bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Proteome Landscape of Human Placentas for Monochorionic Twins with Selective Intrauterine Growth Restriction
Authors: Meng Xin-Lu , Yuan Peng-Bo , Wang Xue-Ju , Hang Jing , Shi Xiao-Ming , Zhao Yang-Yu , Wei Yuan 
Published: September 2022

AbstractIn perinatal medicine, intrauterine growth restriction (IUGR) is one of the greatest challenges. The etiology of IUGR is multifactorial, but most cases are thought to arise from placental insufficiency. However, identifying the placental cause of IUGR can be difficult due to numerous confounding factors. Selective IUGR (sIUGR) would be a good model to investigate how impaired placentation affects fetal development, as the growth discordance between monochorionic twins cannot be explained by confounding genetic or maternal factors. Herein we constructed and analyzed the placental proteomic profiles of IUGR twins and the normal cotwins. Specifically, we identified a total of 5481 proteins and 233 differentially expressed proteins (DEPs), including 57 upregulated and 176 downregulated DEPs in IUGR twins. Bioinformatic analysis indicates that these DEPs are mainly associated with cardiovascular system development and function, organismal survival, and organismal development. Notably, 34 DEPs are significantly enriched in angiogenesis, and diminished placental angiogenesis in IUGR twins has been further elaborately confirmed. Moreover, we found decreased expression of metadherin (MTDH) in placentas for IUGR twins and demonstrated that MTDH contributes to placental angiogenesis and fetal growth in vitro. Collectively, our findings reveal the comprehensive proteomic signature of placentas for sIUGR twins, and the DEPs identified may provide in-depth insights into pathogenesis of placental dysfunction and subsequent impaired fetal growth.
The time-varying association between previous antibiotic use and antibiotic resistance
Authors: Baraz Avi , Chowers Michal , Nevo Daniel , Obolski Uri 
Published: September 2022

AbstractObjectivesThe objective of the study was to estimate how the time elapsed from previous antibiotic use is associated with antibiotic resistance.MethodsData comprised electronic medical records of all patients in an Israeli hospital who had a positive bacterial culture between 2016-19. These included susceptibility testing results, and clinical and demographic data. Mixed-effects time-varying logistic models were fitted to estimate the association between time elapsed since last use of aminoglycosides and gentamicin resistance (n= 13,095), cephalosporins and ceftazidime resistance (n= 13,051), and fluoroquinolones and ciprofloxacin resistance (n= 15,364), while adjusting for multiple covariates.ResultsFor all examined antibiotics, previous antibiotic use had a statistically significant association with resistance (p< 0.001). These associations exhibited a clear decreasing pattern over time, which we present as a flexible function of time. Nonetheless, previous antibiotic use remained a significant risk factor for resistance for at least 180 days, with adjusted odds ratios of 1.94 (95% CI 1.40-2.69), 1.33 (95% CI 1.10-1.61), and 2.25 (95% CI 1.49-3.41), for gentamicin, ceftazidime and ciprofloxacin, respectively.ConclusionsThe association between prior antibiotic use and resistance decreases over time. Commonly used cut-offs for prior antibiotic use can either misclassify patients still at higher risk, when too recent; or provide a diluted estimate of the effects of antibiotic use on future resistance, when too distant. Hence, prior antibiotic use should be considered as a time-dependent risk factor for resistance, both in epidemiological research and clinical practice.
SHINE: Protein Language Model based Pathogenicity Prediction for Inframe Insertion and Deletion Variants
Authors: Fan Xiao , Pan Hongbing , Tian Alan , Chung Wendy K. , Shen Yufeng 
Published: September 2022

AbstractInframe insertion and deletion variants (indels) alter protein sequence and length. Accurate pathogenicity predictions are important in genetic studies of human diseases. Indel Interpretation is challenging due to limitations in the available number of known pathogenic variants for training. Existing methods largely use manually encoded features including conservation, protein structure and function, and allele frequency. Recent advances in deep learning modeling of protein sequences and structures provide an opportunity to improve the representation of salient features based on large numbers of protein sequences. We developed a new pathogenicity predictor for SHort Inframe iNsertion and dEletion (SHINE). SHINE uses pre-trained protein language models to construct a latent representation of an indel and its protein context from protein sequences and multiple protein sequence alignments, and feeds the latent representation into supervised machine learning models for pathogenicity prediction. We curated training data from ClinVar and gnomAD, and created two test datasets from different sources. SHINE achieved better prediction performance than existing methods for both deletion and insertion variants in these two test datasets. Our work suggests that unsupervised protein language models can provide valuable information about proteins, and new methods based on these models can improve variant interpretation in genetic analyses.
Forward genetic screen of homeostatic antibody levels in the Collaborative Cross identifies MBD1 as a novel regulator of B cell homeostasis
Authors: Hampton Brea K. , Plante Kenneth S. , Whitmore Alan C. , Linnertz Colton L. , Madden Emily A. , Noll Kelsey E. , Boyson Samuel P. , Parotti Breantie , Bell Timothy A. , Hock Pablo , Shaw Ginger D. , de Villena Fernando Pardo-Manuel , Ferris Martin T. , Heise Mark T. 
Published: September 2022

AbstractVariation in immune homeostasis, the state in which the immune system is maintained in the absence of stimulation, is highly variable across populations. This variation is attributed to both genetic and environmental factors. However, the identity and function of specific regulators have been difficult to identify in humans. We evaluated homeostatic antibody levels in the serum of the Collaborative Cross (CC) mouse genetic reference population. We found heritable variation in all antibody isotypes and subtypes measured. We identified 4 quantitative trait loci (QTL) associated with 3 IgG subtypes: IgG1, IgG2b, and IgG2c. While 3 of these QTL maps to known immunologically important regions of the genomes,Qih1(associated with variation in IgG1) mapped to a novel locus on Chromosome 18. We further associated this locus with B cell proportions in the spleen and show that Methyl-CpG binding domain protein 1 is a novel regulator of homeostatic IgG1 levels in the serum and marginal zone B cells (MZB) in the spleen, consistent with a role in MZB differentiation to antibody secreting cells.SummaryHomeostatic antibody levels are highly variable between Collaborative Cross (CC) mouse strains. This forward genetic screen in the CC identifies Methyl-CpG binding domain protein 1 (MBD1) as a regulator of homeostatic IgG1 levels and marginal zone B cell differentiation.
Mathematical Modeling of Bottleneck Transmissions of RNA Virus Infecting a Homogeneous Host Population
Authors: Naomi Furuyama Taimá , Ramos Janini Luiz Mario , Guedes de Carvalho Isabel Maria Vicente , Antoneli Fernando Martins 
Published: September 2022

AbstractThere is no consensus about when a potential viral infection event presents greater risk of a successful transmission. Some authors suggest that late infection stages present higher risk of transmission. Others suggest that the early infection stages play a most relevant role in transmission events. However, studies considering the fitness or mutational effects on the viral particles over transmission events are lacking. We propose to approach this question through a two-level mathematical model based on RNA viral population dynamics. The first level of the model represents the intra-host viral population dynamics and the second level of the model represents the host-to-host dynamics of transmission events. The intra-host dynamics model uses the fitness of viral particles as means to track the presence of ‘highly infective’ particles during transmission bottlenecks. More specifically, the intra-host dynamics is described by a stochastic quasispecies, based on a multivariate branching process. The host-to-host dynamics of transmission events is emulated by a ‘putative’ transmission tree with ‘host zero’ at the root and a fixed number of branches emanating from each internal node. A ‘Monte Carlo’ strategy was adopted to explore the tree by sampling random walks along transmission chains along the tree. Viral infections of a single host and several transmission events among hosts were simulated in early and late infection stages scenarios. The results show that the early infection stages may represent a key factor in the viral pandemic. Over the evolution of the viral population within each host the mean fitness decreases due to occurrence of mutations (most of them causing deleterious effects). Despite the small opportunity interval, transmissions that occur in early stages could probably infect new hosts at a higher rate than in late stages. It was observed that a very early transmission scenario could reach a transmission chain 20 times longer than a very late transmission scenario. This indicates that the quality of the viral particles is a relevant factor for transmission events.
Ecological differences among hydrothermal vent symbioses may drive contrasting patterns of symbiont population differentiation
Authors: Breusing Corinna , Xiao Yao , Russell Shelbi L. , Corbett-Detig Russell B. , Li Sixuan , Sun Jin , Chen Chong , Lan Yi , Qian Pei-Yuan , Beinart Roxanne A. 
Published: September 2022

AbstractThe intra-host composition of horizontally transmitted microbial symbionts can vary across host populations due to interactive effects of host genetics, environmental and geographic factors. While adaptation to local habitat conditions can drive geographic subdivision of symbiont strains, it is unknown whether these patterns are universal and how differences in ecological characteristics among host-symbiont associations influence the genomic structure of symbiont populations. To address this question, we sequenced metagenomes of different populations of the deep-sea musselBathymodiolus septemdierum, which are common at Western Pacific deep-sea hydrothermal vents and show characteristic patterns of niche partitioning with sympatric gastropod symbioses.Bathymodiolus septemdierumlives in close symbiotic relationship with sulfur-oxidizing chemosynthetic bacteria but supplements its symbiotrophic diet through filter-feeding, enabling it to occupy ecological niches with less exposure to geochemical reductants. Our analyses indicate that symbiont populations associated withB. septemdierumshow structuring by geographic location, but that the dominant symbiont strain is uncorrelated with vent site. These patterns are in contrast to co-occurringAlviniconchaandIfremeriasymbioses that exhibit greater symbiont nutritional dependence and occupy habitats with higher spatial variability in environmental conditions. Our results suggest that relative habitat homogeneity combined with sufficient symbiont dispersal and genomic mixing might promote persistence of similar symbiont strains across geographic locations, while mixotrophy might decrease selective pressures on the host to affiliate with locally adapted symbiont strains. Overall, these data contribute to our understanding of the potential mechanisms influencing symbiont population structure across a spectrum of marine microbial symbioses that vary in ecological niche and relative host dependency.
Unweighted versus weighted regression methods may be sufficient to analyze complex survey data in the Canadian Longitudinal Study on Aging
Authors: Oremus Mark , Maxwell Colleen , Tyas Suzanne L. , Griffith Lauren E. , van den Heuvel Edwin R. 
Published: September 2022

AbstractIntroductionComplex surveys use stratified or cluster sampling to recruit participants. Researchers analyzing these surveys often wish to make inferences about the source populations from which the participants are drawn. In such cases, methodologists recommend employing sample weights in regression analyses; however, the utilization of weights in studies of associations are not without dispute.Materials and methodsTo help guide analyses of complex surveys, we utilized baseline data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging (CLSA) (n = 30,097) and compared unweighted and weighted regression analyses of the association between social support availability (SSA) and cognitive function. We also conducted simulation studies to validate our findings in the CLSA.ResultsThe regression coefficients for SSA were similar across the unweighted and weighted regression models; the standard errors of the were lower in the unweighted models. The simulation studies mimicking CLSA confirmed these findings. Overall, our findings demonstrated a small advantage for the unweighted analysis of CLSA data due to the smaller standard errors.DiscussionAlthough we cannot guarantee that this would be the case for all association analyses with CLSA data, the current study showed the use of analytical weights was not necessary for our associations of interest.
Temperature-dependent Thermodynamic and Photophysical Properties of SYTO-13 Dye Bound to DNA
Authors: DeJaco Robert F. , Majikes Jacob M. , Liddle J. Alexander , Kearsley Anthony J. 
Published: September 2022

ABSTRACTThe benefits of dyes in nucleic acid assays above room temperature are limited by a nonlinear, highdimensional relationship between fluorescence and the biophysical and chemical processes occurring in solution. To overcome these limitations, we identify an experimental regime that eliminates bias and unnecessary complexities in this relationship, and develop an experimental–computational workflow to generate the property data required to describe the dependence of fluorescence on temperature and concentration. Specifically, we exploit the temperature-cycling capabilities of real-time PCR machine, as well as the utility of numerical optimization, to determine the binding strength and molar fluorescence of the SYTO-13 dye bound to double-stranded (DS) or single-stranded (SS) DNA at more than 60 temperatures. We find that the data analysis approach is robust; it can account for significant well-to-well and plate-to-plate variation. The weak binding strength of SYTO-13 relative to SYBR Green I is consistent with previous reports of its negligible influence on PCR and melting temperature. Discriminating between molar fluorescence and binding strength clarifies the mechanism for the larger fluorescence of a DS/dye solution than a SS/dye solution; in fact, the explanation is different at high temperature than at low temperature. The temperature-dependence of the binding strength allows for ascertainment of the enthalpic and entropic contributions to the free energy, as well as the sign of the differential heat capacity of binding. The temperature-dependence of the molar fluorescence allows for calculation of the brightness (quantum yield times molar extinction coefficient) of SYTO-13 bound to DS relative to SS. The more accurate and complete description of the relationship between solution behavior and fluorescence enabled by this work can lead to more accurate selection of dyes and quantification of nucleic acids.SIGNIFICANCEFluorescent dyes are often used to quantify nucleic acids. The accuracy and precision of quantification, however, is limited by a complex and high-dimensional relationship between fluorescence and solution behavior. This is especially true for assays above room temperature, where empirical approximations are often required in the absence of available property data. In this work, we present an experimental and computational workflow that can more accurately describe this relationship and more efficiently generate the temperature-dependent thermodynamic and photophysical properties required. This approach can improve quantification and selection of high-performing dyes for particular assays.
Activation of ERK by altered RNA splicing in cancer
Authors: Zhang Yushan , Siraj Md Afjalus , Chakraborty Prabir , Tseng Robert , Ku Li-Ting , Das Shamik , Dey Anindya , Dhar Dwivedi Shailendra Kumar , Rao Geeta , Zhang Min , Yang Da , Hossen Md Nazir , Ding Wei-Qun , Fung Kar-Ming , Bhattacharya Resham , Escobar-Hoyos Luisa , Mukherjee Priyabrata 
Published: September 2022

ABSTRACTMany cancers carry change-of-function mutations affecting RNA splicing factors, however, less is known about the functional consequences of upregulated RNA splicing factors in cancer. Here, we demonstrate that SMNDC1, a poorly studied splicing factor, which we found to be upregulated in multiple carcinomas and associated with poor patient prognosis, promotes cell proliferation, clonal expansion, and tumor growth by promoting the retention of G-rich exons, which otherwise would be excluded or retained at a lower rate after RNA splicing in normal cells. Inclusion of exon 4 (E4) of MAPK3 (ERK1), which encodes both kinase phosphorylation sites (Thr202/Tyr204), was among the promoted exons by SMNDC1. Forced exclusion of MAPK3-E4 using anti-sense oligos inhibited the ERK1 phosphorylation, expression of target genes and decreased tumor cell growth. These data support that cancer cells exploit a “splicing switch” to promote ERK kinase activity and offer a druggable alternative to block oncogenic signaling and altered RNA splicing in cancer cellsSIGNIFICANCEERK signaling promotes tumor growth and survival. Exon 4 of MAPK3 (ERK1) encodes the activation phosphorylation sites of ERK1 kinase. Aberrant RNA splicing induced by SMNDC1 in cancer cells increases the retention of exon 4 during mRNA splicing, unleashes the kinase activity. SMNDC1 potentializes as a cancer therapeutic target.
Brain-wide transcriptome-based metabolic alterations in Parkinson’s disease: human inter-region and human-experimental model correlations
Authors: Odongo Regan , Bellur Orhan , Abdik Ecehan , Çakır Tunahan 
Published: September 2022

AbstractAlterations in brain metabolism are closely associated with the molecular hallmarks of Parkinson’s disease (PD). A clear understanding of the main metabolic perturbations in PD is therefore important. Here, we retrospectively analysed the expression of metabolic genes from 34 PD-control post-mortem human brain transcriptome data from literature, spanning multiple brain regions, and found significant metabolic correlations between the Substantia nigra (SN) and cerebral cortical tissues with high perturbations in protein modification, transport, nucleotide and inositol phosphate metabolic pathways. Moreover, three main metabolic clusters of SN tissues were identified from patient cohort studies, each characterised by perturbations in (a) pyruvate, amino acid, neurotransmitter, and complex lipid metabolisms (b) inflammation-related metabolism, and (c) lipid breakdown for energy metabolism. Finally, we analysed 58 PD-control transcriptome data from in vivo/in vitro disease models and identified experimental PD models with significant correlations to matched human brain regions. Collectively, our findings are based on 47 PD transcriptome datasets covering 92 PD-control comparisons spanning more than 1000 samples in total, and they suggest metabolic alterations in several brain regions, heterogeneity in metabolic alterations between study cohorts for the SN tissues and suggest the need to optimize current experimental models to advance research on metabolic aspects of PD.
The RhoGEF protein Plekhg5 regulates medioapical actomyosin dynamics of apical constriction during Xenopus gastrulation
Authors: Baldwin Austin T. , Popov Ivan K. , Keller Ray , Wallingford John B. , Chang Chenbei 
Published: September 2022

AbstractApical constriction results in apical surface reduction in epithelial cells and is a widely used mechanism for epithelial morphogenesis during embryo development. Both medioapical and junctional actomyosin remodeling are involved in apical constriction, but the deployment of medial versus junctional actomyosin in specific developmental processes has not been fully described. Additionally, genetic regulation of actomyosin dynamics during apical constriction is poorly understood in vertebrate systems. In this study, we investigate actomyosin dynamics and their regulation by the RhoGEF protein Plekhg5 in Xenopus bottle cells. Using live imaging and quantitative image analysis, we show that bottle cells assume different shapes, with rounding bottle cells constricting earlier in small clusters followed by fusiform bottle cells forming between the clusters. Though both medioapical and junctional actomyosin accumulate as surface area decreases, apical constriction is better correlated with medioapical actomyosin localization, which may promote formation of microvilli in the apical membrane. Knockdown of plekhg5 disrupts both medioapical actomyosin activity and apical constriction but does not affect initial F-actin dynamics. Taken together, our study reveals distinct cell morphologies, uncovers actomyosin behaviors, and demonstrates the crucial role of a RhoGEF protein in controlling actomyosin dynamics during apical constriction of bottle cells in Xenopus gastrulation.
Effect of genomic and cellular environments on gene expression noise
Authors: Hong Clarice KY , Ramu Avinash , Zhao Siqi , Cohen Barak A 
Published: September 2022

AbstractIndividual cells from isogenic populations often display large cell-to-cell differences in gene expression. This “noise” in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome. To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we performed the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We found that the mean and noise of expression correlate with different histone modifications. We quantified the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assigned the extrinsic component to differences between the CD24+ “stem-like” sub-state and the more “differentiated” sub-state. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for “safe-harbor” loci. Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome, and that the data generated by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.