The intestinal epithelium consists of a single cell layer, which is a critical selectively permeable barrier to both absorb nutrients and avoid the entry of potentially harmful entities, including microorganisms. Epithelial cells are held together by the apical junctional complexes, consisting of adherens junctions, and tight junctions (TJs), and by underlying desmosomes. TJs lay in the apical domain of epithelial cells and are mainly composed by transmembrane proteins such as occludin, claudins, JAMs, and tricellulin, that are associated with the cytoplasmic plaque formed by proteins from the MAGUK family, such as ZO-1/2/3, connecting TJ to the actin cytoskeleton, and cingulin and paracingulin connecting TJ to the microtubule network. Extracellular bacteria such as EPEC and EHEC living in the intestinal lumen inject effectors proteins directly from the bacterial cytoplasm to the host cell cytoplasm, where they play a relevant role in the manipulation of the eukaryotic cell functions by modifying or blocking cell signaling pathways. TJ integrity depends on various cell functions such as actin cytoskeleton, microtubule network for vesicular trafficking, membrane integrity, inflammation, and cell survival. EPEC and EHEC effectors target most of these functions. Effectors encoded inside or outside of locus of enterocyte effacement (LEE) disrupt the TJ strands. EPEC and EHEC exploit the TJ dynamics to open this structure, for causing diarrhea. EPEC and EHEC secrete effectors that mimic host proteins to manipulate the signaling pathways, including those related to TJ dynamics. In this review, we focus on the known mechanisms exploited by EPEC and EHEC effectors for causing TJ disruption.

Since its isolation in Wuhan SARS-Cov2 showed a high mutation rate hindering the ability to properly characterize. Also as a consequence of its size, traditional sequence analysis methods were computationally constrained. However, applying variational autoencoders (VAEs) to custom sequence representations results in a series of clusters sorted by the sunshine duration (SD) rate of change (SDRC) and other solar-derived features. The transition between clusters is characterized by changes in viral genome size, apparent deletions can be found throughout the SARS-Cov2 genome. This series of deletions might behave as an internal clock inside the genome. SDRC-derived features synchronize COVID-19 cases into a single period. Both SDRC-derived features and solar features correlate with COVID-19 cases pointing towards a solar-dependent seasonality. Atmospheric changes that affect solar radiation also showed a correlation with COVID-19 cases. Analyzing viral genome composition as time series displays an attractor-like behavior under different solar-derived time scales. While clustering them by environmental conditions shows a similar pattern as the one found by the VAE models. Further development of analysis techniques will help us to better understand the seasonality and adaptation of pathogenic organisms.

Since its isolation in Wuhan SARS-Cov2 showed a high mutation rate hindering the ability to properly characterize. Also as a consequence of its size, traditional sequence analysis methods were computationally constrained. However, applying variational autoencoders (VAEs) to a custom sequence representation results in a series of clusters sorted by the sunshine duration (SD) rate of change (SDRC). The transition between clusters is characterized by changes in viral genome size, apparent deletions can be found throughout the SARS-Cov2 genome. This series of deletions might behave as an internal clock inside the genome. Using SD-derived features as a time scale results in synchronizing COVID-19 cases into a single period. Both SD-derived features and solar features correlate with COVID-19 cases, except for wavelengths at the SWIR band, pointing towards a solar-dependent seasonality. Further development of analysis techniques will help us to better understand the seasonality and adaptation of pathogenic organisms.

Biological sequences contain information about the function and regulation of different components within a biological system. Accurate modeling will yield a deeper understanding of the system, as well as better experimental designs. Nonetheless, computational constraints of the different available methods continue to prevent its use on large scale. Sliding sub-sampling of biological sequences can generate either vector-based or graph-based sequence encodings. Each one of them can be used to train generative models for an unsupervised representation learning task. Offering a suitable tool to analyze fast-evolving biological systems such as SARS-Cov2. Analysis of variational autoencoders bottleneck representation shows a distinguishable temporal component. Changes in 4-mer composition in the region that codes for the structural SARS-Cov2 proteins. Non-symmetrical changes in 4-mer composition drive the viral temporal adaptation process. Furthermore, mean nucleotide composition and encodings of SARS-Cov2 appear to be constrained by day length. Development and refinement of sequence analysis methods will lead to a better understanding of viral adaptation and evolution.