13273 research outputs found
Sort by
Genetic ancestry inference from cancer-derived molecular data across genomic and transcriptomic platforms
Get PDFGenetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry
Genomic consequences and selection efficacy in sympatric sexual versus asexual kelps
Get PDFGenetic diversity can influence resilience and adaptative capacity of organisms to environmental change. Genetic diversity within populations is largely structured by reproduction, with the prevalence of asexual versus sexual reproduction often underpinning important diversity metrics that determine selection efficacy. Asexual or clonal reproduction is expected to reduce genotypic diversity and slow down adaptation through reduced selection efficacy, yet the evolutionary consequences of clonal reproduction remain unclear for many natural populations. Here, we examine the genomic consequences of sympatric sexual (haplodiplontic) and clonal morphs of the kelp Ecklonia radiata that occur interspersed on reefs in Hamelin Bay, Western Australia. Using genome-wide single nucleotide polymorphisms, we confirm significant asexual reproduction for the clonal populations, indicated by a significantly lower number of multi-locus lineages and higher intra-individual diversity patterns (individual multi-locus heterozygosity, MLH). Nevertheless, co-ancestry analysis and breeding experiments confirmed that sexual reproduction by the clonal morph and interbreeding between the two morphs is still possible, but varies among populations. One clonal population with long-term asexuality showed trends of decreased selection efficacy (increased ratio non- vs. synonymous gene diversities). Yet, all clonal populations showed distinct patterns of putative local adaptation relative to the sexual morph, possibly indicating maladaptation to local environmental conditions and high vulnerability of this unique clonal morph to environmental stress
Diversity oriented clicking delivers β-substituted alkenyl sulfonyl fluorides as covalent human neutrophil elastase inhibitors
Get PDFDiversity Oriented Clicking (DOC) is a discovery method geared toward the rapid synthesis of functional libraries. It combines the best attributes of both classical and modern click chemistries. DOC strategies center upon the chemical diversification of core "SuFExable" hubs-exemplified by 2-Substituted-Alkynyl-1-Sulfonyl Fluorides (SASFs)-enabling the modular assembly of compounds through multiple reaction pathways. We report here a range of stereoselective Michael-type addition pathways from SASF hubs including reactions with secondary amines, carboxylates, 1H-1,2,3-triazole, and halides. These high yielding conjugate addition pathways deliver unprecedented β-substituted alkenyl sulfonyl fluorides as single isomers with minimal purification, greatly enriching the repertoire of DOC and holding true to the fundamentals of modular click chemistry. Further, we demonstrate the potential for biological function - a key objective of click chemistry - of this family of SASF-derived molecules as covalent inhibitors of human neutrophil elastase
Diversifying the genomic data science research community
Get PDFOver the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support
Excitatory and inhibitory D-serine binding to the NMDA receptor
Get PDFN-methyl-D-aspartate receptors (NMDARs) uniquely require binding of two different neurotransmitter agonists for synaptic transmission. D-serine and glycine bind to one subunit, GluN1, while glutamate binds to the other, GluN2. These agonists bind to the receptor's bi-lobed ligand-binding domains (LBDs), which close around the agonist during receptor activation. To better understand the unexplored mechanisms by which D-serine contributes to receptor activation, we performed multi-microsecond molecular dynamics simulations of the GluN1/GluN2A LBD dimer with free D-serine and glutamate agonists. Surprisingly, we observed D-serine binding to both GluN1 and GluN2A LBDs, suggesting that D-serine competes with glutamate for binding to GluN2A. This mechanism is confirmed by our electrophysiology experiments, which show that D-serine is indeed inhibitory at high concentrations. Although free energy calculations indicate that D-serine stabilizes the closed GluN2A LBD, its inhibitory behavior suggests that it either does not remain bound long enough or does not generate sufficient force for ion channel gating. We developed a workflow using pathway similarity analysis to identify groups of residues working together to promote binding. These conformation-dependent pathways were not significantly impacted by the presence of N-linked glycans, which act primarily by interacting with the LBD bottom lobe to stabilize the closed LBD
KMT2C deficiency promotes small cell lung cancer metastasis through DNMT3A-mediated epigenetic reprogramming
No full textSmall cell lung cancer (SCLC) is notorious for its early and frequent metastases, which contribute to it as a recalcitrant malignancy. To understand the molecular mechanisms underlying SCLC metastasis, we generated SCLC mouse models with orthotopically transplanted genome-edited lung organoids and performed multiomics analyses. We found that a deficiency of KMT2C, a histone H3 lysine 4 methyltransferase frequently mutated in extensive-stage SCLC, promoted multiple-organ metastases in mice. Metastatic and KMT2C-deficient SCLC displayed both histone and DNA hypomethylation. Mechanistically, KMT2C directly regulated the expression of DNMT3A, a de novo DNA methyltransferase, through histone methylation. Forced DNMT3A expression restrained metastasis of KMT2C-deficient SCLC through repressing metastasis-promoting MEIS/HOX genes. Further, S-(5'-adenosyl)-L-methionine, the common cofactor of histone and DNA methyltransferases, inhibited SCLC metastasis. Thus, our study revealed a concerted epigenetic reprogramming of KMT2C- and DNMT3A-mediated histone and DNA hypomethylation underlying SCLC metastasis, which suggested a potential epigenetic therapeutic vulnerability
Convergent behavioral strategies and neural computations during vocal turn-taking across diverse species
No full textVocal exchanges between individuals are often coordinated in a temporally precise manner: one party is vocalizing while the other one is listening until the performance roles are switched. This vocal turn-taking behavior is widespread across the animal kingdom and thus provides an opportunity to study the neural circuit mechanisms from a comparative perspective. Although the physical prerequisites of the vocal tracts across animals can be different, the behavioral outcome of turn-taking is often similar with respect to vocal response timing and context-dependent adaptation. Here we review behavioral strategies of vocal turn-taking in diverse animals. Further, we highlight recent advances in studying the neural circuit mechanisms underlying vocal production and perception
Genome-wide identification and analysis of prognostic features in human cancers
Get PDFClinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets. Instead, the strongest adverse biomarkers represent widely expressed cell-cycle and housekeeping genes, and, correspondingly, nearly all therapies directed against these features have failed in clinical trials. In total, our analysis establishes a rich resource for prognostic biomarker analysis and clarifies the use of patient survival data in preclinical cancer research and therapeutic development