bluuebunny/crossref_metadata_2025_split huggingface dataset:
NMR Hydrogen Exchange and Relaxation Reveal Positions Stabilized by p53 Rescue Mutants N239Y and N235K
Authors: Soto Jenaro , Moody Colleen , Alhoshani Ali , Sanchez-Bonilla Marilyn , Martinon Daisy , Cocco Melanie J 
Published: February 2021

AbstractInactivation of p53 is found in over 50% of all cancers; p53 disfunction is often caused by a single missense mutation localized in the DNA binding domain (DBD). Rescue mutants N235K and N239Y stabilize and restore function to multiple p53 cancer mutants. Here, we use NMR to compare protein dynamics between WT and rescue mutants to understand the mechanism of stabilization. We measured and compared folding dynamics by calculating protection factors (PFs) from NMR hydrogen exchange rates of backbone amides. We find that both rescue mutants impose a global stabilizing effect that dampens their motions compared to WT DBD, predominantly in the β-sandwich. However, a few regions become more flexible in rescue mutants. Notably, positions that have increased PFs map to cancer mutants rescued by each mutant. We also compared relaxation results to obtain flexibility information in the ps to ns timescale regime. Protein sequence analysis was used to determine the occurrence of these rescue mutants in nature and showed that 235K is found in mice and rats, but there is no evidence of 239Y occurring naturally in any species. Understanding the mechanism by which stabilizing mutants rescue p53 may reveal novel avenues for the development of cancer therapeutics. Our findings suggest that cancer therapeutics aimed at restoring p53 function could consider protein dynamics as a metric of drug efficacy.Statement of SignificanceTwo mutations (N235K and N239Y) within the DNA binding domain of p53 are known to reverse the effects of multiple cancer mutants. However, the mechanism of rescue is not clear since the crystal structures of these mutants are virtually identical to that of the WT. Here we use NMR methods to show that the protein dynamics of the rescue mutants are significantly different compared to WT, allowing us to describe the stability on a per-residue basis. Although these mutations are only four residues apart, they stabilize the structure distinctly in different regions, consistent with the specific cancer mutations they to which they restore function.
Multidimensional molecular differences between artificial and wildArtemisia rupestrisL.
Authors: Zhou Zhi , Xie Bin , He Bingshu , Zhang Chen , Chen Lulu , Wang Zhonghua , Chen Yanhua , Abliz Zeper 
Published: February 2021

ABSTRACTDifferent ecological environments affect the active ingredients and molecular content of medicinal plants.Artemisia rupestrisL. is a kind of traditional medicinal plant, and the shortages of the wild resource have led to increased use of artificial varieties. However, there have few investigations referring to molecular differences between them in a systematic manner. In the present study, artificial and wildArtemisia rupestrisL. plants were collected in the Altay–Fuyun region, Xinjian, China. Untargeted metabolomics method based on liquid chromatography-mass spectrometry (LC-MS) technology was applied to profile flower, stem, and leaf samples, respectively, and levels of a panel of representative known metabolites in this plant were simultaneously analyzed. The genetic basis of these samples was explored using ade novotranscriptomics approach to investigate differentially expressed genes (DEGs) and their pathway annotations. Results indicated metabolic differences between the two varieties mainly reflected in flavonoids and chlorogenic acid/caffeic acid derivatives. 34 chemical markers (CMs) belonging to these two structural categories were discovered after validation using another batch of samples, including 19 potentially new compounds. After correlation analysis, total of six DEGs in different organs relating to 24 CMs were confirmed using quantitative real-time PCR (qPCR). These findings provided novel insight into the molecular landscape of this medicinal plant through metabolomics-transcriptomics integration strategy, and reference information of its quality control and species identification.ONE SENTENCE SUMMARYA metabolomics-transcriptomics research on Artemisia rupestris L. to discover metabolite differences and the genetic basis between artificial and wild varieties in systematic and novel manner.
Multiple Loci Selection with Multi-way Epistasis in Coalescence with Recombination
Authors: Bose Aritra , Utro Filippo , Platt Daniel E. , Parida Laxmi 
Published: February 2021

AbstractAs studies move into deeper characterization of the impact of selection through non-neutral mutations in whole genome population genetics, modeling for selection becomes crucial. Moreover, epistasis has long been recognized as a significant component in understanding evolution of complex genetic systems. We present a backward coalescent model EpiSimRA, that builds multiple loci selection, with multi-way (k-way) epistasis for any arbitraryk. Starting from arbitrary extant populations with epistatic sites, we trace the Ancestral Recombination Graph (ARG), sampling relevant recombination and coalescent events. Our framework allows for studying different complex evolutionary scenarios in the presence of selective sweeps, positive and negative selection with multiway epistasis. We also present a forward counterpart of the coalescent model based on a Wright-Fisher (WF) process which we use as a validation framework, comparing the hallmarks of the ARG between the two. We provide the first framework that allows a nose-to-nose comparison of multiway epistasis in a coalescent simulator with its forward counterpart with respect to the hallmarks of the ARG. We demonstrate through extensive experiments, that EpiSimRA is consistently superior in term of performance (seconds vs. hours) in comparison to the forward model without compromising on its accuracy. EpiSimRA (both backward and forward) source, executable, user manuals are available at:https://github.com/ComputationalGenomics/SimRA.
Evolution of myoglobin in avian lineages: positive selection for aggregation resistance in penguins and high purifying selection in galliforms
Authors: Rizvi Syed M. , Zheng Wei , Zhang Chengxin , Zhang Yang 
Published: February 2021

AbstractMyoglobin is the major oxygen carrying protein of vertebrate muscle, and high myoglobin net charge is known to hold evolutionary significance as a molecular signature of secondarily aquatic diving capacity in mammals. However, the evolution of myoglobin’s electrostatic properties in non-mammalian vertebrates, such as birds, has not been investigated. Here, we used a new deep learning-based protein folding algorithm to model the tertiary structures of myoglobin from 302 vertebrate species and performed a comparative analysis of their net charge, positively charged solvent-accessible surface area, and negatively charged solvent-accessible surface area. For avian myoglobins, we also calculated selection pressure (ω). The results suggest that the myoglobins of diving avians, specifically those of the penguins (Sphenisciformes) and diving ducks (Aythyini), have highly positively charged electrostatic surfaces, which evolved via positive selection to reduce aggregation propensity and allow greater storage of oxygen for extended underwater foraging. In contrast, galliform myoglobins are under high purifying selection. Distribution of charged atoms on myoglobin surface was more indicative of high myoglobin content than net charge. We also found inter-class differences in net charge; bird myoglobins are the most positively charged and reptile and amphibian myoglobins are the most negatively charged, and net charge seems to be negatively associated with herbivory within mammals. Finally, we propose an equation that describes the relationship between myoglobin net charge and concentration better than the previously suggested logarithmic function. Our findings offer novel insights into the diversification of myoglobin in vertebrate clades and highlight the power of computational structural approaches for zoological and evolutionary research.
Comprehensive analysis of chemical and biological problems associated with browning agents used in aquatic studies
Authors: Scharnweber Kristin , Peura Sari , Attermeyer Katrin , Bertilsson Stefan , Bolender Lucas , Buck Moritz , Einarsdóttir Karólína , Garcia Sarahi L. , Gollnisch Raphael , Grasset Charlotte , Groeneveld Marloes , Hawkes Jeffrey A. , Lindström Eva S. , Manthey Christin , Övergaard Robyn , Rengefors Karin , Sedano-Núñez Vicente T. , Tranvik Lars J. , Székely Anna J. 
Published: February 2021

AbstractInland waters receive and process large amounts of colored organic matter from the terrestrial surroundings. These inputs dramatically affect the chemical, physical, and biological properties of water bodies, as well as their roles as global carbon sinks and sources. To understand the complex changes associated with allochthonous inputs, experiments are needed. However, manipulative studies, especially at ecosystem scales, require large amounts of dissolved organic matter with optical and chemical properties resembling indigenous organic matter. Here we compared the chemical and biological impacts of two leonardite products (HuminFeed (HF) and SuperHume (SH)) and a freshly derived reverse osmosis concentrate of organic matter (RO) in a set of comprehensive mesocosm- and laboratory-scale experiments and analyses.The chemical properties of RO concentrate and the leonardite products were very different with leonardite products being low and RO being high in carboxylic functional groups. Light had a strong impact on the properties of leonardite products, including loss of color and increased particle formation. Furthermore, HF had drastic impacts on bacteria as light stimulated bacterial production and modified community composition, while dark conditions appeared to inhibit bacterial processes. While none of the browning agents inhibited the growth of the tested phytoplankton,Gonyostomum semen, leonardite products had detrimental effects on zooplankton abundance andDaphniareproduction. We conclude that the effects of browning agents extracted from leonardite are in sharp contrast to those originating from terrestrially-derived DOM. Hence, they should be used with great caution in experimental studies on the consequences of terrestrial carbon for aquatic systems.
Plague risk in the western United States over seven decades of environmental change
Authors: Carlson Colin J. , Bevins Sarah N. , Schmid Boris V. 
Published: February 2021

AbstractAfter several pandemics over the last two millennia, the wildlife reservoirs of plague (Yersinia pestis) now persist around the world, including in the western United States. Routine surveillance in this region has generated comprehensive records of human cases and animal seroprevalence, creating a unique opportunity to test how plague reservoirs are responding to environmental change. Here, we develop a new method to detect the signal of climate change in infectious disease distributions, and test whether plague reservoirs and spillover risk have shifted since 1950. We find that plague foci are associated with high-elevation rodent communities, and soil biochemistry may play a key role in the geography of long-term persistence. In addition, we find that human cases are concentrated only in a small subset of endemic areas, and that spillover events are driven by higher rodent species richness (the amplification hypothesis) and climatic anomalies (the trophic cascade hypothesis). Using our detection model, we find that due to the changing climate, rodent communities at high elevations have become more conducive to the establishment of plague reservoirs—with suitability increasing up to 40% in some places—and that spillover risk to humans at mid-elevations has increased as well, although more gradually. These results highlight opportunities for deeper investigation of plague ecology, the value of integrative surveillance for infectious disease geography, and the need for further research into ongoing climate change impacts.
Discover Your Potential: The Influence of Kinematics on a Muscle’s Ability to Contribute to the Sit-to-Stand Transfer
Authors: Roelker Sarah A. , Schmitt Laura C. , Chaudhari Ajit M.W. , Siston Robert A. 
Published: February 2021

AbstractExisting methods for estimating how individual muscles contribute to a movement require extensive time and experimental resources. In this study we developed an efficient method for determining how changes to lower extremity joint kinematics affect the potential of individual muscles to contribute to whole-body center-of-mass vertical (support) and anteroposterior (progression) accelerations. A 4-link 2-dimensional model was used to assess the effect of kinematic changes on muscle function. Joint kinematics were systematically varied throughout ranges observed during the momentum transfer phase of the sit-to-stand transfer. Each muscle’s potential to contribute to support and progression was computed and compared to simulated potentials estimated by traditional dynamic simulation methods for young adults and individuals with knee osteoarthritis (KOA). The new method required 4-10s to compute muscle potentials per kinematic state and computed potentials were consistent with simulated potentials. The new method identified differences in muscle potentials between groups due to kinematic differences, particularly decreased anterior pelvic tilt in young adults, and revealed kinematic and muscle strengthening modifications to increase support. The methods presented provide an efficient, systematic approach to evaluate how joint kinematic adjustments alter a muscle’s ability to contribute to movement and can identify potential sources of pathologic movement and rehabilitation strategies.
Multiple metabolic signals including AMPK and PKA regulate glucose-stimulated double strand break resection in yeast
Authors: Lomonaco Stephanie , Bazzano Dominic , Wilson Thomas E. 
Published: February 2021

AbstractDNA double strand breaks (DSBs) are cytotoxic lesions repaired by non-homologous end joining (NHEJ) and homologous recombination (HR), with 5’ strand resection being the committed step in transition from NHEJ to HR. We previously discovered thatgal1yeast, which cannot metabolize galactose, were unable to perform efficient 5’ resection even though DSBs were formed. Adding glucose or restoringGAL1restored resection, suggesting that carbon source metabolism signals to DSB repair. Here we demonstrate that any fermentable carbon source, including raffinose, can stimulate resection and that the stimulatory effect of glucose was associated with decreased, not increased, cellular ATP. The effect was cell cycle dependent and did not occur in G1, while glucose augmented the G2/M checkpoint arrest even in cells deficient in resection. AMP-activated protein kinase pathway mutants showed only low basal resection despite glucose addition but had normal checkpoint arrest, indicating a primary role for Snf1 specifically in glucose-stimulated resection. The metabolic inputs to resection were multifactorial, however, with loss of the transcriptional repressor Mig1 leading to increased basal resection, three distinct patterns of deficiency with loss of the protein kinase A catalytic subunits, Tpk1, Tpk2 andTpk3, and a resection delay in yeast lacking the lysine demethylase Rph1 that helped separate early and late phase responses to glucose. These results reveal multiple interrelated metabolic signals that optimize DSB resection efficiency while independently amplifying the G2/M checkpoint response.
Programmatic modeling for biological systems
Authors: Lubbock Alexander L.R. , Lopez Carlos F. 
Published: February 2021

AbstractComputational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.HighlightsProgrammatic modeling combines computational modeling with software engineering best practices.An executable model enables users to leverage all available resources from the language.Community benefits include improved collaboration, reusability, and reproducibility.Python has multiple modeling frameworks with a broad, active scientific ecosystem.
Representation learning applications in biological sequence analysis
Authors: Iuchi Hitoshi , Matsutani Taro , Yamada Keisuke , Iwano Natsuki , Sumi Shunsuke , Hosoda Shion , Zhao Shitao , Fukunaga Tsukasa , Hamada Michiaki 
Published: February 2021

ABSTRACTRemarkable advances in high-throughput sequencing have resulted in rapid data accumulation, and analyzing biological (DNA/RNA/protein) sequences to discover new insights in biology has become more critical and challenging. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention, because biological sequences are regarded as sentences and k-mers in these sequences as words. Embedding is an essential step in NLP, which converts words into vectors. This transformation is called representation learning and can be applied to biological sequences. Vectorized biological sequences can be used for function and structure estimation, or as inputs for other probabilistic models. Given the importance and growing trend in the application of representation learning in biology, here, we review the existing knowledge in representation learning for biological sequence analysis.
Novel epitopes of human monoclonal antibodies targeting the influenza virus N1 neuraminidase
Authors: Roubidoux Ericka Kirkpatrick , McMahon Meagan , Carreño Juan Manuel , Capuano Christina , Jiang Kaijun , Simon Viviana , Bakel Harm van , Wilson Patrick , Krammer Florian 
Published: February 2021

AbstractInfluenza virus neuraminidase (NA) targeting antibodies are an independent correlate of protection against infection. Antibodies against the NA act by blocking enzymatic activity, preventing virus release and transmission. As we advance the development of improved influenza virus vaccines that incorporate standard amounts of NA antigen, it is important to identify the antigenic targets of human monoclonal antibodies (mAbs). Additionally, it is important to understand how escape from mAbs changes viral fitness. Here, we describe escape mutants generated by serial passage of A/Netherlands/602/2009 (H1N1) in the presence of human anti-N1 mAbs. We observed escape mutations on the N1 protein around the enzymatic site (S364N, N369T and R430Q) and also detected escape mutations located on the sides and bottom of the NA (N88D, N270D and Q313K/R). We found that a majority of escape mutant viruses had increased fitnessin vitrobut notin vivo. This work increases our understanding of how human antibody responses target the N1 protein.ImportanceAs improved influenza virus vaccines are being developed, the influenza virus neuraminidase (NA) is becoming an important new target for immune responses. By identifying novel epitopes of anti-NA antibodies, we can improve vaccine design. Additionally, characterizing changes in viruses containing mutations in these epitopes aids in identifying effects of NA antigenic drift.
DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution
Authors: Satas Gryte , Zaccaria Simone , El-Kebir Mohammed , Raphael Benjamin J. 
Published: February 2021

AbstractMost tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) – e.g. most solid tumors – the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter themutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs.In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, thedescendant cell fraction(DCF), that quantifies both the prevalence of an SNVandthe past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution.Code availabilitySoftware is available athttps://github.com/raphael-group/decifer