Tissue-tropic effector T cells: generation and targeting opportunities

The localization of effector T cells to extralymphoid tissues is crucial for the generation of an effective immune response, but it also underlies many autoimmune and inflammatory disorders. Recent studies have highlighted a central role for draining lymph nodes and environmentally imprinted dendritic cells in the generation of tissue-tropic effector T cells. Here, I outline our current understanding of the mechanisms that regulate the generation and localization of tissue-tropic effector T cells, and the potential ways in which these pathways can be exploited for immunotherapeutic purposes.     

Co-expression tools for plant biology: opportunities for hypothesis generation and caveats

Gene co‐expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co‐expression analysis asks the question ‘what are the genes that are co‐expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?’. Genes that are highly co‐expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co‐expression results, calculation of co‐expression scores and P values, and the influence of data sets used for co‐expression analysis. Finally, examples from the literature will be presented, wherein co‐expression has been used to corroborate and discover various aspects of plant biology.     

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences.In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.     

Coupling electrochemical CO2 reduction to microbial product generation – identification of the gaps and opportunities

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Livestock damage and wolf presence

Depredation on livestock and wolf pack Canis lupus distribution was investigated in the province of Arezzo, Tuscany, from 1998 to 2001. Although livestock was uniformly distributed, damage level and distribution were focused in the mountainous area inhabited by wolf packs hence there were huge differences between areas with and without wolves. In the whole province a few farms were persistently affected by predation (6%), and they reported 38% of the total attacks and 37% of the total losses. Surplus killing phenomena involved only sheep and goat farms, affecting 18% of the total. Thirty-five attacks (14% of the total attacks) reported 536 kills (44% of the total livestock killed in the whole province of Arezzo). During the period 1998–2001, compensation costs in the province averaged 86 863 Euros (range: 68 805–99 318 Euros). In the same period, no farmer requested prevention funding from the Tuscan region. During the study period wolf population was stable: wolf packs were distributed on 47% of the whole province (1504 km²), with a density estimated at 2.9±0.7 wolves 100 km⁻².     

Epigenetic Effects in Livestock Breeding

Epigenetic effects are considered as a mechanism of the emergence of new inherited traits with their transmission between generations through meiosis. Modern genomic evaluation does not explain the entire phenotypic variance of traits. It is quite obvious that a significant part of the unaccounted dispersion reflects epigenetic effects carried out through DNA methylation, histone and chromatin modifications, and activity of noncoding types of RNA. Epigenetic effects could potentially be used in breeding programs. The obtained data testify to the significant role of epigenetic factors in the expression of imprinting genes, cellular processes, development of muscle tissue, and fat metabolism in animals. The ability of various additives in the diet to induce epigenetic modifications with phenotypic variability has been convincingly proven. However, there are still many contradictions and limitations in the justification of the hereditary component of epigenetics for introduction into animal breeding. Development of modern technologies, such as chromatin immunoprecipitation with microchips of DNA (ChIP-Chip), next-generation sequencing (ChIP-Seq), and epigenomic editing based on CRISPR-Cas9, gives grounds for optimism in solving problems of introducing epigenetic phenomena in livestock breeding.