Expression Pattern of Trace Amine-Associated Receptors during Differentiation of Human Pluripotent Stem Cells to Dopaminergic Neurons.

Trace amine-associated receptors (TAARs), which were discovered only in 2001, are known to be involved in the regulation of a spectrum of neuronal processes and may play a role in the pathogenesis of a number of neuropsychiatric diseases, such as schizophrenia and others. We have previously shown that TAARs also have interconnections with the regulation of neurogenesis and, in particular, with the neurogenesis of dopamine neurons, but the exact mechanisms of this are still unknown. In our work we analyzed the expression of TAARs (TAAR1, TAAR2, TAAR5, TAAR6, TAAR8 and TAAR9) in cells from the human substantia nigra and ventral tegmental areas and in human pluripotent stem cells at consecutive stages of their differentiation to dopaminergic neurons, using RNA sequencing data from open databases, and TaqMan PCR data from the differentiation of human induced pluripotent stem cells in vitro. Detectable levels of TAARs expression were found in cells at the pluripotent stages, and the dynamic of their expression had a trend of increasing with the differentiation and maturation of dopamine neurons. The expression of several TAAR types (particularly TAAR5) was also found in human dopaminergic neuron-enriched zones in the midbrain. This is the first evidence of TAARs expression during neuronal differentiation, which can help to approach an understanding of the role of TAARs in neurogenesis.

Pericentromeric satellite lncRNAs are induced in cancer-associated fibroblasts and regulate their functions in lung tumorigenesis.

The abnormal tumor microenvironment (TME) often dictates the therapeutic response of cancer to chemo- and immuno-therapy. Aberrant expression of pericentromeric satellite repeats has been reported for epithelial cancers, including lung cancer. However, the transcription of tandemly repetitive elements in stromal cells of the TME has been unappreciated, limiting the optimal use of satellite transcripts as biomarkers or anti-cancer targets. We found that transcription of pericentromeric satellite DNA (satDNA) in mouse and human lung adenocarcinoma was observed in cancer-associated fibroblasts (CAFs). In vivo, lung fibroblasts expressed pericentromeric satellite repeats HS2/HS3 specifically in tumors. In vitro, transcription of satDNA was induced in lung fibroblasts in response to TGFß, IL1α, matrix stiffness, direct contact with tumor cells and treatment with chemotherapeutic drugs. Single-cell transcriptome analysis of human lung adenocarcinoma confirmed that CAFs were the cell type with the highest number of satellite transcripts. Human HS2/HS3 pericentromeric transcripts were detected in the nucleus, cytoplasm, extracellularly and co-localized with extracellular vesicles in situ in human biopsies and activated fibroblasts in vitro. The transcripts were transmitted into recipient cells and entered their nuclei. Knock-down of satellite transcripts in human lung fibroblasts attenuated cellular senescence and blocked the formation of an inflammatory CAFs phenotype which resulted in the inhibition of their pro-tumorigenic functions. In sum, our data suggest that satellite long non-coding (lnc) RNAs are induced in CAFs, regulate expression of inflammatory genes and can be secreted from the cells, which potentially might present a new element of cell-cell communication in the TME.

Modulation of Notch Signaling at Early Stages of Differentiation of Human Induced Pluripotent Stem Cells to Dopaminergic Neurons.

Elaboration of protocols for differentiation of human pluripotent stem cells to dopamine neurons is an important issue for development of cell replacement therapy for Parkinson's disease. A number of protocols have been already developed; however, their efficiency and specificity still can be improved. Investigating the role of signaling cascades, important for neurogenesis, can help to solve this problem and to provide a deeper understanding of their role in neuronal development. Notch signaling plays an essential role in development and maintenance of the central nervous system after birth. In our study, we analyzed the effect of Notch activation and inhibition at the early stages of differentiation of human induced pluripotent stem cells to dopaminergic neurons. We found that, during the first seven days of differentiation, the cells were not sensitive to the Notch inhibition. On the contrary, activation of Notch signaling during the same time period led to significant changes and was associated with an increase in expression of genes, specific for caudal parts of the brain, a decrease of expression of genes, specific for forebrain, as well as a decrease of expression of genes, important for the formation of axons and dendrites and microtubule stabilizing proteins.

MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data.

While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis. Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available from https://github.com/ablab/metaGT.

Graph-Based Approaches Significantly Improve the Recovery of Antibiotic Resistance Genes From Complex Metagenomic Datasets.

The lack of control over the usage of antibiotics leads to propagation of the microbial strains that are resistant to many antimicrobial substances. This situation is an emerging threat to public health and therefore the development of approaches to infer the presence of resistant strains is a topic of high importance. The resistome construction of an isolate microbial species could be considered a solved task with many state-of-the-art tools available. However, when it comes to the analysis of the resistome of a microbial community (metagenome), then there exist many challenges that influence the accuracy and precision of the predictions. For example, the prediction sensitivity of the existing tools suffer from the fragmented metagenomic assemblies due to interspecies repeats: usually it is impossible to recover conservative parts of antibiotic resistance genes that belong to different species that occur due to e.g., horizontal gene transfer or residing on a plasmid. The recent advances in development of new graph-based methods open a way to recover gene sequences of interest directly from the assembly graph without relying on cumbersome and incomplete metagenomic assembly. We present GraphAMR-a novel computational pipeline for recovery and identification of antibiotic resistance genes from fragmented metagenomic assemblies. The pipeline involves the alignment of profile hidden Markov models of target genes directly to the assembly graph of a metagenome with further dereplication and annotation of the results using state-of-the art tools. We show significant improvement of the quality of the results obtained (both in terms of accuracy and completeness) as compared to the analysis of an output of ordinary metagenomic assembly as well as different read mapping approaches. The pipeline is freely available from https://github.com/ablab/graphamr.

Hi-C Metagenomics in the ICU: Exploring Clinically Relevant Features of Gut Microbiome in Chronically Critically Ill Patients.

Gut microbiome in critically ill patients shows profound dysbiosis. The most vulnerable is the subgroup of chronically critically ill (CCI) patients - those suffering from long-term dependence on support systems in intensive care units. It is important to investigate their microbiome as a potential reservoir of opportunistic taxa causing co-infections and a morbidity factor. We explored dynamics of microbiome composition in the CCI patients by combining "shotgun" metagenomics with chromosome conformation capture (Hi-C). Stool samples were collected at 2 time points from 2 patients with severe brain injury with different outcomes within a 1-2-week interval. The metagenome-assembled genomes (MAGs) were reconstructed based on the Hi-C data using a novel hicSPAdes method (along with the bin3c method for comparison), as well as independently of the Hi-C using MetaBAT2. The resistomes of the samples were derived using a novel assembly graph-based approach. Links of bacteria to antibiotic resistance genes, plasmids and viruses were analyzed using Hi-C-based networks. The gut community structure was enriched in opportunistic microorganisms. The binning using hicSPAdes was superior to the conventional WGS-based binning as well as to the bin3c in terms of the number, completeness and contamination of the reconstructed MAGs. Using Klebsiella pneumoniae as an example, we showed how chromosome conformation capture can aid comparative genomic analysis of clinically important pathogens. Diverse associations of resistome with antimicrobial therapy from the level of assembly graphs to gene content were discovered. Analysis of Hi-C networks suggested multiple "host-plasmid" and "host-phage" links. Hi-C metagenomics is a promising technique for investigating clinical microbiome samples. It provides a community composition profile with increased details on bacterial gene content and mobile genetic elements compared to conventional metagenomics. The ability of Hi-C binning to encompass the MAG's plasmid content facilitates metagenomic evaluation of virulence and drug resistance dynamics in clinically relevant opportunistic pathogens. These findings will help to identify the targets for developing cost-effective and rapid tests for assessing microbiome-related health risks.