Publications by authors named "Olivier Lichtarge"

135 Publications

Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and greater coronavirus family.

Bioinformatics 2021 May 27. Epub 2021 May 27.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.

Motivation: Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family.

Results: Combining coronavirus family sequence information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent.

Availability: The results of this work are made interactively available at http://cov.lichtargelab.org.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243408PMC
May 2021

Using Interpretable Deep Learning to Model Cancer Dependencies.

Bioinformatics 2021 May 27. Epub 2021 May 27.

Departments of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.

Motivation: Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field.

Results: We design Biological VNN (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks and converges faster. BioVNN also outperforms a neural network based on randomized pathways. More importantly, dependency predictions can be explained by correlating with the neuron output states of relevant pathways, which suggest dependency mechanisms. In feature importance analysis, BioVNN recapitulates known reaction partners and proposes new ones. Such robust and interpretable VNNs may facilitate the understanding of cancer dependency and the development of targeted therapies.

Availability And Implementation: Code and data are available at http://static.lichtargelab.org/BioVNN/.

Supplementary Information: See Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab137DOI Listing
May 2021

A method to delineate de novo missense variants across pathways prioritizes genes linked to autism.

Sci Transl Med 2021 05;13(594)

Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.

Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.
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http://dx.doi.org/10.1126/scitranslmed.abc1739DOI Listing
May 2021

Structure and evolutionary trace-assisted screening of a residue swapping the substrate ambiguity and chiral specificity in an esterase.

Comput Struct Biotechnol J 2021 18;19:2307-2317. Epub 2021 Apr 18.

Institute of Physical Chemistry "Rocasolano", CSIC, 28006 Madrid, Spain.

Our understanding of enzymes with high substrate ambiguity remains limited because their large active sites allow substrate docking freedom to an extent that seems incompatible with stereospecificity. One possibility is that some of these enzymes evolved a set of evolutionarily fitted sequence positions that stringently allow switching substrate ambiguity and chiral specificity. To explore this hypothesis, we targeted for mutation a serine ester hydrolase (EH) that exhibits an impressive 71-substrate repertoire but is not stereospecific ( 50%). We used structural actions and the computational evolutionary trace method to explore specificity-swapping sequence positions and hypothesized that position I244 was critical. Driven by evolutionary action analysis, this position was substituted to leucine, which together with isoleucine appears to be the amino acid most commonly present in the closest homologous sequences (max. identity, . 67.1%), and to phenylalanine, which appears in distant homologues. While the I244L mutation did not have any functional consequences, the I244F mutation allowed the esterase to maintain a remarkable 53-substrate range while gaining stereospecificity properties ( 99.99%). These data support the possibility that some enzymes evolve sequence positions that control the substrate scope and stereospecificity. Such residues, which can be evolutionarily screened, may serve as starting points for further designing substrate-ambiguous, yet chiral-specific, enzymes that are greatly appreciated in biotechnology and synthetic chemistry.
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http://dx.doi.org/10.1016/j.csbj.2021.04.041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105184PMC
April 2021

Variants in PRKAR1B cause a neurodevelopmental disorder with autism spectrum disorder, apraxia, and insensitivity to pain.

Genet Med 2021 Apr 8. Epub 2021 Apr 8.

Institute of Human Genetics, Heidelberg University, Heidelberg, Germany.

Purpose: We characterize the clinical and molecular phenotypes of six unrelated individuals with intellectual disability and autism spectrum disorder who carry heterozygous missense variants of the PRKAR1B gene, which encodes the R1β subunit of the cyclic AMP-dependent protein kinase A (PKA).

Methods: Variants of PRKAR1B were identified by single- or trio-exome analysis. We contacted the families and physicians of the six individuals to collect phenotypic information, performed in vitro analyses of the identified PRKAR1B-variants, and investigated PRKAR1B expression during embryonic development.

Results: Recent studies of large patient cohorts with neurodevelopmental disorders found significant enrichment of de novo missense variants in PRKAR1B. In our cohort, de novo origin of the PRKAR1B variants could be confirmed in five of six individuals, and four carried the same heterozygous de novo variant c.1003C>T (p.Arg335Trp; NM_001164760). Global developmental delay, autism spectrum disorder, and apraxia/dyspraxia have been reported in all six, and reduced pain sensitivity was found in three individuals carrying the c.1003C>T variant. PRKAR1B expression in the brain was demonstrated during human embryonal development. Additionally, in vitro analyses revealed altered basal PKA activity in cells transfected with variant-harboring PRKAR1B expression constructs.

Conclusion: Our study provides strong evidence for a PRKAR1B-related neurodevelopmental disorder.
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http://dx.doi.org/10.1038/s41436-021-01152-7DOI Listing
April 2021

Targeting SARS-CoV-2 Nsp3 macrodomain structure with insights from human poly(ADP-ribose) glycohydrolase (PARG) structures with inhibitors.

Prog Biophys Mol Biol 2021 08 23;163:171-186. Epub 2021 Feb 23.

Department of Molecular and Cellular Oncology, M. D. Anderson Cancer Center, Houston, TX, 77030, USA; Department of Cancer Biology, M.D. Anderson Cancer Center, Houston, TX, 77030, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. Electronic address:

Arrival of the novel SARS-CoV-2 has launched a worldwide effort to identify both pre-approved and novel therapeutics targeting the viral proteome, highlighting the urgent need for efficient drug discovery strategies. Even with effective vaccines, infection is possible, and at-risk populations would benefit from effective drug compounds that reduce the lethality and lasting damage of COVID-19 infection. The CoV-2 MacroD-like macrodomain (Mac1) is implicated in viral pathogenicity by disrupting host innate immunity through its mono (ADP-ribosyl) hydrolase activity, making it a prime target for antiviral therapy. We therefore solved the structure of CoV-2 Mac1 from non-structural protein 3 (Nsp3) and applied structural and sequence-based genetic tracing, including newly determined A. pompejana MacroD2 and GDAP2 amino acid sequences, to compare and contrast CoV-2 Mac1 with the functionally related human DNA-damage signaling factor poly (ADP-ribose) glycohydrolase (PARG). Previously, identified targetable features of the PARG active site allowed us to develop a pharmacologically useful PARG inhibitor (PARGi). Here, we developed a focused chemical library and determined 6 novel PARGi X-ray crystal structures for comparative analysis. We applied this knowledge to discovery of CoV-2 Mac1 inhibitors by combining computation and structural analysis to identify PARGi fragments with potential to bind the distal-ribose and adenosyl pockets of the CoV-2 Mac1 active site. Scaffold development of these PARGi fragments has yielded two novel compounds, PARG-345 and PARG-329, that crystallize within the Mac1 active site, providing critical structure-activity data and a pathway for inhibitor optimization. The reported structural findings demonstrate ways to harness our PARGi synthesis and characterization pipeline to develop CoV-2 Mac1 inhibitors targeting the ADP-ribose active site. Together, these structural and computational analyses reveal a path for accelerating development of antiviral therapeutics from pre-existing drug optimization pipelines.
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http://dx.doi.org/10.1016/j.pbiomolbio.2021.02.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901392PMC
August 2021

Harnessing the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer's disease.

Alzheimers Dement 2021 05 7;17(5):831-846. Epub 2020 Dec 7.

Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, Texas, USA.

The strongest genetic risk factor for idiopathic late-onset Alzheimer's disease (LOAD) is apolipoprotein E (APOE) ɛ4, while the APOE ɛ2 allele is protective. However, there are paradoxical APOE ɛ4 carriers who remain disease-free and APOE ɛ2 carriers with LOAD. We compared exomes of healthy APOE ɛ4 carriers and APOE ɛ2 Alzheimer's disease (AD) patients, prioritizing coding variants based on their predicted functional impact, and identified 216 genes with differential mutational load between these two populations. These candidate genes were significantly dysregulated in LOAD brains, and many modulated tau- or β42-induced neurodegeneration in Drosophila. Variants in these genes were associated with AD risk, even in APOE ɛ3 homozygotes, showing robust predictive power for risk stratification. Network analyses revealed involvement of candidate genes in brain cell type-specific pathways including synaptic biology, dendritic spine pruning and inflammation. These potential modifiers of LOAD may constitute novel biomarkers, provide potential therapeutic intervention avenues, and support applying this approach as larger whole exome sequencing cohorts become available.
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http://dx.doi.org/10.1002/alz.12240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247413PMC
May 2021

Identification of evolutionarily stable sites across the SARS-CoV-2 proteome.

Res Sq 2020 Oct 20. Epub 2020 Oct 20.

Baylor College of Medicine.

Since the first recognized case of COVID-19, more than 30 million people have been infected worldwide. Despite global efforts in drug and vaccine development to fight the disease, there is currently no vaccine or drug cure for COVID-19, though some drugs reduce severity and hasten recovery. Here we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify functional sites that can inform the search for treatments. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that are useful drug targets. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches are based upon evolutionary principles and are agnostic to organism or infective agent.
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http://dx.doi.org/10.21203/rs.3.rs-95030/v1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7587783PMC
October 2020

Uncovering DNA-PKcs ancient phylogeny, unique sequence motifs and insights for human disease.

Prog Biophys Mol Biol 2021 Aug 6;163:87-108. Epub 2020 Oct 6.

Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. Electronic address:

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is a key member of the phosphatidylinositol-3 kinase-like (PIKK) family of protein kinases with critical roles in DNA-double strand break repair, transcription, metastasis, mitosis, RNA processing, and innate and adaptive immunity. The absence of DNA-PKcs from many model organisms has led to the assumption that DNA-PKcs is a vertebrate-specific PIKK. Here, we find that DNA-PKcs is widely distributed in invertebrates, fungi, plants, and protists, and that threonines 2609, 2638, and 2647 of the ABCDE cluster of phosphorylation sites are highly conserved amongst most Eukaryotes. Furthermore, we identify highly conserved amino acid sequence motifs and domains that are characteristic of DNA-PKcs relative to other PIKKs. These include residues in the Forehead domain and a novel motif we have termed YRPD, located in an α helix C-terminal to the ABCDE phosphorylation site loop. Combining sequence with biochemistry plus structural data on human DNA-PKcs unveils conserved sequence and conformational features with functional insights and implications. The defined generally progressive DNA-PKcs sequence diversification uncovers conserved functionality supported by Evolutionary Trace analysis, suggesting that for many organisms both functional sites and evolutionary pressures remain identical due to fundamental cell biology. The mining of cancer genomic data and germline mutations causing human inherited disease reveal that robust DNA-PKcs activity in tumors is detrimental to patient survival, whereas germline mutations compromising function are linked to severe immunodeficiency and neuronal degeneration. We anticipate that these collective results will enable ongoing DNA-PKcs functional analyses with biological and medical implications.
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http://dx.doi.org/10.1016/j.pbiomolbio.2020.09.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021618PMC
August 2021

An Evolutionary Trace method defines functionally important bases and sites common to RNA families.

PLoS Comput Biol 2020 03 24;16(3):e1007583. Epub 2020 Mar 24.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.

Functional non-coding (fnc)RNAs are nucleotide sequences of varied lengths, structures, and mechanisms that ubiquitously influence gene expression and translation, genome stability and dynamics, and human health and disease. Here, to shed light on their functional determinants, we seek to exploit the evolutionary record of variation and divergence read from sequence comparisons. The approach follows the phylogenetic Evolutionary Trace (ET) paradigm, first developed and extensively validated on proteins. We assigned a relative rank of importance to every base in a study of 1070 functional RNAs, including the ribosome, and observed evolutionary patterns strikingly similar to those seen in proteins, namely, (1) the top-ranked bases clustered in secondary and tertiary structures. (2) In turn, these clusters mapped functional regions for catalysis, binding proteins and drugs, post-transcriptional modification, and deleterious mutations. (3) Moreover, the quantitative quality of these clusters correlated with the identification of functional regions. (4) As a result of this correlation, smoother structural distributions of evolutionary important nucleotides improved functional site predictions. Thus, in practice, phylogenetic analysis can broadly identify functional determinants in RNA sequences and functional sites in RNA structures, and reveal details on the basis of RNA molecular functions. As example of application, we report several previously undocumented and potentially functional ET nucleotide clusters in the ribosome. This work is broadly relevant to studies of structure-function in ribonucleic acids. Additionally, this generalization of ET shows that evolutionary constraints among sequence, structure, and function are similar in structured RNA and proteins. RNA ET is currently available as part of the ET command-line package, and will be available as a web-server.
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http://dx.doi.org/10.1371/journal.pcbi.1007583DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7092961PMC
March 2020

Graph-based information diffusion method for prioritizing functionally related genes in protein-protein interaction networks.

Pac Symp Biocomput 2020 ;25:439-450

Integrative Molecular and Biomedical Sciences Graduate Program, and Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA,

Shortest path length methods are routinely used to validate whether genes of interest are functionally related to each other based on biological network information. However, the methods are computationally intensive, impeding extensive utilization of network information. In addition, non-weighted shortest path length approach, which is more frequently used, often treat all network connections equally without taking into account of confidence levels of the associations. On the other hand, graph-based information diffusion method, which employs both the presence and confidence weights of network edges, can efficiently explore large networks and has previously detected meaningful biological patterns. Therefore, in this study, we hypothesized that the graph-based information diffusion method could prioritize genes with relevant functions more efficiently and accurately than the shortest path length approaches. We demonstrated that the graph-based information diffusion method substantially differentiated not only genes participating in same biological pathways (p << 0.0001) but also genes associated with specific human drug-induced clinical symptoms (p << 0.0001) from random. Furthermore, the diffusion method prioritized these functionally related genes faster and more accurately than the shortest path length approaches (pathways: p = 2.7e-28, clinical symptoms: p = 0.032). These data show the graph-based information diffusion method can be routinely used for robust prioritization of functionally related genes, facilitating efficient network validation and hypothesis generation, especially for human phenotype-specific genes.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043368PMC
March 2021

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

Genome Biol 2019 11 19;20(1):244. Epub 2019 Nov 19.

Departments of Bioengineering and Mechanical Engineering, Berkeley, CA, USA.

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.

Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.

Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.
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http://dx.doi.org/10.1186/s13059-019-1835-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864930PMC
November 2019

Discovery of disease- and drug-specific pathways through community structures of a literature network.

Bioinformatics 2020 03;36(6):1881-1888

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

Motivation: In light of the massive growth of the scientific literature, text mining is increasingly used to extract biological pathways. Though multiple tools explore individual connections between genes, diseases and drugs, few extensively synthesize pathways for specific diseases and drugs.

Results: Through community detection of a literature network, we extracted 3444 functional gene groups that represented biological pathways for specific diseases and drugs. The network linked Medical Subject Headings (MeSH) terms of genes, diseases and drugs that co-occurred in publications. The resulting communities detected highly associated genes, diseases and drugs. These significantly matched current knowledge of biological pathways and predicted future ones in time-stamped experiments. Likewise, disease- and drug-specific communities also recapitulated known pathways for those given diseases and drugs. Moreover, diseases sharing communities had high comorbidity with each other and drugs sharing communities had many common side effects, consistent with related mechanisms. Indeed, the communities robustly recovered mutual targets for drugs [area under Receiver Operating Characteristic curve (AUROC)=0.75] and shared pathogenic genes for diseases (AUROC=0.82). These data show that literature communities inform not only just known biological processes but also suggest novel disease- and drug-specific mechanisms that may guide disease gene discovery and drug repurposing.

Availability And Implementation: Application tools are available at http://meteor.lichtargelab.org.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz857DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103064PMC
March 2020

Residues and residue pairs of evolutionary importance differentially direct signaling bias of D2 dopamine receptors.

J Biol Chem 2019 12 1;294(50):19279-19291. Epub 2019 Nov 1.

Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030

The D2 dopamine receptor and the serotonin 5-hydroxytryptamine 2A receptor (5-HT2A) are closely-related G-protein-coupled receptors (GPCRs) from the class A bioamine subfamily. Despite structural similarity, they respond to distinct ligands through distinct downstream pathways, whose dysregulation is linked to depression, bipolar disorder, addiction, and psychosis. They are important drug targets, and it is important to understand how their bias toward G-protein β-arrestin signaling pathways is regulated. Previously, evolution-based computational approaches, difference Evolutionary Trace and Evolutionary Trace-Mutual information (ET-Mip), revealed residues and residue pairs that, when switched in the D2 receptor to the corresponding residues from 5-HT2A, altered ligand potency and G-protein activation efficiency. We have tested these residue swaps for their ability to trigger recruitment of β-arrestin2 in response to dopamine or serotonin. The results reveal that the selected residues modulate agonist potency, maximal efficacy, and constitutive activity of β-arrestin2 recruitment. Whereas dopamine potency for most variants was similar to that for WT and lower than for G-protein activation, potency in β-arrestin2 recruitment for N124H was more than 5-fold higher. T205M displayed high constitutive activity, enhanced dopamine potency, and enhanced efficacy in β-arrestin2 recruitment relative to WT, and L379F was virtually inactive. These striking differences from WT activity were largely reversed by a compensating mutation (T205M/L379F) at residues previously identified by ET-Mip as functionally coupled. The observation that the signs and relative magnitudes of the effects of mutations in several cases are at odds with their effects on G-protein activation suggests that they also modulate signaling bias.
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http://dx.doi.org/10.1074/jbc.RA119.008068DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916503PMC
December 2019

Mutational Landscape of the BAP1 Locus Reveals an Intrinsic Control to Regulate the miRNA Network and the Binding of Protein Complexes in Uveal Melanoma.

Cancers (Basel) 2019 Oct 19;11(10). Epub 2019 Oct 19.

Department of Ophthalmology, University Hospital Bonn, 53127 Bonn, Germany.

The (BRCA1-associated protein 1) gene is associated with a variety of human cancers. With its gene product being a nuclear ubiquitin carboxy-terminal hydrolase with deubiquitinase activity, acts as a tumor suppressor gene with potential pleiotropic effects in multiple tumor types. Herein, we focused specifically on uveal melanoma (UM) in which mutations are associated with a metastasizing phenotype and decreased survival rates. We identified the ubiquitin carboxyl hydrolase (UCH) domain as a major hotspot region for the pathogenic mutations with a high evolutionary action (EA) score. This also includes the mutations at conserved catalytic sites and the ones overlapping with the phosphorylation residues. Computational protein interaction studies revealed that distant BAP1-associated protein complexes (FOXK2, ASXL1, BARD1, BRCA1) could be directly impacted by this mutation paradigm. We also described the conformational transition related to BAP1-BRCA-BARD1 complex, which may pose critical implications for mutations, especially at the docking interfaces of these three proteins. The mutations affect - independent of being somatic or germline - the binding affinity of miRNAs embedded within the locus, thereby altering the unique regulatory network. Apart from UM, BAP1 gene expression and survival associations were found to be predictive for the prognosis in several ( = 29) other cancer types. Herein, we suggest that although BAP1 is conceptually a driver gene in UM, it might contribute through its interaction partners and its regulatory miRNA network to various aspects of cancer. Taken together, these findings will pave the way to evaluate BAP1 in a variety of other human cancers with a shared mutational spectrum.
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http://dx.doi.org/10.3390/cancers11101600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6826957PMC
October 2019

Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

Nat Commun 2019 09 9;10(1):4075. Epub 2019 Sep 9.

Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montréal, QC, H3T 1J4, Canada.

Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics. Taking advantage of these opportunities requires identifying which effectors should be specifically activated or avoided so as to promote desired clinical responses and avoid side effects. However, identifying signaling profiles that support desired clinical outcomes remains challenging. This study describes signaling diversity of mu opioid receptor (MOR) ligands in terms of logistic and operational parameters for ten different in vitro readouts. It then uses unsupervised clustering of curve parameters to: classify MOR ligands according to similarities in type and magnitude of response, associate resulting ligand categories with frequency of undesired events reported to the pharmacovigilance program of the Food and Drug Administration and associate signals to side effects. The ability of the classification method to associate specific in vitro signaling profiles to clinically relevant responses was corroborated using β2-adrenergic receptor ligands.
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http://dx.doi.org/10.1038/s41467-019-11875-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733853PMC
September 2019

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas.

Cell Rep 2019 07;28(5):1370-1384.e5

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.
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http://dx.doi.org/10.1016/j.celrep.2019.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546539PMC
July 2019

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

Hum Mutat 2019 09;40(9):1519-1529

Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α-N-acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population-scale analysis of disease epidemiology and rare variant association analysis.
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http://dx.doi.org/10.1002/humu.23875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156275PMC
September 2019

CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases.

Hum Mutat 2019 09 3;40(9):1373-1391. Epub 2019 Sep 3.

Center for Human Genomics and Precision Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.
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http://dx.doi.org/10.1002/humu.23874DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318886PMC
September 2019

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.

Hum Mutat 2019 09 7;40(9):1436-1454. Epub 2019 Aug 7.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.

Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.
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http://dx.doi.org/10.1002/humu.23873DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900054PMC
September 2019

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants.

Hum Mutat 2019 09 3;40(9):1530-1545. Epub 2019 Sep 3.

Institute of Medical Technology, University of Tampere, Tampere, Finland.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.
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http://dx.doi.org/10.1002/humu.23868DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325732PMC
September 2019

Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants.

Hum Mutat 2019 09 23;40(9):1546-1556. Epub 2019 Aug 23.

Molecular Cancer Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia.

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.
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http://dx.doi.org/10.1002/humu.23861DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744348PMC
September 2019

Assessing predictions on fitness effects of missense variants in calmodulin.

Hum Mutat 2019 09 3;40(9):1463-1473. Epub 2019 Sep 3.

Departments of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas.

This paper reports the evaluation of predictions for the "CALM1" challenge in the fifth round of the Critical Assessment of Genome Interpretation held in 2018. In the challenge, the participants were asked to predict effects on yeast growth caused by missense variants of human calmodulin, a highly conserved protein in eukaryotic cells sensing calcium concentration. The performance of predictors implementing different algorithms and methods is similar. Most predictors are able to identify the deleterious or tolerated variants with modest accuracy, with a baseline predictor based purely on sequence conservation slightly outperforming the submitted predictions. Nevertheless, we think that the accuracy of predictions remains far from satisfactory, and the field awaits substantial improvements. The most poorly predicted variants in this round surround functional CALM1 sites that bind calcium or peptide, which suggests that better incorporation of structural analysis may help improve predictions.
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http://dx.doi.org/10.1002/humu.23857DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744288PMC
September 2019

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5.

Hum Mutat 2019 09 17;40(9):1474-1485. Epub 2019 Aug 17.

Department of Biomedical Sciences, University of Padua, Padua, Italy.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.
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http://dx.doi.org/10.1002/humu.23856DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354699PMC
September 2019

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

Hum Mutat 2019 09 17;40(9):1612-1622. Epub 2019 Aug 17.

Department of Biological Sciences, University of Maryland, Baltimore, Maryland.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.
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http://dx.doi.org/10.1002/humu.23849DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744287PMC
September 2019

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

Hum Mutat 2019 09 12;40(9):1392-1399. Epub 2019 Jul 12.

Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.
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http://dx.doi.org/10.1002/humu.23843DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744327PMC
September 2019
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