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More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer’s disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities’ influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer's disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities' influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Abstract Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and published literature. We developed DES-TOMATO knowledgebase (KB) for exploration of information related to tomato. Information exploration is enabled through terms from 26 dictionaries and combination of these terms. To illustrate the utility of DES-TOMATO, we provide several examples how one can efficiently use this KB to retrieve known or potentially novel information. DES-TOMATO is free for academic and nonprofit users and can be accessed at http://cbrc.kaust.edu.sa/des_tomato/, using any of the mainstream web browsers, including Firefox, Safari and Chrome.

Abstract Gemcitabine is a first-line treatment agent for pancreatic ductal adenocarcinoma (PDAC). Contributing to its cytotoxicity, this chemotherapeutic agent is primarily a DNA replication inhibitor that also induces DNA damage. However, its therapeutic effects are limited owing to chemoresistance. Evidence in the literature points to a role for autophagy in restricting the efficacy of gemcitabine. Autophagy is a catabolic process in which intracellular components are delivered to degradative organelles lysosomes. Interfering with this process sensitizes PDAC cells to gemcitabine. It is consequently inferred that autophagy and lysosomal function need to be tightly regulated to maintain homeostasis and provide resistance to environmental stress, such as those imposed by chemotherapeutic drugs. However, the mechanism(s) through which gemcitabine promotes autophagy remains elusive, and the impact of gemcitabine on lysosomal function remains largely unexplored. Therefore, we applied complementary approaches to define the mechanisms triggered by gemcitabine that support autophagy and lysosome function. We found that gemcitabine elicited ERK-dependent autophagy in PDAC cells, but did not stimulate ERK activity or autophagy in non-tumoral human pancreatic epithelial cells. Gemcitabine also promoted transcription factor EB (TFEB)-dependent lysosomal function in PDAC cells. Indeed, treating PDAC cells with gemcitabine caused expansion of the lysosomal network, as revealed by Lysosome associated membrane protein-1 (LAMP1) and LysoTracker staining. More specific approaches have shown that gemcitabine promotes the activity of cathepsin B (CTSB), a cysteine protease playing an active role in lysosomal degradation. We showed that lysosomal function induced by gemcitabine depends on TFEB, the master regulator of autophagy and lysosomal biogenesis. Interfering with TFEB function considerably limited the clonogenic growth of PDAC cells and hindered the capacity of TFEB-depleted PDAC cells to develop orthotopic tumors.