Autoreactivity to self-antigens LL37 and ADAMTSL5 influences the clinical response to risankizumab in psoriatic patients.

The autoantigens LL37 and ADAMTSL5 contribute to induce pathogenetic T-cells responses in a subset of psoriatic patients. Whether the presence of LL37-and/or ADAMTS5-reactive T-cells influences the clinical response to treatment is still unknown. The aim of the study is to evaluate the clinical responses to the anti-IL-23 risankizumab in LL37 and/or ADAMTSL5-reactive patients in comparison with non-reactive ones and to assess whether genetics (HLA-Cw06.02) or BMI influences the response to treatment. Patients were screened at baseline for the presence of circulating LL37 or/and ADAMTSL5-reactive T-cells and were treated as per protocol with risankizumab. Effectiveness data (PASI scores) were collected at weeks 4, 16, 28, 40 and 52. Data were also analyzed based on HLA-Cw06.02 status and BMI. The overall response to treatment of patients with autoreactivity to LL37 or ADAMTSL5 did not differ compared to the non-reactive cohort as measured as PASI75/90/100 at different time points; however, subjects that had autoreactive T-cells to both LL37 and ADAMTS5 demonstrated suboptimal response to treatment starting at week16. HLA-Cw06:02+ patients demonstrated faster response to risankizumab at week 4 compared to HLA-Cw06:02-. Additionally, the response to treatment was influenced by the BMI with slower responses seen in overweight and obese patients at week 4 and week16. In conclusion, while the presence of either LL37-and ADAMTS5-reactive circulating T-cells do not influence the clinical response to risankizumab, the presence of the double reactivity to both LL37 and ADAMTS5 decreases the clinical responses. Moreover, we evidenced that HLA-Cw06+ respond faster to IL-23 inhibition and that BMI, associated to autoreactivity, can influence the speed in response.

CFTR Modulators Rescue the Activity of CFTR in Colonoids Expressing the Complex Allele p.[R74W;V201M;D1270N]/dele22_24.

An Italian, 46-year-old female patient carrying the complex allele p.[R74W;V201M;D1270N] in trans with CFTR dele22_24 was diagnosed at the Cystic Fibrosis (CF) Center of Verona as being affected by CF-pancreatic sufficient (CF-PS) in 2021. The variant V201M has unknown significance, while both of the other variants of this complex allele have variable clinical consequences, according to the CFTR2 database, with reported clinical benefits for treatment with ivacaftor + tezacaftor and ivacaftor + tezacaftor + elexacaftor in patients carrying the R74W-D1270N complex allele, which are currently approved (in USA, not yet in Italy). She was previously followed up by pneumologists in northern Italy because of frequent bronchitis, hemoptysis, recurrent rhinitis, Pseudomonas aeruginosa lung colonization, bronchiectasis/atelectasis, bronchial arterial embolization and moderately compromised lung function (FEV1: 62%). Following a sweat test with borderline results, she was referred to the Verona CF Center where she presented abnormal values in both optical beta-adrenergic sweat tests and intestinal current measurement (ICM). These results were consistent with a diagnosis of CF. CFTR function analyses were also performed in vitro by forskolin-induced swelling (FIS) assay and short-circuit currents (Isc) in the monolayers of the rectal organoids. Both of these assays showed significantly increased CFTR activity following treatment with the CFTR modulators. Western-blot analysis revealed increased fully glycosylated CFTR protein after treatment with correctors, in line with the functional analysis. Interestingly, tezacaftor, together with elexacaftor, rescued the total organoid area under steady-state conditions, even in the absence of the CFTR agonist forskolin. In conclusion, in ex vivo and in vitro assays, we measured a residual function that was significantly enhanced by in vitro incubation with CFTR modulators, especially by ivacaftor + tezacaftor + elexacaftor, suggesting this combination as a potentially optimal treatment for this case.

Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.

Functional rescue of CFTR in rectal organoids from patients carrying R334W variant by CFTR modulators and PDE4 inhibitor Roflumilast.

BACKGROUND: Many disease-causing variants in the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene remain uncharacterized and untreated. Restoring the function of the impaired CFTR protein is the goal of personalized medicine, particularly in patients carrying rare CFTR variants. In this study, functional defects related to the rare R334W variant were evaluated after treatment with CFTR modulators or Roflumilast, a phosphodiesterase-4 inhibitor (PDE4i). METHODS: Rectal organoids from subjects with R334W/2184insA and R334W/2183AA > G genotypes were used to perform the Forskolin-induced swelling (FIS) assay. Organoids were left drug-untreated or treated with modulators VX-770 (I), VX-445 (E), and VX-661 (T) mixed, and their combination (ETI). Roflumilast (R) was used alone or as a combination of I + R. RESULTS: Our data show a significant increase in FIS rate following treatment with I alone. The combined use of modulators, such as ETI, did not increase further swelling than I alone, nor in protein maturation. Treatment with R shows an increase in FIS response similar to those of I, and the combination R + I significantly increases the rescue of CFTR activity. CONCLUSIONS: Equivalent I and ETI treatment efficacy was observed for both genotypes. Furthermore, significant organoid swelling was observed with combined I + R used that supports the recently published data describing a potentiating effect of only I in patients carrying the variant R334W and, at the same time, corroborating the role of strategies that include PDE4 inhibitors further to potentiate the effect of I for this variant.

How the latent geometry of a biological network provides information on its dynamics: the case of the gene network of chronic myeloid leukaemia.

Background: The concept of the latent geometry of a network that can be represented as a graph has emerged from the classrooms of mathematicians and theoretical physicists to become an indispensable tool for determining the structural and dynamic properties of the network in many application areas, including contact networks, social networks, and especially biological networks. It is precisely latent geometry that we discuss in this article to show how the geometry of the metric space of the graph representing the network can influence its dynamics. Methods: We considered the transcriptome network of the Chronic Myeloid Laeukemia K562 cells. We modelled the gene network as a system of springs using a generalization of the Hooke's law to n-dimension (n ≥ 1). We embedded the network, described by the matrix of spring's stiffnesses, in Euclidean, hyperbolic, and spherical metric spaces to determine which one of these metric spaces best approximates the network's latent geometry. We found that the gene network has hyperbolic latent geometry, and, based on this result, we proceeded to cluster the nodes according to their radial coordinate, that in this geometry represents the node popularity. Results: Clustering according to radial coordinate in a hyperbolic metric space when the input to network embedding procedure is the matrix of the stiffnesses of the spring representing the edges, allowed to identify the most popular genes that are also centres of effective spreading and passage of information through the entire network and can therefore be considered the drivers of its dynamics. Conclusion: The correct identification of the latent geometry of the network leads to experimentally confirmed clusters of genes drivers of the dynamics, and, because of this, it is a trustable mean to unveil important information on the dynamics of the network. Not considering the latent metric space of the network, or the assumption of a Euclidean space when this metric structure is not proven to be relevant to the network, especially for complex networks with hierarchical or modularised structure can lead to unreliable network analysis results.