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PubMed2005

Assessing team performance in the operating room: development and use of a "black-box" recorder and other tools for the intraoperative environment.

BACKGROUND: The objective of this research was to develop a digital system to archive the complete operative environment along with the assessment tools for analysis of this data, allowing prospective studies of operative performance, intraoperative errors, team performance, and communication. Ability to study this environment will yield new insights, allowing design of systems to avoid preventable errors that contribute to perioperative complications. STUDY DESIGN: A multitrack, synchronized, digital audio-visual recording system (RATE tool) was developed to monitor intraoperative performance, including software to synchronize data and allow assignment of independent observational scores. Cases were scored for technical performance, participants' situational awareness (knowledge of critical information), and their comfort and satisfaction with the conduct of the procedure. RESULTS: Laparoscopic cholecystectomy (n = 10) was studied. Technical performance of the RATE tool was excellent. The RATE tool allowed real time, multitrack data collection of all aspects of the operative environment, while permitting digital recording of the objective assessment data in a time synchronized and annotated fashion during the procedure. The mean technical performance score was 73% +/- 28% of maximum (perfect) performance. Situational awareness varied widely among team members, with the attending surgeon typically the only team member having comprehensive knowledge of critical case information. CONCLUSIONS: The RATE tool allows prospective analysis of performance measures such as technical judgments, team performance, and communication patterns, offers the opportunity to conduct prospective intraoperative studies of human performance, and allows for postoperative discussion, review, and teaching. This study also suggests that gaps in situational awareness might be an underappreciated source of operative adverse events. Future uses of this system will aid teaching, failure or adverse event analysis, and intervention research.

Cholecystectomy, Laparoscopic↗

PubMed2005

Quantitative evaluation of protein-DNA interactions using an optimized knowledge-based potential.

Computational evaluation of protein-DNA interaction is important for the identification of DNA-binding sites and genome annotation. It could validate the predicted binding motifs by sequence-based approaches through the calculation of the binding affinity between a protein and DNA. Such an evaluation should take into account structural information to deal with the complicated effects from DNA structural deformation, distance-dependent multi-body interactions and solvation contributions. In this paper, we present a knowledge-based potential built on interactions between protein residues and DNA tri-nucleotides. The potential, which explicitly considers the distance-dependent two-body, three-body and four-body interactions between protein residues and DNA nucleotides, has been optimized in terms of a Z-score. We have applied this knowledge-based potential to evaluate the binding affinities of zinc-finger protein-DNA complexes. The predicted binding affinities are in good agreement with the experimental data (with a correlation coefficient of 0.950). On a larger test set containing 48 protein-DNA complexes with known experimental binding free energies, our potential has achieved a high correlation coefficient of 0.800, when compared with the experimental data. We have also used this potential to identify binding motifs in DNA sequences of transcription factors (TF). The TFs in 79.4% of the known TF-DNA complexes have accurately found their native binding sequences from a large pool of DNA sequences. When tested in a genome-scale search for TF-binding motifs of the cyclic AMP regulatory protein (CRP) of Escherichia coli, this potential ranks all known binding motifs of CRP in the top 15% of all candidate sequences.

Base Sequence↗

PubMed2025

Complex Genetics and Regulatory Drivers of Hypermobile Ehlers-Danlos Syndrome: Insights from Genome-Wide Association Study Meta-analysis.

BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS) is the most common subtype of EDS, a group of heritable connective tissue disorders. Clinically, hEDS is defined by generalized joint hypermobility and chronic musculoskeletal pain, but its impact extends beyond the musculoskeletal system. Affected individuals frequently experience autonomic, gastrointestinal, immune, and neuropsychiatric involvement, highlighting both the multisystemic nature of the condition and challenges of diagnosis. In contrast to other EDS subtypes with defined genetic causes, the molecular basis of hEDS has remained elusive. METHODS: We conducted a genome-wide association study (GWAS) of hEDS across three case controls studies, including 1,815 cases and 5,008 ancestry-matched controls. Fixed-effects meta-analysis of 6.2 million variants was complemented with LDAK gene-based association testing, transcriptome-wide association studies, and integrative annotation across multiple tissues and cell types including eQTLs, enhancer marks and open chromatin accessibility profiles, supported by luciferase assays on one candidate variant. LD-score genetic correlations were assessed between hEDS and 19 frequently reported comorbid conditions. RESULTS: Two loci reached genome-wide significance, including a regulatory region near the atypical chemokine receptor 3 gene (ACKR3) on chromosome 2. Functional annotation supports ACKR3 risk alleles colocalize with eQTLs in tibial nerve, alter enhancer activity, and generate a de novo AHR transcription factor regulatory site, implicating neuroimmune and pain signaling pathways. Gene-based and transcriptome-wide analyses identified common variants in a locus containing multiple candidates, including SLC39A13, a zinc transporter critical for connective tissue development previously implicated in a rare form of EDS, and PSMC3, a gene involved in central nervous system development. LD-score regression revealed significant genetic correlations between hEDS and joint hypermobility, myalgic encephalomyelitis/chronic fatigue syndrome, fibromyalgia, depression, anxiety, autism spectrum disorder, migraine, and gastrointestinal diseases. CONCLUSIONS: These results establish the first evidence of common variant contributions to hEDS, supporting a complex, multisystem model involving neuroimmune-stromal dysregulation. Our findings add novel indications to hEDS pathogenesis and provide solid foundations for future molecular definition and therapeutic discovery.

Genome-wide association study↗

PubMed2004

Prediction of sequence signals for lipid post-translational modifications: insights from case studies.

In silico annotation techniques for post-translational modifications (PTMs) are important to generate biologically meaningful descriptions for sequences of experimentally uncharacterized proteins. Having previously contributed with predictors for lipid PTMs, we summarize our methodological experience. Rather than only looking for the sequence pattern in substrate sequences, a strategy aimed at creating a generalized model of substrate protein/enzyme interaction appears more appropriate since the number of known substrate sequences is small, and some of them are not sufficiently verified experimentally. Such a physical approach (in contrast to a mere textual analysis of substrate sequences) can also take into account other, heterogeneous biological data (mutations of substrate sequences, kinetic data, enzyme sequences/structures) with simple analytical expressions in the score function. Several lipid PTMs are encoded in the form of a small sequence region (with pronounced amino acid type preferences) that is connected to the substrate protein by a linker region with many conformationally flexible, hydrophilic residues. A score function composed of terms penalizing sequence properties known to be incompatible with productive substrate protein/enzyme complexes essentially unselects inappropriate queries. Also, we estimate the number of nonredundant sequences necessary for robust profile computation with statistical criteria, a number that is not reached in most cases of PTM prediction. Finally, we discuss the usage of evolutionary information in evaluating the functional importance of predicted PTMs in cases of motif conservation within sequence families.

Acetyltransferases↗

PubMed2026

Conventional and Shared Genetic Association Analysis Between Diabetes Mellitus and Sensorineural Hearing Loss.

PURPOSE: This study aims to investigate the epidemiological and genetic associations between diabetes mellitus (DM) and sensorineural hearing loss (SNHL) across different subtypes. METHODS: We analyzed 502,490 participants from the UK Biobank using multivariate logistic regression to examine the association between DM and SNHL, considering gender, age, and HbA1c levels. Genetic correlations and causality were examined by linkage disequilibrium score regression and bidirectional Mendelian randomization. Cross-trait meta-analyses identified shared loci between DM and SNHL, followed by gene annotation, functional analysis, and drug candidate exploration for the shared traits. RESULTS: Observational analysis revealed significant associations between DM and SNHL, consistent in subgroups based on age, sex, and certain HbA1c levels. A positive genetic correlation was found between type 2 diabetes mellitus (T2D) and SNHL (Rg = 0.0982, p = 0.0095) between T2D and SNHL, and four loci were identified, with ARHGEF28 and TCF7L2 prioritized as credible pleiotropic genes. Enrichment was indicated in glucose metabolism and organogenesis, with shared heritability in metabolic tissues and outer hair cells. Metformin was identified as potential drug candidates for the T2D-SNHL comorbidity. CONCLUSION: These findings progress our understanding of the epidemiological association, shared genetic basis, and potential therapeutic targets between T2D and SNHL, which might contribute to the management of their comorbidity.

Humans↗

PubMed2026

iMTSS: an integrated framework for biology- and patient-driven prognosis in myelofibrosis undergoing transplantation.

BACKGROUND: Allogeneic hematopoietic cell transplantation is the only curative treatment for myelofibrosis, but failure occurs by two mechanistically distinct routes: relapse of the neoplasm, which reflects its underlying genetics, and non-relapse mortality, which reflects whether the patient and graft tolerate the procedure. Established prognostic systems either lack molecular granularity or were derived in the non-transplant setting, and all collapse these two routes into a single survival estimate. None can indicate why an individual patient is at risk, or which class of intervention might reduce that risk. OBJECTIVE: To determine why an individual patient is at risk and to develop and validate an integrated framework that quantifies biology- and patient-driven prognosis. STUDY DESIGN: We analyzed 1,550 adults undergoing first allogeneic transplantation for primary or secondary myelofibrosis across international centers, the largest genomically annotated transplant cohort in this disease. The cohort was split into development (n=930) and validation (n=620) sets. Overall survival was modeled by Cox regression; relapse and non-relapse mortality were modeled as competing events by Fine-Gray subdistribution-hazard regression at 2 years. Discrimination was assessed by the concordance index with bootstrap confidence intervals. The molecular contribution was quantified by variance decomposition of, and robustness to the analytic choices was examined by resampling. RESULTS: A genetically defined disease-intrinsic axis, including TP53 allelic state, RAS pathway mutations, ASXL1 and driver genotype, blasts and blood counts, predicted 2 year relapse incidence (validation concordance 0.69, 95% CI 0.63 to 0.74), whereas a non-overlapping host and structural axis, including portal vein thrombosis, donor type, patients' performance status, and age predicted 2-year non-relapse mortality (0.63, 95% CI 0.59 to 0.68). The two scores shared only 3.4% of their variance, indicating that a patient's disease genetics carried almost no information about non-relapse mortality. Variance decomposition showed that TP53 allelic state alone accounted for 30% of the relapse score. Recombined, the framework discriminated overall survival (concordance 0.640, 95% CI 0.616 to 0.662) better than every established prognostic system. For proof of concept, 3 risk groups separated in the validation cohort, with 5 year survival of 72%, 58%, and 39% (P<0.001), and the models were well calibrated. CONCLUSIONS: Relapse and non-relapse mortality after transplantation for myelofibrosis are governed by distinct dimensions. Estimating both outcomes independently with genetic and clinical information, in addition to overall survival, establishes an individualized basis for transplant decision-making. The calculator is openly available (https://imtss-calculator.com).

mortality↗

PubMed2026

Identification of Rare Noncoding Variants in Familial Nonmedullary Thyroid Carcinoma.

BACKGROUND: Familial nonmedullary thyroid carcinoma (FNMTC) occurs when three or more family members are affected by usually papillary thyroid carcinoma (PTC), the most common form of NMTC. While the heritability to NMTC is among the highest of all cancers, the genetic determinants among NMTC families are not well understood. Here, we aim to understand the contribution of rare noncoding germline variants in the etiology of FNMTC. METHODS: We previously reported whole-genome sequencing (WGS) and linkage analysis in 17 PTC families and reported on 41 protein-coding variants in 40 genes that cosegregated with PTC in 11 of the families. Herein, we further leveraged our WGS data to include noncoding variants in our analysis for all 17 families. We hypothesized that most of the pathogenic noncoding variants would be located in theoretical or empirically determined regulatory regions that demonstrate at a minimum, basal thyroid expression, a positive family linkage score, and co-segregation among PTC-affected individuals. To test this hypothesis, we adopted a unique filtering strategy to identify variants that occurred in known DNA elements and transcription factor binding sites, near regions known to impact on gene expression or splicing in thyroid tissue, and/or in characterized thyroid enhancers. We annotated variants using two analyses (ENCODE and transcription factor binding site) within the BasePlayer software. We separately analyzed (1) expression quantitative trait loci, (2) splicing quantitative trait loci, and (3) thyroid enhancers. We then ranked variants according to predicted pathogenicity and performed Sanger sequencing in all individuals of each family. RESULTS: In total, 121 variants were selected based on in-silico prediction and our custom ranking analysis in each pedigree. Of these, 56 variants showed cosegregation among all PTC-affected individuals and were absent from unaffected individuals. This included candidate variants from five of the six PTC families for whom no protein-coding variants were previously found. CONCLUSION: Our data suggest that noncoding variants are important in the etiology of FNMTC and provide a framework for identifying noncoding germline variants using a novel approach. Further studies are needed to functionally characterize these variants to better understand the molecular mechanism of their pathogenicity.

Humans↗

PubMed2004

cuticleDB: a relational database of Arthropod cuticular proteins.

BACKGROUND: The insect exoskeleton or cuticle is a bi-partite composite of proteins and chitin that provides protective, skeletal and structural functions. Little information is available about the molecular structure of this important complex that exhibits a helicoidal architecture. Scores of sequences of cuticular proteins have been obtained from direct protein sequencing, from cDNAs, and from genomic analyses. Most of these cuticular protein sequences contain motifs found only in arthropod proteins. DESCRIPTION: cuticleDB is a relational database containing all structural proteins of Arthropod cuticle identified to date. Many come from direct sequencing of proteins isolated from cuticle and from sequences from cDNAs that share common features with these authentic cuticular proteins. It also includes proteins from the Drosophila melanogaster and the Anopheles gambiae genomes, that have been predicted to be cuticular proteins, based on a Pfam motif (PF00379) responsible for chitin binding in Arthropod cuticle. The total number of the database entries is 445: 370 derive from insects, 60 from Crustacea and 15 from Chelicerata. The database can be accessed from our web server at http://bioinformatics.biol.uoa.gr/cuticleDB. CONCLUSIONS: CuticleDB was primarily designed to contain correct and full annotation of cuticular protein data. The database will be of help to future genome annotators. Users will be able to test hypotheses for the existence of known and also of yet unknown motifs in cuticular proteins. An analysis of motifs may contribute to understanding how proteins contribute to the physical properties of cuticle as well as to the precise nature of their interaction with chitin.

Amino Acid Motifs↗

PubMed2000

Estimating local backbone structural deviation in homology models.

After the atomic coordinates themselves, the most important data in a homology model are the spatial reliability estimates associated with each of the atoms (atom annotation). Recent blind homology modeling predictions have demonstrated that principally correct sequence-structure alignments are achievable to sequence identities as low as 25% [Martin, A.C., MacArthur, M.W., Thornton, J.M., 1997. Assessment of comparative modeling in CASP2. Proteins Suppl(1), 14-28]. The locations and extent of spatial deviations in the backbone between correctly aligned homologous protein structures remained very poorly estimated however, and these errors were the cause of errant loop predictions [Abagyan, R., Batalov, S., Cardozo, T., Totrov, M., Webber, J., Zhou, Y., 1997. Homology modeling with internal coordinate mechanics: deformation zone mapping and improvements of models via conformational search. Proteins Suppl(1), 29-37]. In order to derive accurate measures for local backbone deviations, we made a systematic study of static local backbone deviations between homologous pairs of protein structures. We found that 'through space' proximity to gaps and chain termini, local three-dimensional 'density', three-dimensional environment conservation, and B-factor of the template contribute to local deviations in the backbone in addition to local sequence identity. Based on these finding, we have identified the meaningful ranges of values within which each of these parameters correlates with static local backbone deviation and produced a combined scoring function to greatly improve the estimation of local backbone deviations. The optimized function has more than twice the accuracy of local sequence identity or B-factor alone and was validated in a recent blind structure prediction experiment. This method may be used to evaluate the utility of a preliminary homology model for a particular biological investigation (e.g. drug design) or to provide an improved starting point for molecular mechanics loop prediction methods.

Computer Simulation↗

PubMed2006

Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs.

BACKGROUND: High-throughput genotyping technologies represent a highly efficient way to accelerate genetic mapping and enable association studies. As a first step toward this goal, we aimed to develop a resource of candidate Single Nucleotide Polymorphisms (SNP) in white spruce (Picea glauca [Moench] Voss), a softwood tree of major economic importance. RESULTS: A white spruce SNP resource encompassing 12,264 SNPs was constructed from a set of 6,459 contigs derived from Expressed Sequence Tags (EST) and by using the bayesian-based statistical software PolyBayes. Several parameters influencing the SNP prediction were analysed including the a priori expected polymorphism, the probability score (PSNP), and the contig depth and length. SNP detection in 3' and 5' reads from the same clones revealed a level of inconsistency between overlapping sequences as low as 1%. A subset of 245 predicted SNPs were verified through the independent resequencing of genomic DNA of a genotype also used to prepare cDNA libraries. The validation rate reached a maximum of 85% for SNPs predicted with either PSNP > or = 0.95 or > or = 0.99. A total of 9,310 SNPs were detected by using PSNP > or = 0.95 as a criterion. The SNPs were distributed among 3,590 contigs encompassing an array of broad functional categories, with an overall frequency of 1 SNP per 700 nucleotide sites. Experimental and statistical approaches were used to evaluate the proportion of paralogous SNPs, with estimates in the range of 8 to 12%. The 3,789 coding SNPs identified through coding region annotation and ORF prediction, were distributed into 39% nonsynonymous and 61% synonymous substitutions. Overall, there were 0.9 SNP per 1,000 nonsynonymous sites and 5.2 SNPs per 1,000 synonymous sites, for a genome-wide nonsynonymous to synonymous substitution rate ratio (Ka/Ks) of 0.17. CONCLUSION: We integrated the SNP data in the ForestTreeDB database along with functional annotations to provide a tool facilitating the choice of candidate genes for mapping purposes or association studies.

Algorithms↗

PubMed2026

Genetic insights into the relationship between age at menarche and mental health-related phenotypes.

BACKGROUND: Multiple observational studies have reported associations between age at menarche (AAM) and mental health problems, yet their shared genetic architecture remains poorly characterized. METHODS: We leveraged genome-wide association study summary statistics for AAM and 15 mental health-related phenotypes. We conducted a multi-method integrative analysis encompassing linkage disequilibrium score regression, pleiotropic analysis under the composite null hypothesis, functional mapping and annotation, multi-marker analysis of genomic annotation, pathway enrichment, and bidirectional two-sample Mendelian randomization (MR) to explore shared genetic architecture and potential causal relationships. RESULTS: Our study identified significant genetic correlations between AAM and eight mental health-related phenotypes (miserableness, fed-up feelings, nervous feelings, ever thought that life is not worth living, ever self-harmed, depression, ever smoker, and age started smoking in former smokers). A total of 155 pleiotropic loci, 18 colocalized loci (e.g., 6q16.3), and 203 pleiotropic genes (e.g., LIN28B) were identified. These genes are expressed in multiple regions, including the cerebral cortex and hypothalamus, and are involved in various biological processes and signaling pathways. Additionally, MR analysis revealed causal associations between AAM and 5 mental health-related phenotypes (mood swings, miserableness, fed-up feelings, and age at which smokers started smoking in former/current smokers). CONCLUSIONS: Our study revealed extensive genetic associations between AAM and mental health-related phenotypes, and further explored the potential causal relationships between them. These findings enhance our understanding of the relationship from a genetic perspective and establish a foundation for future research to explore the biological pathways and environmental interactions contributing to these associations.

Genome-Wide Association Study↗

PubMed2005

Integrating genomic data to predict transcription factor binding.

Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor's binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes. Binding site clusters are statistically significant clusters of TF binding sites detected by the algorithm ClusterBuster. Gene expression takes into account the idea that when the gene expression profiles of a transcription factor and a potential target gene are correlated, then it is more likely that the gene is a genuine target. Also, genes with highly correlated expression profiles are often regulated by the same TF(s). The GO annotation data takes advantage of the idea that common transcription targets often have related function. Finally, the distribution of the counts of all k-mers of length 4, 5, and 6 in gene's promoter region were examined as means to predict TF binding. In each case the data are compared to known true positives taken from ChIP-chip data, Transfac, and the Saccharomyces Genome Database. First, degeneracy, conservation, expression, and binding site clusters were examined independently and in combination via Bayesian allocation. Then, binding sites were predicted with a support vector machine (SVM) using all methods alone and in combination. The SVM works best when all genomic data are combined, but can also identify which methods contribute the most to accurate classification. On average, a support vector machine can classify binding sites with high sensitivity and an accuracy of almost 80%.

Algorithms↗