Pin-Pointing the Key Hubs in the IFN-γ Pathway Responding to SARS-CoV-2 Infection.

Interferon gamma (IFN-γ) may be potential adjuvant immunotherapy for COVID-19 patients. In this work, we assessed gene expression profiles associated with the IFN-γ pathway in response to SARS-CoV-2 infection. Employing a case-control study from SARS-CoV-2-positive and -negative patients, we identified IFN-γ-associated pathways to be enriched in positive patients. Bioinformatics analyses showed upregulation of MAP2K6, CBL, RUNX3, STAT1, and JAK2 in COVID-19-positive vs. -negative patients. A positive correlation was observed between STAT1/JAK2, which varied alongside the patient's viral load. Expression of MX1, MX2, ISG15, and OAS1 (four well-known IFN-stimulated genes (ISGs)) displayed upregulation in COVID-19-positive vs. -negative patients. Integrative analyses showcased higher levels of ISGs, which were associated with increased viral load and STAT1/JAK2 expression. Confirmation of ISGs up-regulation was performed in vitro using the A549 lung cell line treated with Poly (I:C), a synthetic analog of viral double-stranded RNA; and in different pulmonary human cell lines and ferret tracheal biopsies infected with SARS-CoV-2. A pre-clinical murine model of Coronavirus infection confirmed findings displaying increased ISGs in the liver and lungs from infected mice. Altogether, these results demonstrate the role of IFN-γ and ISGs in response to SARS-CoV-2 infection, highlighting alternative druggable targets that can boost the host response.

Analysis workflow of publicly available RNA-sequencing datasets.

Differential gene expression analysis is widely used to study changes in gene expression profiles between two or more groups of samples (e.g., physiological versus pathological conditions, pre-treatment versus post-treatment, and infected versus non-infected tissues). This protocol aims to identify gene expression changes in a pre-selected set of genes associated with severe acute respiratory syndrome coronavirus 2 viral infection and host cell antiviral response, as well as subsequent gene expression association with phenotypic features using samples deposited in public repositories. For complete details on the use and outcome of this informatics analysis, please refer to Bizzotto et al. (2020).

SARS-CoV-2 Infection Boosts MX1 Antiviral Effector in COVID-19 Patients.

In a published case-control study (GSE152075) from SARS-CoV-2-positive (n = 403) and -negative patients (n = 50), we analyzed the response to infection assessing gene expression of host cell receptors and antiviral proteins. The expression analysis associated with reported risk factors for COVID-19 was also assessed. SARS-CoV-2 cases had higher ACE2, but lower TMPRSS2, BSG/CD147, and CTSB expression compared with negative cases. COVID-19 patients' age negatively affected ACE2 expression. MX1 and MX2 were higher in COVID-19 patients. A negative trend for MX1 and MX2 was observed as patients' age increased. Principal-component analysis determined that ACE2, MX1, MX2, and BSG/CD147 expression was able to cluster non-COVID-19 and COVID-19 individuals. Multivariable regression showed that MX1 expression significantly increased for each unit of viral load increment. Altogether, these findings support differences in ACE2, MX1, MX2, and BSG/CD147 expression between COVID-19 and non-COVID-19 patients and point out to MX1 as a critical responder in SARS-CoV-2 infection.

Non-myogenic tumors display altered expression of dystrophin (DMD) and a high frequency of genetic alterations.

DMD gene mutations have been associated with the development of Dystrophinopathies. Interestingly, it has been recently reported that DMD is involved in the development and progression of myogenic tumors, assigning DMD a tumor suppressor activity in these types of cancer. However, there are only few reports that analyze DMD in non-myogenic tumors. Our study was designed to examine DMD expression and genetic alterations in non-myogenic tumors using public repositories. We also evaluated the overall survival of patients with and without DMD mutations. We studied 59 gene expression microarrays (GEO database) and RNAseq (cBioPortal) datasets that included 9817 human samples. We found reduced DMD expression in 15/27 (56%) pairwise comparisons performed (Fold-Change (FC) ≤ 0.70; p-value range = 0.04-1.5x10-20). The analysis of RNAseq studies revealed a median frequency of DMD genetic alterations of 3.4%, higher or similar to other well-known tumor suppressor genes. In addition, we observed significant poorer overall survival for patients with DMD mutations. The analyses of paired tumor/normal tissues showed that the majority of tumor specimens had lower DMD expression compared to their normal adjacent counterpart. Interestingly, statistical significant over-expression of DMD was found in 6/27 studies (FC ≥ 1.4; p-value range = 0.03-3.4x10-15). These results support that DMD expression and genetic alterations are frequent and relevant in non-myogenic tumors. The study and validation of DMD as a new player in tumor development and as a new prognostic factor for tumor progression and survival are warranted.

Improving risk stratification of patients with childhood acute lymphoblastic leukemia: Glutathione-S-Transferases polymorphisms are associated with increased risk of relapse.

The inclusion of genotype at Acute Lymphoblastic Leukemia (ALL) diagnosis as a genetic predictor of disease outcome is under constant study. However, results are inconclusive and seem to be population specific. We analyzed the predictive value of germline polymorphisms for childhood ALL relapse and survival. We retrospectively recruited 140 Argentine patients with de novo ALL. Genotypes were analyzed using PCR-RFLP (GSTP1 c.313A > G, MDR1 c.3435T > C, and MTHFR c.665C > T) and multiplex PCR (GSTT1 null, GSTM1 null). Patients with the GSTP1 c.313GG genotype had an increased risk for relapse in univariate (OR = 2.65, 95% CI = 1.03-6.82, p = 0.04) and multivariate (OR = 3.22, 95% CI = 1.17-8.83, p = 0.02) models. The combined genotype slightly increased risk for relapse in the univariate (OR = 2.82, 95% CI = 1.09-7.32, p = 0.03) and multivariate (OR = 2.98, 95% CI = 1.14-7.79, p = 0.03) models for patients with 2/3-risk-genotypes (GSTT1 null, GSTM1 null, GSTP1 c.313GG). The Recurrence-Free Survival (RFS) was shorter for GSTP1 c.313GG (p = 0.025) and 2/3-risk-genotypes (p = 0.021). GST polymorphisms increased the risk of relapse and RFS of patients with childhood ALL. The inclusion of these genetic markers in ALL treatment protocols might improve risk stratification and reduce the number of relapses and deaths.