Hidden Markov models for evolution and comparative genomics analysis

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

Flexible comparative genomics of prokaryotic transcriptional regulatory networks

Background

Comparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.

Results

Here we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.

Conclusions

Our results demonstrate the applicability of the CGB pipeline in multiple settings. CGB’s ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network

The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of “Circumjacent Monitoring-Blind Area Inference”. In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.

Ancestral reconstruction of protein interaction networks

The molecular and cellular basis of novelty is an active area of research in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term "phylogenetic latent variable models" (PLVMs, pronounced "plums") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

Proteins Related to the Type I Secretion System Are Associated with Secondary SecA_DEAD Domain Proteins in Some Species of Planctomycetes, Verrucomicrobia, Proteobacteria, Nitrospirae and Chlorobi

A number of bacteria belonging to the PVC (Planctomycetes-Verrucomicrobia-Chlamydiae) super-phylum contain unusual ribosome-bearing intracellular membranes. The evolutionary origins and functions of these membranes are unknown. Some proteins putatively associated with the presence of intracellular membranes in PVC bacteria contain signal peptides. Signal peptides mark proteins for translocation across the cytoplasmic membrane in prokaryotes, and the membrane of the endoplasmic reticulum in eukaryotes, by highly conserved Sec machinery. This suggests that proteins might be targeted to intracellular membranes in PVC bacteria via the Sec pathway. Here, we show that canonical signal peptides are significantly over-represented in proteins preferentially present in PVC bacteria possessing intracellular membranes, indicating involvement of Sec translocase in their cellular targeting. We also characterized Sec proteins using comparative genomics approaches, focusing on the PVC super-phylum. While we were unable to detect unique changes in Sec proteins conserved among membrane-bearing PVC species, we identified (1) SecA ATPase domain re-arrangements in some Planctomycetes, and (2) secondary SecA_DEAD domain proteins in the genomes of some Planctomycetes, Verrucomicrobia, Proteobacteria, Nitrospirae and Chlorobi. This is the first report of potentially duplicated SecA in Gram-negative bacteria. The phylogenetic distribution of secondary SecA_DEAD domain proteins suggests that the presence of these proteins is not related to the occurrence of PVC endomembranes. Further genomic analysis showed that secondary SecA_DEAD domain proteins are located within genomic neighborhoods that also encode three proteins possessing domains specific for the Type I secretion system.

Eighteenth-century genomes show that mixed infections were common at time of peak tuberculosis in Europe

Tuberculosis (TB) was once a major killer in Europe, but it is unclear how the strains and patterns of infection at ‘peak TB' relate to what we see today. Here we describe 14 genome sequences of M. tuberculosis, representing 12 distinct genotypes, obtained from human remains from eighteenth-century Hungary using metagenomics. All our historic genotypes belong to M. tuberculosis Lineage 4. Bayesian phylogenetic dating, based on samples with well-documented dates, places the most recent common ancestor of this lineage in the late Roman period. We find that most bodies yielded more than one M. tuberculosis genotype and we document an intimate epidemiological link between infections in two long-dead individuals. Our results suggest that metagenomic approaches usefully inform detection and characterization of historical and contemporary infections.

Tuberculosis was once a major killer in Europe. Here the authors use metagenomics to obtain genomic sequences of Mycobacterium tuberculosis from human remains from eighteenth-century Hungary, revealing mixed infections within individuals as well as presence of the same strain in two individuals.