You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Metabolism evolution based on network degrees of orthologous enzymes. The evolution of orthologous proteins opens a new era of research where the concepts of orthology and paralogy have become more and more substantial, as the whole-genome comparison allows their identification in complete genomes. Functional specificity of proteins is understood to be conserved among orthologs but it shows much more variability among paralogs. We used this laying claim to identify inter-species interactions based on orthologous protein networks which are crucial for understanding the evolution of orthologous proteins. We analyzed six classes of enzymatic protein sequence data using the node degrees of orthologous proteins. The results demonstrated the evolutionary importance of the fatty acid syntheses and the photosynthetic system in algae. Methods which have successfully exploited network structure at many different levels of detail are a cornerstone of systems biology.
{"Chemical": ["fatty acid"]}