Challenges in IBD Research 2024: Precision Medicine.

Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.

Precision medicine is the practice of getting the most suitable drug or treatment option to each individual patient at the right time. In Crohn's disease and ulcerative colitis, we need to learn more about the diversity of patients to deliver precision medicine.

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology.

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.

Probing the Global Cellular Responses to Lipotoxicity Caused by Saturated Fatty Acids.

Excessive levels of saturated fatty acids are toxic to cells, although the basis for this lipotoxicity remains incompletely understood. Here, we analyzed the transcriptome, lipidome, and genetic interactions of human leukemia cells exposed to palmitate. Palmitate treatment increased saturated glycerolipids, accompanied by a transcriptional stress response, including upregulation of the endoplasmic reticulum (ER) stress response. A comprehensive genome-wide short hairpin RNA (shRNA) screen identified >350 genes modulating lipotoxicity. Among previously unknown genetic modifiers of lipotoxicity, depletion of RNF213, a putative ubiquitin ligase mutated in Moyamoya vascular disease, protected cells from lipotoxicity. On a broader level, integration of our comprehensive datasets revealed that changes in di-saturated glycerolipids, but not other lipid classes, are central to lipotoxicity in this model. Consistent with this, inhibition of ER-localized glycerol-3-phosphate acyltransferase activity protected from all aspects of lipotoxicity. Identification of genes modulating the response to saturated fatty acids may reveal novel therapeutic strategies for treating metabolic diseases linked to lipotoxicity.

Rab18 is not necessary for lipid droplet biogenesis or turnover in human mammary carcinoma cells.

Rab GTPases recruit peripheral membrane proteins and can define organelle identity. Rab18 localizes to the endoplasmic reticulum (ER) but also to lipid droplets (LDs), where it has been implicated in effector protein recruitment and in defining LD identity. Here, we studied Rab18 localization and function in a human mammary carcinoma cell line. Rab18 localized to the ER and to LD membranes on LD induction, with the latter depending on the Rab18 activation state. In cells lacking Rab18, LDs were modestly reduced in size and numbers, but we found little evidence for Rab18 function in LD formation, LD turnover on cell starvation, or the targeting of several proteins to LDs. We conclude that Rab18 is not a general, necessary component of the protein machinery involved in LD biogenesis or turnover.

Atomic structure of a toxic, oligomeric segment of SOD1 linked to amyotrophic lateral sclerosis (ALS).

Fibrils and oligomers are the aggregated protein agents of neuronal dysfunction in ALS diseases. Whereas we now know much about fibril architecture, atomic structures of disease-related oligomers have eluded determination. Here, we determine the corkscrew-like structure of a cytotoxic segment of superoxide dismutase 1 (SOD1) in its oligomeric state. Mutations that prevent formation of this structure eliminate cytotoxicity of the segment in isolation as well as cytotoxicity of the ALS-linked mutants of SOD1 in primary motor neurons and in a Danio rerio (zebrafish) model of ALS. Cytotoxicity assays suggest that toxicity is a property of soluble oligomers, and not large insoluble aggregates. Our work adds to evidence that the toxic oligomeric entities in protein aggregation diseases contain antiparallel, out-of-register ß-sheet structures and identifies a target for structure-based therapeutics in ALS.