Accumulating evidence shows that Mesenchymal Stem/Stromal Cells (MSCs) exert their therapeutic effects by the release of secretome, made of both soluble proteins and nano/microstructured extracellular vesicles (EVs). In this work, for the first time, we proved by a proteomic investigation that adipose-derived (AD)-MSC-secretome contains alpha-1-antitrypsin (AAT), the main elastase inhibitor in the lung, 72 other proteins involved in protease/antiprotease balance, and 46 proteins involved in the response to bacteria. By secretome fractionation, we proved that AAT is present both in the soluble fraction of secretome and aggregated and/or adsorbed on the surface of EVs, that can act as natural carriers promoting AAT in vivo stability and activity. To modulate secretome composition, AD-MSCs were cultured in different stimulating conditions, such as serum starvation or chemicals (IL-1β and/or dexamethasone) and the expression of the gene encoding for AAT was increased. By testing in vitro the anti-elastase activity of MSC-secretome, a dose-dependent effect was observed; chemical stimulation of AD-MSCs did not increase their secretome anti-elastase activity. Finally, MSC-secretome showed anti-bacterial activity on Gram-negative bacteria, especially for Klebsiella pneumoniae. These preliminary results, in addition to the already demonstrated immunomodulation, pave the way for the use of MSC-secretome in the treatment of AAT-deficiency lung diseases.

The clinical manifestations of COVID-19 are heterogeneous: 46.4% of patients admitted into hospital reported to have at least one comorbidity. Comorbidities such as COPD, diabetes, hypertension and malignancy predispose patients with Covid-19 to adverse clinical outcomes. Alpha 1-antitrypsin deficiency (AATD) is a genetic disorder caused by pathological mutation(s) in the SERPINA1 gene resulting in an imbalance in proteinase activity which may lead to premature emphysema and COPD.Our aim was to investigate whether people with severe AAT deficiency (AATD) have an increased risk of (severe) COVID-19 infection.We collected data on COVID-19 symptoms, laboratory-confirmed infection, hospitalization and treatment by means of a telephone survey, directly administered to Italian severe AATD subjects in May 2020. We then compared our findings with data collected by the Istituto Superiore di Sanità on the total population in Italy during the same period.We found an higher frequency of SARS-CoV-2 infection in our cohort (3.8%) compared to national data regarding infection, thus giving severe AATD a relative risk of 8. 8 (95%CI 5.1-20,0; p<0.0001) for symptomatic SARS-CoV-2 infection. Moreover, the relative risk (RR) was higher in AATD patients with pre-existing lung diseases (RR 13.9; 95%CI 8.0-33.6; p<0.001), but with a similar death rate (1 in 8, 12.5%) compared to the general population (13.9%; RR 0.9).These preliminary findings highlight the importance of close surveillance in the spread of COVID-19 in patients with severe AATD and underlines the need for further studies into the role of the antiprotease shield in preventing SARS-Cov-2 infection.

Objectives Alpha1-antitrypsin deficiency (AATD) is an inherited condition that predisposes individuals to an increased risk of developing lung and liver disease. Even though AATD is one of the most widespread inherited diseases in Caucasian populations, only a minority of affected individuals has been detected. Whereas methods have been validated for AATD testing, there is no universally-established algorithm for the detection and diagnosis of the disorder. In order to compare different methods for diagnosing AATD, we carried out a systematic review of the literature on AATD diagnostic algorithms. Methods Complete biochemical and molecular analyses of 5,352 samples processed in our laboratory were retrospectively studied using each of the selected algorithms. Results When applying the diagnostic algorithms to the same samples, the frequency of False Negatives varied from 1.94 to 12.9%, the frequency of True Negatives was 62.91% for each algorithm and the frequency of True Positives ranged from 24.19 to 35.15%. We, therefore, highlighted some differences among Negative Predictive Values, ranging from 0.83 to 0.97. Accordingly, the sensitivity of each algorithm ranged between 0.61 and 0.95. We also postulated 1.108 g/L as optimal AAT cut-off value, in absence of inflammatory status, which points to the possible presence of genetic AATD. Conclusions The choice of the diagnostic algorithm has a significant impact on the correct diagnosis of AATD, which is essential for appropriate treatment and medical care. The fairly large number of possible false negative diagnoses revealed by the present paper should also warn clinicians of negative results in patients with clinically-suspected AATD.