bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing
Authors: Lee Joongho , Kim Minsoo , Kang Keunsoo , Yang Chul-Su , Yoon Seokhyun 
Published: January 2023

AbstractSingle-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.
Genetic control of RNA editing in neurodegenerative disease
Authors: Wu Sijia , Xue Qiuping , Yang Mengyuan , Wang Yanfei , Kim Pora , Zhou Xiaobo , Huang Liyu 
Published: January 2023

Abstract
A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variants. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership-Alzheimer’s Disease and Parkinson’s Progression Markers Initiative. The large-scale and genome-wide identification of 95 198 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA-binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56 and DHX9, since their regulations on multiple RNA-editing events were probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of 3 proteins, expressions of 277 genes and splicing of 449 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.
Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?
Authors: Gu Shukai , Shen Chao , Yu Jiahui , Zhao Hong , Liu Huanxiang , Liu Liwei , Sheng Rong , Xu Lei , Wang Zhe , Hou Tingjun , Kang Yu 
Published: January 2023

AbstractBinding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.
Analysis of affinity purification-related proteomic data for studying protein–protein interaction networks in cells
Authors: Kattan Rebecca Elizabeth , Ayesh Deena , Wang Wenqi 
Published: January 2023

AbstractDuring intracellular signal transduction, protein–protein interactions (PPIs) facilitate protein complex assembly to regulate protein localization and function, which are critical for numerous cellular events. Over the years, multiple techniques have been developed to characterize PPIs to elucidate roles and regulatory mechanisms of proteins. Among them, the mass spectrometry (MS)-based interactome analysis has been increasing in popularity due to its unbiased and informative manner towards understanding PPI networks. However, with MS instrumentation advancing and yielding more data than ever, the analysis of a large amount of PPI-associated proteomic data to reveal bona fide interacting proteins become challenging. Here, we review the methods and bioinformatic resources that are commonly used in analyzing large interactome-related proteomic data and propose a simple guideline for identifying novel interacting proteins for biological research.
Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?
Authors: Ji Kai-Yue , Liu Chong , Liu Zhao-Qian , Deng Ya-Feng , Hou Ting-Jun , Cao Dong-Sheng 
Published: January 2023

AbstractIdentification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand–target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer
Authors: Ji Xiangwen , Wang Edwin , Cui Qinghua 
Published: January 2023

AbstractSomatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.
SGPPI: structure-aware prediction of protein–protein interactions in rigorous conditions with graph convolutional network
Authors: Huang Yan , Wuchty Stefan , Zhou Yuan , Zhang Ziding 
Published: January 2023

AbstractWhile deep learning (DL)-based models have emerged as powerful approaches to predict protein–protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein–protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.
Prediction of transcript structure and concentration using RNA-Seq data
Authors: Sharma Harsh , Pani Trishna , Dasgupta Ujjaini , Batra Jyotsna , Sharma Ravi Datta 
Published: January 2023

AbstractAlternative splicing (AS) is a key post-transcriptional modification that helps in increasing protein diversity. Almost 90% of the protein-coding genes in humans are known to undergo AS and code for different transcripts. Some transcripts are associated with diseases such as breast cancer, lung cancer and glioblastoma. Hence, these transcripts can serve as novel therapeutic and prognostic targets for drug discovery. Herein, we have developed a pipeline, Finding Alternative Splicing Events (FASE), as the R package that includes modules to determine the structure and concentration of transcripts using differential AS. To predict the correct structure of expressed transcripts in given conditions, FASE combines the AS events with the information of exons, introns and junctions using graph theory. The estimated concentration of predicted transcripts is reported as the relative expression in terms of log2CPM. Using FASE, we were able to identify several unique transcripts of EMILIN1 and SLK genes in the TCGA-BRCA data, which were validated using RT-PCR. The experimental study demonstrated consistent results, which signify the high accuracy and precision of the developed methods. In conclusion, the developed pipeline, FASE, can efficiently predict novel transcripts that are missed in general transcript-level differential expression analysis. It can be applied selectively from a single gene to simple or complex genome even in multiple experimental conditions for the identification of differential AS-based biomarkers, prognostic targets and novel therapeutics.
Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network
Authors: Zhao Lianhe , Qi Xiaoning , Chen Yang , Qiao Yixuan , Bu Dechao , Wu Yang , Luo Yufan , Wang Sheng , Zhang Rui , Zhao Yi 
Published: January 2023

AbstractThe determination of transcriptome profiles that mediate immune therapy in cancer remains a major clinical and biological challenge. Despite responses induced by immune-check points inhibitors (ICIs) in diverse tumor types and all the big breakthroughs in cancer immunotherapy, most patients with solid tumors do not respond to ICI therapies. It still remains a big challenge to predict the ICI treatment response. Here, we propose a framework with multiple prior knowledge networks guided for immune checkpoints inhibitors prediction—DeepOmix-ICI (or ICInet for short). ICInet can predict the immune therapy response by leveraging geometric deep learning and prior biological knowledge graphs of gene–gene interactions. Here, we demonstrate more than 600 ICI-treated patients with ICI response data and gene expression profile to apply on ICInet. ICInet was used for ICI therapy responses prediciton across different cancer types—melanoma, gastric cancer and bladder cancer, which includes 7 cohorts from different data sources. ICInet is able to robustly generalize into multiple cancer types. Moreover, the performance of ICInet in those cancer types can outperform other ICI biomarkers in the clinic. Our model [area under the curve (AUC = 0.85)] generally outperformed other measures, including tumor mutational burden (AUC = 0.62) and programmed cell death ligand-1 score (AUC = 0.74). Therefore, our study presents a prior-knowledge guided deep learning method to effectively select immunotherapy-response-associated biomarkers, thereby improving the prediction of immunotherapy response for precision oncology.
Differential co-expression network analysis with DCoNA reveals isomiR targeting aberrations in prostate cancer
Authors: Zhiyanov Anton , Engibaryan Narek , Nersisyan Stepan , Shkurnikov Maxim , Tonevitsky Alexander 
Published: January 2023

AbstractMotivationOne of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential expression analysis, differential co-expression one aims to detect pairs or clusters whose mutual expression changes between two conditions.ResultsWe developed Differential Co-expression Network Analysis (DCoNA)—an open-source statistical tool that allows one to identify pair interactions, which correlation significantly changes between two conditions. Comparing DCoNA with the state-of-the-art analog, we showed that DCoNA is a faster, more accurate and less memory-consuming tool. We applied DCoNA to prostate mRNA/miRNA-seq data collected from The Cancer Genome Atlas (TCGA) and compared predicted regulatory interactions of miRNA isoforms (isomiRs) and their target mRNAs between normal and cancer samples. As a result, almost all highly expressed isomiRs lost negative correlation with their targets in prostate cancer samples compared to ones without the pathology. One exception to this trend was the canonical isomiR of hsa-miR-93-5p acquiring cancer-specific targets. Further analysis showed that cancer aggressiveness simultaneously increased with the expression level of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of P. Hertsen Moscow Oncology Research Institute patients’ cohort analyzed by miRNA microarrays.Availability and implementationSource code and documentation of DCoNA are available at https://github.com/zhiyanov/DCoNA.Supplementary informationSupplementary data are available at Bioinformatics online.
Autophagy in acute kidney injury and maladaptive kidney repair
Authors: Xiang Yu , Fu Ying , Wu Wenwen , Tang Chengyuan , Dong Zheng 
Published: January 2023

Abstract
Acute kidney injury (AKI) is a major renal disease characterized by a sudden decrease in kidney function. After AKI, the kidney has the ability to repair, but if the initial injury is severe the repair may be incomplete or maladaptive and result in chronic kidney problems. Autophagy is a highly conserved pathway to deliver intracellular contents to lysosomes for degradation. Autophagy plays an important role in maintaining renal function and is involved in the pathogenesis of renal diseases. Autophagy is activated in various forms of AKI and acts as a defense mechanism against kidney cell injury and death. After AKI, autophagy is maintained at a relatively high level in kidney tubule cells during maladaptive kidney repair but the role of autophagy in maladaptive kidney repair has been controversial. Nonetheless, recent studies have demonstrated that autophagy may contribute to maladaptive kidney repair after AKI by inducing tubular degeneration and promoting a profibrotic phenotype in renal tubule cells. In this review, we analyze the role and regulation of autophagy in kidney injury and repair and discuss the therapeutic strategies by targeting autophagy.
Identification of Two Novel Hydroperoxide Impurities in Fluocinolone Acetonide Topical Solution by Liquid Chromatography Mass Spectrometry
Authors: Kavitapu DasameswaraRao , Jaishetty Nagadeep , Maruthapillai Arthanareeswari , Murty J N S R C 
Published: January 2023

Abstract
Fluocinolone acetonide topical is used to treat skin discomforts such as swelling, itching and redness by activating the natural substances in the skin. Several process-related impurities and degradation products are identified and reported. But hydroperoxide impurities in Fluocinolone acetonide topical solution are not reported anywhere. In this study, we identify two potential genotoxic isomeric hydroperoxide impurities in Fluocinolone acetonide topical solution by Liquid Chromatography Mass Spectrometry analysis. A possible mechanism for the formation of these two novel hydroperoxide impurities is based on the neighboring group participation effect of adjacent hydroxyl group (Internal SN2) which results in the loss of fluorine atom and formation of epoxide intermediate followed by the addition of the HOOH group. Since most of the hydroperoxide impurities are genotoxic in nature, one should eliminate these impurities from Active Pharmaceutical Ingredient (API) or protect the formulation product from these oxidative impurities.
HISTOPATHOLOGICAL CHARACTERIZATION OF A CHRONIC DSS-INDUCED MOUSE MODEL OF IBD WITH INTESTINAL FIBROSIS
Authors: Nielsen Malte Hasle , Zachariassen Line , Sárvári Anitta Kinga , Marstrand-Jørgensen Adam Bøgh , Hansen Henrik Björk , Feigh Michael 
Published: January 2023

Abstract

RATIONALE
Inflammatory bowel disease (IBD) comprises a group of intestinal disorders, including ulcerative colitis and Crohn's disease. Intestinal fibrosis, as result of chronic inflammation, is a common complication in IBD. High treatment failure rates associated with existing interventions indicate a high unmet need for more effective drugs to improve the management and outcomes of IBD. Consequently, translational animal models of IBD demonstrating chronic, progressive colonic fibrosis are important tools in preclinical drug discovery for IBD. The aim of the present study was to characterize intestinal histopathology in a chronic DSS-induced mouse model of IBD.

METHODS
Ten-weeks-old male C57BL/6JRj mice were randomized into study groups based on body weight and received 3 cycles of DSS (2.5 % w/v) in the drinking water (DSS-IBD) or normal water (CTRL). Terminal endpoints included colon morphometry and quantitative histological markers of inflammation and fibrosis assessed in proximal, middle and distal colonic segments. Using RNA sequencing, transcriptome signatures were profiled in the middle colon segment.

RESULTS
Compared to healthy controls, the chronic DSS-IBD mouse model demonstrated hallmarks of IBD, including mild-to-moderate weight loss and colonic hypertrophy. Histopathological analysis indicated increased deposition of inflammatory (CD45 and CD11b), fibrogenesis (alpha-smooth muscle actin) and fibrosis markers (collagen 1a1, Picrosirius red) in the middle and distal colon. Severe fibrosis was observed throughout all layers of the middle colon. Histological findings were supported by significantly upregulated gene expression markers of inflammation and fibrosis.

CONCLUSIONS
The DSS-IBD mouse is a translational preclinical model suitable for testing novel drug therapies for IBD with intestinal fibrosis.
THE ORAL GLUTMATE CARBOXYPEPTIDASE II INHIBITOR (S)-IBD3540 ROBUSTLY ATTENUATES INFLAMMATION IN SPONTANEOUS IL10 COLITIS
Authors: Peters Diane , Pradhan Manisha , Slusher Barbara 
Published: January 2023

Abstract

BACKGROUND & AIMS
Glutamate carboxypeptidase II (GCPII) is elevated in active inflamed biopsies of both Crohn’s disease and ulcerative colitis patients and is a promising therapeutic target for the treatment of IBD. We have previously presented the development and characterization of a first-in-class, gut-restricted, and orally active GCPII inhibitor, (S)-IBD3540, and have shown that oral (S)-IBD3540 attenuates disease severity in multiple murine colitis models, including acute dextran sulfate sodium (DSS)-induced colitis. Here, we extend our knowledge regarding (S)-IBD3540’s anti-inflammatory effects in spontaneously occurring colitis of interleukin 10 (IL10-/-) mice by performing longitudinal measurement of inflammatory biomarker fecal lipocalin 2 (LCN2) and terminal colon cytokine and chemokine measurements.

METHODS
Female BALB/cAnNTac-Il10em7Tac mice (Taconic, Germantown, NY) were acquired at 4 weeks of age and monitored three times weekly for the onset of clinical signs, including alterations in stool consistency. Following onset of symptoms, groups were randomized and treatment was initiated with vehicle or 100 mg/kg eq. (S)-IBD3540 once daily by oral gavage. Prior to treatment, and biweekly thereafter, fecal LCN2 was quantified using a mouse Lipocalin-2/NGAL DuoSet ELISA (R&D Systems). Upon study termination, proximal colon homogenates were prepared and analyzed by a mouse Cytokine/Chemokine 31-Plex Discovery Assay Array (Eve Technologies).

RESULTS
Impressively, despite initiating treatment in highly symptomatic mice 100 mg/kg oral (S)-IBD3540 was found to reverse abnormalities in stool consistency. Additionally, (S)-IBD3540 had potent anti-inflammatory effects. As expected, fecal LCN2 was observed to increase throughout the study period in vehicle treated mice, however, fecal LCN2 was significantly decreased in (S)-IBD3540 treated mice (p&lt;0.05). Similarly, colon multiplex analysis identified significant reductions in multiple cytokines and chemokines implicated in IL10 colitis pathobiology, with notable reductions in cytokines TNFa, IL-12p40, and IL-17, and chemokines IP-10 and MIP-2.

CONCLUSION
(S)-IBD3540 is a novel, gut-restricted, GCPII inhibitor that has robust anti-inflammatory activity in IL10-/- colitis. Encouragingly, we have now found that in this model (S)-IBD3540 reduces pro-inflammatory cytokines/chemokines that are the targets of existing IBD medications. Studies to compare efficacy to one of these agents, anti-IL-12p40, are in progress.