bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcriptomics based prediction of metastasis in TNBC patients: Challenges in cross-platforms validation
Authors: Devi Naorem Leimarembi , Dhall Anjali , Patiyal Sumeet , Raghava Gajendra P. S. 
Published: September 2021

AbstractTriple-negative breast cancer (TNBC) is more prone to metastasis and recurrence than other breast cancer subtypes. This study aimed to identify genes that can act as diagnostic biomarkers for predicting lymph node metastasis in TNBC patients. The transcriptomic data of TNBC with or without lymph node metastasis was acquired from TCGA, and the differentially expressed genes were identified. Further, logistic-regression method has been used to identify the top 15 genes (or 15 gene signatures) based on their ability to predict metastasis (AUC>0.65). These 15 gene signatures were used to develop machine learning techniques based prediction models; Gaussian Naïve Bayes classifier outperformed other with AUC>0.80 on both training and validation datasets. The best model failed drastically on nine independent microarray datasets obtained from GEO. We investigated the reason for the failure of our best model, and it was observed that the certain genes in 15 gene signatures were showing opposite regulating trends, i.e., genes are upregulated in TCGA-TNBC patients while it is downregulated on other microarray datasets or vice-versa. In conclusion, the 15 gene signatures may act as diagnostic markers for the detection of lymph node metastatic status in TCGA dataset, but quite challenging across multiple platforms. We also identified the prognostic potential of the 15 selected genes and found that overexpression of ZNRF2, FRZB, and TCEAL4 was associated with poor survival with HR>2.3 and p-value≤0.05. In order to provide services to the scientific community, we developed a webserver named “MTNBCPred” for the prediction of metastatic and non-metastatic lymph node status of TNBC patients (http://webs.iiitd.edu.in/raghava/mtnbcpred/).
Non-linear Archetypal Analysis of Single-cell RNA-seq Data by Deep Autoencoders
Authors: Wang Yuge , Zhao Hongyu 
Published: September 2021

AbstractAdvances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.
Directing multicellular organization by varying the aspect ratio of soft hydrogel microwells
Authors: Pahapale Gayatri J. , Tao Jiaxiang , Nikolic Milos , Gao Sammy , Scarcelli Giuliano , Sun Sean , Romer Lewis H. , Gracias David H. 
Published: September 2021

AbstractMulticellular organization with precise spatial definition is an essential step in a wide range of biological processes, including morphogenesis, development, and healing. Gradients and patterns of chemoattractants are well-described guides of multicellular organization, but the influences of three-dimensional geometry of soft hydrogels on multicellular organization are less well defined. Here, we report the discovery of a new mode of self-organization of endothelial cells in ring-like patterns on the perimeters of hydrogel microwells that is independent of protein or chemical patterning and is driven only by geometry and substrate stiffness. We observe quantitatively striking influences of both the microwell aspect ratio (ε = perimeter/depth) and the hydrogel modulus. We systematically investigate the physical factors of cells and substrates that drive this multicellular behavior and present a mathematical model that explains the multicellular organization based upon balancing extracellular and cytoskeletal forces. These forces are determined in part by substrate stiffness, geometry, and cell density. The force balance model predicts the direction and distance of translational cell migration based on the dynamic interaction between tangential cytoskeletal tension and cell-cell and cell-substrate adhesion. We further show that the experimental observations can be leveraged to drive customized multicellular self-organization. Our observation of this multicellular behavior demonstrates the importance of the combinatorial effects of geometry and stiffness in complex biological processes. It also provides a new methodology for direction of cell organization that may facilitate the engineering of bionics and integrated model organoid systems.
SL-Cloud: A Computational Resource to Support Synthetic Lethal Interaction Discovery
Authors: Tercan Bahar , Qin Guangrong , Kim Taek-Kyun , Aguilar Boris , Kemp Christopher J. , Chambwe Nyasha , Shmulevich Ilya 
Published: September 2021

SummarySynthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, an integrated resource and framework to facilitate the prediction of context-specific SLIs by using cloud-based technologies. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the multiple comparable prediction approaches available. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential SLI partners, using multiple computational strategies. Context-specific synthetic lethality potential can also be compared using the framework. We demonstrate various use cases for our cloud-based computational resource and the utility of this approach for customizable and extensible computational inference of SLIs.
Multiplexed transcriptional repression identifies a network of bactericidal interactions between mycobacterial respiratory complexes
Authors: McNeil MB , Ryburn HW. , Tirados J. , Cheung CY. , Cook GM. 
Published: September 2021

AbstractMycobacterium tuberculosisremains a leading cause of infectious disease morbidity and mortality for which new drug combination therapies are needed. Combinations of respiratory inhibitors can have synergistic or synthetic lethal interactions suggesting that regimens with multiple bioenergetic inhibitors will drastically shorten treatment times. However, realizing this potential is hampered by a lack of on-target inhibitors and a poor understanding of which inhibitor combinations have the strongest interactions. To overcome these limitations, we have used CRISPR interference (CRISPRi) to characterize the consequences of transcriptionally inhibiting individual respiratory complexes and identify bioenergetic complexes that when simultaneously inhibited result in cell death. In this study, we identified known and novel synthetic lethal interactions and demonstrate how the engineering of CRISPRi-guide sequences can be used to further explore networks of interacting gene pairs. These results provide fundamental insights into the functions of and interactions between bioenergetic complexes and the utility of CRISPRi in designing drug combinations.
Hydrogen sulfide improves the cold stress resistance through the CsARF5-CsDREB3 module in cucumber
Authors: Zhang Xiao-Wei , Fu Xin , Liu Feng-Jiao , Wang Ya-Nan , Bi Huan-Gai , Ai Xi-Zhen 
Published: September 2021

AbstractHydrogen sulfide (H2S) plays a crucial role in regulating cold tolerance. But the synergistic regulation of H2S and auxin in the plant response to cold stress has not been reported. In the study, we found that sodium hydrosulfide (NaHS, an H2S donor) treatment enhanced the cold tolerance of cucumber seedlings and increased the level of auxin. CsARF5, a cucumber auxin response factor (ARF) gene was isolated and its role in regulating H2S-mediated cold stress tolerance was described. Transgenic cucumber leaves overexpressing CsARF5 were obtained. Physiological analysis indicated that overexpression of CsARF5 enhanced the cold stress tolerance of cucumber and the regulation of the cold stress response by CsARF5 depends on H2S. In addition, molecular assays showed that CsARF5 modulated cold stress response by directly activating the expression of the dehydration-responsive element-binding (DREB)/C-repeat binding factor (CBF) gene CsDREB3, which was identified as a positive regulator of cold stress. Taken together, our results suggest that CsARF5 plays an important role in H2S-mediated cold stress in cucumber. These results shed light on the molecular mechanism by which H2S regulates cold stress response by mediating auxin signaling, and will provide insights for further studies on the molecular mechanism by which H2S regulates cold stress.HighlightAuxin signaling participates in H2S-mediated cold stress through the CsARF5-CsDREB3 module in cucumber.
A computational framework to identify metabolic engineering strategies for the co-production of metabolites
Authors: Raajaraam Lavanya , Raman Karthik 
Published: September 2021

ABSTRACTMicrobial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose an eXtended version of Flux Scanning based on Enforced Objective Flux (XFSEOF), identify intervention strategies to co-optimize for a set of metabolites. XFSEOF can be used to identify all pairs of products that can be co-optimized with ease, by a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.
Alternative polyadenylation characterizes epithelial and fibroblast phenotypic heterogeneity in pancreatic ductal adenocarcinoma
Authors: Venkat Swati , Feigin Michael E. 
Published: September 2021

AbstractHuman tumors are characterized by extensive intratumoral transcriptional variability within the cancer cell and stromal compartments. This variation drives phenotypic heterogeneity, producing cell states with differential pro- and anti-tumorigenic properties. While bulk RNA sequencing cannot achieve cell type specific transcriptional granularity, single cell sequencing has permitted an unprecedented view of these cell states. Despite this knowledge, we lack an understanding of the mechanistic drivers of this transcriptional and phenotypic heterogeneity. 3’ untranslated region alternative polyadenylation (3’ UTR-APA) drives gene expression alterations through regulation of 3’ UTR length. These 3’ UTR alterations modulate mRNA stability, protein expression and protein localization, resulting in cellular phenotypes including differentiation, cell proliferation, and migration. Therefore, we sought to determine whether 3’ UTR-APA events could characterize phenotypic heterogeneity of tumor cell states. Here we analyze the largest single cell human pancreatic ductal adenocarcinoma (PDAC) dataset and resolve 3’ UTR-APA patterns across PDAC cell states. We find that increased proximal 3’ UTR-APA is associated with PDAC progression and characterizes a metastatic ductal epithelial subpopulation and an inflammatory fibroblast population. Furthermore, we find significant 3’ UTR shortening events in cell state-specific marker genes associated with increased expression. Therefore, we propose that 3’ UTR-APA drives phenotypic heterogeneity in cancer.
Characterization of striatal dopamine projections across striatal subregions in behavioral flexibility
Authors: van der Merwe R.K. , Nadel J.A. , Copes-Finke D. , Pawelko S. , Scott J.S. , Fox M , Morehouse C. , Ghanem M. , McLaughlin R. , Maddox C. , Malaki G. , Turocy A. , Jin X. , Howard C.D. 
Published: September 2021

AbstractBehavioral flexibility is key to survival in a dynamic environment. While flexible, goal-directed behaviors are initially dependent on dorsomedial striatum, they become dependent on lateral striatum with extended training as behaviors become inflexible. Similarly, dopamine release shifts from ventromedial to lateral striatum across learning, and impairment of lateral dopamine release disrupts habitual, inflexible responding. This raises the possibility that lateral dopamine release is a causative mechanism in establishing inflexible behaviors late in training, though this has not been directly tested. Here, we utilized optogenetics to activate dopamine terminals in dorsal medial (DMS), dorsal lateral (DLS), and ventral (NAc) striatum in DATcre mice to determine how specific dopamine subpopulations impact behavioral flexibility. Mice performed a reversal task in which they self-stimulated DMS, DLS, or NAc dopamine terminals by pressing one of two levers before action-outcome lever contingencies were reversed. Consistent with presumed ventromedial/lateral striatal function, we found that mice self-stimulating ventromedial dopamine terminals rapidly reversed lever preference following contingency reversal, while mice self-stimulating dopamine terminals in DLS showed impaired reversal learning. These impairments were characterized by more regressive errors and reliance on lose-stay strategies following reversal, suggesting reward insensitivity and overreliance on previously learned actions. This study supports a model of striatal function in which dorsomedial dopamine facilitates goal-directed responding, and dorsolateral dopamine release is a key mechanism in supporting the transition toward inflexible behaviors.
A specific eIF4A paralog facilitates LARP1-mediated translation repression during mTORC1 inhibition
Authors: Shichino Yuichi , Mito Mari , Kashiwagi Kazuhiro , Takahashi Mari , Ito Takuhiro , Ingolia Nicholas T. , Iwasaki Shintaro 
Published: September 2021

AbstractEukaryotic translation initiation factor (eIF) 4A — a DEAD-box RNA-binding protein — plays an essential role in translation initiation. Two mammalian eIF4A paralogs, eIF4A1 and eIF4A2, have been assumed to be redundant because of their high homology, and the difference in their functions has been poorly understood. Here, we show that eIF4A1, but not eIF4A2, enhances translational repression during the inhibition of mechanistic target of rapamycin complex 1 (mTORC1), an essential kinase complex controlling cell proliferation. RNA-immunoprecipitation sequencing (RIP-Seq) of the two eIF4A paralogs revealed that eIF4A1 preferentially binds to mRNAs containing terminal oligopyrimidine (TOP) motifs, whose translation is rapidly repressed upon mTOR inhibition. This biased interaction depends on a La-related RNA-binding protein, LARP1. Ribosome profiling revealed that the deletion ofEIF4A1, but notEIF4A2, rendered the translation of TOP mRNAs resistant to mTOR inactivation. Moreover, eIF4A1 enhances the affinity between TOP mRNAs and LARP1 and thus ensures stronger translation repression upon mTORC1 inhibition. Our data show that the distinct protein interactions of these highly homologous translation factor paralogs shape protein synthesis during mTORC1 inhibition and provide a unique example of the repressive role of a universal translation activator.
Necroptosis contributes to chronic inflammation and fibrosis in aging liver
Authors: Mohammed Sabira , Thadathil Nidheesh , Selvarani Ramasamy , Nicklas Evan H , Wang Dawei , Miller Benjamin F , Richardson Arlan , Deepa Sathyaseelan S. 
Published: September 2021

AbstractInflammaging, characterized by an increase in low-grade chronic inflammation with age, is a hallmark of aging and is strongly associated with various age-related diseases, including chronic liver disease (CLD) and hepatocellular carcinoma (HCC). Because necroptosis is a cell death pathway that induces inflammation through the release of DAMPs, we tested the hypothesis that age-associated increase in necroptosis contributes to chronic inflammation in aging liver. Phosphorylation of MLKL and MLKL-oligomers, markers of necroptosis, as well as phosphorylation of RIPK3 and RIPK1 were significantly upregulated in the livers of old mice relative to young mice and this increase occurred in the later half of life (i.e., after 18 months of age). Markers of M1 macrophages, expression of proinflammatory cytokines (TNFα, IL6 and IL-1β), and markers of fibrosis were significantly upregulated in the liver with age and the change in necroptosis paralleled the changes in inflammation and fibrosis. Hepatocytes and liver macrophages isolated from old mice showed elevated levels of necroptosis markers as well as increased expression of proinflammatory cytokines relative to young mice. Short term treatment with the necroptosis inhibitor, necrostatin-1s (Nec-1s), reduced necroptosis, markers of M1 macrophages, expression of proinflammatory cytokines, and markers of fibrosis in the livers of old mice. Thus, our data show for the first time that liver aging is associated with increased necroptosis and necroptosis contributes to chronic inflammation in the liver, which in turn appears to contribute to liver fibrosis and possibly CLD.Graphical abstract
Structural and biochemical analyses of selectivity determinants in chimeric Streptococcus Class A sortase enzymes
Authors: Gao Melody , Johnson D. Alex , Piper Isabel M. , Kodama Hanna M. , Svendsen Justin E. , Tahti Elise , Vogel Brandon , Antos John M. , Amacher Jeanine F. 
Published: September 2021

AbstractSequence variation in related proteins is an important characteristic that modulates activity and selectivity. An example of a protein family with a large degree of sequence variation is that of bacterial sortases, which are cysteine transpeptidases on the surface of gram-positive bacteria. Class A sortases are responsible for attachment of diverse proteins to the cell wall to facilitate environmental adaption and interaction. These enzymes are also used in protein engineering applications for sortase-mediated ligations (SML) or sortagging of protein targets. We previously investigated SrtA from Streptococcus pneumoniae, identifying a number of putative β7-β8 loop-mediated interactions that affected in vitro enzyme function. We identified residues that contributed to the ability of S. pneumoniae SrtA to recognize several amino acids at the P1’ position of the substrate motif, underlined in LPXTG, in contrast to the strict P1’ Gly recognition of SrtA from Staphylococcus aureus. However, motivated by the lack of a structural model for the active, monomeric form of S. pneumoniae SrtA, here, we expanded our studies to other Streptococcus SrtA proteins. We solved the first monomeric structure of S. agalactiae SrtA which includes the C-terminus, and three others of β7-β8 loop chimeras from S. pyogenes and S. agalactiae SrtA. These structures and accompanying biochemical data support our previously identified β7-β8 loop-mediated interactions and provide additional insight into their role in Class A sortase substrate selectivity. We argue that a greater understanding of individual SrtA sequence and structural determinants of target selectivity can facilitate the design or discovery of improved sortagging tools.
The Transcription Factor Xrp1 is Required for PERK- Mediated Antioxidant Gene Induction in Drosophila
Authors: Brown Brian , Mitra Sahana , Roach Finnegan D. , Vasudevan Deepika , Ryoo Hyung Don 
Published: September 2021

SUMMARYPERK is an endoplasmic reticulum (ER) transmembrane sensor that phosphorylates eIF2α to initiate the Unfolded Protein Response (UPR). eIF2α phosphorylation promotes stress-responsive gene expression most notably through the transcription factor ATF4 that contains a regulatory 5’ leader. Possible PERK effectors other than ATF4 remain poorly understood. Here, we report that the bZIP transcription factor Xrp1 is required for ATF4-independent PERK signaling. Cell type-specific gene expression profiling in Drosophila indicated that delta-family glutathione-S-transferases (gstD) are prominently induced by the UPR-activating transgene Rh1G69D. Perk was necessary and sufficient for such gstD induction, but ATF4 was not required. Instead, Perk and other regulators of eIF2α phosphorylation regulated Xrp1 protein levels to induce gstDs. The Xrp1 5’ leader has a conserved upstream Open Reading Frame (uORF) analogous to those that regulate ATF4 translation. The gstD-GFP reporter induction required putative Xrp1 binding sites. These results indicate that antioxidant genes are highly induced by a previously unrecognized UPR signaling axis consisting of PERK and Xrp1.