Biology subjects

Zhukova, A.

Publications and source records attributed to Zhukova, A..

5 recordsLinked to original sources

bioRxiv2023

The multiplicity of Thioredoxin systems meets the specific needs of Clostridia

Oxidative stress is a highly common stress for cells, which targets proteins with oxidation of cysteine residues. The thioredoxin (Trx) system, which is a ubiquitous system for thiol- and protein-repair, is composed of a thioredoxin (TrxA) and a thioredoxin-reductase (TrxB). TrxAs reduce disulfide bonds of oxidized proteins and are then usually recycled by a single pleiotropic NAD(P)H-dependent TrxB (NTR). However, some Clostridia have also ferredoxin-dependent TrxBs. In this work, we first analyzed the composition of Trx systems across Bacteria. Most of bacteria have only one NTR, but organisms in some Phyla including Firmicutes have several TrxBs. In Firmicutes, this multiplicity of TrxBs is observed only in Clostridia. We thus used Clostridioides difficile as a model to investigate the biological relevance of TrxB multiplicity by studying the physiological roles of the Trx systems in this gut pathogen. Three TrxAs and three TrxBs are present in the 630{Delta}erm strain. We showed that two systems were involved in response to infection-related stresses, allowing survival of vegetative cells to exposure to oxygen, inflammation-related molecules and bile salts. A supplementary TrxB copy present in some C. difficile strains also contributes to this stress-response arsenal. One of the conserved stress-response Trx system was also found to be present in the spore via a dual transcriptional control by different sigma factors. This system contributes to spore survival to hypochlorite and ensure proper germination in the presence of oxygen. Finally, we found that the third Trx system was contributing to sporulation. This involvement was likely linked to the recycling of the glycine-reductase, a Stickland pathway enzyme that allows consumption of glycine, a spore co-germinant. Altogether, our results showed that the multiplicity of Trx systems produced under the control of different regulatory signals and networks and the diversity of TrxBs meet specific needs of Clostridia, i.e., adaptation to strong stress exposure, sporulation and Stickland pathways. More broadly, this multiplicity responds to cell compartmentation and differentiation, which can be transposed to other multiple-TrxBs organisms such as Cyanobacteria or eukaryotes.

microbiology↗

bioRxiv2023

Recent evolutionary origin and localized diversity hotspots of mammalian coronaviruses

Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus-host codiversification have been largely over-estimated.

evolutionary biology↗

bioRxiv2021

Deep learning from phylogenies to uncover the transmission dynamics of epidemics

Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep.

bioinformatics↗

bioRxiv2020

Differing impacts of global and regional responses on SARS-CoV-2 transmission cluster dynamics

Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called "super-spreaders", with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.

genomics↗

bioRxiv2018

A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios

The reconstruction of ancestral scenarios is widely used to study the evolution of characters along a phylogenetic tree. In the likelihood framework one commonly uses the marginal posterior probabilities of the character states, and the joint reconstruction of the most likely scenario. Both approaches are somewhat unsatisfactory. Marginal reconstructions provide users with state probabilities, but these are difficult to interpret and visualize, while joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences.\n\nWe propose a simple and fast approach, which is in between these two extremes. We use decision-theory concepts and the Brier criterion to associate each node in the tree to a set of likely states. A unique state is predicted in the tree regions with low uncertainty, while several states are predicted in the uncertain regions, typically around the tree root. To visualize the results, we cluster the neighboring nodes associated to the same states and use graph visualization tools. The method is implemented in the PastML program and web server.\n\nThe results on simulated data consistently show the accuracy and robustness of the approach. The method is applied to large tree comprising 3,619 sequences from HIV-1M subtype C sampled worldwide, which is processed in a few minutes. Results are very convincing: we retrieve and visualize the main transmission routes of HIV-1C; we demonstrate that drug resistance mutations mostly emerge independently under treatment pressure, but some resistance clusters are found, corresponding to transmissions among untreated patients.

evolutionary biology↗