Inducing cancer indolence by targeting mitochondrial Complex I is potentiated by blocking macrophage-mediated adaptive responses

Converting carcinomas in benign oncocytomas has been suggested as a potential anti-cancerstrategy. One of the oncocytoma hallmarks is the lack of respiratory complex I (CI). Herewe use genetic ablation of this enzyme to induce indolence in two cancer types, andshow this is reversed by allowing the stabilization of Hypoxia Inducible Factor-1 alpha(HIF-1α). We further show that on the long run CI-deficient tumors re-adapt to their inabilityto respond to hypoxia, concordantly with the persistence of human oncocytomas. Wedemonstrate that CI-deficient tumors survive and carry out angiogenesis, despite theirinability to stabilize HIF-1α. Such adaptive response is mediated by tumor associated mac-rophages, whose blockage improves the effect of CI ablation. Additionally, the simultaneouspharmacological inhibition of CI function through metformin and macrophage infiltrationthrough PLX-3397 impairs tumor growth in vivo in a synergistic manner, setting the basisfor an efficient combinatorial adjuvant therapy in clinical trials

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

A New Method for the Validation of Ultraviolet Reactors by Means of Photochromic Materials

The interest in ultraviolet (UV) water sterilization has grown significantly in recent years. The main difficulty in designing a UV reactor is to assess the UV dose delivered. In fact, that dose depends on both the UV radiation field and the flow distribution within the reactor. At the design phase, computational fluid dynamics (CFD) helps to predict the UV dose distribution, but it requires a validation; nowadays, such validation is possible only using the Lagrangian actinometry method. This promising technique, however, requires a complex and expensive equipment, which makes it difficult to apply in most of the real contexts. The purpose of this work is to develop a new method to make the validation of the UV reactor performance a faster, less expensive, and more sustainable procedure. To this aim, we used two photochromic materials, sensitive to the UV-C radiation. Each material has been characterized by relating its color variation with the absorbed UV dose. Samples of such materials, in some cases stuck on supports characterized by different densities, were then inserted within a pilot UV reactor under three different flow rates, to measure the dose distributions. These latter were then compared with the results obtained by the CFD simulations performed on the same reactor geometry, and by biodosimetry analyses. The best results, both in terms of average value and distributions of the UV dose, were obtained from the photochromic amorphous polypropylene samples, having a density similar to that of water. This method emerges then as a promising validation technique, able also to assess the dose distribution of a UV reactor

A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest

Assigning a pathogenic role to mitochondrial DNA (mtDNA) variants and unveiling the potential involvement of the mitochondrial genome in diseases are challenging tasks in human medicine. Assuming that rare variants are more likely to be damaging, we designed a phylogeny-based prioritization workflow to obtain a reliable pool of candidate variants for further investigations. The prioritization workflow relies on an exhaustive functional annotation through the mtDNA extraction pipeline MToolBox and includes Macro Haplogroup Consensus Sequences to filter out fixed evolutionary variants and report rare or private variants, the nucleotide variability as reported in HmtDB and the disease score based on several predictors of pathogenicity for non-synonymous variants. Cutoffs for both the disease score as well as for the nucleotide variability index were established with the aim to discriminate sequence variants contributing to defective phenotypes. The workflow was validated on mitochondrial sequences from Leber’s Hereditary Optic Neuropathy affected individuals, successfully identifying 23 variants including the majority of the known causative ones. The application of the prioritization workflow to cancer datasets allowed to trim down the number of candidate for subsequent functional analyses, unveiling among these a high percentage of somatic variants. Prioritization criteria were implemented in both standalone (http://sourceforge.net/projects/mtoolbox/) and web version (https://mseqdr.org/mtoolbox.php) of MToolBox

A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest

Assigning a pathogenic role to mitochondrial DNA (mtDNA) variants and unveiling the potential involvement of the mitochondrial genome in diseases are challenging tasks in human medicine. Assuming that rare variants are more likely to be damaging, we designed a phylogeny-based prioritization workflow to obtain a reliable pool of candidate variants for further investigations. The prioritization workflow relies on an exhaustive functional annotation through the mtDNA extraction pipeline MToolBox and includes Macro Haplogroup Consensus Sequences to filter out fixed evolutionary variants and report rare or private variants, the nucleotide variability as reported in HmtDB and the disease score based on several predictors of pathogenicity for non-synonymous variants. Cutoffs for both the disease score as well as for the nucleotide variability index were established with the aim to discriminate sequence variants contributing to defective phenotypes. The workflow was validated on mitochondrial sequences from Leber's Hereditary Optic Neuropathy affected individuals, successfully identifying 23 variants including the majority of the known causative ones. The application of the prioritization workflow to cancer datasets allowed to trim down the number of candidate for subsequent functional analyses, unveiling among these a high percentage of somatic variants. Prioritization criteria were implemented in both standalone ( http://sourceforge.net/projects/mtoolbox/ ) and web version ( https://mseqdr.org/mtoolbox.php ) of MToolBox

mtDNA mutations in cancer

Mitochondrial DNA (mtDNA) mutations have been described in virtually all cancer types. However, due to the peculiarities of mitochondrial genetics and cancer heterogeneity, it has been difficult to assess their role in tumorigenesis and cancer progression. The advent of massive sequencing and large public data repositories are allowing to gain insight about the evolution of mtDNA variants and somewhat predict their functional effects. Here, the current knowledge of mtDNA mutation landscape in cancer is described, which generally implies to a negative selection of severely pathogenic lesions. The interplay between mtDNA mutations and different stages of progressing solid tumors is discussed, together with the potential of mtDNA variants to be used as diagnostic markers in certain cancer contexts