Moral Inquiry into Nordic Animal Work in Tourism: The Role of Emotions and Reasoning

Human-nonhuman animal relationship in tourism reveals a dichotomy between tourism prosperity and ethical concerns. For long nonhuman animals have been involved in different forms of tourism activities with functions ranging from entertainment and profit generation to marketing and education. At the same time, recent talks of animal rights and welfare call to ponder on the moral aspects of nonhuman animal involvement. The currently accelerating wave of social awareness of anthropogenic impacts on our globe’s ecosystems only further pushes both researchers and society to reassess human-nonhuman animal relationship within tourism. Within the field of tourism studies, human-nonhuman animal relationship has been predominantly researched from normative ethics perspectives and viewed either through the context of animal captivity or tourist-animal relationship, with little research taking the worker-animal relationship perspective. Theoretically, this study draws upon the normative discourse of academic literature and major animal ethics theories, joining a critical paradigm which highlights the need to shift the research focus away from justification or application of absolute normative principles towards the inquiry of morally problematic situations. It suggests a turn from a monistic viewpoint towards more intersubjective-interpretive approach. This study aims to explore how cognitive and emotional attributes of animal workers in Northern Europe facilitate moral deliberations of the use of nonhuman animals in tourism. After conducting participatory and non-participatory observations during winter period of 2019-2020, the empirical data was collected through semi-structured in-depth interviews with 6 tourism animal workers in Finnish Lapland in the winter period of 2020-2021. The data was then analysed through the qualitative interpretive content analysis to facilitate the exploratory disposition of the study. The empirical data of the study indicates that emotional motivations and emotional relationship with nonhuman animals facilitate moral positioning of nonhuman animal labour. At the same time, tourism animal workers utilize certain emotional management mechanisms to cope with the difficulties and specifics of the job. Overall, the results of the study on the theoretical level suggest reflective equilibrium as an approach to achieve an endpoint of moral inquiry

The Perseus computational platform for comprehensive analysis of (prote)omics data

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical toots for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. ALL activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets

Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome

Background: The oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow. Methods: We used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study interindividual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called "split by taxonomy id" that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification. Results: Microgram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing. Conclusion: Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host-pathogen interactions and clinical saliva diagnostics

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification. The software platform MaxDIA streamlines analysis of data-independent acquisition proteomics

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.publishedVersio

Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry

Although mutations may represent attractive targets for immunotherapy, direct identification of mutated peptide ligands isolated from human leucocyte antigens (HLA) on the surface of native tumour tissue has so far not been successful. Using advanced mass spectrometry (MS) analysis, we survey the melanoma-associated immunopeptidome to a depth of 95,500 patient-presented peptides. We thereby discover a large spectrum of attractive target antigen candidates including cancer testis antigens and phosphopeptides. Most importantly, we identify peptide ligands presented on native tumour tissue samples harbouring somatic mutations. Four of eleven mutated ligands prove to be immunogenic by neoantigen-specific T-cell responses. Moreover, tumour-reactive T cells with specificity for selected neoantigens identified by MS are detected in the patient's tumour and peripheral blood. We conclude that direct identification of mutated peptide ligands from primary tumour material by MS is possible and yields true neoepitopes with high relevance for immunotherapeutic strategies in cancer

Computational Methods for Understanding Mass Spectrometry-Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry-based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear

Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome

Background: The oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow. Methods: We used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study interindividual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called "split by taxonomy id" that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification. Results: Microgram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing. Conclusion: Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host-pathogen interactions and clinical saliva diagnostics