Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and ...compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.
Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related ...to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.
Polar PIN Localization Directs Auxin Flow in Plants Wiśniewska, Justyna; Xu, Jian; Seifertová, Daniela ...
Science (American Association for the Advancement of Science),
05/2006, Letnik:
312, Številka:
5775
Journal Article
Recenzirano
Polar flow of the phytohormone auxin requires plasma membrane-associated PIN proteins and underlies multiple developmental processes in plants. Here we address the importance of the polarity of ...subcellular PIN localization for the directionality of auxin transport in Arabidopsis thaliana. Expression of different PINs in the root epidermis revealed the importance of PIN polar positions for directional auxin flow and root gravitropic growth. Interfering with sequence-embedded polarity signals directly demonstrates that PIN polarity is a primary factor in determining the direction of auxin flow in meristematic tissues. This finding provides a crucial piece in the puzzle of how auxin flow can be redirected via rapid changes in PIN polarity.
The development of in silico tools able to predict bioactivity and toxicity of chemical substances is a powerful solution envisioned to assess toxicity as early as possible. To enable the development ...of such tools, the ToxCast program has generated and made publicly available in vitro bioactivity data for thousands of compounds. The goal of the present study is to characterize and explore the data from ToxCast in terms of Machine Learning capability. For this, a large scale analysis on the entire database has been performed to build models to predict bioactivities measured in in vitro assays. Simple classical QSAR algorithms (ANN, SVM, LDA, random forest, and Bayesian) were first applied on the data, and the results of these algorithms suggested that they do not seem to be well-suited for data sets with a high proportion of inactive compounds. The study then showed for the first time that the use of an ensemble method named “Stacked generalization” could improve the model performance on this type of data. Indeed, for 61% of 483 models, the Stacked method led to models with higher performance. Moreover, the combination of this ensemble method with an applicability domain filter allows one to assess the reliability of the predictions for further compound prioritization. In particular we showed that for 50% of the models, the ROC score is better if we do not consider the compounds that are not within the applicability domain.
The emergence and quick spread of SARS-CoV-2 has pointed at a low capacity response for testing large populations in many countries, in line of material, technical and staff limitations. The ...traditional RT-qPCR diagnostic test remains the reference method and is by far the most widely used test. These assays are limited to a few probe sets, require large sample PCR reaction volumes, along with an expensive and time-consuming RNA extraction step. Here we describe a quantitative nanofluidic assay that overcomes some of these shortcomings, based on the BiomarkTM instrument from Fluidigm. This system offers the possibility of performing 4608 qPCR end-points in a single run, equivalent to 192 clinical samples combined with 12 pairs of primers/probe sets in duplicate, thus allowing the monitoring of SARS-CoV-2 including the detection of specific SARS-CoV-2 variants, as well as the detection other pathogens and/or host cellular responses (virus receptors, response markers, microRNAs). The 10 nL-range volume of BiomarkTM reactions is compatible with sensitive and reproducible reactions that can be easily and cost-effectively adapted to various RT-qPCR configurations and sets of primers/probe. Finally, we also evaluated the use of inactivating lysis buffers composed of various detergents in the presence or absence of proteinase K to assess the compatibility of these buffers with a direct reverse transcription enzymatic step and we propose several protocols, bypassing the need for RNA purification. We advocate that the combined utilization of an optimized processing buffer and a high-throughput real-time PCR device would contribute to improve the turn-around-time to deliver the test results to patients and increase the SARS-CoV-2 testing capacities.
Current demand for SARS‐CoV‐2 testing is straining material resource and labor capacity around the globe. As a result, the public health and clinical community are hindered in their ability to ...monitor and contain the spread of COVID‐19. Despite broad consensus that more testing is needed, pragmatic guidance toward realizing this objective has been limited. This paper addresses this limitation by proposing a novel and geographically agnostic framework (the 4Ps framework) to guide multidisciplinary, scalable, resource‐efficient, and achievable efforts toward enhanced testing capacity. The 4Ps (Prioritize, Propagate, Partition, and Provide) are described in terms of specific opportunities to enhance the volume, diversity, characterization, and implementation of SARS‐CoV‐2 testing to benefit public health. Coordinated deployment of the strategic and tactical recommendations described in this framework has the potential to rapidly expand available testing capacity, improve public health decision‐making in response to the COVID‐19 pandemic, and/or to be applied in future emergent disease outbreaks.
This paper proposes a novel 4Ps Framework (Prioritize, Propagate, Partition, and Provide) to guide multidisciplinary, scalable, resource‐efficient, and achievable efforts towards enhanced SARS‐CoV‐2 testing capacity and improved public health decision‐making in response to the COVID‐19 pandemic.
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
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.
Developing novel bioactive molecules is time-consuming, costly and rarely successful. As a mitigation strategy, we utilize, for the first time, cellular morphology to directly guide the de novo ...design of small molecules. We trained a conditional generative adversarial network on a set of 30 000 compounds using their cell painting morphological profiles as conditioning. Our model was able to learn chemistry-morphology relationships and influence the generated chemical space according to the morphological profile. We provide evidence for the targeted generation of known agonists when conditioning on gene overexpression profiles, even though no information on biological targets was used during training. Based on a target-agnostic readout, our approach facilitates knowledge transfer between biological pathways and can be used to design bioactives for many targets under one unified framework. Prospective application of this proof-of-concept to larger chemical spaces promises great potential for hit generation in drug and phytopharmaceutical discovery and chemical safety.
In rice, several allergens have been identified such as the non-specific lipid transfer protein-1, the α-amylase/trypsin-inhibitors, the α-globulin, the 33 kDa glyoxalase I (Gly I), the 52-63 kDa ...globulin, and the granule-bound starch synthetase. The goal of the present study was to define optimal rice extraction and detection methods that would allow a sensitive and reproducible measure of several classes of known rice allergens. In a three-laboratory ring-trial experiment, several protein extraction methods were first compared and analyzed by 1D multiplexed SDS-PAGE. In a second phase, an inter-laboratory validation of 2D-DIGE analysis was conducted in five independent laboratories, focusing on three rice allergens (52 kDa globulin, 33 kDa glyoxalase I, and 14-16 kDa α-amylase/trypsin inhibitor family members). The results of the present study indicate that a combination of 1D multiplexed SDS-PAGE and 2D-DIGE methods would be recommended to quantify the various rice allergens.
Representative multiplexed detection and 2D-DIGE profile of rice endogenous allergens.
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