Opt-in versus universal codeine provision for medical abortion up to 10 weeks of gestation at British Pregnancy Advisory Service: a cross-sectional evaluation.

OBJECTIVE: To assess patient experiences of pain management during medical abortion up to 10 weeks' gestation with opt-in versus universal codeine provision. METHODS: We invited patients who underwent medical abortion up to 10 weeks of gestation to participate in an online, anonymous, English-language survey from November 2021 to March 2022. We performed ordinal regression analyses to compare satisfaction with pain management (5-point Likert scale) and maximum abortion pain score (11-point numerical rating scale) in the opt-in versus universal codeine provision groups. RESULTS: Of 11 906 patients invited to participate, 1625 (13.6%) completed the survey. Participants reported a mean maximum pain score of 6.8±2.2. A total of 1149 participants (70.7%) reported using codeine for pain management during their abortion. Participants in the opt-in codeine provision group were significantly more likely to be satisfied with their pain management than those in the universal group (aOR 1.48, 95% CI 1.12 to 1.96, p<0.01). Maximum abortion pain scores were lower on average among the opt-in codeine provision group (OR 0.80, 95% CI 0.66 to 0.96, p=0.02); however, this association was not statistically significant in the model adjusted for covariates (aOR 0.85, 95% CI 0.70 to 1.03, p=0.09). CONCLUSION: Our findings suggest that patients have a better experience with pain management during medical abortion when able to opt-in to codeine provision following counselling versus receiving this medication routinely.

Clinical profiling of specific diagnostic subgroups of women with chronic pelvic pain.

Introduction: Chronic pelvic pain (CPP) is a common condition affecting up to 26.6% of women, with many suffering for several years before diagnosis and/or treatment. Its clinical presentation is varied and there are frequently comorbid conditions both within and outside the pelvis. We aim to explore whether specific subgroups of women with CPP report different clinical symptoms and differing impact of pain on their quality of life (QoL). Methods: The study is part of the Translational Research in Pelvic Pain (TRiPP) project which is a cross-sectional observational cohort study. The study includes 769 female participants of reproductive age who completed an extensive set of questions derived from standardised WERF EPHect questionnaires. Within this population we defined a control group (reporting no pelvic pain, no bladder pain syndrome, and no endometriosis diagnosis, N = 230) and four pain groups: endometriosis-associated pain (EAP, N = 237), interstitial cystitis/bladder pain syndrome (BPS, N = 72), comorbid endometriosis-associated pain and BPS (EABP, N = 120), and pelvic pain only (PP, N = 127). Results: Clinical profiles of women with CPP (13-50 years old) show variability of clinical symptoms. The EAP and EABP groups scored higher than the PP group (p < 0.001) on the pain intensity scales for non-cyclical pelvic pain and higher than both the BPS and PP groups (p < 0.001) on the dysmenorrhoea scale. The EABP group also had significantly higher scores for dyspareunia (p < 0.001), even though more than 50% of sexually active participants in each pain group reported interrupting and/or avoiding sexual intercourse due to pain in the last 12 months. Scores for the QoL questionnaire (SF-36) reveal that CPP patients had significantly lower QoL across all SF-36 subscales (p < 0.001). Significant effects were also observed between the pain groups for pain interference with their work (p < 0.001) and daily lives (p < 0.001), with the EABP suffering more compared to the EAP and PP groups (p < 0.001). Discussion: Our results demonstrate the negative impact that chronic pain has on CPP patients' QoL and reveal an increased negative impact of pain on the comorbid EABP group. Furthermore, it demonstrates the importance of dyspareunia in women with CPP. Overall, our results demonstrate the need for further exploration of interventions targeting QoL more broadly and suggest that novel approaches to classifying women with CPP are needed.

Comprehensive quantitative sensory testing shows altered sensory function in women with chronic pelvic pain: results from the Translational Research in Pelvic Pain (TRiPP) Study.

ABSTRACT: Chronic pelvic pain (CPP), despite its high prevalence, is still relatively poorly understood mechanistically. This study, as part of the Translational Research in Pelvic Pain (TRiPP) project, has used a full quantitative sensory testing (QST) paradigm to profile n = 85 women with and without CPP (endometriosis or bladder pain specifically). We used the foot as a control site and abdomen as the test site. Across 5 diagnostically determined subgroups, we found features which are common across different aetiologies, eg, gain of function in pressure pain threshold (PPT) when assessing responses from the lower abdomen or pelvis (referred pain site). However, disease-specific phenotypes were also identified, eg, greater mechanical allodynia in endometriosis, despite there being large heterogeneities within diagnostic groups. The most common QST sensory phenotype was mechanical hyperalgesia (>50% across all the groups). A "healthy' sensory phenotype was seen in <7% of CPP participants. Specific QST measures correlated with sensory symptoms assessed by the painDETECT questionnaire (pressure-evoked pain [painDETECT] and PPT [QST] [ r = 0.47, P < 0.001]; mechanical hyperalgesia (painDETECT) and mechanical pain sensitivity [MPS from QST] [ r = 0.38, P = 0.009]). The data suggest that participants with CPP are sensitive to both deep tissue and cutaneous inputs, suggesting that central mechanisms may be important in this cohort. We also see phenotypes such as thermal hyperalgesia, which may be the result of peripheral mechanisms, such as irritable nociceptors. This highlights the importance of stratifying patients into clinically meaningful phenotypes, which may have implications for the development of better therapeutic strategies for CPP.

The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions.

Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention.

Human phospho-signaling networks of SARS-CoV-2 infection are rewired by population genetic variants.

SARS-CoV-2 infection hijacks signaling pathways and induces protein-protein interactions between human and viral proteins. Human genetic variation may impact SARS-CoV-2 infection and COVID-19 pathology; however, the genetic variation in these signaling networks remains uncharacterized. Here, we studied human missense single nucleotide variants (SNVs) altering phosphorylation sites modulated by SARS-CoV-2 infection, using machine learning to identify amino acid substitutions altering kinase-bound sequence motifs. We found 2,033 infrequent phosphorylation-associated SNVs (pSNVs) that are enriched in sequence motif alterations, potentially reflecting the evolution of signaling networks regulating host defenses. Proteins with pSNVs are involved in viral life cycle and host responses, including RNA splicing, interferon response (TRIM28), and glucose homeostasis (TBC1D4) with potential associations with COVID-19 comorbidities. pSNVs disrupt CDK and MAPK substrate motifs and replace these with motifs of Tank Binding Kinase 1 (TBK1) involved in innate immune responses, indicating consistent rewiring of signaling networks. Several pSNVs associate with severe COVID-19 and hospitalization (STARD13, ARFGEF2). Our analysis highlights potential genetic factors contributing to inter-individual variation of SARS-CoV-2 infection and COVID-19 and suggests leads for mechanistic and translational studies.

ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update).

Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.

State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing.

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods' limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks.

A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR < 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.

Pathogenic Germline Variants in 10,389 Adult Cancers.

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins.

Interpretation of genetic variation is needed for deciphering genotype-phenotype associations, mechanisms of inherited disease, and cancer driver mutations. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 21% of disease-associated amino acid substitutions corresponding to missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants through the lens of PTMs. We integrated >385,000 published PTM sites with ∼3.6 million substitutions from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and human genome sequencing projects. The database includes site-specific interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated mutations. Users can upload mutation datasets interactively and use our application programming interface in pipelines. Integrative analysis of mutations and PTMs may help decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of TP53, BRCA2 and VHL. The open-source database is available at https://www.ActiveDriverDB.org.