Distinguishing activated T regulatory cell and T conventional cells by single-cell technologies

Resting conventional T cells (Tconv) can be distinguished from T regulatory cells (Treg) by the canonical markers FOXP3, CD25 and CD127. However, the expression of these proteins alters after T-cell activation leading to overlap between Tconv and Treg. The objective of this study was to distinguish resting and antigen-responsive T effector (Tconv) and Treg using single-cell technologies. CD4+ Treg and Tconv cells were stimulated with antigen and responsive and non-responsive populations processed for targeted and non-targeted single-cell RNAseq. Machine learning was used to generate a limited set of genes that could distinguish responding and non-responding Treg and Tconv cells and which was used for single-cell multiplex qPCR and to design a flow cytometry panel. Targeted scRNAseq clearly distinguished the four-cell populations. A minimal set of 27 genes was identified by machine learning algorithms to provide discrimination of the four populations at >95% accuracy. In all, 15 of the genes were validated to be differentially expressed by single-cell multiplex qPCR. Discrimination of responding Treg from responding Tconv could be achieved by a flow cytometry strategy that included staining for CD25, CD127, FOXP3, IKZF2, ITGA4, and the novel marker TRIM which was strongly expressed in Tconv and weakly expressed in both responding and non-responding Treg. A minimal set of genes was identified that discriminates responding and non-responding CD4+ Treg and Tconv cells and, which have identified TRIM as a marker to distinguish Treg by flow cytometry

Maximum depth sequencing reveals an ON/OFF replication slippage switch and apparent in vivo selection for bifidobacterial pilus expression

The human gut microbiome, of which the genus Bifidobacterium is a prevalent and abundant member, is thought to sustain and enhance human health. Several surface-exposed structures, including so-called sortase-dependent pili, represent important bifidobacterial gut colonization factors. Here we show that expression of two sortase-dependent pilus clusters of the prototype Bifidobacterium breve UCC2003 depends on replication slippage at an intragenic G-tract, equivalents of which are present in various members of the Bifidobacterium genus. The nature and extent of this slippage is modulated by the host environment. Involvement of such sortase-dependent pilus clusters in microbe-host interactions, including bacterial attachment to the gut epithelial cells, has been shown previously and is corroborated here for one case. Using a Maximum Depth Sequencing strategy aimed at excluding PCR and sequencing errors introduced by DNA polymerase reagents, specific G-tract sequences in B. breve UCC2003 reveal a range of G-tract lengths whose plasticity within the population is functionally utilized. Interestingly, replication slippage is shown to be modulated under in vivo conditions in a murine model. This in vivo modulation causes an enrichment of a G-tract length which appears to allow biosynthesis of these sortase-dependent pili. This work provides the first example of productive replication slippage influenced by in vivo conditions. It highlights the potential for microdiversity generation in “beneficial” gut commensals

Integrating gene annotation with orthology inference at scale

[INTRODUCTION] Comparative genomics provides valuable insights into gene function, phylogeny, molecular evolution, and associations between phenotypic and genomic differences. Such analyses require knowledge about which genes originated from a speciation event (orthologs) or from a duplication event (paralogs). Existing methods to detect orthologs in turn require knowledge of the location of genes in the genome (gene annotation), which is itself a challenging problem, resulting in a growing gap between sequenced and annotated genomes.[RATIONALE] We developed TOGA (Tool to infer Orthologs from Genome Alignments), a genomics method that integrates orthology inference and gene annotation. TOGA takes as input a gene annotation of a reference species (e.g., human, mouse, or chicken) and a whole-genome alignment between the reference and a query genome (e.g., other mammals or birds). It infers orthologous gene loci in the query genome, annotates and classifies orthologous genes, detects gene losses and duplications, and generates protein and codon alignments. Orthology detection relies on the principle that orthologous sequences are generally more similar to each other than to paralogous sequences. Whereas existing methods work with annotated protein-coding sequences, TOGA extends this similarity principle to non-exonic regions (introns and intergenic regions) and uses machine learning to detect orthologous gene loci based on alignments of intronic and intergenic regions.[RESULTS] We demonstrate that TOGA’s machine learning classifier detects orthologous gene loci with a very high accuracy, and also works for orthologous genes that underwent translocations or inversions. TOGA improves ortholog detection and comprehensively annotates conserved genes, even if transcriptomics data are available. Although homology-based methods such as TOGA cannot annotate orthologs of genes that are not present in the reference, we show that reference bias can be effectively counteracted by integrating annotations generated with multiple reference species. TOGA can also be applied to highly fragmented genome assemblies, where genes are often split across scaffolds. By accurately identifying and joining orthologous gene fragments, TOGA annotates entire genes and thus increases the utility of fragmented genomes for comparative analyses. TOGA’s gene classification explicitly distinguishes between genes with missing sequences (indicative of assembly incompleteness) and genes with inactivating mutations (potentially indicative of base errors). We show that this classification provides a superior benchmark for assembly completeness and quality. As genomes are generated at an increasing rate, annotation and orthology inference methods that can handle hundreds or thousands of genomes are needed. TOGA’s reference species methodology scales linearly with the number of query species. By applying TOGA with human and mouse as references to 488 placental mammal assemblies and using chicken as a reference for 501 bird assemblies, we created large comparative resources for mammals and birds that comprise gene annotations, ortholog sets, lists of inactivated genes, and multiple codon alignments.[CONCLUSION] TOGA provides a general strategy to cope with the annotation and orthology inference bottleneck. We envision three major uses. First, TOGA enables phylogenomic analyses of orthologous genes and screens for gene changes (e.g., selection, loss, and duplication) that are associated with phenotypic differences. Second, TOGA provides annotations of genes that are conserved in newly sequenced genomes, which can be supplemented with transcriptomics data to detect lineage-specific genes or exons. Finally, TOGA’s gene classification provides a powerful genome assembly quality benchmark.[ABSTRACT] Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.This work was supported by the LOEWE-Centre for Translational Biodiversity Genomics (TBG), the German Research Foundation (grants HI1423/4-1 and HI1423/5-1), and the Max Planck Society.Peer reviewe

Redox control of cancer cell destruction

Redox regulation has been proposed to control various aspects of carcinogenesis, cancer cell growth, metabo-lism, migration, invasion, metastasis and cancer vascularization. As cancer has many faces, the role of redoxcontrol in different cancers and in the numerous cancer-related processes often point in different directions. Inthis review, we focus on the redox control mechanisms of tumor cell destruction. The review covers the tumor-intrinsic role of oxidants derived from the reduction of oxygen and nitrogen in the control of tumor cell pro-liferation as well as the roles of oxidants and antioxidant systems in cancer cell death caused by traditionalanticancer weapons (chemotherapeutic agents, radiotherapy, photodynamic therapy). Emphasis is also put onthe role of oxidants and redox status in the outcome following interactions between cancer cells, cytotoxiclymphocytes and tumor infiltrating macrophage

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field