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Auffret, M.

Publications and source records attributed to Auffret, M..

6 recordsLinked to original sources

Distinct iron metabolism regulation during cachexia progression in murine models of intestinal and pancreatic cancer

Background. Cancer cachexia is a multifactorial syndrome characterized by substantial metabolic disturbances including systemic inflammation, and muscle atrophy, which may affect the regulation of iron metabolism in patients. This study aimed to characterize this regulation in two murine models of colorectal and pancreatic cancer, both associated with a high prevalence of cachexia. Methods. Histological, biochemical and molecular analyses were performed in serum, skeletal muscle, liver, spleen and tumor from UN-KC-6141 pancreatic tumor bearing mice and from ApcMin/+ mice (colorectal cancer model), both models exhibiting variable cachexia severity. Results. Both models induce cachexia, but diverge markedly in their regulation of iron metabolism. UNKC mice display reduced plasma iron availability (-9.6% transferrin saturation, p<0.001) without concomitant anemia, while hepatic hepcidin synthesis is upregulated (+100.6%, p<0.001) by inflammation, promoting ferroportin (FPN) downregulation (-42.4%, p<0.001) and iron sequestration in the spleen (+134% total iron content, p<0.001). In the atrophied gastrocnemius, iron concentration is preserved through increased ferritin (+86.6%, p=0.010) and hemoproteins (i.e., myoglobin, CYSC, COXIV) expression. Conversely, ApcMin/+ mice exhibit anemia together with hepatic and splenic iron depletion (-48.4%, p<0.001 and -84.9%, p<0.001, respectively). Hepcidin synthesis is downregulated (-99.9%, p<0.001) despite persistent inflammation, indicating that hypoxia and/or iron-deficiency signals probably predominate over the inflammatory pathway. FPN is markedly decreased (-94.2%, p<0.001) in the iron-depleted spleen despite low hepcidin. In the atrophied quadriceps of ApcMin/+ mice, iron deficiency is associated with decreased FPN and ferritin (-87.9%, p<0.001, and -38.4%, p=0.016, respectively), and increased TfR1 expression (+305%, p=0.107). Finally, tumor iron import may play a significant role in cachexia progression, as suggested by the association between tumor weight, tumor TfR1 protein levels and body weight loss in UNKC mice. Conclusion. These findings reveal cancer type-specific signatures of iron regulation in cachexia, which appear to be more driven by cancer type specific features than by cachexia severity itself, with skeletal muscle iron metabolism impaired mainly when cachexia is associated with anemia. This suggests that the clinical management of iron metabolism in cachectic patients should be guided by the underlying cancer type.

physiology↗

Electrophysiological signatures of anxiety in Parkinson's disease

Anxiety is a common non-motor symptom in Parkinsons disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N=18) to both PD non-anxious (PD-NA, N=50) and HC groups (N=25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and diagnosis and can ultimately support the development of new therapeutics strategies.

neuroscience↗

Deep Learning Allows Assessment of Risk of Metastatic Relapse from Invasive Breast Cancer Histological Slides

BackgroundCorrectly classifying early estrogen receptor-positive and HER2-negative (ER+/HER2) breast cancer (EBC) cases allows to propose an adapted adjuvant systemic treatment strategy. We developed a new AI-based tool to assess the risk of distant relapse at 5 years for ER+/HER2-EBC patients from pathological slides. Patients and MethodsThe discovery dataset (GrandTMA) included 1429 ER+/HER2-EBC patients, with long-term follow-up and an available hematoxylin-eosin and saffron (HES) whole slide image (WSI). A Deep Learning (DL) network was trained to predict metastasis free survival (MFS) at five years, based on the HES WSI only (termed RlapsRisk). A combined score was then built using RlapsRisk and well established prognostic factors. A threshold corresponding to a probability of MFS event of 5% at 5 years was applied to dichotomize patients into low or high-risk groups. The external validation, as well as assessment of the additional prognosis value of the DL model beyond standard clinico-pathologic factors were carried out on an independent, prospective cohort (CANTO, NCT01993498) including 889 HES WSI of ER+/HER2-EBC patients. ResultsRlapsRisk was an independent prognostic factor of MFS in multivariable analysis adjusted for established clinico-pathological factors (p<0.005 in GrandTMA and CANTO). Combining RlapsRisk score and the clinico-pathological factors improved the prognostic discrimination as compared to the clinico-pathological factors alone (increment of c-index in the validation set 0.80 versus 0.76, +0.04, p-value < 0.005). After dichotomization, the Combined Model showed a higher cumulative sensitivity on the entire population (0.76 vs 0.61) for an equal dynamic specificity (0.76) in comparison with the clinical score alone. ConclusionsOur deep learning model developed on digitized HES slides provided additional prognostic information as compared to current clinico-pathological factors and has the potential of valuably informing the decision making process in the adjuvant setting when combined with current clinico-pathological factors.

pathology↗

Open prediction of polysaccharide utilisation loci (PUL) in 5414 public Bacteroidetes genomes using PULpy

Polysaccharide utilisation loci (PUL) are regions within bacterial genomes that encode all the necessary machinery for the cleavage of particular carbohydrates. For the Bacteroidetes phylum, prediction of PUL from genomic data alone involves the identification of carbohydrate-active enzymes (CAZymes) co-localised with susCD gene pairs. Here we present the open prediction of PUL in 5414 public Bacteroidetes genomes, and an open-source pipeline to reproduce or extend the results.

microbiology↗

MAGpy: a reproducible pipeline for the downstream analysis of metagenome-assembled genomes (MAGs)

Recent advances in bioinformatics have enabled the rapid assembly of genomes from metagenomes (MAGs), and there is a need for reproducible pipelines that can annotate and characterise thousands of genomes simultaneously. Here we present MAGpy, a Snakemake pipeline that takes FASTA input and compares MAGs to several public databases, checks quality, assigns a taxonomy and draws a phylogenetic tree.

bioinformatics↗

Assembly of hundreds of microbial genomes from the cow rumen reveals novel microbial species encoding enzymes with roles in carbohydrate metabolism

The cow rumen is a specialised organ adapted for the efficient breakdown of plant material into energy and nutrients, and it is the rumen microbiome that encodes the enzymes responsible. Many of these enzymes are of huge industrial interest. Despite this, rumen microbes are under-represented in the public databases. Here we present 220 high quality bacterial and archaeal genomes assembled directly from 768 gigabases of rumen metagenomic sequence data. Comparative analysis with current publicly available genomes reveals that the majority of these represent previously unsequenced strains and species of bacteria and archaea. The genomes contain over 13,000 proteins predicted to be involved in carbohydrate metabolism, over 90% of which do not have a good match in the public databases. Inclusion of the 220 genomes presented here improves metagenomic read classification by 2-3-fold, both in our data and in other publicly available rumen datasets. This release improves the coverage of rumen microbes in the public databases, and represents a hugely valuable resource for biomass-degrading enzyme discovery and studies of the rumen microbiome

genomics↗