as well as unravel biological mechanisms underlying the disease.We have shown that methylation patterns from AECs and NECs might serve as better biomarkers for the diagnosis of asthma compared with methylation patterns derived from PBMCs. The comparable diagnostic accuracy and efficiency levels of NECs and AECs suggest that the immune-related genetic functional measurements derived from NECs might serve as biomarkers associated with pathological changes in the central airway. In addition, the comparable diagnostic accuracy between NECs and AECs suggests that NECs may be considered as practical surrogate biomarkers for the diagnostics of asthma. Our data also suggest that DNA methylation profiling based on PBMCs may not accurately reflect DNAm profiles seen in the airway. Furthermore, NEC-based or AEC-based DNAm profiles associated with asthma reflect more genetic networks centered on immune regulation, compared with PBMC-based DNA methylation signatures associated with asthma. Profiling DNAm levels in tissues/cells that directly contribute to asthma pathogenesis is likely to aid the discovery of novel drug targets and biomarkers. Therefore, caution needs to be exercised when interpreting the results from epigenetic studies using DNA extracted from PBMCs.In conclusion, the main purpose of this study is to identify a panel of biomarkers to aid asthma diagnostics. This purpose is different from the attempt to search for genuine biological causes of the disease. The prediction using biomarkers is likely less susceptible to biases that arise from un-adjusted or unmeasured confounders, compared with studies that aim to identify etiological factors. Nevertheless, some confounders, such as certain environmental exposures, might predict the risk of asthma well. Adding such confounders might further enhance diagnostic performance of biomarkers. Future analysis should consider additional confounders to increase the predictive value of the predicting CpG sites.Supplementary Information


CFTR variants in 50,778 exomes. In 24 cases with bi-allelic pathogenic CFTR variants, there were 21 true-positives. We considered three cases “potential” false-positives due to limitations in available EHR phenotype data. This genomic screening exhibited a positive predictive value of 87.5%, negative predictive value of 99.9%, sensitivity of 95.5%, and a specificity of 99.9%. Despite EHR-based phenotyping limitations in three cases, the presence or absence of pathogenic CFTR variants has strong predictive value for CF diagnosis when EHR data is used as the sole phenotyping source. Accurate ascertainment of the predictive value of DNA-based screening requires condition-specific phenotyping beyond available EHR data.Doubts have been raised about the value of DNA-based screening for low-prevalence monogenic conditions following reports of testing this approach using available electronic health record (EHR) as the sole phenotyping source. We hypothesized that a better model for EHR-focused examination of DNA-based screening is Cystic Fibrosis (CF) since the diagnosis is proactively sought within the healthcare system. We reviewed  These CF diagnostic efforts, incorporating both genetic and non-genetic diagnostic testing, have led to standardized diagnostic approaches to suspected cases of CF.CFTR) gene.13 The most common pathogenic CFTR allele, F508del, accounts for ~70% of known CF-causing alleles.14CF is an autosomal recessive disorder that affects one in 3200 in the United States and is caused by bi-allelic pathogenic variants  in the cystic fibrosis transmembrane conductance regulator  as the initial screen followed by either repeat IRT (IRT/IRT) or DNA-based testing (IRT/DNA) as the follow-up screen.17 Genome-first approaches to case identification of monogenic disease are increasingly feasible in large cohorts with genomic sequence data.20 Despite promising data regarding the use of this approach for two conditions with observed screen positive rates of 1 in 190 to 1 in 256 , the efforts cited above using genomic screening in this manner to identify less prevalent monogenic conditions have demonstrated suboptimal performance characteristics for case identification via this approach.2“Genome-first” describes a screening process that starts with detection of pathogenic DNA variants independent of any demographic or clinical information.A genome-first approach to case identification for autosomal recessive diseases in general, and to CF specifically, has not been previously examined in a large sequenced cohort. Given the existence of available diagnostic approaches and the anticipated robust diagnostic workup data of suspected cases in the EHR, we hypothesized that modeling a genotype-first screening approach to CF case identification using EHRs would perform better than previous efforts in LQTS and ARVC.21 The data for this study came from 50,778 adult and pediatric participants in the DiscovEHR dataset recruited through the Geisinger MyCode Community Health Initiative (MyCode) in Pennsylvania. IRT-based newborn screening for CF began in Pennsylvania in 1995; therefore, any documented cases in individuals older than 23 years in Pennsylvania would be expected to have almost exclusively occurred through clinical presentations with signs or symptoms.In this manuscript, we report the results of applying a genotype-first approach to CF case identification within the DiscovEHR dataset that links de-identified exome sequence data with participant’s de-identified EHR data.n = 50,778) was queried for CF-causing variants, defined as those variants identified in the CFTR2 database as CF-causal, and variants identified as pathogenic or likely pathogenic in the ClinVar database.The DiscovEHR WES database  variant  and F508del and R1239S in the (FN). Importantly the S1235R variant has been judged non-pathogenic in both CFTR2 and ClinVar, and R1239S has not been evaluated for inclusion in either database. In addition to evidence of R1239S pathogenicity in case #22, there is additional bioinformatic support for the pathogenicity of this variant from our studies  individuals with CF, and we observed a carrier rate of one in 25 individuals (data not shown).In the United States, the prevalence of CF in individuals of northern European background is 1:3200 (or 0.03%), with a carrier frequency of one in 28 individuals.23 It is therefore noteworthy that the population prevalence of CF, LQTS, and ARVC are estimated to be: CF 1:3200, LQTS 1:2500, and AVRC 1:1250.8 Although the authors did not calculate PPV in their manuscripts, a liberal estimate of PPV using the same definitions for EHR-based TP and FP would be 3.2% for LQTS and 0% for ARVC.2 A PPV of 87.5% was found for CF in our study.The positive predictive value (PPV) of screening tests are known to be influenced by the prevalence of the disease in the population that is being screened; more prevalent disease tends to correlate with higher PPV.This study carried out a genome-first DNA-based retrospective analysis of clinical data in a cohort with an average of 14 years of EHR data and an average age of 63 years. In this setting we demonstrated excellent predictive value for genetic screening-based identification of CF.24 There have been numerous strategies proposed to improve the performance characteristics of the newborn CF screen.25 Vernooij-van Langen et al.26 carried out screening in 145,499 newborns in the Netherlands. The strategy that performed the best used two non-DNA screens (IRT and pancreatitis-associated-protein) followed by a two DNA screens (panel and sequencing) to get a sensitivity of 95.0%, a specificity of 100% and a PPV of 87.5%. The current study using DNA sequencing in a non-newborn cohort had a sensitivity, specificity, and PPV similar to the Vernooij-van Langen study.Newborn screening for CF is usually based on IRT, a non-DNA-based test. Kloosterboer et al. reported a sensitivity of 80.2% for the IRT/IRT screening algorithm.CFTR variants and an EHR showing a negative sweat test result as part of a diagnostic CF workup. So although EHR diagnostic coding included a CF diagnosis, that has been revised to CRMS. It is noteworthy that the DiscovEHR exome sequence data, array genotype data, and clinical genetic tests results, when available, were completely concordant.Of the 26 potential cases reviewed with DNA data, 22 were classified as definite CF, three were not classified as CF due to insufficient data, and one was classified as CRMS. The individual with CRMS, was judge a TN. This case had absence of bi-allelic pathogenic The exome sequence data identified three possible previously undiagnosed CF cases that were conservatively judged FP (see evidence support pathogenicity for these variants with milder disease phenotypes in Bioinformatic Narrative in the supplements). In one of these individuals there had been clinical suspicion of CF and a record of completed clinical genetic testing for CF at an outside institution. The other two (cases 24 and 25) had no evidence of clinical testing for CF in their EHR, but exome sequencing identified bi-allelic pathogenic variants (F508del and L206W), which have been classified as a CF-causing combination in both the CFTR2 and ClinVar databases.This work relied on the existence of robust variant interpretation databases, and the importance of maintaining and expanding these datasets was highlighted by two of the cases. The clinical testing of case 26 with the heterozygous combination of F508del and Q1476X in 2009 failed to identify a second pathogenic allele; her second pathogenic allele identified in this study was first entered into public data in 2013.One previously not observed variant that we encountered was R1239S; this variant was found in combination with F508del in case 22 (designated FN). Designation of R1239S as a newly recognized pathogenic variant is supported by our patient’s CF diagnosis as well as the bioinformatics analysis in Table CFTR-CF in more diverse cohorts where there is a greater potential for additional pathogenic variants not currently in the databases.CF is a disease that is most prevalent in people of European ancestry, and the MyCode cohort has 98% European-Americans  expert disputes over what to interpret as pathogenic genetic variants, (2) a lack of targeted condition-specific diagnostic evaluations in the EHR data associated with cases presenting with partial phenotypes (i.e. minor diagnostic criteria), and (3) the likelihood that we currently have an incomplete understanding of condition-specific genotype-phenotype correlations .2Growing enthusiasm for using DNA-based screening is tempered by legitimate concerns that this could lead to over-diagnosis and over-estimates of risk.28 the second can be overcome in clinical settings where patients are called back for more extensive phenotyping,29 and the third will require more research into basic genotype-phenotype correlation.2With regard to these limitations in the setting of these two cardiogenetic conditions, the first is being overcome through efforts such as ClinVar and ClinGen,CFTR variant calls, a clinical data source (i.e. Geisinger’s EHR) where comprehensive diagnostic phenotyping and clinical genetic testing of suspected cases is common, and a gene-disease example with clearly established diagnostic criteria that are routinely interrogated by expert clinicians in suspected cases. In this setting there is little evidence of over-estimate of CF risk given the finding that 87.5% (21 out of 24) of those who screened positive had confirmed CF, and there is little expectation of over-diagnosis since the remaining cases who lack sufficient diagnostic evidence could potentially undergo a clinical evaluation and secondary screening with sweat chloride testing to reach a clear conclusion.In the case of CF, examined in this work, the limitations observed in the LQTS and ARVC examples are largely overcome through the use of: expert consensus on pathogenic Prior to this CF example, given the data on LQTS and AVRC, it would have been tempting to conclude that poor PPVs were inescapable when pursuing population-based DNA-screening for monogenic disease risk in unselected patient groups. However, since this work demonstrates that a desirable PPV can be attained for CF, further research focused on a deeper examination of the factors surrounding these three conditions that result in dramatic differences in PPV should be pursued.CFTR gene are known, the molecular consequences of those changes are understood, and expertly curated publically available databases containing these variants exist. The sweat chloride test has been in use for 60 years.31 This routinely employed test provides non-DNA-based laboratory evidence of CFTR dysfunction and can be employed to confirm CF diagnoses. CF is clinically distinct without commonly encountered phenocopies.32 The range of clinical manifestations of CF including the mild end of the spectrum is well studied.CF is a particularly well-studied monogenic condition. CF is a recessively inherited disorder with nearly 100% penetrance in homozygous or compound heterozygous individuals. Proactive efforts aimed at case identification and diagnosis have occurred within healthcare for more than two decades. A large number of pathogenic variations in the Together these features have allowed us to test genome-first screening through a cross-sectional study design using the Geisinger EHR, which has been in place for over 20 years, as the sole clinical data source.With this design and in this setting, we were able to measure both the PPV and the NPV, as well as the sensitivity and specificity of using DNA variant detection as a primary screening technique. Our strict definition of FP led to 3 FP cases amongst 24 positives, even though 2 of those 3 are probable CF and would likely be converted to TP on follow-up evaluation. Therefore, the measured PPV of 87.5% for genome-first case detection of CF in this cohort is perhaps mildly suppressed by our retrospective EHR-based study design and demonstrates the need for condition-specific phenotyping beyond the available EHR data for incidental or secondary genetic findings.CF likely constitutes a gold-standard for the performance of genome-first case identification for monogenetic conditions in population cohorts where the individuals tested have a low prior probability. It is likely that the prospective use of genome-first screening will vary in its diagnostic accuracy and predictive value depending on the cohort and the evidence-base surrounding the chosen gene-condition pair. Importantly, the application of genome-first screening will need to accommodate for additional variables such as age-related penetrance (e.g. for cancer or heart disease) in those gene-condition pairs where it is a major factor and there are no secondary clinical tests of gene function such as the sweat chloride test.34 Minimum gene-condition test performance standards may be needed in order to support the employment of genome-first screening outside of the research setting. The development of expert consensus criteria could help establish when a gene-condition pair has the sufficient evidence-base to support population screening.It seems likely that an evidence-based genome-first gene-condition pair list will be a more restricted list of gene-condition pairs compared to the list for secondary findings within data generated for unrelated diagnoses.Since there is an existing population screen for CF that is effective and does not use DNA as the primary screening tool, CF may be amongst the conditions least likely to see significant changes in diagnoses if a DNA-based population screening were pursued. However, using CF as an exemplar in this finding, we demonstrate that an accurate ascertainment of the predictive value of DNA-based screening requires condition-specific phenotyping beyond available EHR data.18 and are summarized in Table The study cohort consisted of individuals who consented to participate in MyCode, an Institutional Review Board (IRB)-approved program to create a biorepository of blood, serum, and DNA samples for broad research use, including genomic analysis. This study was reviewed by the Geisinger IRB and determined to not be human subjects research as defined in 45CFR46.102(f) in written consent (Study #2017–054). Data obtained from analysis of MyCode samples is linked to Geisinger EHR; study participants have a median of 14 years of EHR data, providing a longitudinal database of clinical diagnosis, procedures, medications, and laboratory results. As part of the DiscovEHR collaboration between Geisinger and the Regeneron Genetics Center, DNA samples undergo microarray genotyping and whole exome sequencing (WES). The data analyzed for this study consisted of exome variants from the first 50,778 participants, including children. The cohort characteristics have been described previously35 WES variants were confirmed using genotyping data when the markers were available. The same DNA samples were genotyped using the Infinium OmniExpressExome microarray Beadchip . Following project level quality controls , 59,273 samples and 889,966 variants remained for downstream analysis. Of the CFTR variants identified in the patients in the present study, a subset  was available as markers on the BeadChip; all samples with markers on the BeadChip confirmed WES calls, providing a confirmation of the patient genotype status.WES for the DiscovEHR collaboration has been described previously.37 All variants were transcribed to RefSeq NM_000492.3 and translated to NP_000483.3.Sequence variants were annotated to coding DNA and functional proteins using the NCBI RefSeq Gene definitions, selecting for the transcript with the longest coding sequence among the transcripts with a Locus Reference Genome (LRG) annotation, and excluding transcripts without annotated start and stop codons .CFTR database  and the ClinVar database  were used as the source of validated pathogenic and non-pathogenic CFTR variants.39The Clinical and Functional Translation of CFTR variants; cases not previously identified through the variant process were sent for open chart review by 2 CF experts (described below) to adjudicate their CF diagnosis.CF ICD10 codes  identified all EHRs among the 50,778 participants who had a relevant CF diagnosis. This list was cross referenced with the list of cases with bi-allelic pathogenic CFTR variants identified by DNA analysis (WES plus array), or a CF diagnosis in the EHR without pathogenic bi-allelic CFTR variants underwent open chart review by experienced CF clinicians (CF physician and CF program nurse coordinator). All relevant clinical data were extracted from the EHR, including clinical history as available in progress notes, diagnoses, biochemical parameters, commercial genetic testing, prescription history, spirometry results, and radiographic studies. CF diagnoses was confirmed when an individual had both a clinical presentation of the disease and evidence of CFTR dysfunction or abundant clinical evidence of CF.Twenty-six cases with either pathogenic bi-allelic CFTR dysfunction, defined as sweat chloride >60 mmol/L, and clinical CFTR genetic analysis showing a combination of two CF-causing CFTR pathogenic variants found in CFTR2 or ClinVar, as per current guidelines from the CF Foundation, [2] if absent sweat chloride testing then CF diagnosis required genetics that were consistent with CF causing pathogenic variants and consistent clinical features, such as longitudinal care by CF physicians, bronchiectasis, and exocrine pancreatic insufficiency. No diagnosis was assumed in those patients lacking sufficient clinical information, irrespective of genetic results.CF was also considered present in the following: [1] The list of patients currently managed in the Geisinger CF clinic and their clinical genetic test results were extracted from EHR. The subset who were sequenced and genotyped as volunteers in MyCode were identified. Correlation of variant findings in clinical testing and research sequencing was determined. Clinical genetic testing data was not available on cases of confirmed CF where disease testing and management was carried out at a non-Geisinger facility.Standard statistical analyses and all data were plotted using GraphPad Prism .Supplemental Material


The current case report represents a warning against serious hyperkalaemia and acidosis induced by ACE-I during surgical stress while normal renal function could deceive the attending anaesthetist. Arterial gas analysis for follow-up of haemoglobin loss accidentally discovered hyperkalaemia and acidosis. Glucose-insulin and furosemide successfully corrected hyperkalaemia after 25 minutes and acidosis after 3 hours. These complications could be explained by a deficient steroid stress response to surgery secondary to suppression by ACE-I. Event analysis and database search found that ACE-I induced aldosterone deficiency aggravated by surgical stress response with an inadequate increase in aldosterone secretion due to angiotensin II deficiency as a sequel of ACE-I leading to defective secretion of H+ and K+. Furosemide is recommended to secrete H+ and K+ compensating for aldosterone deficiency in addition to other antihyperkalaemia measures. Anaesthetising an ACE-I treated patient requires considering ACE-I as a potential cause of hyperkalaemia and acidosis. We present a case of accidentally discovered, unexpected intraoperative acidosis and hyperkalaemia in an ACE-I treated patient with no renal impairment or diabetes. Written consent was obtained from the patient for publication.It is not uncommon for the anaesthetists to manage a hypertensive patient treated with Angiotensin Converting Enzyme Inhibitors (ACE-I) scheduled for either elective or emergency surgery. ACE-I are well known to be associated with a potential risk of perioperative side effects and complications as intraoperative hypotension and need for vasopressors, preoperative hyperkalaemia, perioperative drug-induced angioedema, and possible postoperative hypertension .The risk of hyperkalaemia in ACE-I treated patients is higher in patients with type II diabetes or renal insufficiency. At least 10% of patients treated with ACE-I will experience even mild hyperkalaemia during their course of treatment with ACE-I . The mecA 59-year-old male was scheduled for elective open retropubic prostatectomy for a benign enlarged prostate weighing approximately 65 grams. The patient's weight was 89 kg, ASA physical status II, diagnosed with essential hypertension two years ago, and controlled with ACE-I, Ramipril 10 mg once daily. No other morbidities were associated and no other medications were taken by the patient. The preoperative assessment did not reveal any other abnormality related to anaesthesia with normal vital signs, omitting Ramipril for 48 hours before the operation and normal baseline laboratory results including renal profile .Following discussion with the patient and the surgical team, the anaesthetic plan was general anesthesia (GA) with postoperative patient-controlled analgesia (PCA) with morphine. Relatively uneventful induction of GA by propofol (2mg/kg), fentanyl (100 micrograms), and rocuronium (0.6 mg/kg) with endotracheal intubation, radial arterial cannulation for IBP monitoring, and two wide-bore peripheral cannulas (18G) were inserted. Induction was accompanied by hypotension (BP dropped from 112/68 to 73/46) and bradycardia (HR dropped from 78/min. to 38/min.) that required two successive doses of ephedrine each 6 mg were followed by restoration of BP and HR. Baseline arterial blood gas (ABG) after positioning was normal . At 2 hoDespite normalization of potassium level (k 4.1 mmol/L) following the measures mentioned above, the acidosis persisted with maintained normal bicarbonate level and normal PCo2 . From thPostoperative follow-up of the renal function tests and electrolytes  revealedAt the end of surgery, the estimated blood loss was about 635 ml, UOP was 700 ml (over an operative time of 4 hours), and the infused fluids included 450 ml of Hartman's solution (over the first 2 hours), 950 ml of 0.9% normal saline, and 500 ml of Gelofusine 4% (over the second 2 hours) in addition to 1 litre of glucose 10% with insulin. No blood transfusion was required and no MAP <70 mmHg was recorded.Systematic analysis of the event did not reveal any cause for the hyperkalaemia and acidosis except the history of treatment with ACE-I that may be associated with renal tubular acidosis (RTA), normal anion gap acidosis, and hyperkalaemia –13; thisThe patient did not receive blood transfusion and did not experience hypotensive events apart from a single induction related hypotension event that was corrected by a small dose of ephedrine; this excludes the possibility of either transfusion related or acute kidney injury due to hypotension as possible causes of hyperkalaemia. A single dose of propofol was used for induction of anaesthesia making the possibility of propofol infusion syndrome extremely unlikely.The steroid hormone aldosterone acts on a mineralocorticoid receptor in the renal collecting ducts through a protein kinase mechanism leading to stimulation of Na+ reabsorption and active secretion of both H+ and K with potential risk for hyperkalaemia and acidosis in case of aldosterone deficiency , 5.Angiotensin II is essential for the secretion of aldosterone  with ACEFurosemide can be beneficial in type IV renal tubular acidosis by stimulation of K+ and H+ secretion from distal collecting tubules in case of hypoaldosteronism .Typically, surgical induced stress response is associated with increased secretion of cortisol and aldosterone through stimulation of the hypothalamic-pituitary-adrenocortical axis (HPA-axis) with subsequent electrolyte changes including salt and water retention with hypokalaemia .Figure 1).Experimental research demonstrated that angiotensin II and ACE could intervene with the pituitary hormones secretion like corticotropin (ACTH) and enhances the stimulatory effects of corticotropin-releasing hormone (CRH), thus contributing to the stress-related activation of the hypothalamic-pituitary-adrenocortical (HPA) axis , 14. SubAlthough, our patient stopped ACE-I 48 hours before surgery, it has been proved that aldosterone level does not change up to 15 days after cessation of Ramipril . This auIt is known that renal impairment and diabetes can be associated with increased risk of hyperkalaemia and acidosis in ACE-I treated patients who may warn the anaesthetists to suspect, monitor, and detect such complication; however, we reported a case of unexpected hyperkalaemia and acidosis in a surgical patient treated with ACE-I with normal baseline renal function, with adequate response to dextrose insulin and furosemide and correction of both hyperkalaemia and acidosis. This event in our case report may be triggered by surgical stress response with deficient steroid secretion especially aldosterone; this is supported by the ability of furosemide to reverse the effects of aldosterone deficiency by secreting hydrogen ion and potassium.Although the lactate level increased during the surgery, this cannot be explained by increased production as no haemodynamic instability was recorded but may be related to impaired excretion of lactate as a part of renal tubular acidosis.The current patient experienced a mild increase in creatinine level and a mild drop in in glomerular filtration rate. The derangement in renal function postoperatively may be due to the use of furosemide intraoperatively, which has been recorded before to produce drop GFR .Acidosis persisted despite potassium normalization with insulin, until successful correction was achieved with the diuretic effect of furosemide. Insulin administration produced correction of potassium but not acidosis as insulin may participate to acidosis  and thisThe possibility that catecholamines may shifted potassium intracellularly is not possible as catecholamines are also suppressed with ACE-I Patients on ACE-I are recommended for perioperative monitoring of potassium for early detection and management of such possible complications especially with unexplained ECG signs of hyperkalaemia and haemodynamic instability. In case of developing hyperkalaemia and acidosis, furosemide is recommended to secrete H+ and K+ compensating for aldosterone deficiency in addition to other antihyperkalaemia measures.A limitation in our case report is that we did not measure the aldosterone level after the event.No previous studies  examinedA future research study can assess the risk of hyperkalaemia and acidosis during the operative period in patients treated with ACE-I in addition to assessment of cortisol stress response in this category of patients.