SUMMARY The barrier to curing HIV-1 is thought to reside primarily in CD4+ T cells containing silent proviruses. To characterize these latently infected cells, we studied the integration profile of HIV-1 in viremic progressors, individuals receiving antiretroviral therapy, and viremic controllers. Clonally expanded T cells represented the majority of all integrations and increased during therapy. However, none of the 75 expanded T cell clones assayed contained intact virus. In contrast, the cells bearing single integration events decreased in frequency over time on therapy, and the surviving cells were enriched for HIV-1 integration in silent regions of the genome. Finally, there was a strong preference for integration into, or in close proximity to Alu repeats, which were also enriched in local hotspots for integration. The data indicate that dividing clonally expanded T cells contain defective proviruses, and that the replication competent reservoir is primarily found in CD4+ T cells that remain relatively quiescent.

signeR is implemented in R and C ++, and is available as a R package at http://bioconductor.org/packages/signeR CONTACT: itojal@cipe.accamargo.org.brSupplementary information: Supplementary data are available at Bioinformatics online.

Type-II ryanodine receptor channels (RYRs) play a fundamental role in intracellular Ca2+ dynamics in heart. The processes of activation, inactivation, and regulation of these channels have been the subject of intensive research and the focus of recent debates. Typically, approaches to understand these processes involve statistical analysis of single RYRs, involving signal restoration, model estimation, and selection. These tasks are usually performed by following rather phenomenological criteria that turn models into self-fulfilling prophecies. Here, a thorough statistical treatment is applied by modeling single RYRs using aggregated hidden Markov models. Inferences are made using Bayesian statistics and stochastic search methods known as Markov chain Monte Carlo. These methods allow extension of the temporal resolution of the analysis far beyond the limits of previous approaches and provide a direct measure of the uncertainties associated with every estimation step, together with a direct assessment of why and where a particular model fails. Analyses of single RYRs at several Ca2+ concentrations are made by considering 16 models, some of them previously reported in the literature. Results clearly show that single RYRs have Ca2+-dependent gating modes. Moreover, our results demonstrate that single RYRs responding to a sudden change in Ca2+ display adaptation kinetics. Interestingly, best ranked models predict microscopic reversibility when monovalent cations are used as the main permeating species. Finally, the extended bandwidth revealed the existence of novel fast buzz-mode at low Ca2+ concentrations.

Motivation Despite of the fast development of highly effective vaccines to control the current COVID–19 pandemics, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) variants of concern. Therefore, it is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spread of the disease and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Nevertheless, the lack of appropriate analytical tools to quickly and effectively access the viral composition in meta-transcriptomic sequencing data, including environmental surveillance, represent possible challenges that may impact the fast adoption of this approach to mitigate the spread and transmission of viruses. Results We propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the VCF format. Results obtained by using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants in complex mixtures such as waste water and environmental samples. Availability http://github.com/rvalieris/LCS Supplementary information Supplementary data are available at Bioinformatics online.