Paris Biotech Santé

In the colon, the single-layered epithelium forms crypts that extend into the mucosa and are surrounded by a fibroblast network essential for extracellular matrix (ECM) production, remodeling and epithelial support. Fibroblasts are heterogeneous, but inconsistent nomenclature and lack of markers have hindered their classification. Using single-cell RNA sequencing (scRNA-seq), we identified six distinct fibroblast subpopulations in mouse colonic mucosa, each with unique molecular profiles and specialized functions. Some fibroblasts focus on ECM production and remodeling, whereas others show high contractility. Certain subsets of the fibroblasts secrete cytokines promoting epithelial differentiation or stem cell niche maintenance. Spatial mapping revealed their organization within the mucosa, and trajectory analysis suggested distinct differentiation pathways. Cell cycle scoring confirmed that fibroblasts remain largely non-proliferative under homeostasis. By integrating our dataset with published ones, we identify conserved fibroblast populations and propose a standardized nomenclature for intestinal fibroblasts. This framework enhances communication and understanding of fibroblast diversity and their roles in gut homeostasis and disease.

Abstract Abstract FigureGraphical abstractIn the colon, single-layered epithelium lines the crypts, which descend into the underlying mucosa. Colonic crypts are surrounded by a network of fibroblasts essential for various functions, including the production and remodeling of the extracellular matrix (ECM), supporting the epithelial stem cell niche, and promoting the differentiation of epithelial cells. Recent studies indicate that fibroblasts are not a homogeneous cell population. However, differences in nomenclature and a lack of exact markers hindered their classification and functional understanding. Here, we used single-cell RNA-sequencing (scRNA-seq) to identify six distinct fibroblast subpopulations in mouse colonic mucosa, each with unique molecular signatures and functional specialization. Our analysis reveals that some fibroblasts are primarily involved in ECM production and remodeling, while others exhibit high contractility. Additionally, a subset of fibroblasts produces cytokines that promote epithelial cell differentiation, whereas another group secrete cytokines essential for maintaining the epithelial stem cell niche. We also map the spatial distribution of these fibroblast subpopulations within the colonic mucosa. Differentiation trajectory analysis suggests distinct pathways for fibroblast differentiation, while cell cycle scoring reveals that fibroblasts do not proliferate under homeostatic conditions. Furthermore, we integrated our scRNA-seq data with previously published datasets to identify common fibroblast populations and propose a standardized nomenclature for intestinal fibroblasts. This unified framework aims to improve communication within the research community and enhance understanding of fibroblast roles in gut homeostasis and gastrointestinal diseases.

Ostrya carpinifolia L., a member of the Betulaceae family, is a tree endemic to the Mediterranean basin that is well known for the hardness of its wood. In this study, we assess the anti-pollution activities of a hydroalcoholic extract of O. carpinifolia twigs using several judiciously selected in vitro cosmetic bioassays. The extract's capacity to counteract excessive production of reactive oxygen species following a cutaneous exposure to atmospheric pollution was evaluated using a combination of several antioxidant assays: DPPH, FRAP and β-carotene bleaching assays. These antioxidant assays were complemented by anti-elastase, anti-collagenase, anti-hyaluronidase and anti-lipoxygenase assays to evaluate the capacity of the extract to preserve the integrity of the skin. The hydroalcoholic extract of O. carpinifolia demonstrates intriguing biological antioxidant activities, with approximately 50 % inhibition observed in DPPH and β-carotene assays. Furthermore, its anti-lipoxygenase, anti-hyaluronidase, and anti-collagenase activities are noteworthy, exceeding 50 % inhibition. The two major compounds of O. carpinifolia ethanolic extract were isolated and identified as myricitrin (1) and quercitrin (2). Myricitrin and quercitrin exhibit antioxidant and anti-hyaluronidase properties; we explored the correlation of these properties with the activity of the crude hydroalcoholic extract. Notably, these compounds have not been previously described in the Ostrya genus.

ObjectiveA fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA).Materials and methods2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (AllE), or from the 1st echo only (1stE) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the AllE U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation.ResultsThe agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (AllE/1stE) and 0.88 ± 0.03/0.88 ± 0.03 (AllE/1stE) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to − 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms (AllE/1stE). The AllE U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen’s D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the AllE U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41).ConclusionThe fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis.Trial registrationClinicaltrials.gov identification: NCT00080171.

The performance of dairy cows is influenced by the microbial communities hosted within their digestive tract. While the rumen microbiota has long been associated with host phenotypes, the impact of the faecal microbiota remains elusive. In this study, we collected 697 faecal samples from commercial Holstein cows and analysed them with 16S rRNA gene analyses. For each animal, routinely recorded data, i.e., milk yield, fat yield, protein yield, fat content, protein content, and an aggregate production trait (pINEL) based on the French economic dairy index, were available to assess the links between the faecal microbiota and host production. Our findings revealed a strong and significant association between the structure of the bacterial and prokaryote community (β-diversity) and dairy production. In addition, differential abundance analyses identified 48 genera whose abundances were significantly associated with pINEL, milk, fat and protein yield. Among these genera, the increased abundance of Bifidobacterium, and particularly an amplicon sequence variant with a 16S rRNA V3-V4 gene region identical to B. globosum and B. pseudolongum, was found to be the most important for high-yielding animals. Bifidobacterium seemed to be a potential key member of the bovine faecal microbiota that should be further investigated. Conversely, the p-1088-a5 gut group genus was found more abundant in low-productive cows. In conclusion, this study demonstrates significant associations between the faecal microbiota and the performance of dairy cows at the whole lactation scale. A better understanding of the physiology of the gut microbiota could help to improve dairy cow production.

Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees. We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner. Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 vs. 52.1±2.1 ms for manual; P<0.001). T2 correlations were r=0.91-0.99 for deep and r=0.86-0.97 for superficial layers across regions. The only statistically significant T2 increase over 1 year was observed in the deep layer of the lateral femur [standardized response mean (SRM) =0.58 for automated vs. 0.52 for manual analysis; P<0.001]. There was no relevant difference in baseline/longitudinal T2 values/changes between the ACL-injured groups and healthy participants, with either segmentation method. This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).

Glenzocimab is a novel antithrombotic agent which targets platelet glycoprotein VI (GPVI) and does not induce haemorrhage. SARS-CoV-2 triggers a prothrombotic state and lung injury whose mechanisms include coagulopathy, endothelial dysfunction, and inflammation with dysregulated platelets. GARDEN was a randomised double-blind, exploratory phase II study of glenzocimab in SARS-CoV-2 respiratory failure (NCT04659109). PCR+ adults in Brazil and France (7 centres) were randomized to standard-of-care (SOC) plus glenzocimab (1000 mg/dayx3 days) or placebo, followed for 40 days. Primary efficacy endpoint was clinical progression at Day 4. All analyses concerned the intention-to-treat population. Between December 2020 and August 2021, 61 patients received at least one dose (30 glenzocimab vs 32 placebo) and 58 completed the study (29 vs 29). Clinical progression of COVID-19 ARDS was not statistically different between glenzocimab and placebo arms (43.3% and 29.0%, respectively; p = 0.245). Decrease in the NEWS-2 category at D4 was statistically significant (p = 0.0290) in the glenzocimab arm vs placebo. No Serious Adverse Event (SAE) was deemed related to study drug; bleeding related events were reported in 6 patients (7 events) and 4 patients (4 events) in glenzocimab and placebo arms, respectively. Therapeutic GPVI inhibition assessment during COVID-19 was conducted in response to a Public Health emergency. Glenzocimab in coagulopathic patients under therapeutic heparin was neither associated with increased bleeding, nor SAE. Clinical impact of glenzocimab on COVID-19 ARDS was not demonstrated. A potential role for GPVI inhibition in other types of ARDS deserves further experimentation. Glenzocimab is currently studied in stroke (ACTISAVE: NCT05070260) and cardiovascular indications.