bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy
Authors: Guo Binjie , Zheng Hanyu , Jiang Haohan , Li Xiaodan , Guan Naiyu , Zuo Yanming , Zhang Yicheng , Yang Hengfu , Wang Xuhua
Published: January 2023

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.
Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps
Authors: Nakamura Andrew , Meng Hanze , Zhao Minglei , Wang Fengbin , Hou Jie , Cao Renzhi , Si Dong
Published: January 2023

Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.
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

Single-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

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

Binding 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

During 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

Identification 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

Somatic 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

While 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

Alternative 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

The 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

Motivation

One 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.

Results

We 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 implementation

Source code and documentation of DCoNA are available at https://github.com/zhiyanov/DCoNA.

Supplementary information

Supplementary 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

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

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

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

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<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.
MACHINE LEARNING-BASED PREDICTION OF GEBOES SCORE AND HISTOLOGIC IMPROVEMENT AND REMISSION THRESHOLDS IN ULCERATIVE COLITIS
Authors: Shanis Zahil , Padigela Harshith , Sucipto Kathleen , Shamshoian John , Li Jin , Walker Andrew , Fahy Darren , Lin Mary , Montalto Mike , Beck Andrew , Mehta Jimish , Wapinski Ilan , Khosla Archit , Pokkalla Harsha , Najdawi Fedaa , Jayson Christina
Published: January 2023

BACKGROUND

Histology is emerging as a key therapeutic endpoint for ulcerative colitis driven by associations between histologic response and long-term outcomes. However, existing scoring systems are subjective and consequently have variable inter- and intra-reader variability. Geboes scoring is a well-established system for ulcerative colitis histologic assessment that has previously been used to define thresholds for histo-endoscopic mucosal improvement (Geboes Score ≤3.1, together with Mayo score 0 or 1) and histologic remission (Geboes Score <2). Here we report the first machine learning (ML)-based prediction of the Geboes Score, and Geboes Score-derived thresholds of histologic improvement and remission, directly from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained mucosal biopsies.

METHODS

3,148 WSI were scored by three expert gastrointestinal pathologists and the median consensus score was used to determine the Geboes score for each slide as ground truth. ML models were trained on median consensus scores to predict the Geboes score and subscores for each slide. Model performance vs. pathologist median consensus score was measured using accuracy and the F1 score, which accounts for both false positive and false negative errors.

RESULTS

The ML-based model performance, measured against median consensus scores of three pathologists, showed strong performance at predicting overall Geboes Score, with a quadratic kappa of 0.89. The model was also able to predict both histologic improvement and histologic remission with high accuracy. For predicting histological improvement as defined by a Geboes Score of ≤3.1, the model showed accuracy of 0.92 and F1 score of 0.92 (Figure 1). For predicting histological remission as defined by a Geboes Score of < 2, the model showed accuracy of 0.91 and F1 score of 0.89 (Figure 2).

CONCLUSIONS

We report a ML-based approach for predicting Geboes score and Geboes score-based key thresholds of histologic improvement and histologic remission. Model predictions show high accuracy compared to median consensus pathologist scores. This approach may enable standardized, reproducible and accurate prediction of these clinically relevant thresholds to better measure histologic disease activity and treatment response in clinical trials.
METHODS TO DERIVE A CROHN'S DISEASE ACTIVITY INDEX FROM ELECTRONIC HEALTH RECORDS DATA
Authors: Swenson Anna , Womack Katherine , Quinn Meaghan , Curhan Gary
Published: January 2023

BACKGROUND

The Crohn’s Disease Activity Index (CDAI) is currently the gold-standard for measuring disease activity in Crohn’s disease (CD) patients. It is the most commonly used outcome measure in clinical trials, however, due to the burden of completion, it is infrequently used in routine care. Real world data, including electronic health records (EHR), are useful for studying longitudinal disease and treatment outcomes in the real world, but most EHR databases do not include CDAI. Building off the methods presented by Rudrapatna et al. 2021, we calculated a derived CDAI using data elements available in EHRs.

METHODS

Data were derived from the OM1 PremiOM UC Dataset (OM1, Boston, MA), a multisource real-world database with linked healthcare claims and EHR data on US patients with CD (2013-present). Weight, body temperature and hematocrit were sourced from structured vital signs and laboratory data. Ideal body weight was calculated from height and gender using the Devine formula. Extraintestinal manifestations/complications were evaluated based on the presence or absence of ICD diagnosis codes for each condition. Abdominal mass was abstracted from clinical notes. Number of stools in the past 7 days, average daily abdominal pain, and general well being were extrapolated from single-day measures extracted from clinical notes rather than based on 7-day diaries. Each component value was multiplied by the weights outlined in Best et al. 1976. Components recorded within 4 months were added to calculate a derived CDAI. Multiple CDAIs could be calculated for each patient over time, but each component was allowed to contribute to only one CDAI score.

RESULTS

Among 13,159 CD patients, only 12 had a CDAI recorded in their notes. After applying the algorithm, we were able to calculate a derived CDAI for 1,378 patients. 489 patients had > 1 score derived. Figure 1 shows the distribution of derived CDAIs. Patients with a derived CDAI were younger and a higher proportion were treated with biologics and corticosteroids than those who could not have a CDAI calculated (Table 1).

CONCLUSIONS

A derived CDAI can be calculated from data that may be routinely recorded in EHRs. Efforts to standardize the collection of CDAI components in EHRs would further increase the availability of derived CDAIs. Further work to validate these derived scores against a gold standard and to replicate the findings in other real-world datasets should be considered. Validation was not feasible in this dataset due to the lack of recorded CDAIs. Derived CDAIs could potentially be used to study longitudinal changes in disease status and treatment effectiveness in settings where observed CDAIs are unavailable.
ILEOSTOMY AND RISK OF ACUTE KIDNEY INJURY IN PATIENTS WITH CROHN'S DISEASE
Authors: Abomhya Ahmed , Ayaz Salman , Haider Salman
Published: January 2023

BACKGROUND

Chron's disease is an inflammatory disorder that can involve any part of GI tract but most commonly involves the small intestine. Many of the patients with Cohn’s disease end up requiring surgery, and the 10-year risk of requiring surgery is estimated to be around 50%. The most common type of surgery in patients with Crohn’s disease is ileocecal resection. More than 40,000 ileostomies are formed annually in the United States. Up to 15% of patients who receive ileostomy experience a de novo or community-onset AKI within 90 days. Oral rehydration with iso-osmolar fluids is the mainstay of outpatient management. Both hypotonic fluids, and hypertonic fluids can cause a net flow of fluid into the bowel lumen increasing the ostomy output. We aimed to study the risk of AKI within 30 days in CD patients after an ileostomy procedure.

METHODS

We collected data from the Healthcare Cost and Utilization Project- (HCUP) Nationwide Readmission Database- 2016- 2018. CD patients were identified and those who received an ileostomy procedure were identified using ICD-10 and PCS-10 codes respectively. Median and IQR were used to describe Continuous variables, and proportions were used with categorical variables. Comparison between groups was performed by Mann Whitney test for continuous variables and the Chi-Square test for Categorical variables.

RESULTS

We identified 214,622 index hospitalizations with CD, 3,789 of whom received an ileostomy procedure. Of those who received ileostomy, 2.1% were readmitted within 30 days for a primary diagnosis of AKI while 2.9% had 30-day readmission with a secondary diagnosis of AKI. CD patients admitted for AKI after ileostomy had a median length of stay of 14 days (IQR: 8-24). CD patients who received ileostomy had a higher rate of 30-day admission for AKI (2.1% vs. 0.3%, P <0.001) and a higher rate of 30-day readmission with a secondary diagnosis of AKI (2.9% vs. 1.5%, P <0.001) compared to CD patients who didn’t receive ileostomy during the index hospitalization. CD patients who received an ileostomy procedure and were readmitted for AKI within 30 days had an average cost of hospitalization of $140,544 (IQR: $85,072 - $242,059). CD patients admitted for AKI within 30 days after an ileostomy were older (59, IQR: 48-69 vs. 47, IQR: 33-61, P <0.001) compared to those who didn’t have a 30day readmission for AKI respectively.

CONCLUSION

In a nationwide cohort of patients hospitalized with Crohn’s disease, patients who received ileostomy had a significant risk of AKI within 30-days. The development of AKI was associated with significant morbidity, hospital readmission, and elevated costs of hospitalization. Further studies are needed to investigate novel measures to decrease the risk of AKI in this patient population.
PREDICTORS OF DEVELOPING MULTIPLE EXTRA-INTESTINAL MANIFESTATIONS OF INFLAMMATORY BOWEL DISEASE
Authors: Alizadeh Madeline , Motwani Kiran , Cross Raymond
Published: January 2023

Extraintestinal Manifestations (EIMs) of Inflammatory Bowel Diseases (IBD) are a frequent feature of disease, occurring in 25-40% of patients with IBD. While associations with IBD type, gender, and age at diagnosis are known, less is known about the associations between different EIMs and which patients are most likely to develop more than 1 EIM. The goal of our study was to better characterize which patients may be at higher risk of developing multiple EIMs.

METHODS: A retrospective review of patients enrolled in the Study of Prospective Adult Research Cohort with IBD (SPARC IBD) registry was performed and demographics, clinical data, and patient-reported outcomes were analyzed. A total of 1,202 patients with data available on EIMs were included, where EIMs included IBD-associated arthropathy, ocular complications including uveitis/iritis, primary sclerosing cholangitis, erythema nodosum, pyoderma gangrenosum, and thrombotic complications. Welch’s t-test and Fisher’s exact test were used to assess differences among continuous and categorical variables between those with versus without EIMs, as well as for comparisons between individual EIM groups.

RESULTS: Three hundred forty-seven patients with at least 1 EIM were identified, compared to 855 patients without any EIMs. Two hundred eight four patients were identified as having 1 EIM, 54 patients had 2 EIMs, and 9 patients had 3 EIMs. Patients with Crohn’s disease and women were more likely to have 2 or more EIMs (p-value = 0.00517 and 0.000285, respectively). Patients with ocular manifestations were likeliest to have at least 2 EIMs (with 27/37 or 73.0% having multiple), while those with pyoderma gangrenosum and IBD associated arthritis were least likely to have additional EIMs (p-value = 0.000500). Further, no patients with PSC developed pyoderma gangrenosum (Fisher’s multiple comparison p-value = 0.00187). Most patients with 3 EIMs had IBD-associated arthritis and/or erythema nodosum (88% having each), while only 1 patient (11%) with 3 EIMs had PSC (p-value = 0.00161).

CONCLUSION: Women and patients with Crohn’s disease are more likely to develop multiple EIMs and patients with ocular EIMs are more likely to have another EIM. Understanding how individual EIMs relate to one another can be used to better identify patients at risk of developing future EIMs and may help with risk stratification when choosing treatments.
SEPSIS IN THE SETTINGS OF INFLAMMATORY BOWEL DISEASE: STUDYING THE OUTCOMES OF OVER EIGHT-MILLION PATIENTS
Authors: Aldiabat Mohammad , Kilani Yassine , Aljabiri Yazan , Al-bashabsheh Abdel-qader
Published: January 2023

BACKGROUND

Inflammatory bowel disease (IBD) is associated with increased risk of infections, attributed to the nature of the disease itself, and the use of immunosuppressive agents in these patients as well. However, the impact of IBD on patients with sepsis is poorly studied. In this analysis, authors aim to investigate the outcomes of sepsis in patients with IBD.

METHODS

This is an analysis of the National Inpatient Sample Database of the years 2016 to 2019. Using ICD-10 codes, authors identified patients who were admitted with a principal diagnosis of sepsis and those with history of Crohn’s disease (CD) and ulcerative colitis (UC). Primary outcome was mortality difference AND risk of sepsis-related complications. Secondary outcomes included length of stay and charge of care. Multivariate logistic analysis was performed using Stata/BE 17.0. Baseline patients and facilities characteristics were incorporated into the analysis. Data was considered statistically significant if p-value was <0.05.

RESULTS

Among a total number of 8,403,578 patients who were admitted for sepsis and included in the study, 61390 (0.73%) and 47504 (0.56%) had a history of CD and UC, respectively. When compared to general population, adjusted odds ratio for mortality were lower in patients with CD than in UC (OR 1.01, p<0.001, 95% CI 0.74 – 0.87 vs OR 0.81, p=0.758, 95% CI 0.93 – 1.09). Increased risk of septic shock observed in UC-sepsis group more than in CD-sepsis group (OR 1.28, p<0.001, 95% CI 1.21 – 1.34 vs OR 1.08, p=0.001, 95% CI 1.03 – 1.13), acute kidney injury (UC OR 1.18, p<0.001, 95% CI 1.13 – 1.23, CD OR 1.11, p<0.001, 95% CI 1.06 – 1.15) and non-variceal upper GI bleeding (UC OR 1.64, 95% CI 1.50 – 1.79, p<0.001vs CD OR 1.15, 95% CI 1.04 – 1.27, p=0.004). Both groups had prolonged length of stay, more prolonged with UC (UC Coefficient 1.04 days, 95% CI 0.85 – 1.24, p<0.001, vs CD Coefficient 0.67, 95% CI 0.51 – 0.82, p<0.001) and in charge of care (UC Coefficient 13505$, 95% CI 10561 – 16449, p<0.001 vs CD Coefficient 5855$, 95% CI 3251 – 8189, p<0.001) when compared to admitted sepsis patients without prior history of IBD.

CONCLUSION

Patient admitted with sepsis have worse outcomes in the settings of inflammatory bowel diseases, represented by higher risk septic shock, acute kidney injury, non-variceal upper GI bleeding, prolonged length of stay and increased charge of care. These outcomes noticed to be more severe in sepsis patients with ulcerative colitis than those with Crohn’s disease. Clinicians and intensivist should be aware of the high risk associated with inflammatory bowel disease in sepsis patients, especially in those with ulcerative colitis.
DEMOCRATIC DELIBERATION TO ASSESS VETERAN PREFERENCES FOR BIOSIMILAR MEDICATION SWITCHING FOR THE TREATMENT OF INFLAMMATORY BOWEL DISEASE IN A RESOURCE CONSCIOUS SETTING
Authors: Ryan Kerry , Hou Jason , Baker Jessica , Krenz Christopher , Waljee Akbar , De Vries Raymond
Published: January 2023

BACKGROUND

To optimize care while being resource conscious, the Veterans Health Administration (VHA) must develop equitable policies that incorporate Veterans’ values and beliefs. Switching patients with Inflammatory Bowel Disease (IBD) from biologic medications (originators) to less expensive biosimilars offers the opportunity to explore potential policies. Unlike originators, the FDA did not require disease-specific randomized controlled trials for approval of biosimilars, creating provider and patient concerns about the safety of switching to these medications. Deliberative democratic (DD) methods allow us to assess Veteran views of switching programs, a necessary step in creating equitable policy approaches.

METHODS

We carried out two 2-day sessions with Veterans from the Ann Arbor VA (n=17) and Houston VA (n=12). Deliberation began with education about IBD, use of biologics (including biosimilars), and related implementation challenges. Participants engaged in facilitated small group discussions designed to 1) identify Veterans views about biosimilar switching and their preferences for informing Veterans about switching, and 2) evaluate responses to five approaches for switching: (a) “status quo”–switching policies vary by VHA facility, (b) “sickest last”–Veterans with the most severe IBD are switched last, (c) “opt-out”–Veterans may opt-out of switching, (d) “next appointment”–Veterans are switched at their next appointment, and (e) “lottery”–patients are switched based on random selection. Participants ranked options (1=most preferred, 5=least preferred) and proposed modifications to these approaches. Participants completed pre- and post-DD session surveys. Discussion transcripts were analyzed using qualitative methods.

RESULTS

Participant demographics are in Table 1. Most Veterans sessions preferred the “sickest last” (mean rank: 1.8, standard deviation: 0.9) or “opt-out” (2.4,1.4) approaches over the “status quo” (3.1,1.0) or “lottery” (4.6,0.9); preferences did not significantly change after deliberation (Figure 1). Preferences reflected Veterans’ concerns about the risk of switching to biosimilars for the most vulnerable Veterans and their strong preference for information and transparency around biosimilar switching. Participants emphasized the importance of informed choice, prioritizing based on health status, personalization, and shared decision-making (e.g., treatment should be geared to individual patient in discussion with their provider).

CONCLUSION

DD methods are effective for soliciting Veterans' opinions and preferences for health policies. Veterans favored policies that provided information and control, promoted shared decision-making, and reduced risk to vulnerable patients. Incorporating patient preferences and insights is critical when implementing health policies in the VHA and other resource-limited settings.
EPITHELIAL CELLS OF ULCERATIVE COLITIS PATIENTS REQUIRE NORMAL LEVELS OF MITOCHONDRIA AND BETA-CATENIN TO UTILIZE BUTYRATE METABOLISM AND PROMOTE ULCER HEALING
Authors: Erinkitola Iyabo , Abomhya Ahmed , Hassan Syed Adeel , Conder Evan , Rosenau Kian , Kapur Neeraj , Goretsky Tatiana , Barrett Terrence
Published: January 2023

BACKGROUND

Under normal conditions butyrate produced by obligate anaerobes (e.g. firmicutes) provides the dominant energy source for colonocytes via β-oxidation and the tricarboxylic acid cycle. Ulcerative Colitis (UC) is an inflammatory bowel disease (IBD) characterized by reduced butyrate metabolism and intestinal epithelial cell (IEC) mitochondria (needed for β-oxidation). Given that nonhealing ulcers in UC are associated with reduced crypt fissioning and branching, we asked whether there was a relationship between mitochondrial deficiency and butyrate-induced crypt branching using colonoid cultures grown from human biopsies.

METHODS

IECs from biopsies taken from active and uninvolved areas of the same UC patient were grown in colonoid cultures. After the second passage, the colonoids with crypt branching were quantified. In separate studies, we modeled mitochondrial deficiency by shRNA knockdown of transcription mitochondrial factor A (Tfam) in human colonoids, a gene responsible for mitochondrial DNA stability and transcription. Both shControl and shTfam colonoids were treated with 5mM of Na-butyrate overnight. NCM460 cells were also treated with 5mM of butyrate overnight, and both ATP levels and TCF/LEF (b-catenin transcriptional activity) were examined.

RESULTS

Colonoids from active UC segments demonstrated 27% less crypt branching than uninvolved areas. These areas also displayed reduced mitochondria complex levels by WB of IEC. To directly examine the role of mitochondria in these effects, crypt branching was examined in shTFAM-treated colonoids where crypt branching levels were 31% less than shCont-treated colonoids (Fig. 1). Butyrate treatment of both shCont and shTfam colonoids increased crypt formation by 11% and 8%, respectively compared to untreated groups. In addition, butyrate increased levels of ATP by 55% and b-catenin transcriptional activity (TCF-LEF) by >600%.

CONCLUSION

This data was consistent with the notion that the failure to heal ulceration in UC patients involves both 1) reduction in butyrate availability from the microbiome (ie reduced stimulation) and 2) reduced mitochondria needed to metabolize butyrate. Upon effective anti-inflammatory treatment, we believe butyrate levels and IEC mitochondrial metabolism increase which contributes to IEC b-catenin signaling and enhanced crypt fissioning/branching needed for effective ulcer healing. We believe this data provide a potential mechanism for how enhanced mitochondrial function contributes to ulcer healing in IBD.