bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of unequal vaccination coverage and migration on long-term pathogen evolution in a metapopulation
Authors: Walter Alicia , Gandon Sylvain , Lion Sébastien 
Published: December 2022

AbstractWorldwide inequalities in vaccine availability are expected to affect the spread and spatial distribution of infectious diseases. It is unclear, however, how spatial variation in vaccination coverage can affect the long-term evolution of pathogens. Here we use an analytical model and numerical simulations to analyse the influence of different imperfect vaccines on the potential evolution of pathogen virulence in a two-population model where vaccination coverage varies between populations. We focus on four vaccines, with different modes of action on the life-cycle of a pathogen infecting two host populations coupled by migration. We show that, for vaccines that reduce infection risk or transmissibility, spatial heterogeneity has little effect on pathogen prevalence and host mortality, and no effect on the evolution of pathogen virulence. In contrast, vaccines that reduce pathogen virulence can select for more virulent pathogens and may lead to the coexistence of different pathogen strains, depending on the degree of spatial heterogeneity in the metapopulation. This heterogeneity is driven by two parameters: pathogen migration and the difference in the vaccination rate between the two populations. We show that vaccines that only reduce pathogen virulence select mainly for a single pathogen strategy in the long term while vaccines that reduce both transmission and virulence can favor the coexistence of two pathogen genotypes. We discuss the implications and potential extensions of our analysis.
Varying molecular interactions explain crowder-dependent enzyme function of a viral protease
Authors: Ostrowska Natalia , Feig Michael , Trylska Joanna 
Published: December 2022

ABSTRACTBiochemical processes in cells, including enzyme-catalyzed reactions, occur in crowded conditions with various background macromolecules occupying up to 40% of cytoplasm’s volume. Viral enzymes in the host cell also encounter such crowded conditions as they often function at the endoplasmic reticulum membranes. We focus on an enzyme encoded by the hepatitis C virus, the NS3/4A protease, which is crucial for viral replication. We have previously found experimentally that synthetic crowders, polyethylene glycol (PEG) and branched polysucrose (Ficoll), differently affect the kinetic parameters of peptide hydrolysis catalyzed by NS3/4A. To gain understanding of the reasons for such behavior, we perform atomistic molecular dynamics simulations of NS3/4A in the presence of either PEG or Ficoll crowders and with and without the peptide substrates. We find that both crowder types make nanosecond long contacts with the protease and slow down its diffusion. However, they also affect the enzyme structural dynamics; crowders induce functionally relevant helical structures in the disordered parts of the protease cofactor, NS4A, with the PEG effect being more pronounced. Overall, PEG interactions with NS3/4A are slightly stronger but Ficoll forms more hydrogen bonds with NS3. The crowders also interact with substrates; we find that the substrate diffusion is reduced much more in the presence of PEG than Ficoll. However, contrary to NS3, the substrate interacts more strongly with Ficoll than with PEG crowders, with the substrate diffusion being similar to crowder diffusion. Importantly, crowders affect also the substrate-enzyme interactions. We observe that both PEG and Ficoll enhance the presence of substrates near the active site, especially near catalytic His57 but Ficoll crowders increase substrate binding more than PEG molecules. The presence of crowders also enhances the stability of Zn2+ion coordination necessary for structural stability of NS3/4A enabling catalysis.AUTHOR SUMMARYEnzyme-catalyzed reactions in reality occur in the crowded environment of the cell. Therefore, viruses entering the host cells also encounter a crowded surrounding in which the viral enzymes are replicated. One such enzyme is the NS3/4A protease encoded by the hepatitis C virus. This enzyme is crucial for viral replication and is used as the therapeutic target for clinically approved drugs. To gain understanding of this enzyme function and explain our previous experiments on itsin vitroactivity, we performed atomistic molecular dynamics simulations in the presence of synthetic crowders (polyethylene glycol and polysucrose) mimicking the cellular crowd. Based on these simulations we describe in detail how and why these crowders affect the diffusion and structural dynamics of this enzyme and enzyme-substrate interactions. In fact, crowders enhance substrate binding, which may have vast consequences for its function in the host cell and drug-design.
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
Authors: Joodaki Mehdi , Shaigan Mina , Parra Victor , Bülow Roman D. , Kuppe Christoph , Hölscher David L. , Cheng Mingbo , Nagai James S. , Goedertier Michaël , Bouteldja Nassim , Tesar Vladimir , Barratt Jonathan , Roberts Ian S.D. , Coppo Rosanna , Kramann Rafael , Boor Peter , Costa Ivan G. 
Published: December 2022

ABSTRACTAlthough clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell and pathomics data to find sample level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics and pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell and pathomics data. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression, and tissue structures related to the disease trajectories supporting interpretation of predictions.
Protein Language Model for Prediction of Subcellular Localization of Protein Sequences from Gram-negative bacteria (ProtLM.SCL)
Authors: Singh Gurpreet , Tyagi Ravi , Singh Anjana , Kapil Shruti , Parida Pratap Kumar , Scarcelli Maria , Dumitru Dan , Sathiyamoorthy Nanda Kumar , Phogat Sanjay , Essaghir Ahmed 
Published: December 2022

AbstractThe prediction of bacterial protein Sub-Cellular Localization (SCL) is critical for antigen identification and reverse vaccinology, especially when determining protein localization in the lab is time consuming, expensive and not possible for all species. While PSORTb is one of the most widely used tool for predicting SCL, it has several limitations, including the tendency to label a large number of proteins as ‘Unknown’. To address these shortcomings, we present a protein language model capable of predicting the subcellular localization of a given protein (ProtLM.SCL) from gram-negative bacteria. By performing 10-fold cross validation on the PSORTb public data set, we demonstrate that ProtLM.SCL is more accurate and precise than PSORTb. When compared to empirically validated published data, our models also outperformed PSORTb, particularly when categorizing difficult occurrences.
Adaptive tuning of human learning and choice variability to unexpected uncertainty
Authors: Lee Junseok K. , Rouault Marion , Wyart Valentin 
Published: December 2022

Human value-based decisions are strikingly variable under uncertainty. This variability is known to arise from two distinct sources: variable choices aimed at exploring available options, and imprecise learning of option values due to limited cognitive resources. However, whether these two sources of decision variability are tuned to their specific costs and benefits remains unclear. To address this question, we compared the effects of expected and unexpected uncertainty on decision-making in the same reinforcement learning task. Across two large behavioral datasets, we found that humans choose more variably between options but simultaneously learn less imprecisely their values in response to unexpected uncertainty. Using simulations of learning agents, we demonstrate that these opposite adjustments reflect adaptive tuning of exploration and learning precision to the structure of uncertainty. Together, these findings indicate that humans regulate not only how much they explore uncertain options, but also how precisely they learn the values of these options.TeaserHumans regulate not only how much they explore uncertain options, but also how precisely they learn their values.
Quantifying constraint in the human mitochondrial genome
Authors: Lake Nicole J. , Liu Wei , Battle Stephanie L. , Laricchia Kristen M. , Tiao Grace , Puiu Daniela , Compton Alison G. , Cowie Shannon , Christodoulou John , Thorburn David R. , Zhao Hongyu , Arking Dan E. , Sunyaev Shamil R. , Lek Monkol 
Published: December 2022

AbstractMitochondrial DNA (mtDNA) has an important, yet often overlooked, role in health and disease. Constraint models quantify the removal of deleterious variation from the population by selection, representing a powerful tool for identifying genetic variation underlying human phenotypes1–4. However, a constraint model for the mtDNA has not been developed, due to its unique features. Here we describe the development of a mitochondrial constraint model and its application to the Genome Aggregation Database (gnomAD), a large-scale population dataset reporting mtDNA variation across 56,434 humans5. Our results demonstrate strong depletion of expected variation, suggesting most deleterious mtDNA variants remain undiscovered. To aid their identification, we compute constraint metrics for every mitochondrial protein, tRNA, and rRNA gene, revealing a spectrum of intolerance to variation. We characterize the most constrained regions within genes via regional constraint, and positions across the entire mtDNA via local constraint, showing their enrichment in pathogenic variation and functionally critical sites, including topological clustering in 3D protein and RNA structures. Notably, we identify constraint at often overlooked sites, such as rRNAs and non-coding regions. Lastly, we demonstrate how these metrics can improve the discovery of mtDNA variation underlying rare and common human phenotypes.
ATR inhibition augments the efficacy of lurbinectedin in small cell lung cancer
Authors: Schultz Christopher W. , Zhang Yang , Elmeskini Rajaa , Zimmermann Astrid , Fu Haiqing , Murai Yasuhisa , Wangsa Darawalee , Kumar Suresh , Takahashi Nobuyuki , Atkinson Devon , Saha Liton Kumar , Lee Chien-Fei , Elenbaas Brian , Desai Parth , Sebastian Robin , Ried Thomas , Aladjem Mirit , Zenke Frank T. , Weaver Zoe , Pommier Yves , Thomas Anish 
Published: December 2022

Abstract/SummarySmall cell lung cancer (SCLC) is the most lethal type of lung cancer. Specifically, MYC-driven non-neuroendocrine SCLC are particularly resistant to standard therapies. Lurbinectedin was recently approved for the treatment of relapsed SCLC, but combinatorial approaches are needed to increase the depth and duration of responses to lurbinectedin. Using high-throughput screens, we found inhibitors of ataxia telangiectasia–mutated and rad3-related (ATR) as the most effective agents for augmenting lurbinectedin efficacy. First-in-class ATR inhibitor berzosertib synergized with lurbinectedin in multiple SCLC cell lines, organoid andin-vivomodels. Mechanistically, ATR inhibition abrogated S-phase arrest induced by lurbinectedin and forced cell-cycle progression causing mitotic catastrophe and cell death. HighCDKN1A/p21 expression was associated with decreased synergy due to G1 arrest, while increased levels ofERCC5/XPG were predictive of increased combination efficacy. Importantly, MYC-driven non-neuroendocrine tumors which were resistant to first-line therapies displayed decreasedCDKN1A/p21 expression and increasedERCC5/XPG indicating they were primed for response to lurbinectedin-berzosertib combination. The combination is being assessed in a clinical trialNCT04802174.Graphical Abstract
30×30 biodiversity gains rely on national coordination
Authors: Eckert Isaac , Brown Andrea , Caron Dominique , Riva Federico , Pollock Laura J. 
Published: December 2022

AbstractProtecting 30% of land by 2030 is an invaluable opportunity to combat the ongoing biodiversity crisis, but critical questions remain regarding what biodiversity to prioritize, how to coordinate protection, and how to incorporate global change. Here, we evaluate how well different 30×30 expansion scenarios capture the climatically viable ranges of Canadian terrestrial vertebrates, plants, and butterflies. We find that national coordination protects vastly more biodiversity (65% of species; 40% of species-at-risk) than regional approaches, which safeguard at least 33% fewer species and 75% fewer species-at-risk. Whereas prioritizing different taxa or biodiversity facets (e.g., phylogenetic diversity) incur smaller trade-offs. Surprisingly, national priorities closely match transnational ones, indicating that national coordination could efficiently contribute to global targets while protecting Canada’s biodiversity at large.
A phylogenetics-based nomenclature system for steroid receptors in teleost fishes
Authors: Hoadley Andrew P. , Munley Kathleen M. , Alward Beau A. 
Published: December 2022

AbstractTeleost fishes have emerged as tractable models for studying the neuroendocrine regulation of social behavior via molecular genetic techniques, such as CRISPR/Cas9 gene editing. Moreover, teleosts provide an opportunity to investigate the evolution of steroid receptors and their functions, as species within this lineage possess novel steroid receptor paralogs that resulted from a teleost-specific whole genome duplication. Although teleost fishes have grown in popularity as models for behavioral neuroendocrinology, there is not a consistent nomenclature system for steroid receptors and their genes, which may impede a clear understanding of steroid receptor paralogs and their functions. Here, we used a phylogenetic approach to assess the relatedness of protein sequences encoding steroid receptor paralogs in 18 species from 12 different orders of the Infraclass Teleostei. While most similarly named sequences grouped based on the established phylogeny of the teleost fish lineage, our analysis revealed several inconsistencies in the nomenclature of steroid receptor paralogs, particularly for sequences encoding estrogen receptor beta (ERβ). Based on our results, we propose a nomenclature system for teleosts in which Greek symbols refer to proteins and numbers refer to genes encoding different subtypes of steroid receptors within the five major groups of this nuclear receptor subfamily. Collectively, our results bridge a critical gap by providing a cohesive naming system for steroid receptors in teleost fishes, which will serve to improve communication, promote collaboration, and enhance our understanding of the evolution and function of steroid receptors across vertebrates.
Analysis of Gene Expression Heterogeneity Reveals Therapeutic Targets and Novel Regulators of Metastasis
Authors: Yang Dongbo , Dann Christopher , Valdespino Andrea , Robinson-Mailman Lydia , Henn Madeline , Chen Mengje , Balázsi Gábor , Rosner Marsha Rich 
Published: December 2022

AbstractTumor cell heterogeneity has been implicated in metastatic progression of solid tumors such as triple-negative breast cancer (TNBC), leading to resistance and recurrence. We hypothesized that genes with low cell-to-cell transcriptional variability may be effective therapeutic targets, and that analysis of variability may facilitate identification of new metastatic regulators. Here we demonstrate, using single cell RNA sequencing, that the metastasis suppressor Raf Kinase Inhibitory Protein (RKIP) reduced overall transcriptional variability in TNBC xenograft tumors. Focusing on genes with reduced variability in response to RKIP, we identified targetable gene sets such as oxidative phosphorylation and showed that metformin could inhibit RKIP-expressing but not control tumor growth. We also found many regulators of cancer progression including a novel epigenetic metastasis suppressor, KMT5C. These studies demonstrate that a metastatic regulator can alter transcriptional variability in tumors and reveal the importance of genes involved in heterogeneity as potential therapeutic targets and regulators of metastatic progression in cancer.
A Potential Pitfall in Detection of Diabetes Mellitus Non-invasively Using the Second-Derivative of Photoplethysmography: Coexisting Hypertension May Blunt the Distinctive Difference in Early Negative to Initial Positive Wave Ratio (b/a) Between People With and Without Diabetes
Authors: Tas Ahmet , Alan Yaren , Kara Ilke , Savas Abdullah , Bayhan Muhammed Ikbal , Ekici Diren , Atay Zeynep , Sezer Fatih , Kitapli Cagla 
Published: December 2022

AbstractBackgroundDigital biomarkers have attracted increasing attention as possible non-invasive diagnostic tools for frequent cardiovascular pathologies. Alterations of features extracted from the derivatives and raw signal of photoplethysmography have been found in the presence of cardiovascular risk factors including but not limited to diabetes, hypertension, arterial stiffness, and endothelial dysfunction. In parallel, recent studies have shown promising results regarding the utilization of PPG as a non-invasive diagnostic tool for diabetes. Whether the presence of hypertension impedes the classification due to mutual endothelial insult resulting in peripheral hemodynamic perturbations remains unknown.MethodsThe previously described ratio between the amplitudes of early-negative(b) and initial-positive(a) waves (b/a) was derived from the second derivative of the PPG signal. Patients were classified according to diabetes mellitus type 2 and hypertension. Standard statistical tests were used to compare the means between unpaired subgroups.ResultsFinal analysis included 132 patients. Compared to healthy control group DM2(-)HT(-) (-0.263 ± 0.115), both DM2(+)HT(-) (-0.361 ± 0.122, p<0.001,d= 0.83) and HT(+)DM2(-) (-0.319 ± 0.127, p:0.033,d= 0.48) groups had significantly lower b/a values. However, subgroups with one of two conditions had statistically indifferent mean b/a values (DM2(+)HT(-) (-0.361 ± 0.122) vs HT(+)DM2(-) (-0.319 ± 0.127, p:0.212,d:0.32). Moreover, significant difference in b/a between DM2 and non-DM2 subgroups of non-HT group (p<0.001) disappeared in subgroups with HT(p:0.665,d:0.16).ConclusionThe utilization of the second-derivative of PPG for the detection of diabetes non-invasively may be impeded in patients with cardiovascular comorbidities, especially in the hypertensive population since both HT and DM2 induce parallel b/a ratio alterations due to the mutual undesired perturbations in the cardiovascular system, affecting peripheral flow dynamics.
Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech
Authors: Sezen U. Uzay , Shue Jessica E. , Worthy Samantha J. , Davies Stuart J. , McMahon Sean M. , Swenson Nathan G. 
Published: December 2022

AbstractTranscriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33,042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the SNP-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-fall transitions for two consecutive years involved 4197 differentially expressed protein coding genes. UsingPopulusorthologs we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed GO terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech (Fagus grandifolia).
NeuroDecodeR: A package for neural decoding analyses in R
Authors: Meyers Ethan M. 
Published: December 2022

AbstractNeural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.
A refined characterization of large-scale genomic differences in the first complete human genome
Authors: Yang Xiangyu , Wang Xuankai , Zou Yawen , Zhang Shilong , Xia Manying , Vollger Mitchell R. , Chen Nae-Chyun , Taylor Dylan J. , Harvey William T. , Logsdon Glennis A. , Meng Dan , Shi Junfeng , McCoy Rajiv C. , Schatz Michael C. , Li Weidong , Eichler Evan E. , Lu Qing , Mao Yafei 
Published: December 2022

AbstractThe first telomere-to-telomere (T2T) human genome assembly (T2T-CHM13) release was a milestone in human genomics. The T2T-CHM13 genome assembly extends our understanding of telomeres, centromeres, segmental duplication, and other complex regions. The current human genome reference (GRCh38) has been widely used in various human genomic studies. However, the large-scale genomic differences between these two important genome assemblies are not characterized in detail yet. Here, we identify 590 discrepant regions (∼226 Mbp) in total. In addition to the previously reported ‘non-syntenic’ regions, we identify 67 additional large-scale discrepant regions and precisely categorize them into four structural types with a newly developed website tool (SynPlotter). The discrepant regions (∼20.4 Mbp) excluding telomeric and centromeric regions are highly structurally polymorphic in humans, where copy number variation are likely associated with various human disease and disease susceptibility, such as immune and neurodevelopmental disorders. The analyses of a newly identified discrepant region—theKLRCgene cluster—shows that the depletion ofKLRC2by a single deletion event is associated with natural killer cell differentiation in ∼20% of humans. Meanwhile, the rapid amino acid replacements within KLRC3 is consistent with the action of natural selection during primate evolution. Our study furthers our understanding of the large-scale structural variation differences between these two crucial human reference genomes and future interpretation of studies of human genetic variation.