BACKGROUND: Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.RESULTS: First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.CONCLUSIONS: The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving. <<<

BACKGROUND: Lymphocytic leukemia (LL) is a primary malignant tumor of hematopoietic tissue, which seriously affects the health of children and the elderly. The study aims to establish a new detection method for screening acute/chronic LL using time-resolved fluorescence immunoassay (TRFIA) via quantitative detection of S100 calcium binding protein A8 (S100A8) and leucine-rich alpha-2-glycoprotein 1 (LRG1) in serum.METHODS: Here a sandwich TRFIA was optimized and established: Anti-S100A8/LRG1 caputre antibodies immobilized on 96-well plates captured S100A8/LRG1, and then banded together with the anti-S100A8/LRG1 detection antibodies labeled with Europium(III) (Eu3+)/samarium(III) (Sm3+) chelates. Finally time resolved fluorometry measured the fluorescence intensity.RESULTS: The sensitivity of S100A8 was 1.15 ng/mL(LogY = 3.4027 + 0.4091 × LogX, R2 = 0.9828, P < 0.001, dynamic range: 2.1-10,000 ng/mL), and 3.2 ng/mL for LRG1 (LogY = 3.3009 + 0.4082 × LogX, R2 = 0.9748, P < 0.001, dynamic range: 4.0-10,000 ng/mL). The intra-assay and inter-assay CVs were low, ranging from 5.75% to 8.23% for S100A8 and 5.30% to 9.45% for LRG1 with high specificity and affinity in serum samples. Bland-Altman plots indicated TRFIA and ELISA kits have good agreement in clinical serum samples. Additionally, the cutoff values for S100A8 and LRG1 were 1849.18 ng/mL and 588.08 ng/mL, respectively.CONCLUSION: The present TRFIA method could be used for the quantitative detection of S100A8 and LRG1 in serum, and it has high sensitivity, accuracy and specificity. Clinically, this TRFIA method could be suitable for screening of LL via the quantitative detection of S100A8 and LRG1. <<<

Children with genetic diseases endure a prolonged and costly "diagnostic odyssey." The use of whole exome sequencing (WES) and whole genome sequencing (WGS) has improved the diagnosis rate, ending the odyssey. However, the additional costs associated WES/WGS has impeded their adoption in Asian settings. We aim to estimate the expected change to the mean number of diagnostic tests used, and the associated costs from a decision to use WES early in the diagnostic pathways of pediatric phenotypes, as compared to Existing Practice. Retrospective data from a patient cohort recruited under the Singapore Undiagnosed Disease Program from a tertiary hospital in Singapore, for the period October 2004 to September 2020, was analyzed. Four phenotype-specific subgroups were used: multiple congenital anomalies (MCA) without developmental delay; global developmental delay (GDD); neuromuscular disorder (NMD) and primary immunodeficiency disorder (PID). Patients had undergone a traditional diagnostic pathway and received a diagnosis either through clinical exome or WES or WGS. A costs only analysis was performed, by tabulating the outcomes "test quantity" and "test costs" incurred by patients. The outcomes were compared with alternate diagnostic pathways which incorporates the early introduction of WES trio testing. To include uncertainty in cost outcomes, simulation studies were done on uncertain parameters. Cost outcomes are reported in Singapore dollars (S$). The 92 included patients had MCA (n = 48), GDD (n = 29), NMD (n = 10), or PID (n = 5). Patients were aged between 18 days and 26 years, 52.2% were males. The majority were of Chinese ethnicity (81.5%). If patients had access to WES directly, test quantity reduced by 97.38% for MCA, 96.98% for GDD, 96.56% for NMD, and 99.84% for PID. The expected cost savings per patient were $5940 for MCA (US$4433), $5342 for GDD (US$3986), $4622 for NMD (US$3449), and $58,497 for PID (US$43,654). Uncertainty assessment for MCA and GDD patients showed a respective likelihood of 86.9% and 97.4% for cost savings. Adoption of alternate diagnostic pathways with early WES in selected pediatric subgroups are likelt to reduce costs, when compared to Existing Practice. Benefits arising from earlier diagnosis, and the potential cost savings could mitigate the large initial cost of implementing WES in Asian settings. <<<

Zhi-Wei Guo , Ke Wang , Xiang Huang , Kun Li , Guojun Ouyang , Xu Yang , Jiayu Tan , Haihong Shi , Liangping Luo , Xincai Zhang , Min Zhang , Bo-Wei Han , Xiangming Zhai , Yingsong Wu , Fang Yang , Xue-Xi Yang , Jia Tang <<<

Preterm birth (PTB) occurs in around 11% of all births worldwide, resulting in significant morbidity and mortality for both mothers and offspring. Identification of pregnancies at risk of preterm birth in early pregnancy may help improve intervention and reduce its incidence. However, there exist few methods for PTB prediction developed with large sample size, high throughput screening and validation in independent cohorts. Here, we established a large scale, multi center, and case control study that included 2,590 pregnancies (2,072 full term and 518 preterm pregnancies) from three independent hospitals to develop a preterm birth classifier. We implemented whole genome sequencing on their plasma cfDNA and then their promoter profiling (read depth spanning from -1 KB to +1 KB around the transcriptional start site) was analyzed. Using three machine learning models and two feature selection algorithms, classifiers for predicting preterm delivery were developed. Among them, a classifier based on the support vector machine model and backward algorithm, named PTerm (Promoter profiling classifier for preterm prediction), exhibited the largest AUC value of 0.878 (0.852-0.904) following LOOCV cross validation. More importantly, PTerm exhibited good performance in three independent validation cohorts and achieved an overall AUC of 0.849 (0.831-0.866). Taken together, PTerm could be based on current noninvasive prenatal test (NIPT) data without changing its procedure or adding detection cost, which can be easily adapted for preclinical tests. <<<

Ja Hye Kim, Shinwon Hwang, Hyeonju Son, Dongsun Kim, Il Bin Kim, Myeong-Heui Kim, Nam Suk Sim, Dong Seok Kim, Yoo-Jin Ha, Junehawk Lee, Hoon-Chul Kang, Jeong Ho Lee, Sangwoo Kim <<<

Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues depending on the developmental stage and affected organs. However, the effect of human developmental stages or mutations of different organs on the features of somatic mutations is still unclear. Here, we performed a systemic and comprehensive analysis of low-level somatic mutations using deep whole-exome sequencing (average read depth ~500×) of 498 multiple organ tissues with matched controls from 190 individuals. Our results showed that early clone-forming mutations shared between multiple organs were lower in number but showed higher allele frequencies than late clone-forming mutations [0.54 vs. 5.83 variants per individual; 6.17% vs. 1.5% variant allele frequency (VAF)] along with less nonsynonymous mutations and lower functional impacts. Additionally, early and late clone-forming mutations had unique mutational signatures that were distinct from mutations that originated from tumors. Compared with early clone-forming mutations that showed a clock-like signature across all organs or tissues studied, late clone-forming mutations showed organ, tissue, and cell-type specificity in the mutation counts, VAFs, and mutational signatures. In particular, analysis of brain somatic mutations showed a bimodal occurrence and temporal-lobe-specific signature. These findings provide new insights into the features of somatic mosaicism that are dependent on developmental stage and brain regions. <<<

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand. <<<

Long-read sequencing (LRS) has been around for more than a decade, but widespread adoption of the technology has been slow due to the perceived high error rates and high sequencing cost. This is changing due to the recent advancements to produce highly accurate sequences and the reducing costs. LRS promises significant improvement over short read sequencing in four major areas: (1) better detection of structural variation (2) better resolution of highly repetitive or nonunique regions (3) accurate long-range haplotype phasing and (4) the detection of base modifications natively from the sequencing data. Several successful applications of LRS have demonstrated its ability to resolve molecular diagnoses where short-read sequencing fails to identify a cause. However, the argument for increased diagnostic yield from LRS remains to be validated. Larger cohort studies may be required to establish the realistic boundaries of LRS's clinical utility and analytical validity, as well as the development of standards for clinical applications. We discuss the limitations of the current standard of care, and contrast with the applications and advantages of two major LRS platforms, PacBio and Oxford Nanopore, for molecular diagnostics of constitutional disorders, and present a critical argument about the potential of LRS in diagnostic settings. <<<

Cancer subtypes identification is one of the critical steps toward advancing personalized anti-cancerous therapies. Accumulation of a massive amount of multi-platform omics data measured across the same set of samples provides an opportunity to look into this deadly disease from several views simultaneously. Few integrative clustering approaches are developed to capture shared information from all the views to identify cancer subtypes. However, they have certain limitations. The challenge here is identifying the most relevant feature space from each omic view and systematically integrating them. Both the steps should lead toward a global clustering solution with biological significance. In this respect, a novel multi-omics clustering algorithm named RISynG (Recursive Integration of Synergised Graph-representations) is presented in this study. RISynG represents each omic view as two representation matrices that are Gramian and Laplacian. A parameterised combination function is defined to obtain a synergy matrix from these representation matrices. Then a recursive multi-kernel approach is applied to integrate the most relevant, shared, and complementary information captured via the respective synergy matrices. At last, clustering is applied to the integrated subspace. RISynG is benchmarked on five multi-omics cancer datasets taken from The Cancer Genome Atlas. The experimental results demonstrate RISynG's efficiency over the other approaches in this domain. <<<

Gene transcription is a highly regulated process in all animals. In Drosophila, two major transcriptional programs, housekeeping and developmental, have promoters with distinct regulatory compatibilities and nucleosome organization. However, it remains unclear how the differences in chromatin structure relate to the distinct regulatory properties and which chromatin remodelers are required for these programs. Using rapid degradation of core remodeler subunits in Drosophila melanogaster S2 cells, we demonstrate that developmental gene transcription requires SWI/SNF-type complexes, primarily to maintain distal enhancer accessibility. In contrast, wild-type-level housekeeping gene transcription requires the Iswi and Ino80 remodelers to maintain nucleosome positioning and phasing at promoters. These differential remodeler dependencies relate to different DNA-sequence-intrinsic nucleosome affinities, which favor a default ON state for housekeeping but a default OFF state for developmental gene transcription. Overall, our results demonstrate how different transcription-regulatory strategies are implemented by DNA sequence, chromatin structure, and remodeler activity. <<<

Jeffrey K Ng, Pankaj Vats, Elyn Fritz-Waters, Stephanie Sarkar, Eleanor I Sams, Evin M Padhi, Zachary L Payne, Shawn Leonard, Marc A West, Chandler Prince, Lee Trani, Marshall Jansen, George Vacek, Mehrzad Samadi, Timothy T Harkins, Craig Pohl, Tychele N Turner <<<

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. To accelerate DNV calling, we developed a graphics processing units-based workflow. We applied our workflow to whole-genome sequencing data from three parent-child sequenced cohorts including the Simons Simplex Collection (SSC), Simons Foundation Powering Autism Research (SPARK), and the 1000 Genomes Project (1000G) that were sequenced using DNA from blood, saliva, and lymphoblastoid cell lines (LCLs), respectively. The SSC and SPARK DNV callsets were within expectations for number of DNVs, percent at CpG sites, phasing to the paternal chromosome of origin, and average allele balance. However, the 1000G DNV callset was not within expectations and contained excessive DNVs that are likely cell line artifacts. Mutation signature analysis revealed 30% of 1000G DNV signatures matched B-cell lymphoma. Furthermore, we found variants in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites and significant excess of protein-coding DNVs in IGLL5; a gene known to be involved in B-cell lymphomas. Our study provides a new rapid DNV caller for the field and elucidates important implications of using sequencing data from LCLs for reference building and disease-related projects. <<<

Xiao Han, Xuanye Cao, Vanessa Aguiar-Pulido, Wei Yang, Menuka Karki, Paula Andrea Pimienta Ramirez, Robert M Cabrera, Ying Linda Lin, Bogdan J Wlodarczyk, Gary M Shaw, M Elizabeth Ross, Cuilian Zhang, Richard H Finnell, Yunping Lei <<<

Neural tube defects (NTDs) are congenital malformations resulting from abnormal embryonic development of the brain, spine, or spinal column. The genetic etiology of human NTDs remains poorly understood despite intensive investigation. CIC, homolog of the Capicua transcription repressor, has been reported to interact with ataxin-1 (ATXN1) and participate in the pathogenesis of spinocerebellar ataxia type 1. Our previous study demonstrated that CIC loss of function (LoF) variants contributed to the cerebral folate deficiency syndrome by downregulating folate receptor 1 (FOLR1) expression. Given the importance of folate transport in neural tube formation, we hypothesized that CIC variants could contribute to increased risk for NTDs by depressing embryonic folate concentrations. In this study, we examined CIC variants from whole-genome sequencing (WGS) data of 140 isolated spina bifida cases and identified eight missense variants of CIC gene. We tested the pathogenicity of the observed variants through multiple in vitro experiments. We determined that CIC variants decreased the FOLR1 protein level and planar cell polarity (PCP) pathway signaling in a human cell line (HeLa). In a murine cell line (NIH3T3), CIC loss of function variants downregulated PCP signaling. Taken together, this study provides evidence supporting CIC as a risk gene for human NTD. <<<

INTRODUCTION: In 2020, prior to COVID-19 vaccine rollout, the Brighton Collaboration created a priority list, endorsed by the World Health Organization, of potential adverse events relevant to COVID-19 vaccines. We adapted the Brighton Collaboration list to evaluate serious adverse events of special interest observed in mRNA COVID-19 vaccine trials.METHODS: Secondary analysis of serious adverse events reported in the placebo-controlled, phase III randomized clinical trials of Pfizer and Moderna mRNA COVID-19 vaccines in adults (NCT04368728 and NCT04470427), focusing analysis on Brighton Collaboration adverse events of special interest.RESULTS: Pfizer and Moderna mRNA COVID-19 vaccines were associated with an excess risk of serious adverse events of special interest of 10.1 and 15.1 per 10,000 vaccinated over placebo baselines of 17.6 and 42.2 (95 % CI -0.4 to 20.6 and -3.6 to 33.8), respectively. Combined, the mRNA vaccines were associated with an excess risk of serious adverse events of special interest of 12.5 per 10,000 vaccinated (95 % CI 2.1 to 22.9); risk ratio 1.43 (95 % CI 1.07 to 1.92). The Pfizer trial exhibited a 36 % higher risk of serious adverse events in the vaccine group; risk difference 18.0 per 10,000 vaccinated (95 % CI 1.2 to 34.9); risk ratio 1.36 (95 % CI 1.02 to 1.83). The Moderna trial exhibited a 6 % higher risk of serious adverse events in the vaccine group: risk difference 7.1 per 10,000 (95 % CI -23.2 to 37.4); risk ratio 1.06 (95 % CI 0.84 to 1.33). Combined, there was a 16 % higher risk of serious adverse events in mRNA vaccine recipients: risk difference 13.2 (95 % CI -3.2 to 29.6); risk ratio 1.16 (95 % CI 0.97 to 1.39).DISCUSSION: The excess risk of serious adverse events found in our study points to the need for formal harm-benefit analyses, particularly those that are stratified according to risk of serious COVID-19 outcomes. These analyses will require public release of participant level datasets. <<<

Motivation: Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another, and vice versa. Results: To understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with an arbitrary distribution of reading frames. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias, and the distribution of selection biases (dN/dS) in the respective reading frames. Availability and implementation: Our simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License (GPL) version 3, and is available at https://github.com/PoonLab/HexSE. <<<

In the evolutionary model of dosage compensation, per-allele expression level of the X chromosome has been proposed to have twofold up-regulation to compensate its dose reduction in males (XY) compared to females (XX). However, the expression regulation of X-linked genes is still controversial, and comprehensive evaluations are still lacking. By integrating multi-omics datasets in mammals, we investigated the expression ratios including X to autosomes (X:AA ratio) and X to orthologs (X:XX ratio) at the transcriptome, translatome, and proteome levels. We revealed a dynamic spatial-temporal X:AA ratio during development in humans and mice. Meanwhile, by tracing the evolution of orthologous gene expression in chickens, platypuses, and opossums, we found a stable expression ratio of X-linked genes in humans to their autosomal orthologs in other species (X:XX ≈ 1) across tissues and developmental stages, demonstrating stable dosage compensation in mammals. We also found that different epigenetic regulations contributed to the high tissue specificity and stage specificity of X-linked gene expression, thus affecting X:AA ratios. It could be concluded that the dynamics of X:AA ratios were attributed to the different gene contents and expression preferences of the X chromosome, rather than the stable dosage compensation. <<<

Graham Scott Erwin , Gamze Gursoy , Rashid Al-Abri , Ashwini Suriyaprakash , Egor Dolzhenko , Kevin Zhu , Christian R Hoerner , Shannon M White , Lucia Ramirez , Ananya Vadlakonda , Alekhya Vadlakonda , Konor von Kraut , Julia Park , Charlotte M Brannon , Daniel A Sumano , Raushun A Kirtikar , Alicia A Erwin , Thomas J Metzner , Ryan K. C. Yuen , Alice C Fan , John T Leppert , Michael A Eberle , Mark Gerstein , Michael P Snyder <<<

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs (STRs), a phenomenon termed microsatellite instability (MSI); however larger repeat expansions have not been systematically analyzed in cancer. Here, we identified TR expansions in 2,622 cancer genomes, spanning 29 cancer types. In 7 cancer types, we found 160 recurrent repeat expansions (rREs); most of these (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with an enrichment near candidate cis-regulatory elements, suggesting a role in gene regulation. One rRE located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, targeting cells harboring this rRE with a rationally designed, sequence-specific DNA binder led to a dose-dependent decrease in cell proliferation. Overall, our results demonstrate that rREs are an important but unexplored source of genetic variation in human cancers, and we provide a comprehensive catalog for further study. <<<

BACKGROUND: Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations.RESULTS: Our proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets.CONCLUSIONS: Our model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions. <<<

Konrad J Karczewski, Matthew Solomonson, Katherine R Chao, Julia K Goodrich, Grace Tiao, Wenhan Lu, Bridget M Riley-Gillis, Ellen A Tsai, Hye In Kim, Xiuwen Zheng, Fedik Rahimov, Sahar Esmaeeli, A Jason Grundstad, Mark Reppell, Jeff Waring, Howard Jacob, David Sexton, Paola G Bronson, Xing Chen, Xinli Hu, Jacqueline I Goldstein, Daniel King, Christopher Vittal, Timothy Poterba, Duncan S Palmer, Claire Churchhouse, Daniel P Howrigan, Wei Zhou, Nicholas A Watts, Kevin Nguyen, Huy Nguyen, Cara Mason, Christopher Farnham, Charlotte Tolonen, Laura D Gauthier, Namrata Gupta, Daniel G MacArthur, Heidi L Rehm, Cotton Seed, Anthony A Philippakis, Mark J Daly, J Wade Davis, Heiko Runz, Melissa R Miller, Benjamin M Neale <<<

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results. <<<

Mitchell E Fane, Yash Chhabra, Gretchen M Alicea, Devon A Maranto, Stephen M Douglass, Marie R Webster, Vito W Rebecca, Gloria E Marino, Filipe Almeida, Brett L Ecker, Daniel J Zabransky, Laura Hüser, Thomas Beer, Hsin-Yao Tang, Andrew Kossenkov, Meenhard Herlyn, David W Speicher, Wei Xu, Xiaowei Xu, Elizabeth M Jaffee, Julio A Aguirre-Ghiso, Ashani T Weeraratna <<<

Disseminated cancer cells from primary tumours can seed in distal tissues, but may take several years to form overt metastases, a phenomenon that is termed tumour dormancy. Despite its importance in metastasis and residual disease, few studies have been able to successfully characterize dormancy within melanoma. Here we show that the aged lung microenvironment facilitates a permissive niche for efficient outgrowth of dormant disseminated cancer cells-in contrast to the aged skin, in which age-related changes suppress melanoma growth but drive dissemination. These microenvironmental complexities can be explained by the phenotype switching model, which argues that melanoma cells switch between a proliferative cell state and a slower-cycling, invasive state. It was previously shown that dermal fibroblasts promote phenotype switching in melanoma during ageing. We now identify WNT5A as an activator of dormancy in melanoma disseminated cancer cells within the lung, which initially enables the efficient dissemination and seeding of melanoma cells in metastatic niches. Age-induced reprogramming of lung fibroblasts increases their secretion of the soluble WNT antagonist sFRP1, which inhibits WNT5A in melanoma cells and thereby enables efficient metastatic outgrowth. We also identify the tyrosine kinase receptors AXL and MER as promoting a dormancy-to-reactivation axis within melanoma cells. Overall, we find that age-induced changes in distal metastatic microenvironments promote the efficient reactivation of dormant melanoma cells in the lung. <<<

Daniel Bottomly, Nicola Long, Anna Reister Schultz, Stephen E Kurtz, Cristina E Tognon, Kara Johnson, Melissa Abel, Anupriya Agarwal, Sammantha Avaylon, Erik Benton, Aurora Blucher, Uma Borate, Theodore P Braun, Jordana Brown, Jade Bryant, Russell Burke, Amy Carlos, Bill H Chang, Hyun Jun Cho, Stephen Christy, Cody Coblentz, Aaron M Cohen, Amanda d'Almeida, Rachel Cook, Alexey Danilov, Kim-Hien T Dao, Michie Degnin, James Dibb, Christopher A Eide, Isabel English, Stuart Hagler, Heath Harrelson, Rachel Henson, Hibery Ho, Sunil K Joshi, Brian Junio, Andy Kaempf, Yoko Kosaka, Ted Laderas, Matt Lawhead, Hyunjung Lee, Jessica T Leonard, Chenwei Lin, Evan F Lind, Selina Qiuying Liu, Pierrette Lo, Marc M Loriaux, Samuel Luty, Julia E Maxson, Tara Macey, Jacqueline Martinez, Jessica Minnier, Andrea Monteblanco, Motomi Mori, Quinlan Morrow, Dylan Nelson, Justin Ramsdill, Angela Rofelty, Alexandra Rogers, Kyle A Romine, Peter Ryabinin, Jennifer N Saultz, David A Sampson, Samantha L Savage, Robert Schuff, Robert Searles, Rebecca L Smith, Stephen E Spurgeon, Tyler Sweeney, Ronan T Swords, Aashis Thapa, Karina Thiel-Klare, Elie Traer, Jake Wagner, Beth Wilmot, Joelle Wolf, Guanming Wu, Amy Yates, Haijiao Zhang, Christopher R Cogle, Robert H Collins, Michael W Deininger, Christopher S Hourigan, Craig T Jordan, Tara L Lin, Micaela E Martinez, Rachel R Pallapati, Daniel A Pollyea, Anthony D Pomicter, Justin M Watts, Scott J Weir, Brian J Druker, Shannon K McWeeney, Jeffrey W Tyner <<<

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. <<<

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone. <<<