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In our previous paper (Kurakin et al., 2020, https://doi.org/10.1134/S0006297920090059), we found that bacterial lipoxygenases might be associated with multicellularity. This probably implies an evolutionary link between oxylipin signalling and multicellularity. However, in biochemically characterized organisms, oxylipin signalling is dependent not only on lipoxygenases, but also on cyclooxygenases or their homologs (alpha dioxygenases in plants and PPO enzymes in fungi). Therefore, a bioinformatic survey of cyclooxygenase homologs in bacteria is needed.
We used BLAST searches for cyclooxygenase homologs, phylogenetic analysis and a simple statistical analysis to estimate taxonomic associations of bacterial cyclooxygenase-like proteins.
We found that cyclooxygenases, alpha dioxygenases and, possibly, fungal PPO enzymes evolved independently from a large superfamily of peroxidase/catalase-like enzymes. However, animal cyclooxygenases are relatively closely related to a large set of bacterial proteins, one of which (in Nostoc punctiforme) has been characterized as having a lipoxygenase function (Brash et al., 2014, https://doi.org/10.1074/jbc.M114.555904). They are expected to share a common PUFA-dioxygenating ancestor and to be oxylipin biosynthetic enzymes.
When we assessed the taxonomic distribution of these enzymes, we revealed that they are overrepresented in multicellular bacterial taxa — cyanobacteria and actinomycetes. This could evidence that another family of oxylipin biosynthetic enzymes is associated with multicellularity.

The human genome has been studied for decades, and its application as a reference genome has increased since the release of ""The Human Genome Project."" The latest reference genome available from Genome Reference Consortium (i.e., GRCh38) includes alternate haplotypes to represent population diversity. Yet, 151 Mbp of gap sequences are distributed throughout the genome in GRCh38. Recently, a Telomere-to-Telomere (T2T) Consortium has released a complete human genome sequence, including gapless assemblies for all chromosomes.
In our work, we analysed next-generation sequencing (NGS) data using the T2T genome as a reference to evaluate its potential advantages. Using the GRCh38 and T2T genomes, we compared the alignment of NGS data of diverse populations obtained from the 1000 Genomes Project Consortium. We examined six chromosomes (e.g., 9, 13, 15, 20, 21, and 22) to identify additional variants present in the T2T alignment, albeit absent in the GRCh38 alignments.
Our results revealed that the T2T alignment identified a higher number of genetic variants than the GRCh38 alignment, including increased single-nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms, and insertions. These findings suggest that the T2T genome can improve the accuracy and sensitivity of NGS data analysis, particularly in detecting population-specific genetic variants.

We present reference-quality genome assemblies of two mammals, the blue whale Balaenoptera musculus and Nile rat Arvicanthis niloticus. The blue whale is the world’s largest animal, while the Nile rat is a promising animal model of type 2 diabetes. Both assemblies were built using multiple data types and state-of-the-art genome assembly workflows by the Vertebrates Genomes Project (VGP). The Nile rat is one of only a few diploid genomes whose two haplotypes have been fully resolved using the trio binning genome assembly workflow. We analyzed both genomes for heterozygosity and segmental duplications. We also compared them to related species in an effort to find features that may be responsible for large body size in the blue whale and diabetes susceptibility in the Nile rat.

Unlike bacteria that can be deactivated by the antibiotics, viruses evolve in the host body cause a constant effort to eliminate them. The problem becomes worse, if they target somatic stem cells. Consequent infection process transfers them to cancer where the cancer stem cells are resistant to drug therapy.
To address this issue, it is helpful to identify the stem cells either in the case of immunization or for the treatment. Therefore, the first step is to identify organoids which are derived from stem cells by image processing. The next step is to identify the somatic stem cells-specific functional units which play the significant role in transferring them to cancer stem cells.
With respect to the role of connectors in the regulatory network of the blood cell development as cell fate biomarkers and the role of the dominators in the interwoven network of stem cells as a minimum connected dominating set to control and maintain the organs, it is postulated that the virus evolutionary path complies with the evolutionary path of the blood cells. By circulating blood, the connectors of the virus evolutionary path play a significant role in spreading the disease to the organs for the maximum efficiency to survive.
In the case of cancer, the healthy surrounding parts can be systematically connected using deep learning methods to improve treatment time and accuracy. As mentioned in this work, stomach, large bowel and small bowel were considered as independent layers connected with each other based on the donkey theorem.


Unlike bacteria that can be deactivated by the antibiotics, viruses evolve in the host body cause a constant effort to eliminate them. The problem becomes worse, if they target somatic stem cells. Consequent infection process transfers them to cancer where the cancer stem cells are resistant to drug therapy.
To address this issue, it is helpful to identify the stem cells either in the case of immunization or for the treatment. Therefore, the first step is to identify organoids which are derived from stem cells by image processing. The next step is to identify the somatic stem cells-specific functional units which play the significant role in transferring them to cancer stem cells.
With respect to the role of connectors in the regulatory network of the blood cell development as cell fate biomarkers and the role of the dominators in the interwoven network of stem cells as a minimum connected dominating set to control and maintain the organs, it is postulated that the virus evolutionary path complies with the evolutionary path of the blood cells. By circulating blood, the connectors of the virus evolutionary path play a significant role in spreading the disease to the organs for the maximum efficiency to survive.
In the case of cancer, the healthy surrounding parts can be systematically connected using deep learning methods to improve treatment time and accuracy. As mentioned in this work, stomach, large bowel and small bowel were considered as independent layers connected with each other based on the donkey theorem.


Determining and comparing nucleotide sequences in DNA, or DNA sequence alignment, has proved effective in identifying pathogenic microorganisms for diagnosing cancer and infectious diseases. In this paper, we propose a novel, information-theory-based algorithm for DNA sequence alignment called Huffman-Levenshtein Alignment (HLA). The HLA first converts the two DNA sequences in question using Huffman coding and then calculates a Levenshtein distance to determine the similarity. We applied the HLA on the DNA sequences of five SARS-CoV-2 variants, evaluated two performance measures, namely alignment accuracy and computation time, and compared them with an established method called Smith-Waterman Algorithm (SWA) as the baseline. We found that compared to SWA, the HLA achieved similar accuracies but significantly shorter computation times (avg. 11 minutes vs. 15 hours). Our findings suggest that the HLA may reduce the computational cost for larger-scale DNA sequence alignment tasks.

YihW is a local transcription factor from Escherichia coli regulating the yih genes responsible for the degradation of sulfoquinovose (SQ) and, presumably, lactose. SQ and lactose may potentially serve as effectors for YihW. Initial findings from our laboratory suggest YihW having an alternative shortened form.

We aimed to examine the conservation of an alternative start methionine in YihW from E. coli among YihW homologs in diverse bacterial species, and estimate potential differences in binding SQ and lactose between two protein YihW variants.

Phylogenetic analysis was conducted to study the occurrence pattern of alternative translation start for YihW in different bacterial species. Binding of SQ and lactose to both YihW forms was modeled using molecular docking

Protein phylogenetic tree of YihW indicates that potential start methionine of the truncated YihW emerged in a common ancestor of Enterobacteriales. The shortened YihW may bind lactose and SQ in the interdomain pocket with higher affinity compared to the full-length YihW. The last one probably interacts with SQ in the interdomain space and prefers binding of lactose on the surface of the C-terminal domain.

Our results suggest the existence of the functional truncated YihW protein providing yet a rare example among regulators in bacteria.

Accurate reconstruction of clonal evolution is essential for the application of precision oncology. It allows for the early detection of newly developing, highly aggressive subclones that might be resistant to therapy and can potentially lead to relapse. However, analysis is characterized by challenges: Often, data on only few time points are available. As a consequence, clonal evolution is incomplete, lacking information on a tumor's development over time and its response to therapy. Furthermore, bi-allelic events, which are considered of high relevance with respect to many cancers, cannot be depicted properly.
To address these challenges, we developed clevRvis – an R/Bioconductor package providing a wide set of innovative visualization techniques for clonal evolution in R. Three different representations are available: shark plots (graph-based), dolphin plots (fish plot-like) and plaice plots (allele-aware). Phylogeny-aware color coding is available by default. Plots are generated on the basis of a seaObject, optionally containing automatically interpolated time points and/or estimated therapy effect. Alternative trees, determined on the basis of the user-defined cancer cell fractions, can be explored interactively. A shiny interface allows for user-friendly analysis.
Concluding, clevRvis provides novel techniques for visualizing clonal evolution, contributing to a better understanding of a tumor's development.