bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative proteomics identifies secreted diagnostic biomarkers as well as tumor-dependent prognostic targets for clear cell Renal Cell Carcinoma
Authors: Senturk Aydanur , Sahin Ayse Tugce , Armutlu Ayse , Kiremit Murat Can , Acar Omer , Erdem Selcuk , Bagbudar Sidar , Esen Tarik , Tuncbag Nurcan , Ozlu Nurhan 
Published: February 2021

AbstractClear cell Renal Cell Carcinoma (ccRCC) is the third most common and most malignant urological cancer, with a 5-year survival rate of 10% for patients with advanced tumors. Here, we identified 10,160 unique proteins by in-depth quantitative proteomics, of which 955 proteins were significantly regulated between tumor and normal adjacent tissues. We verified 4 putatively secreted biomarker candidates, namely PLOD2, FERMT3, SPARC and SIRPα, as highly expressed proteins that are not affected by intra- and inter-tumor heterogeneity. Moreover, SPARC displayed a significant increase in urine samples of ccRCC patients, making it a promising marker for clinical screening assays. Furthermore, based on molecular expression profiles, we propose a biomarker panel for the robust classification of ccRCC tumors into two main clusters, which significantly differed in patient outcome with an almost three times higher risk of death for cluster 1 tumors compared to cluster 2 tumors. Moreover, among the most significant clustering proteins, 13 were targets of repurposed inhibitory FDA-approved drugs. Our rigorous proteomics approach identified promising diagnostic and tumor-discriminative biomarker candidates which can serve as therapeutic targets for the treatment of ccRCC.
Recognition of N6-methyladenosine by the YTHDC1 YTH domain studied by molecular dynamics and NMR spectroscopy: The role of hydration
Authors: Krepl Miroslav , Damberger Fred Franz , von Schroetter Christine , Theler Dominik , Pokorná Pavlína , Allain Frédéric H.-T. , Šponer Jiří 
Published: February 2021

AbstractThe YTH domain of YTHDC1 belongs to a class of protein “readers”, recognizing the N6-methyladenosine (m6A) chemical modification in mRNA. Static ensemble-averaged structures revealed details of N6-methyl recognition via a conserved aromatic cage. Here, we performed molecular dynamics (MD) simulations along with nuclear magnetic resonance (NMR) and isothermal titration calorimetry (ITC) to examine how dynamics and solvent interactions contribute to the m6A recognition and negative selectivity towards unmethylated substrate. The structured water molecules surrounding the bound RNA and the methylated substrate’s ability to exclude bulk water molecules contribute to the YTH domain’s preference for m6A. Intrusions of bulk water deep into the binding pocket disrupt binding of unmethylated adenosine. The YTHDC1’s preference for the 5′-Gm6A-3′ motif is partially facilitated by a network of water-mediated interactions between the 2-amino group of the guanosine and residues in the m6A binding pocket. The 5′-Im6A-3′ (where I is inosine) motif can be recognized too but disruption of the water network lowers affinity. The D479A mutant also disrupts the water network and destabilizes m6A binding. Our interdisciplinary study of YTHDC1 protein/RNA complex reveals an unusual physical mechanism by which solvent interactions contributes towards m6A recognition.
Rapid spread of a symbiotic virus in a major crop pest following wide-scale adoption of Bt-cotton in China
Authors: Xiao Yutao , Li Wenjing , Yang Xianming , Xu Pengjun , Jin Minghui , Yuan He , Zheng Weigang , Soberón Mario , Bravo Alejandra , Wilson Kenneth , Wu Kongming 
Published: February 2021

AbstractBacillus thuringiensis(Bt) crops have been widely planted and the effects of Bt-crops on populations of the target and non-target insect pests were well studied. However, the effects of Bt-crops exposure on microorganisms that interact with crop pests haven’t previously been quantified. Here, we use laboratory and field data to show that infection ofHelicoverpa armigerawith a symbiotic densovirus (HaDV2) is associated with its enhanced growth and resistance to Bt-cotton. Moreover, field monitoring showed a much higher incidence of cotton bollworm infection with HaDV2 in regions cultivated with Bt-cotton than in regions without it, with the rate of densovirus infection increasing with increasing use of Bt-cotton. RNA-seq suggested resistance to both baculovirus and Cry1Ac were enhanced via the immune-related pathways. These suggest that the exposure to Bt-crops has selected for beneficial interactions between the target pest and a symbiotic microorganism that enhances its performance on Bt-crops under field conditions.
SALTS – SURFR (sncRNA) And LAGOOn (lncRNA) Transcriptomics Suite
Authors: Kasukurthi Mohan V , Houserova Dominika , Huang Yulong , Barchie Addison A. , Roberts Justin T. , Li Dongqi , Wu Bin , Huang Jingshan , Borchert Glen M 
Published: February 2021

ABSTRACTThe widespread utilization of high-throughput sequencing technologies has unequivocally demonstrated that eukaryotic transcriptomes consist primarily (>98%) of non-coding RNA (ncRNA) transcripts significantly more diverse than their protein-coding counterparts.ncRNAs are typically divided into two categories based on their length. (1) ncRNAs less than 200 nucleotides (nt) long are referred as small non-coding RNAs (sncRNAs) and include microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), transfer ribonucleic RNAs (tRNAs), etc., and the majority of these are thought to function primarily in controlling gene expression. That said, the full repertoire of sncRNAs remains fairly poorly defined as evidenced by two entirely new classes of sncRNAs only recently being reported, i.e., snoRNA-derived RNAs (sdRNAs) and tRNA-derived fragments (tRFs). (2) ncRNAs longer than 200 nt long are known as long ncRNAs (lncRNAs). lncRNAs represent the 2ndlargest transcriptional output of the cell (behind only ribosomal RNAs), and although functional roles for several lncRNAs have been reported, most lncRNAs remain largely uncharacterized due to a lack of predictive tools aimed at guiding functional characterizations.Importantly, whereas the cost of high-throughput transcriptome sequencing is now feasible for most active research programs, tools necessary for the interpretation of these sequencings typically require significant computational expertise and resources markedly hindering widespread utilization of these datasets. In light of this, we have developed a powerful new ncRNA transcriptomics suite, SALTS, which is highly accurate, markedly efficient, and extremely user-friendly. SALTS stands forSURFR (sncRNA)AndLAGOOn (lncRNA)TranscriptomicsSuite and offers platforms for comprehensive sncRNA and lncRNA profiling and discovery, ncRNA functional prediction, and the identification of significant differential expressions among datasets. Notably, SALTS is accessed through an intuitive Web-based interface, can be used to analyze either user-generated, standard next-generation sequencing (NGS) output file uploads (e.g., FASTQ) or existing NCBI Sequence Read Archive (SRA) data, and requires absolutely no dataset pre-processing or knowledge of library adapters/oligonucleotides.SALTS constitutes the first publically available, Web-based, comprehensive ncRNA transcriptomic NGS analysis platform designed specifically for users with no computational background, providing a much needed, powerful new resource capable of enabling more widespread ncRNA transcriptomic analyses. The SALTS WebServer is freely available online athttp://salts.soc.southalabama.edu.
Root-to-shoot iron partitioning in Arabidopsis requires IRON-REGULATED TRANSPORTER1 (IRT1)
Authors: Quintana Julia , Bernal María I. , Scholle Marleen , Holländer-Czytko Heike , Nga Nguyen , Piotrowski Markus , Mendoza-Cózatl David G. , Haydon Michael J. , Krämer Ute 
Published: February 2021

ABSTRACTIRON-REGULATED TRANSPORTER1 (IRT1) is the root high-affinity ferrous iron uptake system and indispensable for the completion of the life cycle ofArabidopsis thalianawithout vigorous iron (Fe) supplementation. Here we provide evidence supporting a second role of IRT1 in root-to-shoot mobilization of Fe. We show that theirt1-2 (pam42) mutant over-accumulates Fe in roots, most prominently in the cortex of the differentiation zone, when compared to the wild type. Shoots ofirt1-2 are severely Fe-deficient according to Fe content and marker transcripts, as expected. We generatedirt1-2 lines producing IRT1 mutant variants carrying single amino-acid substitutions of key residues in transmembrane helices IV and V, Ser206and His232, which are required for transport activity in yeast. In the transgenic Arabidopsis lines, short-term root Fe uptake rates and secondary substrate Mn accumulation resemble those ofirt1-2, suggesting that these plants remain incapable of IRT1-mediated root Fe uptake. Yet, IRT1S206Apartially complements rosette dwarfing and leaf chlorosis, as well as root-to-shoot Fe partitioning and gene expression defects ofirt1-2, all of which are fully complemented by wild-type IRT1. Taken together, these results suggest a function for IRT1 in root-to-shoot Fe partitioning that does not require Fe transport activity of IRT1. Among the genes of which transcript levels are partially dependent on IRT1, we identifyMYB DOMAIN PROTEIN10,MYB DOMAIN PROTEIN72andNICOTIANAMINE SYNTHASE4as candidates for effecting IRT1-dependent Fe mobilization in roots. Understanding the biological functions of IRT1 will help to improve iron nutrition and the nutritional quality of agricultural crops.
The protection of Gαz-null NOD mice from hyperglycemia is sexually dimorphic and only partially β-cell autonomous
Authors: Fenske Rachel J. , Peter Darby C. , Wienkes Haley N. , Schaid Michael D. , Reuter Austin , Carbajal Kathryn A. , Kimple Michelle E. 
Published: February 2021

AbstractThe mechanisms that underlie the β-cell pathophysiology of Type 1 Diabetes (T1D) are not fully understood. Our group has defined the unique heterotrimeric G protein alpha-subunit, Gαz, as a key negative regulator of β-cell signal transduction pathways. Non-obese diabetic (NOD) mice lacking Gαzthroughout the body are protected from developing T1D-like hyperglycemia. To determine whether this phenotype is β-cell autonomous, we generated and validated a β-cell-specific Gαzknockout (βKO) on the NOD background and characterized the phenotype of female and male cohorts. Long-term hyperglycemia incidence was lower in GαzβKO mice as compared to wild-type (WT) controls, but, unlike global Gαzknockout mice, this protection was incomplete. While young male and female GαzβKO NOD mice had improved glucose tolerance, WT NOD males were significantly less glucose tolerant than females, and only female GαzβKO mice retained improved glucose tolerance at 28-29 weeks of age. Conversely, β-cell-specific Gαzloss only influenced insulitis in 28-29-week old male NOD mice, a phenotype correlating directly with body burden of glucose during oral glucose challenge. Using surrogates for β-cell function and apoptosis, the partial penetrance of euglycemia in GαzβKO NOD was best explained by an early failure to up-regulate β-cell proliferation. We conclude β-cell Gαzis an important regulator of the sexually-dimorphic T1D-like phenotype of NOD mice. Yet, other factors must be important in imparting full protection from the disease.
Chloroplast redox state changes indicate cell-to-cell signalling during the hypersensitive response
Authors: Lukan Tjaša , Županič Anže , Povalej Tjaša Mahkovec , Brunkard Jacob O. , Juteršek Mojca , Baebler Špela , Gruden Kristina 
Published: February 2021

AbstractHypersensitive response (HR)-conferred resistance is associated with an induction of programmed cell death (PCD) and pathogen spread restriction in its proximity. While the pivotal role of salicylic acid (SA) in the restriction of pathogen spread during HR has been confirmed, the exact role of chloroplastic reactive oxygen species and the link between SA signalling and chloroplast redox state during HR remain unexplained. To unravel these relationships, we performed detailed spatiotemporal analysis of chloroplast redox response to potato virus Y (PVY) infection in resistantNy-1-gene-bearing potato and its transgenic counterpart with impaired SA accumulation and compromised resistance. We found that the chloroplasts are highly oxidized in the cells adjacent to the cell death zone at different stages after virus inoculation in both genotypes. Moreover, we detected individual cells with moderately oxidized chloroplasts, which we call “signalling cells”, in close proximity as well as farther from the cell death zone. These are relatively rare in SA-deficient plants, suggesting their role in signalling for HR-conferred resistance. This hypothesis is further supported by highly induced formation of stroma filled tubules that extend from chloroplasts (stromules) in the cells adjacent to signalling cells. Unexpectedly, the cells with the highest occurrence of stromules have chloroplasts in more reduced state than the adjacent ones. In addition, we show that stromules are induced also at the edge of PVY multiplication zone. We conclude that chloroplast redox state and stromule formation are tightly spatiotemporally regulated by SA-signalling which leads to effective HR response.
The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian
Authors: Zhang Haoyang , Xiu Xuehao , Xue Angli , Yang Yuedong , Yang Yuanhao , Zhao Huiying 
Published: February 2021

AbstractBackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship.MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614,Ncontrol=155,150) and cataract (Ncase=24,622,Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality.ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60×10−6) between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genesMIR4453HG(βSMR=−0.34, p-value=6.41×10−8) andKCNK17(βSMR=−0.07, p-value=2.49×10−10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract.ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients.Key MessagesWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases.We performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract.We revealed two candidate genesMIR4453HGandKCNK17whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract.Our study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice
Patient-specific cell communication networks associate with disease progression in cancer
Authors: Gibbs David L , Aguilar Boris , Thorsson Vésteinn , Ratushny Alexander V , Shmulevich Ilya 
Published: February 2021

AbstractThe maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling ‘axes’ with clinical outcomes. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).
Harmful Algal Bloom-Forming Organism Responds to Nutrient Stress Distinctly From Model Phytoplankton
Authors: McLean Craig , Haley Sheean T. , Swarr Gretchen J. , Soule Melissa C. Kido , Dyhrman Sonya T. , Kujawinski Elizabeth B. 
Published: February 2021

SummaryResources such as nitrogen (N) and phosphorus (P) play an important role in primary production and constraining phytoplankton bloom dynamics. Models to predict bloom dynamics require mechanistic knowledge of algal metabolic shifts in response to resource limitation. For well-studied model phytoplankton like diatoms, this information is plentiful. However, for less-studied groups such as the raphidophytes, there remain significant gaps in understanding metabolic changes associated with nutrient limitation.Using a novel combination of metabolomics and transcriptomics, we examined how the harmful algal bloom-forming raphidophyteHeterosigma akashiwoshifts its metabolism under N- and P-stress. We choseH. akashiwobecause of its ubiquity within estuarine environments worldwide, where bloom dynamics are influenced by N and P availability.Our results show that each stress phenotype is distinct in both the allocation of carbon and the recycling of macromolecules. Further, we identified biomarkers of N- and P-stress that may be appliedin situto help modelers and stakeholders manage, predict, and prevent future blooms.These findings provide a mechanistic foundation to model the metabolic traits and trade-offs associated with N- and P-stress inH. akashiwo, and evaluate the extent to which these metabolic responses can be inferred in other phytoplankton groups.
Single-cell transitional dynamics unravel stimulus- and cell type-dependent signaling outputs of distinct p53 regulatory feedback motifs
Authors: Xie Jun , Zhang Lichun , Zhu Yanting , Liang Xiao , Shi Jue 
Published: February 2021

AbstractDespite myriad of systemic analysis, our quantitative understanding of signaling networks and their differential outputs is still limited, especially at the dynamic level. Here we employed a network motif approach to dissect key regulatory units of p53 pathway and determined how their collective activities generated context-specific dynamic responses. By combining single-cell imaging and mathematical modeling of dose-dependent p53 dynamics induced by three chemotherapeutics of distinct mechanism-of-action, including Etoposide, Nutlin-3a and 5-Fluouracil, and in five cancer cell types, we identified novel p53 dynamic modes and their mechanistic origins with new insight on previously unknown drug mediators and phenotypic heterogeneity of cancer cells. Our results established the functional roles of unique feedback motifs of p53 pathway in generating the stimulus- and cell type-specific signaling responses and demonstrated that transitional dynamics activated at intermediate stimulus levels can be exploited as novel quantitative readouts to uncover and elucidate the key building blocks of bio-networks.
The Population Structure and Selection Signal Analysis of Shenxian Pigs based on Genome Resequencing Technology
Authors: Diao Liu , Chunlian Lu , Shang Li , Mengyu Jia , Sai Li , Liqin Ren , Yutao Miao , Hongzhan Cao 
Published: February 2021

ABSTRACTShenxian pigs are the only local black pig of Hebei Province, and were listed in the Genetics of Livestock and Poultry Resources of China in 2016. This breed of pig is considered to be a valuable local pig germplasm genetic resource in China. In the present study, in order to understand the genetic variations of Shenxian pigs, identify selected regions related to superior traits, and accelerate the breeding processes of Shenxian pigs, the whole genome of the Shenxian pigs was resequenced and compared with that of large white pigs. The goal was to explore the germplasm characteristics of Shenxian pigs.The results obtained in this research investigation revealed that the genetic relationships of the Shenxian pig breed were complex, and that sub-populations could be identified within the general population. A total of 23M SNP sites were obtained by whole genome resequencing, and 1,509 selected sites were obtained via bioinformatics analyses. It was determined after annotation that a total of 19 genes were enriched in three items of bioengineering, molecular function, and cell composition.During this research investigation, the aforementioned 19 genes were subjected to GO and KEGG analyses. Subsequently, the candidate genes related to cell proliferation were obtained (DMTF1 and WDR5), which were considered to possibly be related to the slow growth and development of Shenxian pigs. In addition, the candidate genes related to lactation were obtained (CSN2 and CSN3).
Deep learning for robust and flexible tracking in behavioral studies forC. elegans
Authors: Bates Kathleen , Le Kim , Lu Hang 
Published: February 2021

ABSTRACTRobust and accurate behavioral tracking is essential for ethological studies. Common methods for tracking and extracting behavior rely on user adjusted heuristics that can significantly vary across different individuals, environments, and experimental conditions. As a result, they are difficult to implement in large-scale behavioral studies with complex, heterogenous environmental conditions. Recently developed deep-learning methods for object recognition such as Faster R-CNN have advantages in their speed, accuracy, and robustness. Here, we show that Faster R-CNN can be employed for identification and detection ofCaenorhabditis elegansin a variety of life stages in complex environments. We applied the algorithm to track animal speeds during development, fecundity rates and spatial distribution in reproductive adults, and behavioral decline in aging populations. By doing so, we demonstrate the flexibility, speed, and scalability of Faster R-CNN across a variety of experimental conditions, illustrating its generalized use for future large-scale behavioral studies.