The Americas were the last inhabitable continents to be occupied by humans, with a growing multidisciplinary consensus for entry 15–25 thousand years ago (kya) from northeast Asia via the former ...Beringia land bridge 1–4. Autosomal DNA analyses have dated the separation of Native American ancestors from the Asian gene pool to 23 kya or later 5, 6 and mtDNA analyses to ∼25 kya 7, followed by isolation (“Beringian Standstill” 8, 9) for 2.4–9 ky and then a rapid expansion throughout the Americas. Here, we present a calibrated sequence-based analysis of 222 Native American and relevant Eurasian Y chromosomes (24 new) from haplogroups Q and C 10, with four major conclusions. First, we identify three to four independent lineages as autochthonous and likely founders: the major Q-M3 and rarer Q-CTS1780 present throughout the Americas, the very rare C3-MPB373 in South America, and possibly the C3-P39/Z30536 in North America. Second, from the divergence times and Eurasian/American distribution of lineages, we estimate a Beringian Standstill duration of 2.7 ky or 4.6 ky, according to alternative models, and entry south of the ice sheet after 19.5 kya. Third, we describe the star-like expansion of Q-M848 (within Q-M3) starting at 15 kya 11 in the Americas, followed by establishment of substantial spatial structure in South America by 12 kya. Fourth, the deep branches of the Q-CTS1780 lineage present at low frequencies throughout the Americas today 12 may reflect a separate out-of-Beringia dispersal after the melting of the glaciers at the end of the Pleistocene.
•We sequenced 20 Native American Y chromosomes chosen for their genetic diversity•A Beringian Standstill of <4,600 years led to both Siberian and American Y-lineages•Y-lineage split times rule out occupation of the Americas before 19,500 years ago•Present-day male population structure in South America arose before 12,000 years ago
Pinotti et al. provide a genetic analysis of male history in the Americas that reveals three or four founding lineages, an occupation of Beringia for no longer than 4,600 years, entry south of the ice sheets after 19,500 years ago, and the establishment of the present-day male population structure in South America by 12,000 years ago.
Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) ...structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 μM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.
Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to ...discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson's disease.
In this paper we propose a structure-based methodology, which is extensively validated, for the discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves molecular docking studies against both receptors and the evaluation of different scoring functions fusion strategies for maximizing the initial virtual screening enrichment of known dual ligands.
The developed methodology provides high values of enrichment of known ligands, which outperform that of the individual scoring functions. At the same time, the obtained ensemble can be translated in a sequence of steps that should be followed to maximize the enrichment of dual target Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors.
Information relative to docking scores to both targets have to be combined for achieving high dual ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable strategy for improving the enrichment relative to single scoring function in virtual screening experiments.
Background: In the context of the current drug discovery efforts to find disease modifying
therapies for Parkinson's disease (PD) the current single target strategy has proved inefficient.
...Consequently, the search for multi-potent agents is attracting more and more attention due to the
multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the
monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a
promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical
scaffolds has been proposed as potential dual MAO-B inhibitors/A2AAR antagonists (caffeine
derivatives and benzothiazinones).
Methods: In this study, we conduct a series of chemoinformatics analysis in order to evaluate and
advance the potential of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold.
Results: The information provided by SAR data mining analysis based on network similarity graphs
and molecular docking studies support the suitability of the chromone nucleus as a potential MAOB/
A2AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion
similarity search approach was developed for the prioritization of potential MAO-B/A2AAR dual
binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK
GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library
potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and
sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches.
Conclusion: The information and tools provided in this work represent valuable decision making
elements in the search of novel chromone derivatives with a favorable dual binding profile as MAOB
inhibitors and A2AAR antagonists with the potential to act as a disease-modifying therapeutic for
Parkinson's disease.
In this work we report the first attempt to study the effect of activity cliffs over the generalization ability of machine learning (ML) based QSAR classifiers, using as study case a previously ...reported diverse and noisy dataset focused on drug induced liver injury (DILI) and more than 40 ML classification algorithms. Here, the hypothesis of structure-activity relationship (SAR) continuity restoration by activity cliffs removal is tested as a potential solution to overcome such limitation. Previously, a parallelism was established between activity cliffs generators (ACGs) and instances that should be misclassified (ISMs), a related concept from the field of machine learning. Based on this concept we comparatively studied the classification performance of multiple machine learning classifiers as well as the consensus classifier derived from predictive classifiers obtained from training sets including or excluding ACGs. The influence of the removal of ACGs from the training set over the virtual screening performance was also studied for the respective consensus classifiers algorithms. In general terms, the removal of the ACGs from the training process slightly decreased the overall accuracy of the ML classifiers and multi-classifiers, improving their sensitivity (the weakest feature of ML classifiers trained with ACGs) but decreasing their specificity. Although these results do not support a positive effect of the removal of ACGs over the classification performance of ML classifiers, the "balancing effect" of ACG removal demonstrated to positively influence the virtual screening performance of multi-classifiers based on valid base ML classifiers. Specially, the early recognition ability was significantly favored after ACGs removal. The results presented and discussed in this work represent the first step towards the application of a remedial solution to the activity cliffs problem in QSAR studies.
The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which ...represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches.
A consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks.
The consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools.
The proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics.
Many single-nucleotide polymorphisms (SNPs) in the non-recombining region of the human Y chromosome have been described in the last decade. High-coverage sequencing has helped to characterize new ...SNPs, which has in turn increased the level of detail in paternal phylogenies. However, these paternal lineages still provide insufficient information on population history and demography, especially for Native Americans. The present study aimed to identify informative paternal sublineages derived from the main founder lineage of the Americas-haplogroup Q-L54-in a sample of 1841 native South Americans. For this purpose, we used a Y-chromosomal genotyping multiplex platform and conventional genotyping methods to validate 34 new SNPs that were identified in the present study by sequencing, together with many Y-SNPs previously described in the literature. We updated the haplogroup Q phylogeny and identified two new Q-M3 and three new Q-L54*(xM3) sublineages defined by five informative SNPs, designated SA04, SA05, SA02, SA03 and SA29. Within the Q-M3, sublineage Q-SA04 was mostly found in individuals from ethnic groups belonging to the Tukanoan linguistic family in the northwest Amazon, whereas sublineage Q-SA05 was found in Peruvian and Bolivian Amazon ethnic groups. Within Q-L54*, the derived sublineages Q-SA03 and Q-SA02 were exclusively found among Coyaima individuals (Cariban linguistic family) from Colombia, while Q-SA29 was found only in Maxacali individuals (Jean linguistic family) from southeast Brazil. Furthermore, we validated the usefulness of several published SNPs among indigenous South Americans. This new Y chromosome haplogroup Q phylogeny offers an informative paternal genealogy to investigate the pre-Columbian history of South America.Journal of Human Genetics advance online publication, 31 March 2016; doi:10.1038/jhg.2016.26.
Over the past decades, consistent studies have shown that race/ethnicity have a great impact on cancer incidence, survival, drug response, molecular pathways and epigenetics. Despite the influence of ...race/ethnicity in cancer outcomes and its impact in health care quality, a comprehensive understanding of racial/ethnic inclusion in oncological research has never been addressed. We therefore explored the racial/ethnic composition of samples/individuals included in fundamental (patient-derived oncological models, biobanks and genomics) and applied cancer research studies (clinical trials). Regarding patient-derived oncological models (n = 794), 48.3% have no records on their donor's race/ethnicity, the rest were isolated from White (37.5%), Asian (10%), African American (3.8%) and Hispanic (0.4%) donors. Biobanks (n = 8,293) hold specimens from unknown (24.56%), White (59.03%), African American (11.05%), Asian (4.12%) and other individuals (1.24%). Genomic projects (n = 6,765,447) include samples from unknown (0.6%), White (91.1%), Asian (5.6%), African American (1.7%), Hispanic (0.5%) and other populations (0.5%). Concerning clinical trials (n = 89,212), no racial/ethnic registries were found in 66.95% of participants, and records were mainly obtained from Whites (25.94%), Asians (4.97%), African Americans (1.08%), Hispanics (0.16%) and other minorities (0.9%). Thus, two tendencies were observed across oncological studies: lack of racial/ethnic information and overrepresentation of Caucasian/White samples/individuals. These results clearly indicate a need to diversify oncological studies to other populations along with novel strategies to enhanced race/ethnicity data recording and reporting.
Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying ...new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.
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