Human respiratory syncytial virus (hRSV) is the leading cause of acute lower respiratory tract infections in children under five years of age and older adults worldwide. During hRSV infection, host cells undergo changes in endomembrane organelles, including mitochondria. This organelle is responsible for energy production in the cell and plays an important role in the antiviral response. The present study focuses on characterizing the ultrastructural and functional changes during hRSV infection using thin-section transmission electron microscopy and RT-qPCR. Here we report that hRSV infection alters mitochondrial morphodynamics by regulating the expression of key genes in the antiviral response process, such as Mfn1, VDAC2, and PINK1. Our results suggest that hRSV alters mitochondrial morphology during infection, producing a mitochondrial phenotype with shortened cristae, swollen matrix, and damaged membrane. We also observed that hRSV infection modulates the expression of the aforementioned genes, possibly as an evasion mechanism in the face of cellular antiviral response. Taken together, these results advance our knowledge of the ultrastructural alterations associated with hRSV infection and might guide future therapeutic efforts to develop effective antiviral drugs for hRSV treatment.

In recent years, wastewater (WW)-based epidemiology has been increasingly used for surveillance of SARS-CoV-2 and has emerged as a potential tool for monitoring other respiratory viruses. Most evidence on the use of WW for detecting multiple respiratory viruses comes from developed countries. In this study, we assessed the feasibility of multi-respiratory virus sewage surveillance in a middle-income country and explored signals that may be potentially used as early warning signs for Public Health authorities. We examined the presence of SARS-CoV-2, influenza virus, respiratory syncytial virus (RSV), and human metapneumovirus (hMPV) in 238 WW samples collected from three treatment plants in San Luis Potosí, Mexico, over one year. The weekly detection of each virus was compared with the weekly number of hospital admissions for respiratory infections caused by that virus in pediatric patients. SARS-CoV-2, influenza, hMPV, and RSV were detected in 152 (63.9%), 108 (45.4%), 95 (39.9%), and 24 (10.1%) samples, respectively. There was no significant correlation between viral detection in WW and the number of hospitalizations during that week. However, analyses of WW viral detection with hospitalizations in subsequent weeks showed an increasing correlation reaching a maximum correlation for a lag of 12 weeks for SARS-CoV-2 (r_s = 0.63, p = 0.001), 9 weeks for influenza (r_s 0.62, p = 0.0001), 2 weeks for RSV (r_s = 0.30, p = 0.05), and 3 weeks for hMPV (r_s = 0.39, p = 0.009). In addition, we identified time-periods of SARS-CoV-2, influenza, and RSV widespread circulation (several consecutive weeks in which viruses were detected in the three treatment plants); most hospitalizations caused by these viruses occurred after widespread circulation was detected in WW, suggesting this may be used as an early alert for public health systems. Overall, our results show that WW-based surveillance of multiple respiratory viruses is feasible and has potential applications as an early warning system in middle-income countries.

Viruses can induce various mitochondrial morphological changes, which are associated with the type of immune response. Therefore, characterization and analysis of mitochondrial ultrastructural changes could provide insights into the kind of immune response elicited, especially when compared to uninfected cells. However, this analysis is highly time-consuming and susceptible to observer bias. This work presents the development of a deep learning-based approach for the automatic identification, segmentation, and analysis of mitochondria from thin-section transmission electron microscopy images of cells infected with two SARS-CoV-2 variants or the Zika virus, utilizing a convolutional neural network with a U-Net architecture. A comparison between manual and automatic segmentations, along with morphological metrics, was performed, yielding an accuracy greater than 85% with no statistically significant differences between the manual and automatic metrics. This approach significantly reduces processing time and enables a prediction of the immune response to viral infections by allowing the detection of both intact and damaged mitochondria. Therefore, the proposed deep learning-based tool may represent a significant advancement in the study and understanding of cellular responses to emerging pathogens. Additionally, its applicability could be extended to the analysis of other organelles, thereby opening up new opportunities for automated studies in cell biology.

In the original publication [1], Figure 1b was previously published by the authors in the PLoS One, but the citation (reference 14) was inadvertently omitted