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Interaction between a drug and its protein target results in a shift in thermal stability of that protein. The cellular thermal shift assay–CETSA–exploits this to characterize intracellular drug-protein engagement. Thermal shift assays are used to study thermal stabilization of proteins upon ligand binding. Such assays have been used extensively on purified proteins in the drug discovery industry and in academia to detect interactions. Recently, we published a proof-of-principle study describing the implementation of thermal shift assays in a cellular format, which we call the cellular thermal shift assay (CETSA). The method allows studies of target engagement of drug candidates in a cellular context, herein exemplified with experimental data on the human kinases p38α and ERK1/2. The assay involves treatment of cells with a compound of interest, heating to denature and precipitate proteins, cell lysis, and the separation of cell debris and aggregates from the soluble protein fraction. Whereas unbound proteins denature and precipitate at elevated temperatures, ligand-bound proteins remain in solution. We describe two procedures for detecting the stabilized protein in the soluble fraction of the samples. One approach involves sample workup and detection using quantitative western blotting, whereas the second is performed directly in solution and relies on the induced proximity of two target-directed antibodies upon binding to soluble protein. The latter protocol has been optimized to allow an increased throughput, as potential applications require large numbers of samples. Both approaches can be completed in a day.

Target engagement is a critical factor for therapeutic efficacy. Assessment of compound binding to native target proteins in live cells is therefore highly desirable in all stages of drug discovery. We report here the first compound library screen based on biophysical measurements of intracellular target binding, exemplified by human thymidylate synthase (TS). The screen selected accurately for all the tested known drugs acting on TS. We also identified TS inhibitors with novel chemistry and marketed drugs that were not previously known to target TS, including the DNA methyltransferase inhibitor decitabine. By following the cellular uptake and enzymatic conversion of known drugs we correlated the appearance of active metabolites over time with intracellular target engagement. These data distinguished a much slower activation of 5-fluorouracil when compared with nucleoside-based drugs. The approach establishes efficient means to associate drug uptake and activation with target binding during drug discovery. Drugs therapeutic efficacy relies on their capability of binding the relevant targets in a physiological environment, which has so far been hard to measure. Here, the authors present a compound library screen based on a target engagement assay that reports on protein stability upon ligands binding in cell.

The efficacy of therapeutics is dependent on a drug binding to its cognate target. Optimization of target engagement by drugs in cells is often challenging, because drug binding cannot be monitored inside cells. We have developed a method for evaluating drug binding to target proteins in cells and tissue samples. This cellular thermal shift assay (CETSA) is based on the biophysical principle of ligand-induced thermal stabilization of target proteins. Using this assay, we validated drug binding for a set of important clinical targets and monitored processes of drug transport and activation, off-target effects and drug resistance in cancer cell lines, as well as drug distribution in tissues. CETSA is likely to become a valuable tool for the validation and optimization of drug target engagement.

To understand both the beneficial and the side effects of a drug, one would need to know its full binding profile to all cellular proteins. Savitski et al. take significant steps toward meeting this daunting challenge. They monitored the unfolding or “melting” of over 7000 human proteins and measured how small-molecule binding changes individual melting profiles. As a proof of principle, over 50 targets were identified for an inhibitor known to bind a broad spectrum of kinases. Two cancer drugs, vemurafib and Alectinib, are known to have a side effect of photosensitivity. The thermal profiling approach identified drug-protein interactions responsible for these side effects. Science , this issue 10.1126/science.1255784 Monitoring drug effects on the thermal profile of a cell’s proteins identifies drug targets and off-targets. The thermal stability of proteins can be used to assess ligand binding in living cells. We have generalized this concept by determining the thermal profiles of more than 7000 proteins in human cells by means of mass spectrometry. Monitoring the effects of small-molecule ligands on the profiles delineated more than 50 targets for the kinase inhibitor staurosporine. We identified the heme biosynthesis enzyme ferrochelatase as a target of kinase inhibitors and suggest that its inhibition causes the phototoxicity observed with vemurafenib and alectinib. Thermal shifts were also observed for downstream effectors of drug treatment. In live cells, dasatinib induced shifts in BCR-ABL pathway proteins, including CRK/CRKL. Thermal proteome profiling provides an unbiased measure of drug-target engagement and facilitates identification of markers for drug efficacy and toxicity.

The gut microbiota influences both local and systemic inflammation. Inflammation contributes to development, progression, and treatment of cancer, but it remains unclear whether commensal bacteria affect inflammation in the sterile tumor microenvironment. Here, we show that disruption of the microbiota impairs the response of subcutaneous tumors to CpG-oligonucleotide immunotherapy and platinum chemotherapy. In antibiotics-treated or germ-free mice, tumor-infiltrating myeloid-derived cells responded poorly to therapy, resulting in lower cytokine production and tumor necrosis after CpG-oligonucleotide treatment and deficient production of reactive oxygen species and cytotoxicity after chemotherapy. Thus, optimal responses to cancer therapy require an intact commensal microbiota that mediates its effects by modulating myeloid-derived cell functions in the tumor microenvironment. These findings underscore the importance of the microbiota in the outcome of disease treatment.

Table-top extreme ultraviolet (EUV) microscopy offers unique opportunities for label-free investigation of biological samples. Here, we demonstrate ptychographic EUV imaging of two dried, unstained model specimens: germlings of a fungus ( Aspergillus nidulans ), and bacteria ( Escherichia coli ) cells at 13.5 nm wavelength. We find that the EUV spectral region, which to date has not received much attention for biological imaging, offers sufficient penetration depths for the identification of intracellular features. By implementing a position-correlated ptychography approach, we demonstrate a millimeter-squared field of view enabled by infrared illumination combined with sub-60 nm spatial resolution achieved with EUV illumination on selected regions of interest. The strong element contrast at 13.5 nm wavelength enables the identification of the nanoscale material composition inside the specimens. Our work will advance and facilitate EUV imaging applications and enable further possibilities in life science.

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

Androgen Receptor (AR) is a key driver in prostate cancer. Direct targeting of AR has valuable therapeutic potential. However, the lack of disease relevant cellular methodologies capable of discriminating between inhibitors that directly bind AR and those that instead act on AR co-regulators has made identification of novel antagonists challenging. The Cellular Thermal Shift Assay (CETSA) is a technology enabling confirmation of direct target engagement with label-free, endogenous protein in living cells. We report the development of the first high-throughput CETSA assay (CETSA HT) to identify direct AR binders in a prostate cancer cell line endogenously expressing AR. Using this approach, we screened a pharmacology library containing both compounds reported to directly engage AR, and compounds expected to target AR co-regulators. Our results show that CETSA HT exclusively identifies direct AR binders, differentiating them from co-regulator inhibitors where other cellular assays measuring functional responses cannot. Using this CETSA HT approach we can derive apparent binding affinities for a range of AR antagonists, which represent an intracellular measure of antagonist-receptor Ki performed for the first time in a label-free, disease-relevant context. These results highlight the potential of CETSA HT to improve the success rates for novel therapeutic interventions directly targeting AR.

This research focuses on pre-university students' perceptions of algorithmic biases in artificial intelligence. Six types of biases (generational, gender, functional diversity, ethnicity, geographical origin and economic reasons) are examined on the basis of four variables (age, sex, educational level and academic year) of young people. A quantitative method is employed using a questionnaire. ANOVA, T-test and Kruskall-Wallis test are used. The results show statistically significant differences in the variables analysed and, in general terms, young people have a medium-high perception of possible biases. The highest number of differences between groups was found in the level of education (secondary education/baccalaureate/vocational training). The least differences were found in age (less than 12 years/12-14 years/15-17 years/18-21 years) and sex of the participants (male/female). Students in vocational training have a higher perception of bias and those in baccalaureate have the lowest means. In this case, significant differences were found. The results also show significant differences in biases produced by functional diversity, geographical origin and economic reasons. In relation to age, significant differences were found in two groups of students. According to sex, males have a higher perception of gender and ethnicity biases. These results have consequences for educational practice, as they highlight the aspects that should be addressed in the training of young people in artificial intelligence. It also has implications for research as it opens up new questions to be analysed.

The pretreatment of biomass remains a critical requirement for bio-renewable fuel production from lignocellulose. Although current processes primarily involve chemical and physical approaches, the biological breakdown of lignin using enzymes and microorganisms is quickly becoming an interesting eco-friendly alternative to classical processes. As a result, bioprospection of wild fungi from naturally occurring lignin-rich sources remains a suitable method to uncover and isolate new species exhibiting ligninolytic activity. In this study, wild species of white rot fungi were collected from Colombian forests based on their natural wood decay ability and high capacity to secrete oxidoreductases with high affinity for phenolic polymers such as lignin. Based on high activity obtained from solid-state fermentation using a lignocellulose source from oil palm as matrix, we describe the isolation and whole-genome sequencing of Dictyopanus pusillus, a wild basidiomycete fungus exhibiting ABTS oxidation as an indication of laccase activity. Functional characterization of a crude enzymatic extract identified laccase activity as the main enzymatic contributor to fungal extracts, an observation supported by the identification of 13 putative genes encoding for homologous laccases in the genome. To the best of our knowledge, this represents the first report of an enzymatic extract exhibiting laccase activity in the Dictyopanus genera, offering means to exploit this species and its enzymes for the delignification process of lignocellulosic by-products from oil palm.