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Abstract Objective To identify a convergent, multitarget proliferation characteristic for astrocytoma transformation that could be targeted for therapy discovery. Methods Using an integrated functional genomics approach, we prioritized networks associated with astrocytoma progression using the following criteria: differential co‐expression between grade II and grade III IDH1‐mutated and 1p/19q euploid astrocytomas, preferential enrichment for genetic risk to cancer, association with patient survival and sample‐level genomic features. Drugs targeting the identified multitarget network characteristic for astrocytoma transformation were computationally predicted using drug transcriptional perturbation data and validated using primary human astrocytoma cells. Results A single network, M2, consisting of 177 genes, was associated with glioma progression on the basis of the above criteria. Functionally, M2 encoded physically interacting proteins regulating cell cycle processes and analysis of genome‐wide gene‐regulatory interactions using mutual information and DNA–protein interactions revealed the known regulators of cell cycle processes FoxM1, B‐Myb, and E2F2 as key regulators of M2. These results suggest functional disruption of M2 via gene mutation or altered expression as a convergent pathway regulating astrocytoma transformation. By considering M2 as a multitarget drug target regulating astrocytoma transformation, we identified several drugs that are predicted to restore M2 expression in anaplastic astrocytoma toward its low‐grade profile and of these, we validated the known antiproliferative drug resveratrol as down‐regulating multiple nodes of M2 including at nanomolar concentrations achievable in human cerebrospinal fluid by oral dosing. Interpretation Our results identify M2 as a multitarget network characteristic for astrocytoma progression and encourage M2‐based drug screening to identify new compounds for preventing glioma transformation.

The identification of new drug targets is highly challenging, particularly for diseases of the brain. This study describes a general computational gene regulatory framework called CRAFT for drug target discovery, and the authors use CRAFT to identify the microglial membrane receptor Csf1R as a potential therapeutic target for epilepsy.

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.

Microglial activation is a key modulator of brain vulnerability in response to intra-uterine growth restriction (IUGR). However, the consequences of IUGR on microglial development and the microglial proteome are still unknown. We used a model of IUGR induced by a gestational low-protein diet (LPD) in rats. Microglia, isolated from control and growth-restricted animals at P1 and P4, showed significant changes in the proteome between the two groups. The expression of protein sets associated with fetal growth, inflammation, and the immune response were significantly enriched in LPD microglia at P1 and P4. Interestingly, upregulation of protein sets associated with the oxidative stress response and reactive oxygen species production was observed at P4 but not P1. During development, inflammation-associated proteins were upregulated between P1 and P4 in both control and LPD microglia. By contrast, proteins associated with DNA repair and senescence pathways were upregulated in only LPD microglia. Similarly, protein sets involved in protein retrograde transport were significantly downregulated in only LPD microglia. Overall, these data demonstrate significant and multiple effects of LPD-induced IUGR on the developmental program of microglial cells, leading to an abnormal proteome within the first postnatal days.