Explore the vast range of titles available.

Background Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Results Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. Conclusions Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies.

Multidrug-resistant tuberculosis (MDR-TB) is among the most frequent causes of death due to antimicrobial resistance. Although only 3% of global TB cases are MDR, geographical hotspots with up to 40% of MDR-TB have been observed in countries of the former Soviet Union. While the quality of TB control and patient-related factors are known contributors to such hotspots, the role of the pathogen remains unclear. Here we show that in the country of Georgia, a known hotspot of MDR-TB, MDR Mycobacterium tuberculosis strains of lineage 4 (L4) transmit less than their drug-susceptible counterparts, whereas most MDR strains of L2 suffer no such defect. Our findings further indicate that the high transmission fitness of these L2 strains results from epistatic interactions between the rifampicin resistance-conferring mutation RpoB S450L, compensatory mutations in the RNA polymerase, and other pre-existing genetic features of L2/Beijing clones that circulate in Georgia. We conclude that the transmission fitness of MDR M. tuberculosis strains is heterogeneous, but can be as high as drug-susceptible forms, and that such highly drug-resistant and transmissible strains contribute to the emergence and maintenance of hotspots of MDR-TB. As these strains successfully overcome the metabolic burden of drug resistance, and given the ongoing rollout of new treatment regimens against MDR-TB, proper surveillance should be implemented to prevent these strains from acquiring resistance to the additional drugs. Geographical hotspots with high frequency of multi-drug resistant tuberculosis (MDR-TB) have been observed in several locations, such as the country of Georgia. Here, the authors analyse genomic sequences from tuberculosis bacteria isolated from Georgia to show that the transmission fitness of MDR-TB strains is heterogeneous, and highly drug-resistant and transmissible isolates contribute to the emergence and maintenance of MDR-TB hotspots.

Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE’s effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence. Current methods to generate sequence-function data at large scale are either technically complex or limited to specific applications. Here the authors introduce DNA-based phenotypic recording to overcome these limitations and enable deep learning for accurate prediction of function from sequence.

During spermatogenesis most histones are replaced with protamines. The distribution of the products of Trithorax and Polycomb histone modifications are now examined in both mouse and human sperm, showing conservation of histone methylation distribution across species. The authors propose a role for the Polycomb complex in transgenerational inheritance. In higher eukaryotes, histone methylation is involved in maintaining cellular identity during somatic development. As most nucleosomes are replaced by protamines during spermatogenesis, it is unclear whether histone modifications function in paternal transmission of epigenetic information. Here we show that two modifications important for Trithorax- and Polycomb-mediated gene regulation have methylation-specific distributions at regulatory regions in human spermatozoa. Histone H3 Lys4 dimethylation (H3K4me2) marks genes that are relevant in spermatogenesis and cellular homeostasis. In contrast, histone H3 Lys27 trimethylation (H3K27me3) marks developmental regulators in sperm, as in somatic cells. However, nucleosomes are only moderately retained at regulatory regions in human sperm. Nonetheless, genes with extensive H3K27me3 coverage around transcriptional start sites in particular tend not to be expressed during male and female gametogenesis or in preimplantation embryos. Promoters of orthologous genes are similarly modified in mouse spermatozoa. These data are compatible with a role for Polycomb in repressing somatic determinants across generations, potentially in a variegating manner.

The Mycobacterium tuberculosis complex (MTBC) causes tuberculosis (TB) in humans and various other mammals. The human-adapted members of the MTBC comprise seven phylogenetic lineages that differ in their geographical distribution. There is growing evidence that this phylogeographic diversity modulates the outcome of TB infection and disease. For decades, TB research and development has focused on the two canonical MTBC laboratory strains H37Rv and Erdman, both of which belong to Lineage 4. Relying on only a few laboratory-adapted strains can be misleading as study results might not be directly transferrable to clinical settings where patients are infected with a diverse array of strains, including drug-resistant variants. Here, we argue for the need to expand TB research and development by incorporating the phylogenetic diversity of the MTBC. To facilitate such work, we have assembled a group of 20 genetically well-characterized clinical strains representing the seven known human-adapted MTBC lineages. With the \"MTBC clinical strains reference set\" we aim to provide a standardized resource for the TB community. We hope it will enable more direct comparisons between studies that explore the physiology of MTBC beyond the laboratory strains used thus far. We anticipate that detailed phenotypic analyses of this reference strain set will increase our understanding of TB biology and assist in the development of new control tools that are broadly effective.

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK. The bioinformatics method COJAC enables improved population-level surveillance of the emergence and spread of SARS-CoV-2 variants in wastewater.

The omentum, a visceral adipose tissue with critical metabolic, immunological, and stem cell functions is the preferred site for ovarian cancer metastasis. However, its role in maintaining homeostasis and its responses to metastatic colonization remain incompletely understood. Using single-cell transcriptomics, we profile different anatomical regions of the omentum in patients with benign conditions and metastasis. We catalog the benign omentum and observe a stable cell type composition and a preserved stem and progenitor niche. Upon metastatic colonization, we report on increased immune heterogeneity and a concomitant reduction of mesothelial and progenitor cells. The lesser omentum, which is not routinely removed during surgical debulking, is identified as a premetastatic niche characterized by neutrophil infiltration, extracellular trap formation, and the presence of micrometastases. At established metastatic sites, resident cells exhibit cancer-associated phenotypes with regulatory, anti-adipogenic, and immunosuppressive properties. Cellular reprogramming across the omentum is associated with signaling profiles of tumor cells, suggesting potential influences on both proximal and distal tissue regions. This cell atlas illuminates the cellular and molecular determinants of organ homeostasis and reveals a high degree of plasticity and cellular reprogramming promoted by cancer colonization. The omentum is the main metastatic site for ovarian cancer. Here, the authors present a human cell atlas of both benign and omentum metastatic ovarian cancer, providing insights into established metastatic sites but also of the omentum pre-metastatic niche.

Neural stem cells (NSCs) generate neurons of the cerebral cortex with distinct morphologies and functions. How specific neuron production, differentiation and migration are orchestrated is unclear. Hippo signaling regulates gene expression through Tead transcription factors (TFs). We show that Hippo transcriptional coactivators Yap1/Taz and the Teads have distinct functions during cortical development. Yap1/Taz promote NSC maintenance and Satb2 + neuron production at the expense of Tbr1 + neuron generation. However, Teads have moderate effects on NSC maintenance and do not affect Satb2 + neuron differentiation. Conversely, whereas Tead2 blocks Tbr1 + neuron formation, Tead1 and Tead3 promote this early fate. In addition, we found that Hippo effectors regulate neuronal migration to the cortical plate (CP) in a reciprocal fashion, that ApoE , Dab2 and Cyr61 are Tead targets, and these contribute to neuronal fate determination and migration. Our results indicate that multifaceted Hippo signaling is pivotal in different aspects of cortical development.

Genomic imprinting is an epigenetic phenomenon leading to parent-of-origin specific differential expression of maternally and paternally inherited alleles. In plants, genomic imprinting has mainly been observed in the endosperm, an ephemeral triploid tissue derived after fertilization of the diploid central cell with a haploid sperm cell. In an effort to identify novel imprinted genes in Arabidopsis thaliana, we generated deep sequencing RNA profiles of F1 hybrid seeds derived after reciprocal crosses of Arabidopsis Col-0 and Bur-0 accessions. Using polymorphic sites to quantify allele-specific expression levels, we could identify more than 60 genes with potential parent-of-origin specific expression. By analyzing the distribution of DNA methylation and epigenetic marks established by Polycomb group (PcG) proteins using publicly available datasets, we suggest that for maternally expressed genes (MEGs) repression of the paternally inherited alleles largely depends on DNA methylation or PcG-mediated repression, whereas repression of the maternal alleles of paternally expressed genes (PEGs) predominantly depends on PcG proteins. While maternal alleles of MEGs are also targeted by PcG proteins, such targeting does not cause complete repression. Candidate MEGs and PEGs are enriched for cis-proximal transposons, suggesting that transposons might be a driving force for the evolution of imprinted genes in Arabidopsis. In addition, we find that MEGs and PEGs are significantly faster evolving when compared to other genes in the genome. In contrast to the predominant location of mammalian imprinted genes in clusters, cluster formation was only detected for few MEGs and PEGs, suggesting that clustering is not a major requirement for imprinted gene regulation in Arabidopsis.

Background Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it. Results We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies. Conclusions openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.