Janus kinase inhibitors are potential therapeutics for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a poorly treated multifactorial neurodegenerative disease associated with multiple cell types and subcellular organelles. As with other multifactorial diseases, it is likely that drugs will need to target multiple disease processes and cell types to be effective. We review here the role of Janus kinase (JAK)/Signal transducer and activator of transcription (STAT) signalling in ALS, confirm the association of this signalling with fundamental ALS disease processes using the BenevolentAI Knowledge Graph, and demonstrate that inhibitors of this pathway could reduce the ALS pathophysiology in neurons, glia, muscle fibres, and blood cells. Specifically, we suggest that inhibition of the JAK enzymes by approved inhibitors known as Jakinibs could reduce STAT3 activation and modify the progress of this disease. Analysis of the Jakinibs highlights baricitinib as a suitable candidate due to its ability to penetrate the central nervous system and exert beneficial effects on the immune system. Therefore, we recommend that this drug be tested in appropriately designed clinical trials for ALS.

Associating biological context with protein-protein interactions through text mining at PubMed scale.

Inferring knowledge from known relationships between drugs, proteins, genes, and diseases has great potential for clinical impact, such as predicting which existing drugs could be repurposed to treat rare diseases. Incorporating key biological context such as cell type or tissue of action into representations of extracted biomedical knowledge is essential for principled pharmacological discovery. Existing global, literature-derived knowledge graphs of interactions between drugs, proteins, genes, and diseases lack this essential information. In this study, we frame the task of associating biological context with protein-protein interactions extracted from text as a classification task using syntactic, semantic, and novel meta-discourse features. We introduce the Insider corpora, which are automatically generated PubMed-scale corpora for training classifiers for the context association task. These corpora are created by searching for precise syntactic cues of cell type and tissue relevancy to extracted regulatory relations. We report F1 scores of 0.955 and 0.862 for identifying relevant cell types and tissues, respectively, for our identified relations. By classifying with this framework, we demonstrate that the problem of context association can be addressed using intuitive, interpretable features. We demonstrate the potential of this approach to enrich text-derived knowledge bases with biological detail by incorporating cell type context into a protein-protein network for dengue fever.

SCIRT lncRNA Restrains Tumorigenesis by Opposing Transcriptional Programs of Tumor-Initiating Cells.

In many tumors, cells transition reversibly between slow-proliferating tumor-initiating cells (TIC) and their differentiated, faster-growing progeny. Yet, how transcriptional regulation of cell-cycle and self-renewal genes is orchestrated during these conversions remains unclear. In this study, we show that as breast TIC form, a decrease in cell-cycle gene expression and increase in self-renewal gene expression are coregulated by SOX2 and EZH2, which colocalize at CpG islands. This pattern was negatively controlled by a novel long noncoding RNA (lncRNA) that we named Stem Cell Inhibitory RNA Transcript (SCIRT), which was markedly upregulated in tumorspheres but colocalized with and counteracted EZH2 and SOX2 during cell-cycle and self-renewal regulation to restrain tumorigenesis. SCIRT specifically interacted with EZH2 to increase EZH2 affinity to FOXM1 without binding the latter. In this manner, SCIRT induced transcription at cell-cycle gene promoters by recruiting FOXM1 through EZH2 to antagonize EZH2-mediated effects at target genes. Conversely, on stemness genes, FOXM1 was absent and SCIRT antagonized EZH2 and SOX2 activity, balancing toward repression. These data suggest that the interaction of an lncRNA with EZH2 can alter the affinity of EZH2 for its protein-binding partners to regulate cancer cell state transitions. SIGNIFICANCE: These findings show that a novel lncRNA SCIRT counteracts breast tumorigenesis by opposing transcriptional networks associated with cell cycle and self-renewal.See related commentary by Pardini and Dragomir, p. 535.

Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs.

Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1-3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

Noncoding RNAs and the control of signalling via nuclear receptor regulation in health and disease.

Nuclear receptors belong to a superfamily of proteins that play central roles in human biology, orchestrating a large variety of biological functions in both health and disease. Understanding the interactions and regulatory pathways of NRs will allow development of potential therapeutic interventions for a multitude of disease processes. Non-coding RNAs have recently been discovered to have significant interactions with NR signalling pathways via a variety of biological connections. This review summarises the known interactions between ncRNAs and the NR superfamily in health, embryogenesis and a plethora of human diseases.

Growth arrest-specific transcript 5 associated snoRNA levels are related to p53 expression and DNA damage in colorectal cancer.

BACKGROUND: The growth arrest-specific transcript 5 gene (GAS5) encodes a long noncoding RNA (lncRNA) and hosts a number of small nucleolar RNAs (snoRNAs) that have recently been implicated in multiple cellular processes and cancer. Here, we investigate the relationship between DNA damage, p53, and the GAS5 snoRNAs to gain further insight into the potential role of this locus in cell survival and oncogenesis both in vivo and in vitro. METHODS: We used quantitative techniques to analyse the effect of DNA damage on GAS5 snoRNA expression and to assess the relationship between p53 and the GAS5 snoRNAs in cancer cell lines and in normal, pre-malignant, and malignant human colorectal tissue and used biological techniques to suggest potential roles for these snoRNAs in the DNA damage response. RESULTS: GAS5-derived snoRNA expression was induced by DNA damage in a p53-dependent manner in colorectal cancer cell lines and their levels were not affected by DICER. Furthermore, p53 levels strongly correlated with GAS5-derived snoRNA expression in colorectal tissue. CONCLUSIONS: In aggregate, these data suggest that the GAS5-derived snoRNAs are under control of p53 and that they have an important role in mediating the p53 response to DNA damage, which may not relate to their function in the ribosome. We suggest that these snoRNAs are not processed by DICER to form smaller snoRNA-derived RNAs with microRNA (miRNA)-like functions, but their precise role requires further evaluation. Furthermore, since GAS5 host snoRNAs are often used as endogenous controls in qPCR quantifications we show that their use as housekeeping genes in DNA damage experiments can lead to inaccurate results.

The p53 miRNA interactome and its potential role in the cancer clinic.

p53 is one of the most frequently mutated tumor suppressors. It regulates protein-coding genes and noncoding RNAs involved in many cellular processes, functioning predominantly at the transcriptional level but also through nontranscriptional processes. miRNAs have recently been identified as key mediators of the p53 stress-response pathway. p53 regulates miRNA transcription and processing, and miRNAs regulate p53 activity and expression and, accordingly, various feedback/feed-forward loops have been identified. Many chemotherapeutic agents induce cancer cell death or senescence via DNA damage and the subsequent activation of p53. Resistance to chemotherapy can occur due to the mutation of components in p53 signaling networks. A better understanding of the role of the various components within these pathways and their interactions with each other may allow the modification and improvement of current treatments, and the design of novel therapies. Improving our knowledge of the role of miRNAs in such p53 signaling networks may be crucial to achieving this.