Aldehyde dehydrogenases (ALDHs) oxidize aldehydes to the corresponding carboxylic acids using either NAD or NADP as a coenzyme. Aldehydes are highly reactive aliphatic or aromatic molecules that play ...an important role in numerous physiological, pathological, and pharmacological processes. ALDHs have been discovered in practically all organisms and there are multiple isoforms, with multiple subcellular localizations. More than 160 ALDH cDNAs or genes have been isolated and sequenced to date from various sources, including bacteria, yeast, fungi, plants, and animals. The eukaryote ALDH genes can be subdivided into several families; the human genome contains 19 known ALDH genes, as well as many pseudogenes. Noteworthy is the fact that elevated activity of various ALDHs, namely ALDH1A2, ALDH1A3, ALDH1A7, ALDH2*2, ALDH3A1, ALDH4A1, ALDH5A1, ALDH6, and ALDH9A1, has been observed in normal and cancer stem cells. Consequently, ALDHs not only may be considered markers of these cells, but also may well play a functional role in terms of self-protection, differentiation, and/or expansion of stem cell populations. The ALDH3 family includes enzymes able to oxidize medium-chain aliphatic and aromatic aldehydes, such as peroxidic and fatty aldehydes. Moreover, these enzymes also have noncatalytic functions, including antioxidant functions and some structural roles. The gene of the cytosolic form, ALDH3A1, is localized on chromosome 17 in human beings and on the 11th and 10th chromosome in the mouse and rat, respectively. ALDH3A1 belongs to the phase II group of drug-metabolizing enzymes and is highly expressed in the stomach, lung, keratinocytes, and cornea, but poorly, if at all, in normal liver. Cytosolic ALDH3 is induced by polycyclic aromatic hydrocarbons or chlorinated compounds, such as 2,3,7,8-tetrachlorodibenzo-p-dioxin, in rat liver cells and increases during carcinogenesis. It has been observed that this increased activity is directly correlated with the degree of deviation in hepatoma and lung cancer cell lines, as is the case in chemically induced hepatoma in rats. High ALDH3A1 expression and activity have been correlated with cell proliferation, resistance against aldehydes derived from lipid peroxidation, and resistance against drug toxicity, such as oxazaphosphorines. Indeed, cells with a high ALDH3A1 content are more resistant to the cytostatic and cytotoxic effects of lipidic aldehydes than are those with a low content. A reduction in cell proliferation can be observed when the enzyme is directly inhibited by the administration of synthetic specific inhibitors, antisense oligonucleotides, or siRNA or indirectly inhibited by the induction of peroxisome proliferator-activated receptor γ (PPARγ) with polyunsaturated fatty acids or PPARγ transfection. Conversely, cell proliferation is stimulated by the activation of ALDH3A1, whether by inhibiting PPARγ with a specific antagonist, antisense oligonucleotides, siRNA, or a medical device (i.e., composite polypropylene prosthesis for hernia repair) used to induce cell proliferation. To date, the mechanisms underlying the effects of ALDHs on cell proliferation are not yet fully clear. A likely hypothesis is that the regulatory effect is mediated by the catabolism of some endogenous substrates deriving from normal cell metabolism, such as 4-hydroxynonenal, which have the capacity to either stimulate or inhibit the expression of genes involved in regulating proliferation.
► Aldehyde dehydrogenases (ALDHs) exist as multiple isoforms with multiple subcellular localizations. ► ALDHs are elevated in normal and cancer stem cells. ► ALDH3A1 activity is correlated with cell proliferation and drug resistance. ► ALDH3A1 is modulated by PPARγ, indirectly via NF-κB and AP1, or directly via PPRE. ► ALDH3A1 regulates proliferation via gene modulation or lipid peroxidation products.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Chemical space networks (CSNs) have recently been introduced as an alternative to other coordinate-free and coordinate-based chemical space representations. In CSNs, nodes represent compounds and ...edges pairwise similarity relationships. In addition, nodes are annotated with compound property information such as biological activity. CSNs have been applied to view biologically relevant chemical space in comparison to random chemical space samples and found to display well-resolved topologies at low edge density levels. The way in which molecular similarity relationships are assessed is an important determinant of CSN topology. Previous CSN versions were based on numerical similarity functions or the assessment of substructure-based similarity. Herein, we report a new CSN design that is based upon combined numerical and substructure similarity evaluation. This has been facilitated by calculating numerical similarity values on the basis of maximum common substructures (MCSs) of compounds, leading to the introduction of MCS-based CSNs (MCS-CSNs). This CSN design combines advantages of continuous numerical similarity functions with a robust and chemically intuitive substructure-based assessment. Compared to earlier version of CSNs, MCS-CSNs are characterized by a further improved organization of local compound communities as exemplified by the delineation of drug-like subspaces in regions of biologically relevant chemical space.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with ...speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at
https://www.difacquim.com/english/events/2022-colloquium/
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Chemical Space Networks (CSNs) are generated for different compound data sets on the basis of pairwise similarity relationships. Such networks are thought to complement and further extend traditional ...coordinate-based views of chemical space. Our proof-of-concept study focuses on CSNs based upon fingerprint similarity relationships calculated using the conventional Tanimoto similarity metric. The resulting CSNs are characterized with statistical measures from network science and compared in different ways. We show that the homophily principle, which is widely considered in the context of social networks, is a major determinant of the topology of CSNs of bioactive compounds, designed as threshold networks, typically giving rise to community structures. Many properties of CSNs are influenced by numerical features of the conventional Tanimoto similarity metric and largely dominated by the edge density of the networks, which depends on chosen similarity threshold values. However, properties of different CSNs with constant edge density can be directly compared, revealing systematic differences between CSNs generated from randomly collected or bioactive compounds.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Chemical space networks (CSNs) have been introduced as a coordinate-free representation of chemical space. In CSNs, nodes represent compounds and edges pairwise similarity relationships. These ...network representations are mostly used to navigate sections of biologically relevant chemical space. Different types of CSNs have been designed on the basis of alternative similarity measures including continuous numerical similarity values or substructure-based similarity criteria. CSNs can be characterized and compared on the basis of statistical concepts from network science. Herein, a new CSN design is introduced that is based upon asymmetric similarity assessment using the Tversky coefficient and termed TV-CSN. Compared to other CSNs, TV-CSNs have unique features. While CSNs typically contain separate compound communities and exhibit small world character, many TV-CSNs are also scale-free in nature and contain hubs, i.e., extensively connected central compounds. Compared to other CSNs, these hubs are a characteristic of TV-CSN topology. Hub-containing compound communities are of particular interest for the exploration of structure–activity relationships.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Medicinal chemists are frequently asked to review lists of compounds to assess their drug- or leadlike nature and to evaluate the suitability of lead compounds based on their “attractiveness” and/or ...synthetic feasibility as a basis for launching a drug-discovery campaign. It is often felt that one medicinal chemist's opinion is as good as any other, but is it? In an attempt to answer this question, an experiment was performed in conjunction with a recent compound acquisition program (CAP) conducted at Pharmacia. Historically, the CAP included a review of many thousands of compounds by medicinal chemists who eliminate anything deemed undesirable for any reason. In a review conducted in 2002, about 22 000 compounds requiring review by medicinal chemists were broken down into 11 lists of approximately 2000 compounds each. Unknown to the medicinal chemists, a subset of 250 compounds, previously rejected by a very experienced senior medicinal chemist, was added to each of the lists. Most of the 13 medicinal chemists who participated in this process reviewed two lists, although some only reviewed a single list and one reviewed three lists. Those compounds that were deemed unacceptable were recorded and tabulated in various ways to assess the consistency of the reviews. It was found that medicinal chemists were not very consistent in the compounds they rejected as being undesirable. The inconsistency arises from the subjective analysis that all humans utilize when considering “data sets” of any kind. This has important implications for pharmaceutical project teams where individual medicinal chemists review lists of primary screening hits to identify those compounds suitable for follow-up. Once a compound is removed from a list, it and other structurally similar compounds are effectively removed from further consideration. This can also have an impact on computational chemists who are developing models for assessing the desirability or attractiveness of different classes of compounds for lead discovery.
Cachexia, the most severe paraneoplastic syndrome, occurs in about 80% of patients with advanced cancer; it cannot be reverted by conventional, enteral, or parenteral nutrition. For this reason, ...nutritional interventions must be based on the use of substances possessing, alongside nutritional and energetic properties, the ability to modulate production of the pro-inflammatory factors responsible for the metabolic changes characterising cancer cachexia. In light of their nutritional and anti-inflammatory properties, polyunsaturated fatty acids (PUFAs), and in particular n-3, have been investigated for treating cachexia; however, the results have been contradictory.
Since both n-3 and n-6 PUFAs can affect cell functions in several ways, this research investigated the possibility that the effects of both n-3 and n-6 PUFAs could be mediated by their major aldehydic products of lipid peroxidation, 4-hydroxyhexenal (HHE) and 4-hydroxynonenal (HNE), and by their anti-inflammatory properties. An “in vitro” cancer cachexia model, consisting of human lung cancer cells (A427) and murine myoblasts (C2C12), was used.
The results showed that: 1) both n-3 and n-6 PUFAs reduced the growth of lung cancer cells without causing cell death, increased lipid peroxidation and Peroxisome Proliferator-Activated Receptor (PPAR)α, and decreased TNFα; 2) culture medium conditioned by A427 cells grown in the absence of PUFAs blocked myosin production and the differentiation of C2C12 muscle cells; conversely, muscle cells grown in culture medium conditioned by the same cells in the presence of PUFAs showed myosin expression and formed myotubes; 3) adding HHE or HNE directly to C2C12 cells maintained in culture medium conditioned by A427 cells in the absence of PUFAs stimulated myosin production and myotube formation; 4) putative consensus sequences for (PPARs) have been found in genes encoding fast isoforms of myosin heavy chain, by a bioinformatics approach.
The overall results show, first, the ability of both n-3 and n-6 PUFAs and their lipid peroxidation products to prevent the blocking of myosin expression and myotube formation caused in C2C12 cells by medium conditioned by human lung tumour cells. The C2C12 cell differentiation can be due to direct effect of lipid peroxidation products, as evidenced by treating C2C12 cells with HHE and HNE, and to the decrease of pro-inflammatory TNFα in A427 cell culture medium. The presence of consensus sequences for PPARs in genes encoding the fast isoforms of myosin heavy chain suggests that the effects of PUFAs, HHE, and HNE are PPAR-mediated.
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•n-3 and n-6 PUFAs decrease growth, induce lipid peroxidation and PPARα, and decrease TNFα in human lung cancer cells A427.•Culture medium conditioned by lung cancer cells in the absence of PUFAs blocks myoblast differentiation.•Culture medium conditioned by lung cancer cells in the presence of PUFAs allows myoblast differentiation.•HHE and HNE added to myoblasts stimulate myosin production and myotube formation.•Genes encoding the fast isoforms of myosin heavy chain contain consensus sequences for PPARs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We present the first determination of the energy-dependent amplitudes of N⁎ resonances extracted from their decay in KΛ pairs in p+p→pK+Λ reactions. A combined Partial Wave Analysis of seven data ...samples with exclusively reconstructed p+p→pK+Λ events measured by the COSY-TOF, DISTO, FOPI and HADES Collaborations in fixed target experiments at kinetic energies between 2.14 to 3.5 GeV is used to determine the amplitude of the resonant and non-resonant contributions into the associated strangeness final state. The contribution of seven N⁎ resonances with masses between 1650 MeV/c2 and 1900 MeV/c2 for an excess energy between 0 and 600 MeV has been considered. The Σ–p cusp and final state interactions for the p–Λ channel are also included as coherent contributions in the PWA. The N⁎ contribution is found to be dominant with respect to the phase space emission of the pKΛ+ final state at all energies demonstrating the important role played by both N⁎ and interference effects in hadron–hadron collisions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP