What Can RNA-Based Therapy Do for Monogenic Diseases?

The use of RNA-based approaches to treat monogenic diseases (i.e., hereditary disorders caused by mutations in single genes) has been developed on different fronts. One approach uses small antisense oligonucleotides (ASOs) to modulate RNA processing at various stages; namely, to enhance correct splicing, to stimulate exon skipping (to exclude premature termination codon variants), to avoid undesired messenger RNA (mRNA) transcript degradation via the nonsense-mediated decay (NMD) pathway, or to induce mRNA degradation where they encode toxic proteins (e.g., in dominant diseases). Another approach consists in administering mRNA, which, like gene therapy, is a mutation-agnostic approach with potential application to any recessive monogenic disease. This is simpler than gene therapy because instead of requiring targeting of the nucleus, the mRNA only needs to be delivered to the cytoplasm. Although very promising (as demonstrated by COVID-19 vaccines), these approaches still have potential for optimisation, namely regarding delivery efficiency, adverse drug reactions and toxicity.

Integrity and Stability of PTC Bearing CFTR mRNA and Relevance to Future Modulator Therapies in Cystic Fibrosis.

Major advances have recently been made in the development and application of CFTR (cystic fibrosis transmembrane conductance regulator) mutation class-specific modulator therapies, but to date, there are no approved modulators for Class I mutations, i.e., those introducing a premature termination codon (PTC) into the CFTR mRNA. Such mutations induce nonsense-mediated decay (NMD), a cellular quality control mechanism that reduces the quantity of PTC bearing mRNAs, presumably to avoid translation of potentially deleterious truncated CFTR proteins. The NMD-mediated reduction of PTC-CFTR mRNA molecules reduces the efficacy of one of the most promising approaches to treatment of such mutations, namely, PTC readthrough therapy, using molecules that induce the incorporation of near-cognate amino acids at the PTC codon, thereby enabling translation of a full-length protein. In this study, we measure the effect of three different PTC mutations on the abundance, integrity, and stability of respective CFTR mRNAs, using CFTR specific RT-qPCR-based assays. Altogether, our data suggest that optimized rescue of PTC mutations has to take into account (1) the different steady-state levels of the CFTR mRNA associated with each specific PTC mutation; (2) differences in abundance between the 3' and 5' regions of CFTR mRNA, even following PTC readthrough or NMD inhibition; and (3) variable effects on CFTR mRNA stability for each specific PTC mutation.

Enhanced Expression of Human Epididymis Protein 4 (HE4) Reflecting Pro-Inflammatory Status Is Regulated by CFTR in Cystic Fibrosis Bronchial Epithelial Cells.

Decreased human epididymis protein 4 (HE4) plasma levels were reported in cystic fibrosis (CF) patients under CFTR potentiator ivacaftor therapy, which inversely correlated with lung function improvement. In this study, we investigated whether HE4 expression was affected via modulation of CFTR function in CF bronchial epithelial (CFBE) cells in vitro. HE4 protein levels were measured in the supernatants of CFBE 41o- cells expressing F508del-CFTR or wild-type CFTR (wt-CFTR) after administration of lumacaftor/ivacaftor or tezacaftor/ivacaftor, while HE4 expression in CFBE 41o- cells were also analyzed following application of adenylate cyclase activators Forskolin/IBMX or CFTRinh172. The effect of all of these compounds on CFTR function was monitored by the whole-cell patch-clamp technique. Induced HE4 expression was studied with interleukin-6 (IL-6) in F508del-CFTR CFBE 41o- cells under TNF-α stimulation for 1 h up to 1 week in duration. In parallel, plasma HE4 was determined in CF subjects homozygous for p.Phe508del-CFTR mutation receiving lumacaftor/ivacaftor (Orkambi®) therapy. NF-κB-mediated signaling was observed via the nuclear translocation of p65 subunit by fluorescence microscopy together with the analysis of IL-6 expression by an immunoassay. In addition, HE4 expression was examined after NF-κB pathway inhibitor BAY 11-7082 treatment with or without CFTR modulators. CFTR modulators partially restored the activity of F508del-CFTR and reduced HE4 concentration was found in F508del-CFTR CFBE 41o- cells that was close to what we observed in CFBE 41o- cells with wt-CFTR. These data were in agreement with decreased plasma HE4 concentrations in CF patients treated with Orkambi®. Furthermore, CFTR inhibitor induced elevated HE4 levels, while CFTR activator Forskolin/IBMX downregulated HE4 in the cell cultures and these effects were more pronounced in the presence of CFTR modulators. Higher activation level of baseline and TNF-α stimulated NF-κB pathway was detected in F508del-CFTR vs. wt-CFTR CFBE 41o- cells that was substantially reduced by CFTR modulators based on lower p65 nuclear positivity and IL-6 levels. Finally, HE4 expression was upregulated by TNF-α with elevated IL-6, and both protein levels were suppressed by combined administration of NF-κB pathway inhibitor and CFTR modulators in CFBE 41o- cells. In conclusion, CFTR dysfunction contributes to abnormal HE4 expression via NF-κB in CF.

Mutant CFTR Drives TWIST1 mediated epithelial-mesenchymal transition.

Cystic fibrosis (CF) is a monogenetic disease resulting from mutations in the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene encoding an anion channel. Recent evidence indicates that CFTR plays a role in other cellular processes, namely in development, cellular differentiation and wound healing. Accordingly, CFTR has been proposed to function as a tumour suppressor in a wide range of cancers. Along these lines, CF was recently suggested to be associated with epithelial-mesenchymal transition (EMT), a latent developmental process, which can be re-activated in fibrosis and cancer. However, it is unknown whether EMT is indeed active in CF and if EMT is triggered by dysfunctional CFTR itself or a consequence of secondary complications of CF. In this study, we investigated the occurrence of EMT in airways native tissue, primary cells and cell lines expressing mutant CFTR through the expression of epithelial and mesenchymal markers as well as EMT-associated transcription factors. Transepithelial electrical resistance, proliferation and regeneration rates, and cell resistance to TGF-ß1induced EMT were also measured. CF tissues/cells expressing mutant CFTR displayed several signs of active EMT, namely: destructured epithelial proteins, defective cell junctions, increased levels of mesenchymal markers and EMT-associated transcription factors, hyper-proliferation and impaired wound healing. Importantly, we found evidence that the mutant CFTR triggered EMT was mediated by EMT-associated transcription factor TWIST1. Further, our data show that CF cells are over-sensitive to EMT but the CF EMT phenotype can be reversed by CFTR modulator drugs. Altogether, these results identify for the first time that EMT is intrinsically triggered by the absence of functional CFTR through a TWIST1 dependent mechanism and indicate that CFTR plays a direct role in EMT protection. This mechanistic link is a plausible explanation for the high incidence of fibrosis and cancer in CF, as well as for the role of CFTR as tumour suppressor protein.

Organoids as a personalized medicine tool for ultra-rare mutations in cystic fibrosis: The case of S955P and 1717-2A>G.

BACKGROUND: For most of the >2000 CFTR gene variants reported, neither the associated disease liability nor the underlying basic defect are known, and yet these are essential for disease prognosis and CFTR-based therapeutics. Here we aimed to characterize two ultra-rare mutations - 1717-2A > G (c.1585-2A > G) and S955P (p.Ser955Pro) - as case studies for personalized medicine. METHODS: Patient-derived rectal biopsies and intestinal organoids from two individuals with each of these mutations and F508del (p.Phe508del) in the other allele were used to assess CFTR function, response to modulators and RNA splicing pattern. In parallel, we used cellular models to further characterize S955P independently of F508del and to assess its response to CFTR modulators. RESULTS: Results in both rectal biopsies and intestinal organoids from both patients evidence residual CFTR function. Further characterization shows that 1717-2A > G leads to alternative splicing generating <1% normal CFTR mRNA and that S955P affects CFTR gating. Finally, studies in organoids predict that both patients are responders to VX-770 alone and even more to VX-770 combined with VX-809 or VX-661, although to different levels. CONCLUSION: This study demonstrates the high potential of personalized medicine through theranostics to extend the label of approved drugs to patients with rare mutations.

KLF4 Acts as a wt-CFTR Suppressor through an AKT-Mediated Pathway.

Cystic Fibrosis (CF) is caused by >2000 mutations in the CF transmembrane conductance regulator (CFTR) gene, but one mutation-F508del-occurs in ~80% of patients worldwide. Besides its main function as an anion channel, the CFTR protein has been implicated in epithelial differentiation, tissue regeneration, and, when dysfunctional, cancer. However, the mechanisms that regulate such relationships are not fully elucidated. Krüppel-like factors (KLFs) are a family of transcription factors (TFs) playing central roles in development, stem cell differentiation, and proliferation. Herein, we hypothesized that these TFs might have an impact on CFTR expression and function, being its missing link to differentiation. Our results indicate that KLF4 (but not KLF2 nor KLF5) is upregulated in CF vs. non-CF cells and that it negatively regulates wt-CFTR expression and function. Of note, F508del-CFTR expressing cells are insensitive to KLF4 modulation. Next, we investigated which KLF4-related pathways have an effect on CFTR. Our data also show that KLF4 modulates wt-CFTR (but not F508del-CFTR) via both the serine/threonine kinase AKT1 (AKT) and glycogen synthase kinase 3 beta (GSK3ß) signaling. While AKT acts positively, GSK3ß is a negative regulator of CFTR. This crosstalk between wt-CFTR and KLF4 via AKT/ GSK3ß signaling, which is disrupted in CF, constitutes a novel mechanism linking CFTR to the epithelial differentiation.

TMEM16A chloride channel does not drive mucus production.

Airway mucus obstruction is the main cause of morbidity in cystic fibrosis, a disease caused by mutations in the CFTR Cl- channel. Activation of non-CFTR Cl- channels such as TMEM16A can likely compensate for defective CFTR. However, TMEM16A was recently described as a key driver in mucus production/secretion. Here, we have examined whether indeed there is a causal relationship between TMEM16A and MUC5AC production, the main component of respiratory mucus. Our data show that TMEM16A and MUC5AC are inversely correlated during differentiation of human airway cells. Furthermore, we show for the first time that the IL-4-induced TMEM16A up-regulation is proliferation-dependent, which is supported by the correlation found between TMEM16A and Ki-67 proliferation marker during wound healing. Consistently, the notch signaling activator DLL4 increases MUC5AC levels without inducing changes neither in TMEM16A nor in Ki-67 expression. Moreover, TMEM16A inhibition decreased airway surface liquid height. Altogether, our findings demonstrate that up-regulation of TMEM16A and MUC5AC is only circumstantial under cell proliferation, but with no causal relationship between them. Thus, although essential for airway hydration, TMEM16A is not required for MUC5AC production.

BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies.

BACKGROUND: Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data. RESULTS: We propose a new model to detect and classify relations in text, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using open biomedical ontologies, specifically Chemical Entities of Biological Interest (ChEBI), Human Phenotype, and Gene Ontology. We assessed the performance of BO-LSTM with drug-drug interactions mentioned in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved the F1-score of both the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. We adapted an existing DDI extraction model with our ontology-based method, obtaining a higher F1 score than the original model. Furthermore, we developed and made available a corpus of 228 abstracts annotated with relations between genes and phenotypes, and demonstrated how BO-LSTM can be applied to other types of relations. CONCLUSIONS: Our findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.

The effect of premature termination codon mutations on CFTR mRNA abundance in human nasal epithelium and intestinal organoids: a basis for read-through therapies in cystic fibrosis.

A major challenge in cystic fibrosis (CF) research is applying mutation-specific therapy to individual patients with diverse and rare CF transmembrane conductance regulator (CFTR) genotypes. Read-through agents are currently the most promising approach for Class I mutations that introduce premature termination codons (PTCs) into CFTR mRNA. However, variations in degradation of PTC containing transcripts by nonsense mediated decay (NMD) might lower read-through efficacy. Allele specific quantitative real time (qRT)-PCR was used to measure variations in CFTR mRNA abundance for several PTC mutations in respiratory cells and intestinal organoids. The majority of PTC mutations were associated with reduced levels of relative mRNA transcript abundance (∼33% and 26% of total CFTR mRNA in respiratory cells and intestinal organoids, respectively, compared to >50% for non-PTC causing mutations). These levels were generally not affected by PTC mutation type or position, but there could be twofold variations between individuals bearing the same genotype. Most PTC mutations in CFTR are subject to similar levels of NMD, which reduce but do not abolish PTC bearing mRNAs. Measurement of individual NMD levels in intestinal organoids and HNE cells might, therefore, be useful in predicting efficacy of PTC read-through in the context of personalized CFTR modulator therapy.

Generating a Tolerogenic Cell Therapy Knowledge Graph from Literature.

Tolerogenic cell therapies provide an alternative to conventional immunosuppressive treatments of autoimmune disease and address, among other goals, the rejection of organ or stem cell transplants. Since various methodologies can be followed to develop tolerogenic therapies, it is important to be aware and up to date on all available studies that may be relevant to their improvement. Recently, knowledge graphs have been proposed to link various sources of information, using text mining techniques. Knowledge graphs facilitate the automatic retrieval of information about the topics represented in the graph. The objective of this work was to automatically generate a knowledge graph for tolerogenic cell therapy from biomedical literature. We developed a system, ICRel, based on machine learning to extract relations between cells and cytokines from abstracts. Our system retrieves related documents from PubMed, annotates each abstract with cell and cytokine named entities, generates the possible combinations of cell-cytokine pairs cooccurring in the same sentence, and identifies meaningful relations between cells and cytokines. The extracted relations were used to generate a knowledge graph, where each edge was supported by one or more documents. We obtained a graph containing 647 cell-cytokine relations, based on 3,264 abstracts. The modules of ICRel were evaluated with cross-validation and manual evaluation of the relations extracted. The relation extraction module obtained an F-measure of 0.789 in a reference database, while the manual evaluation obtained an accuracy of 0.615. Even though the knowledge graph is based on information that was already published in other articles about immunology, the system we present is more efficient than the laborious task of manually reading all the literature to find indirect or implicit relations. The ICRel graph will help experts identify implicit relations that may not be evident in published studies.

Extracting microRNA-gene relations from biomedical literature using distant supervision.

Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.

Functional coherence metrics in protein families.

BACKGROUND: Biological sequences, such as proteins, have been provided with annotations that assign functional information. These functional annotations are associations of proteins (or other biological sequences) with descriptors characterizing their biological roles. However, not all proteins are fully (or even at all) annotated. This annotation incompleteness limits our ability to make sound assertions about the functional coherence within sets of proteins. Annotation incompleteness is a problematic issue when measuring semantic functional similarity of biological sequences since they can only capture a limited amount of all the semantic aspects the sequences may encompass. METHODS: Instead of relying uniquely on single (reductive) metrics, this work proposes a comprehensive approach for assessing functional coherence within protein sets. The approach entails using visualization and term enrichment techniques anchored in specific domain knowledge, such as a protein family. For that purpose we evaluate two novel functional coherence metrics, mUI and mGIC that combine aspects of semantic similarity measures and term enrichment. RESULTS: These metrics were used to effectively capture and measure the local similarity cores within protein sets. Hence, these metrics coupled with visualization tools allow an improved grasp on three important functional annotation aspects: completeness, agreement and coherence. CONCLUSIONS: Measuring the functional similarity between proteins based on their annotations is a non trivial task. Several metrics exist but due both to characteristics intrinsic to the nature of graphs and extrinsic natures related to the process of annotation each measure can only capture certain functional annotation aspects of proteins. Hence, when trying to measure the functional coherence of a set of proteins a single metric is too reductive. Therefore, it is valuable to be aware of how each employed similarity metric works and what similarity aspects it can best capture. Here we test the behaviour and resilience of some similarity metrics.

Human Epididymis Protein 4: A Novel Serum Inflammatory Biomarker in Cystic Fibrosis.

BACKGROUND: Increased expression of the human epididymis protein 4 (HE4) was previously described in lung biopsy samples from patients with cystic fibrosis (CF). It remains unknown, however, whether serum HE4 concentrations are elevated in CF. METHODS: Seventy-seven children with CF from six Hungarian CF centers and 57 adult patients with CF from a Czech center were enrolled. In addition, 94 individuals with non-CF lung diseases and 117 normal control subjects with no pulmonary disorders were analyzed. Serum HE4 levels were measured by using an immunoassay, and their expression was further investigated via the quantification of HE4 messenger RNA by using quantitative reverse transcription polymerase chain reaction in CF vs non-CF respiratory epithelium biopsy specimens. The expression of the potential regulator miR-140-5p was analyzed by using an UPL-based quantitative reverse transcription polymerase chain reaction assay. HE4 was measured in the supernatants from unpolarized and polarized cystic fibrosis bronchial epithelial cells expressing wild-type or F508del-CFTR. RESULTS: Median serum HE4 levels were significantly elevated in children with CF (99.5 [73.1-128.9] pmol/L) compared with control subjects (36.3 [31.1-43.4] pmol/L; P < .0001). This observation was replicated in adults with CF (115.7 [77.8-148.7] pmol/L; P < .0001). In contrast, abnormal but lower HE4 concentrations were found in cases of severe bronchitis, asthma, pneumonia, and bronchiectasis. In patients with CF, the concentrations of HE4 were positively correlated with overall disease severity and C-reactive protein concentrations, whereas a significant inverse relationship was found between HE4 and the spirometric FEV1 value. Relative HE4 mRNA levels were significantly upregulated (P = .011) with a decreased miR-140-5p expression (P = .020) in the CF vs non-CF airway biopsy specimens. Twofold higher HE4 concentrations were recorded in the supernatant of polarized F508del-CF transmembrane conductance regulator/bronchial epithelial cells compared with wild-type cells. CONCLUSIONS: HE4 serum levels positively correlate with the overall severity of CF and the degree of pulmonary dysfunction. HE4 may thus be used as a novel inflammatory biomarker and possibly also as a measure of treatment efficacy in CF lung disease.

Correction of a Cystic Fibrosis Splicing Mutation by Antisense Oligonucleotides.

Cystic fibrosis (CF), the most common life-threatening genetic disease in Caucasians, is caused by ∼2,000 different mutations in the CF transmembrane conductance regulator (CFTR) gene. A significant fraction of these (∼13%) affect pre-mRNA splicing for which novel therapies have been somewhat neglected. We have previously described the effect of the CFTR splicing mutation c.2657+5G>A in IVS16, showing that it originates transcripts lacking exon 16 as well as wild-type transcripts. Here, we tested an RNA-based antisense oligonucleotide (AON) strategy to correct the aberrant splicing caused by this mutation. Two AONs (AON1/2) complementary to the pre-mRNA IVS16 mutant region were designed and their effect on splicing was assessed at the RNA and protein levels, on intracellular protein localization and function. To this end, we used the 2657+5G>A mutant CFTR minigene stably expressed in HEK293 Flp-In cells that express a single copy of the transgene. RNA data from AON1-treated mutant cells show that exon 16 inclusion was almost completely restored (to 95%), also resulting in increased levels of correctly localized CFTR protein at the plasma membrane (PM) and with increased function. A novel two-color CFTR splicing reporter minigene developed here allowed the quantitative monitoring of splicing by automated microscopy localization of CFTR at the PM. The AON strategy is thus a promising therapeutic approach for the specific correction of alternative splicing.

Comparative ex vivo, in vitro and in silico analyses of a CFTR splicing mutation: Importance of functional studies to establish disease liability of mutations.

The Cystic Fibrosis p.Ile1234Val missense mutation actually creates a new dual splicing site possibly used either as a new acceptor or donor. Here, we aimed to test the accuracy of in silico predictions by comparing them with in vitro and ex vivo functional analyses of this mutation for an accurate CF diagnosis/prognosis. To this end, we applied a new in vitro strategy using a CFTR mini-gene which includes the complete CFTR coding sequence plus intron 22 (short version) which allows the assessment of alternatively spliced mRNA levels as well as the properties of the resulting abnormal CFTR protein regarding processing, intracellular localization and function. Our data demonstrate that p.Ile1234Val leads to usage of the alternative splicing donor (but not acceptor) resulting in alternative CFTR transcripts lacking 18 nts of exon 22 which produce a truncated CFTR protein with residual Cl- channel function. These results recapitulate data from native tissues of a CF patient. In conclusion, the existing in silico prediction models have limited application and ex vivo functional assessment of mutation effects should be made. Alternatively the in vitro strategy adopted here can be applied to assess the disease liability of mutations for an accurate CF diagnosis/prognosis.

Transcriptome meta-analysis reveals common differential and global gene expression profiles in cystic fibrosis and other respiratory disorders and identifies CFTR regulators.

A meta-analysis of 13 independent microarray data sets was performed and gene expression profiles from cystic fibrosis (CF), similar disorders (COPD: chronic obstructive pulmonary disease, IPF: idiopathic pulmonary fibrosis, asthma), environmental conditions (smoking, epithelial injury), related cellular processes (epithelial differentiation/regeneration), and non-respiratory "control" conditions (schizophrenia, dieting), were compared. Similarity among differentially expressed (DE) gene lists was assessed using a permutation test, and a clustergram was constructed, identifying common gene markers. Global gene expression values were standardized using a novel approach, revealing that similarities between independent data sets run deeper than shared DE genes. Correlation of gene expression values identified putative gene regulators of the CF transmembrane conductance regulator (CFTR) gene, of potential therapeutic significance. Our study provides a novel perspective on CF epithelial gene expression in the context of other lung disorders and conditions, and highlights the contribution of differentiation/EMT and injury to gene signatures of respiratory disease.

Measurements of Functional Responses in Human Primary Lung Cells as a Basis for Personalized Therapy for Cystic Fibrosis.

BACKGROUND: The best investigational drug to treat cystic fibrosis (CF) patients with the most common CF-causing mutation (F508del) is VX-809 (lumacaftor) which recently succeeded in Phase III clinical trial in combination with ivacaftor. This corrector rescues F508del-CFTR from its abnormal intracellular localization to the cell surface, a traffic defect shared by all Class II CFTR mutants. Our goal here is to test the efficacy of lumacaftor in other Class II mutants in primary human bronchial epithelial (HBE) cells derived from CF patients. METHODS: The effect of lumacaftor was investigated in primary HBE cells from non-CF and CF patients with F508del/F508del, A561E/A561E, N1303K/G542X, F508del/G542X and F508del/Y1092X genotypes by measurements of Forskolin plus Genistein-inducible equivalent short-circuit current (Ieq-SC-Fsk + Gen) in perfused open-circuit Ussing chambers. Efficacy of corrector C18 was also assessed on A561E/A561E and F508del/F508del cells. RESULTS: Our data indicate that A561E (when present in both alleles) responds positively to lumacaftor treatment at equivalent efficacy of F508del in primary HBE cells. Similarly, lumacaftor has a positive impact on Y1092X, but not on N1303K. Our data also show that cells with only one copy of F508del-CFTR respond less to VX-809. Moreover, there is great variability in lumacaftor responses among F508del-homozygous cells from different donors. Compound C18 failed to rescue A561E-CFTR but not in F508del-CFTR, thus plausibly it has a different mechanism of action distinct from lumacaftor. CONCLUSIONS: CF patients with A561E (and likely also those with Y1029X) can potentially benefit from lumacaftor. Moreover, the methodology used here exemplifies how ex vivo approaches may apply personalized therapies to CF and possibly other respiratory diseases.

GRYFUN: a web application for GO term annotation visualization and analysis in protein sets.

Functional context for biological sequence is provided in the form of annotations. However, within a group of similar sequences there can be annotation heterogeneity in terms of coverage and specificity. This in turn can introduce issues regarding the interpretation of actual functional similarity and overall functional coherence of such a group. One way to mitigate such issues is through the use of visualization and statistical techniques. Therefore, in order to help interpret this annotation heterogeneity we created a web application that generates Gene Ontology annotation graphs for protein sets and their associated statistics from simple frequencies to enrichment values and Information Content based metrics. The publicly accessible website http://xldb.di.fc.ul.pt/gryfun/ currently accepts lists of UniProt accession numbers in order to create user-defined protein sets for subsequent annotation visualization and statistical assessment. GRYFUN is a freely available web application that allows GO annotation visualization of protein sets and which can be used for annotation coherence and cohesiveness analysis and annotation extension assessments within under-annotated protein sets.

Annotation extension through protein family annotation coherence metrics.

Protein functional annotation consists in associating proteins with textual descriptors elucidating their biological roles. The bulk of annotation is done via automated procedures that ultimately rely on annotation transfer. Despite a large number of existing protein annotation procedures the ever growing protein space is never completely annotated. One of the facets of annotation incompleteness derives from annotation uncertainty. Often when protein function cannot be predicted with enough specificity it is instead conservatively annotated with more generic terms. In a scenario of protein families or functionally related (or even dissimilar) sets this leads to a more difficult task of using annotations to compare the extent of functional relatedness among all family or set members. However, we postulate that identifying sub-sets of functionally coherent proteins annotated at a very specific level, can help the annotation extension of other incompletely annotated proteins within the same family or functionally related set. As an example we analyse the status of annotation of a set of CAZy families belonging to the Polysaccharide Lyase class. We show that through the use of visualization methods and semantic similarity based metrics it is possible to identify families and respective annotation terms within them that are suitable for possible annotation extension. Based on our analysis we then propose a semi-automatic methodology leading to the extension of single annotation terms within these partially annotated protein sets or families.

High-content siRNA screen reveals global ENaC regulators and potential cystic fibrosis therapy targets.

Dysfunction of ENaC, the epithelial sodium channel that regulates salt and water reabsorption in epithelia, causes several human diseases, including cystic fibrosis (CF). To develop a global understanding of molecular regulators of ENaC traffic/function and to identify of candidate CF drug targets, we performed a large-scale screen combining high-content live-cell microscopy and siRNAs in human airway epithelial cells. Screening over 6,000 genes identified over 1,500 candidates, evenly divided between channel inhibitors and activators. Genes in the phosphatidylinositol pathway were enriched on the primary candidate list, and these, along with other ENaC activators, were examined further with secondary siRNA validation. Subsequent detailed investigation revealed ciliary neurotrophic factor receptor (CNTFR) as an ENaC modulator and showed that inhibition of (diacylglycerol kinase, iota) DGKι, a protein involved in PiP2 metabolism, downgrades ENaC activity, leading to normalization of both Na+ and fluid absorption in CF airways to non-CF levels in primary human lung cells from CF patients.