Regulation and coordination of the different DNA damage responses in Drosophila.

Cells have evolved mechanisms that allow them to respond to DNA damage to preserve genomic integrity and maintain tissue homeostasis. These responses include the activation of the cell cycle checkpoints and the repair mechanisms or the induction of apoptosis that eventually will eliminate damaged cells. These "life" vs. "death" decisions differ depending on the cell type, stages of development, and the proliferation status of the cell. The apoptotic response after DNA damage is of special interest as defects in its induction could contribute to tumorigenesis or the resistance of cancer cells to therapeutic agents such as radiotherapy. Multiples studies have elucidated the molecular mechanisms that mediate the activation of the DNA damage response pathway (DDR) and specifically the role of p53. However, much less is known about how the different cellular responses such as cell proliferation control and apoptosis are coordinated to maintain tissue homeostasis. Another interesting question is how the differential apoptotic response to DNA damage is regulated in distinct cell types. The use of Drosophila melanogaster as a model organism has been fundamental to understand the molecular and cellular mechanisms triggered by genotoxic stress. Here, we review the current knowledge regarding the cellular responses to ionizing radiation as the cause of DNA damage with special attention to apoptosis in Drosophila: how these responses are regulated and coordinated in different cellular contexts and in different tissues. The existence of intrinsic mechanisms that might attenuate the apoptotic pathway in response to this sort of DNA damage may well be informative for the differences in the clinical responsiveness of tumor cells after radiation therapy.

The Janus Face of IL-33 Signaling in Tumor Development and Immune Escape.

Interleukin-33 (IL-33), a member of the IL-1 cytokine family, plays a critical role in maintaining tissue homeostasis as well as pathological conditions, such as allergy, infectious disease, and cancer, by promoting type 1 and 2 immune responses. Through its specific receptor ST2, IL-33 exerts multifaceted functions through the activation of diverse intracellular signaling pathways. ST2 is expressed in different types of immune cells, including Th2 cells, Th1 cells, CD8+ T cells, regulatory T cells (Treg), cytotoxic NK cells, group 2 innate lymphoid cells (ILC2s), and myeloid cells. During cancer initiation and progression, the aberrant regulation of the IL-33/ST2 axis in the tumor microenvironment (TME) extrinsically and intrinsically mediates immune editing via modulation of both innate and adaptive immune cell components. The summarized results in this review suggest that IL-33 exerts dual-functioning, pro- as well as anti-tumorigenic effects depending on the tumor type, expression levels, cellular context, and cytokine milieu. A better understanding of the distinct roles of IL-33 in epithelial, stromal, and immune cell compartments will benefit the development of a targeting strategy for this IL-33/ST2 axis for cancer immunotherapy.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Location, location! cellular relocalization primes specialized metabolic diversification.

Specialized metabolites are structurally diverse and cell- or tissue-specific molecules produced in restricted plant lineages. In contrast, primary metabolic pathways are highly conserved in plants and produce metabolites essential for all of life, such as amino acids and nucleotides. Substrate promiscuity - the capacity to accept non-native substrates - is a common characteristic of enzymes, and its impact is especially apparent in generating specialized metabolite variation. However, promiscuity only leads to metabolic diversity when alternative substrates are available; thus, enzyme cellular and subcellular localization directly influence chemical phenotypes. We review a variety of mechanisms that modulate substrate availability for promiscuous plant enzymes. We focus on examples where evolution led to modification of the 'cellular context' through changes in cell-type expression, subcellular relocalization, pathway sequestration, and cellular mixing via tissue damage. These varied mechanisms contributed to the emergence of structurally diverse plant specialized metabolites and inform future metabolic engineering approaches.

Atoh1 and other related key regulators in the development of auditory sensory epithelium in the mammalian inner ear: function and interplay.

Damage or loss of auditory hair cells leads to irreversible sensorineural hearing loss in human, thus regeneration of these cells to reconstruct auditory sensory epithelium holds the promise for the treatment of deafness. Regulatory factors involved in the development of auditory sensory epithelium play crucial roles in hair cell regeneration and hearing restoration. Here, we first focus on the transcription factor Atoh1 which is critical for hair cell development and regeneration, and comprehensively summarize the current understanding of the protein structure, target binding motif, developmental expression pattern, functional role, and upstream and downstream regulatory mechanism of Atoh1 in the context of controlling the cell fate commitment to hair cells or transdifferentiation from supporting cells. We also discuss cellular context dependency of Atoh1 in hair cell induction which should be taken into consideration when using Atoh1 gene therapy for hair cell regeneration. Next, we review the roles of Gfi1, Pou4f3, and Barhl1 in hair cell maturation and maintenance, and suggest that manipulation of these genes and their downstream targets will be helpful for the generation of functional hair cells with long-term viability. Finally, we provide an overview of the interplay between Notch, Wnt, Shh, and FGF signaling pathways during auditory sensory epithelium development. By analyzing crosstalk between these pathways, we suggest that combination of Wnt signaling activation with Hey1 and Hey2 inhibition will be crucial for hair cell regeneration and hearing restoration. Furthermore, this review highlights the importance of deeper understanding of the cellular context for hair cell development and the interconnection between these key regulators in developing new strategies to treat sensorineural hearing loss.

Remote focusing multifocal plane microscopy for the imaging of 3D single molecule dynamics with cellular context.

Three-dimensional (3D) single molecule fluorescence microscopy affords the ability to investigate subcellular traffcking at the level of individual molecules. An imaged single molecule trajectory, however, often reveals only limited information about the underlying biological process when insuffcient information is available about the organelles and other cellular structures with which the molecule interacts. A new 3D fluorescence microscopy imaging modality is described here that enables the simultaneous imaging of the trajectories of fast-moving molecules and the associated cellular context. The new modality is called remote focusing multifocal plane microscopy (rMUM), as it extends multifocal plane microscopy (MUM) with a remote focusing module. MUM is a modality that uses multiple detectors to image distinct focal planes within the specimen at the same time, and it has been demonstrated to allow the determination of 3D single molecule trajectories with high accuracy. Remote focusing is a method that makes use of two additional objective lenses to enable the acquisition of a z-stack of the specimen without having to move the microscope's objective lens or sample stage, components which are required by MUM to be fixed in place. rMUM's remote focusing module thus allows the cellular context to be imaged in the form of z-stacks as the trajectories of molecules or other objects of interest are imaged by MUM. In addition to a description of the modality, a discussion of rMUM data analysis and an example of data acquired using an rMUM setup are provided in this paper.