Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

The Reactome Pathway Knowledgebase 2024.

The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.

ChatGPT usage in the Reactome curation process.

Appreciating the rapid advancement and ubiquity of generative AI, particularly ChatGPT, a chatbot using large language models like GPT, we endeavour to explore the potential application of ChatGPT in the data collection and annotation stages within the Reactome curation process. This exploration aimed to create an automated or semi-automated framework to mitigate the extensive manual effort traditionally required for gathering and annotating information pertaining to biological pathways, adopting a Reactome "reaction-centric" approach. In this pilot study, we used ChatGPT/GPT4 to address gaps in the pathway annotation and enrichment in parallel with the conventional manual curation process. This approach facilitated a comparative analysis, where we assessed the outputs generated by ChatGPT against manually extracted information. The primary objective of this comparison was to ascertain the efficiency of integrating ChatGPT or other large language models into the Reactome curation workflow and helping plan our annotation pipeline, ultimately improving our protein-to-pathway association in a reliable and automated or semi-automated way. In the process, we identified some promising capabilities and inherent challenges associated with the utilisation of ChatGPT/GPT4 in general and also specifically in the context of Reactome curation processes. We describe approaches and tools for refining the output given by ChatGPT/GPT4 that aid in generating more accurate and detailed output.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.

Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection.

Background: The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles. Methodology: A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection. Results: The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker. Conclusion: Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.

Using the Reactome Database.

Pathway databases provide descriptions of the roles of proteins, nucleic acids, lipids, carbohydrates, and other molecular entities within their biological cellular contexts. Pathway-centric views of these roles may allow for the discovery of unexpected functional relationships in data such as gene expression profiles and somatic mutation catalogues from tumor cells. For this reason, there is a high demand for high-quality pathway databases and their associated tools. The Reactome project (a collaboration between the Ontario Institute for Cancer Research, New York University Langone Health, the European Bioinformatics Institute, and Oregon Health & Science University) is one such pathway database. Reactome collects detailed information on biological pathways and processes in humans from the primary literature. Reactome content is manually curated, expert-authored, and peer-reviewed and spans the gamut from simple intermediate metabolism to signaling pathways and complex cellular events. This information is supplemented with likely orthologous molecular reactions in mouse, rat, zebrafish, worm, and other model organisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Browsing a Reactome pathway Basic Protocol 2: Exploring Reactome annotations of disease and drugs Basic Protocol 3: Finding the pathways involving a gene or protein Alternate Protocol 1: Finding the pathways involving a gene or protein using UniProtKB (SwissProt), Ensembl, or Entrez gene identifier Alternate Protocol 2: Using advanced search Basic Protocol 4: Using the Reactome pathway analysis tool to identify statistically overrepresented pathways Basic Protocol 5: Using the Reactome pathway analysis tool to overlay expression data onto Reactome pathway diagrams Basic Protocol 6: Comparing inferred model organism and human pathways using the Species Comparison tool Basic Protocol 7: Comparing tissue-specific expression using the Tissue Distribution tool.

What affects farmers in choosing better agroforestry practice as a strategy of climate change adaptation? An experience from the mid-hills of Nepal.

Determinants for choosing climate change adaptation strategies and selecting improved agroforestry practices have rarely been explored, while numerous studies have been conducted on climate change and agroforestry. This paper discusses; local understanding of climate change, climatic impacts, and factors that affect farmers' choices of adaptation strategies, and agroforestry practices. We focused on three districts located in the mid-hills of Nepal, where farmers were adopting agroforestry practices in two forms; traditional and improved practices. We followed three techniques of social survey; household survey (n = 420), focus group discussions (n = 6), and key informant interviews (n = 24). Almost all farmers of the study areas were experiencing climatic challenges, but only 59.29% of them accepted that the challenges are induced by climate change and, likewise, 55.24% have adopted climate change adaptation measures. Diversifying crop production, shifting farming practices, changing occupation, and emigration were local adaptation strategies. Livelihood improvement, income generation, and food production were the primary motives for adopting agroforestry practices in the study area. Agroforestry as an adaptation measure to climate change was considered secondary by most farmers. Statistical analysis using a logit model revealed that age, education, and habit of growing commercial species significantly influenced farmers adopting climate change adaptation strategies. Likewise, age, education, gender, habit of growing commercial species, and income from tree products significantly influenced the choice of improved agroforestry practices as a better option. Though agroforestry was widely considered a strategy to combat climate change, only some farmers accepted it due to their awareness level. Therefore, education programs such as training, farmer field schools, door-to-door visits, etc., should be intensified to sensitize farmers about climate change and encourage them to adopt improved agroforestry practices. The findings of the study could reinforce local, national, and international allied agencies to design operative actions in the days to come.

Reproducibility in systems biology modelling.

Reproducibility of scientific results is a key element of science and credibility. The lack of reproducibility across many scientific fields has emerged as an important concern. In this piece, we assess mathematical model reproducibility and propose a scorecard for improving reproducibility in this field.

BioModels Parameters: a treasure trove of parameter values from published systems biology models.

MOTIVATION: One of the major bottlenecks in building systems biology models is identification and estimation of model parameters for model calibration. Searching for model parameters from published literature and models is an essential, yet laborious task. RESULTS: We have developed a new service, BioModels Parameters, to facilitate search and retrieval of parameter values from the Systems Biology Markup Language models stored in BioModels. Modellers can now directly search for a model entity (e.g. a protein or drug) to retrieve the rate equations describing it; the associated parameter values (e.g. degradation rate, production rate, Kcat, Michaelis-Menten constant, etc.) and the initial concentrations. Currently, BioModels Parameters contains entries from over 84,000 reactions and 60 different taxa with cross-references. The retrieved rate equations and parameters can be used for scanning parameter ranges, model fitting and model extension. Thus, BioModels Parameters will be a valuable service for systems biology modellers. AVAILABILITY AND IMPLEMENTATION: The data are accessible via web interface and API. BioModels Parameters is free to use and is publicly available at https://www.ebi.ac.uk/biomodels/parameterSearch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BioModels-15 years of sharing computational models in life science.

Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world's largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.

Personalization of cancer treatment using predictive simulation.

BACKGROUND: The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. METHODS: We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. RESULTS: Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. CONCLUSIONS: These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.

A sensitive WST-8-based bioassay for PEGylated granulocyte colony stimulating factor using the NFS-60 cell line.

CONTEXT: Granulocyte colony stimulating factor (G-CSF) has been commonly used to treat neutropenia caused by chemotherapy, radiotherapy, and organ transplants. Improved in vitro efficacy of G-CSF has already been observed by conjugating it to polyethylene glycol (PEG). OBJECTIVE: The in vivo bioassay using tetrazolium dye with the NFS-60 cell line has been recommended for G-CSF but no such monographs are available for PEGylated G-CSF in pharmacopeias. In the present study, the assay recommended for G-CSF was evaluated for its suitability to PEGylated G-CSF. MATERIALS AND METHODS: The generally used MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium]-based assay was compared with a bioassay employing a water-soluble tetrazolium dye, WST-8 [2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium], using NFS-60 cells at a concentration of 7 × 10(5) cells/ml against 800 IU/ml of PEGylated G-CSF at 24, 48, 72, and 72 h time points to determine the efficacy of PEGylated G-CSF. Further, the optimized WST-8 dye-based assay was used to test the potency of various commercially available PEGylated G-CSF preparations. RESULTS: The results demonstrated enhanced sensitivity of the WST-8-based assay over the conventional MTS-based assay for determining the potency of PEGylated G-CSF using the NFS-60 cell line. CONCLUSION: Our study demonstrates the potential application of WST-8-based bioassays for other biotherapeutic proteins of human and veterinary interest.

Elastin sequences trigger transient proinflammatory responses by human dermal fibroblasts.

Following penetrating injury of the skin, a highly orchestrated and overlapping sequence of events helps to facilitate wound resolution. Inflammation is a hallmark that is initiated early, but the reciprocal relationship between cells and matrix molecules that triggers and maintains inflammation is poorly appreciated. Elastin is enriched in the deep dermis of skin. We propose that deep tissue injury encompasses elastin damage, yielding solubilized elastin that triggers inflammation. As dermal fibroblasts dominate the deep dermis, this means that a direct interaction between elastin sequences and fibroblasts would reveal a proinflammatory signature. Tropoelastin was used as a surrogate for elastin sequences. Tropoelastin triggered fibroblast expression of the metalloelastase MMP-12, which is normally expressed by macrophages. MMP-12 expression increased 1056 ± 286-fold by 6 h and persisted for 24 h. Chemokine expression was more transient, as chemokine C-X-C motif ligand 8 (CXCL8), CXCL1, and CXCL5 transcripts increased 11.8 ± 2.6-, 10.2 ± 0.4-, and 8593 ± 996-fold, respectively, by 6-12 h and then decreased. Through the use of specific inhibitors and protein truncation, we found that transduction of the tropoelastin signal was mediated by the fibroblast elastin binding protein (EBP). In silico modeling using a predictive computational fibroblast model confirmed the up-regulation, and simulations revealed PKA as a key part of the signaling circuit. We tested this prediction with 1 µM PKA inhibitor H-89 and found that 2 h of exposure correspondingly reduced expression of MMP-12 (63.9±12.3%) and all chemokine markers, consistent with the levels seen with EBP inhibition, and validated PKA as a novel node and druggable target to ameliorate the proinflammatory state. A separate trigger that utilized C-terminal RKRK of tropoelastin reduced marker expression to 65.0-76.5% and suggests the parallel involvement of integrin αVß3. We propose that the solubilization of elastin as a result of dermal damage leads to rapid chemokine up-regulation by fibroblasts that is quenched when exposed elastin is removed by MMP-12.

Clinically detected gastroenteropancreatic neuroendocrine tumors are on the rise: epidemiological changes in Germany.

AIM: To study the epidemiologic changes of gastroenteropancreatic neuroendocrine tumors (GEP-NET) in Germany, we analyzed two time periods 1976-1988 and 1998-2006. METHODS: We evaluated epidemiological data of GEP-NET from the former East German National Cancer Registry (DDR Krebsregister, 1976-1988) and its successor, the Joint Cancer Registry (GKR, 1998-2006), which was founded after German reunification. Due to a particularly substantial database the epidemiological data from the federal states of Mecklenburg-Western Pomerania, Saxony, Brandenburg and Thuringia, covering a population of more than 10.8 million people, were analyzed. Survival probabilities were calculated using life table analysis. In addition, GEP-NET patients were evaluated for one or more second (non-GEP-NET) primary malignancies. RESULTS: A total of 2821 GEP neuroendocrine neoplasms were identified in the two registries. The overall incidence increased significantly between 1976 and 2006 from 0.31 (per 100.000 inhabitants per year) to 2.27 for men and from 0.57 to 2.38 for women. In the later period studied (2004-2006), the small intestine was the most common site. Neuroendocrine (NE) neoplasms of the small intestine showed the largest absolute increase in incidence, while rectal NE neoplasms exhibited the greatest relative increase. Only the incidence of appendiceal NET in women showed little change between 1976 and 2006. Overall survival of patients varied for sex, tumor site and the two periods studied but improved significantly over time. Interestingly, about 20% of the GEP-NET patients developed one or more second malignancies. Their most common location was the gastrointestinal tract. GEP-NET patients without second malignancies fared better than those with one or more of them. CONCLUSION: The number of detected GEP-NET increased about 5-fold in Germany between 1976 and 2006. At the same time, their anatomic distribution changed, and the survival of GEP-NET patients improved significantly. Second malignancies are common and influence the overall survival of GEP-NET patients. Thus, GEP-NET warrant our attention as well as intensive research on their tumorigenesis.

Determinants of farmers' adoption of improved soil conservation technology in a middle mountain watershed of Central Nepal.

This study explores different socio-economic and institutional factors influencing the adoption of improved soil conservation technology (ISCT) on Bari land (Rainfed outward sloping terraces) in the Middle Mountain region of Central Nepal. Structured questionnaire survey and focus group discussion methods were applied to collect the necessary information from farm households. The logistic regression model predicted seven factors influencing the adoption of improved soil conservation technology in the study area including years of schooling of the household head, caste of the respondent, land holding size of the Bari land, cash crop vegetable farming, family member occupation in off farm sector, membership of the Conservation and Development Groups, and use of credit. The study showed that technology dissemination through multi-sectoral type community based local groups is a good option to enhance the adoption of improved soil conservation technology in the Middle Mountain farming systems in Nepal. Planners and policy makers should formulate appropriate policies and programs considering the farmers' interest, capacity, and limitation in promoting improved soil conservation technology for greater acceptance and adoption by the farmers.

Moraxella catarrhalis is internalized in respiratory epithelial cells by a trigger-like mechanism and initiates a TLR2- and partly NOD1-dependent inflammatory immune response.

Moraxella catarrhalis is an important pathogen in patients with chronic obstructive lung disease (COPD). While M. catarrhalis has been categorized as an extracellular bacterium so far, the potential to invade human respiratory epithelium has not yet been explored. Our results obtained by electron and confocal microscopy demonstrated a considerable potential of M. catarrhalis to invade bronchial epithelial (BEAS-2B) cells, type II pneumocytes (A549) and primary small airway epithelial cells (SAEC). Moraxella invasion was dependent on cellular microfilament as well as on bacterial viability, and characterized by macropinocytosis leading to the formation of lamellipodia and engulfment of the invading organism into macropinosomes, thus indicating a trigger-like uptake mechanism. In addition, the cells examined expressed TLR2 as well as NOD1, a recently found cytosolic protein implicated in the intracellular recognition of bacterial cell wall components. Importantly, inhibition of TLR2 or NOD1 expression by RNAi significantly reduced the M. catarrhalis-induced IL-8 secretion. The role of TLR2 and NOD1 was further confirmed by overexpression assays in HEK293 cells. Overall, M. catarrhalis may employ lung epithelial cell invasion to colonize and to infect the respiratory tract, nonetheless, the bacteria are recognized by cell surface TLR2 and the intracellular surveillance molecule NOD1.

Adhesion of Moraxella catarrhalis to human bronchial epithelium characterized by a novel fluorescence-based assay.

Moraxella catarrhalis is a major cause of infectious exacerbations of chronic obstructive lung disease. Adhesion of this pathogen to epithelial cells is critical for its pathogenicity. Although much work has been done on identifying surface molecules of M. catarrhalis as adhesins, several adhesion assays were used in these studies which has never been validated or compared to each other. In the present study, we have examined the capacity of M. catarrhalis to adhere to different human epithelial cells. By using the two most commonly used adhesion assays based on the enumeration of colony-forming units or on the counting of adherent bacteria per epithelial cell by light microscopy, we identified significant limitations of both methods. These arose either from differences in strain-specific adhesion pattern on the epithelial cell surface or the dependence on the state of confluence of the epithelial cell layer. We developed a new fluorescence-based adhesion assay and compared our results to the two conventional methods. We demonstrated that the fluorescence-based adhesion assay offers a reliable and convenient method for the quantification of M. catarrhalis adhesion to confluent epithelial cell monolayers.