Summary The human gut microbiota is a metabolic organ whose cellular composition is determined by a dynamic process of selection and competition. To identify microbial genes required for establishment of human symbionts in the gut, we developed an approach (insertion-sequencing, or INSeq) based on a mutagenic transposon that allows capture of adjacent chromosomal DNA to define its genomic location. We used massively parallel sequencing to monitor the relative abundance of tens of thousands of transposon mutants of a saccharolytic human gut bacterium, Bacteroides thetaiotaomicron, as they established themselves in wild-type and immunodeficient gnotobiotic mice, in the presence or absence of other human gut commensals. In vivo selection transforms this population, revealing functions necessary for survival in the gut: we show how this selection is influenced by community composition and competition for nutrients (vitamin B12). INSeq provides a broadly applicable platform to explore microbial adaptation to the gut and other ecosystems.

Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10%). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

Proteins, such as many transcription factors, that bind to specific DNA sequences are essential for the proper regulation of gene expression. Identifying the specific sequences that each factor binds can help to elucidate regulatory networks within cells and how genetic variation can cause disruption of normal gene expression, which is often associated with disease. Traditional methods for determining the specificity of DNA-binding proteins are slow and laborious, but several new high-throughput methods can provide comprehensive binding information much more rapidly. Combined with in vivo determinations of transcription factor binding locations, this information provides more detailed views of the regulatory circuitry of cells and the effects of variation on gene expression.

We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.

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Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

ObjectiveAs the current therapeutic strategies for human hepatocellular carcinoma (HCC) have been proven to have limited effectiveness, immunotherapy becomes a compelling way to tackle the disease. We aim to provide humanised mouse (humice) models for the understanding of the interaction between human cancer and immune system, particularly for human-specific drug testing.DesignPatient-derived xenograft tumours are established with type I human leucocyte antigen matched human immune system in NOD-scid Il2rg−/− (NSG) mice. The longitudinal changes of the tumour and immune responses as well as the efficacy of immune checkpoint inhibitors are investigated.ResultsSimilar to the clinical outcomes, the human immune system in our model is educated by the tumour and exhibits exhaustion phenotypes such as a significant declination of leucocyte numbers, upregulation of exhaustion markers and decreased the production of human proinflammatory cytokines. Notably, cytotoxic immune cells decreased more rapidly compared with other cell types. Tumour infiltrated T cells have much higher expression of exhaustion markers and lower cytokine production compared with peripheral T cells. In addition, tumour-associated macrophages and myeloid-derived suppressor cells are found to be highly enriched in the tumour microenvironment. Interestingly, the tumour also changes gene expression profiles in response to immune responses by upregulating immune checkpoint ligands. Most importantly, in contrast to the NSG model, our model demonstrates both therapeutic and side effects of immune checkpoint inhibitors pembrolizumab and ipilimumab.ConclusionsOur work provides a model for immune-oncology study and a useful parallel-to-human platform for anti-HCC drug testing, especially immunotherapy.

Metastatic spread is the mechanism in more than 90 percent of cancer deaths and current therapeutic options, such as systemic chemotherapy, are often ineffective. Here we provide a proof of principle for a novel two-pronged modality referred to as Synergistic Immuno Photothermal Nanotherapy (SYMPHONY) having the potential to safely eradicate both primary tumors and distant metastatic foci. Using a combination of immune-checkpoint inhibition and plasmonic gold nanostar (GNS)–mediated photothermal therapy, we were able to achieve complete eradication of primary treated tumors and distant untreated tumors in some mice implanted with the MB49 bladder cancer cells. Delayed rechallenge with MB49 cancer cells injection in mice that appeared cured by SYMPHONY did not lead to new tumor formation after 60 days observation, indicating that SYMPHONY treatment induced effective long-lasting immunity against MB49 cancer cells.