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Human settlements on the Moon, crewed missions to Mars and space tourism will become a reality in the next few decades. Human presence in space, especially for extended periods of time, will therefore steeply increase. However, despite more than 60 years of spaceflight, the mechanisms underlying the effects of the space environment on human physiology are still not fully understood. Animals, ranging in complexity from flies to monkeys, have played a pioneering role in understanding the (patho)physiological outcome of critical environmental factors in space, in particular altered gravity and cosmic radiation. The use of animals in biomedical research is increasingly being criticized because of ethical reasons and limited human relevance. Driven by the 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, major efforts have been focused in the past decades on the development of alternative methods that fully bypass animal testing or so-called new approach methodologies. These new approach methodologies range from simple monolayer cultures of individual primary or stem cells all up to bioprinted 3D organoids and microfluidic chips that recapitulate the complex cellular architecture of organs. Other approaches applied in life sciences in space research contribute to the reduction of animal experimentation. These include methods to mimic space conditions on Earth, such as microgravity and radiation simulators, as well as tools to support the processing, analysis or application of testing results obtained in life sciences in space research, including systems biology, live-cell, high-content and real-time analysis, high-throughput analysis, artificial intelligence and digital twins. The present paper provides an in-depth overview of such methods to replace or reduce animal testing in life sciences in space research. 

Abstract In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com. 

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight. 

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical.