Since December 2019, SARS-CoV-2 infection has been still rapidly spreading, resulting in a pandemic, followed by an increasing number of cases in countries throughout the world. The severity of the disease depends on the patient’s overall medical condition but no appropriate markers are available to establish the prognosis of the patients. We performed a 16S rRNA gene sequencing, revealing an altered composition of the nasal/oropharyngeal (NOP) microbiota in 21 patients affected by COVID-19, paucisymptomatic or in an Intensive Care Unit (ICU), as compared to 10 controls negative for COVID-19 or eight affected by a different Human Coronavirus (HKU, NL63 and OC43). A significant decrease in Chao1 index was observed when patients affected by COVID-19 (in ICU) were compared to paucisymptomatic. Furthermore, patients who were in ICU, paucisymptomatic or affected by other Coronaviruses all displayed a decrease in the Chao1 index when compared to controls, while Shannon index significantly decreased only in patients under ICU as compared to controls and paucisymptomatic patients. At the phylum level, Deinococcus-Thermus was present only in controls as compared to SARS-CoV-2 patients admitted to ICU, paucisymptomatic or affected by other coronaviruses. Candidatus Saccharibacteria (formerly known as TM7) was strongly increased in negative controls and SARS-CoV-2 paucisymptomatic patients as compared to SARS-CoV-2 ICU patients. Other modifications were observed at a lower taxonomy level. Complete depletion of Bifidobacterium and Clostridium was exclusively observed in ICU SARS-CoV-2 patients, which was the only group characterized by the presence of Salmonella, Scardovia, Serratia and Pectobacteriaceae. In conclusion, our preliminary results showed that nasal/oropharyngeal microbiota profiles of patients affected with SARS-CoV-2 may provide valuable information in order to facilitate the stratification of patients and may open the way to new interventional strategies in order to ameliorate the outcome of the patients.

In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia--Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed. The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.

The pathogenesis of ulcerative colitis (UC) is unknown, although genetic loci and altered gut microbiota have been implicated. Up to a third of patients with moderate to severe UC require proctocolectomy with ileal pouch ano-anastomosis (IPAA). We aimed to explore the mucosal microbiota of UC patients who underwent IPAA. Methods: For microbiome analysis, mucosal specimens were collected from 34 IPAA individuals. Endoscopic and histological examinations of IPAA were normal in 21 cases, while pouchitis was in 13 patients. 19 specimens from the healthy control (10 from colonic and 9 from ileum) were also analyzed. Data were analyzed using an ensemble of software packages: QIIME2, coda-lasso, clr-lasso, PICRUSt2, and ALDEx2. Results: IPAA specimens had significantly lower bacterial diversity as compared to normal. The microbial composition of the normal pouch was also decreased also when compared to pouchitis. Faecalibacterium prausnitzii, Gemmiger formicilis, Blautia obeum, Ruminococcus torques, Dorea formicigenerans, and an unknown species from Roseburia were the most uncommon in pouch/pouchitis, while an unknown species from Enterobacteriaceae was over-represented. Propionibacterium acnes and Enterobacteriaceae were the species most abundant in the pouchitis and in the normal pouch, respectively. Predicted metabolic pathways among the IPAA bacterial communities revealed an important role of immunometabolites such as SCFA, butyrate, and amino acids. Conclusions: Our findings showed specific bacterial signature hallmarks of dysbiosis and could represent bacterial biomarkers in IPAA patients useful to develop novel treatments in the future by modulating the gut microbiota through the administration of probiotic immunometabolites-producing bacterial strains and the addition of specific prebiotics and the faecal microbiota transplantation.