Ofir Cohen

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma

Summary Large-scale genomic characterization of tumors from prospective cohort studies may yield new insights into cancer pathogenesis. We performed whole-exome sequencing of 619 incident colorectal cancers (CRCs) and integrated the results with tumor immunity, pathology, and survival data. We identified recurrently mutated genes in CRC, such as BCL9L, RBM10, CTCF, and KLF5, that were not previously appreciated in this disease. Furthermore, we investigated the genomic correlates of immune-cell infiltration and found that higher neoantigen load was positively associated with overall lymphocytic infiltration, tumor-infiltrating lymphocytes (TILs), memory T cells, and CRC-specific survival. The association with TILs was evident even within microsatellite-stable tumors. We also found positive selection of mutations in HLA genes and other components of the antigen-processing machinery in TIL-rich tumors. These results may inform immunotherapeutic approaches in CRC. More generally, this study demonstrates a framework for future integrative molecular epidemiology research in colorectal and other malignancies.

FastML: a web server for probabilistic reconstruction of ancestral sequences

Ancestral sequence reconstruction is essential to a variety of evolutionary studies. Here, we present the FastML web server, a user-friendly tool for the reconstruction of ancestral sequences. FastML implements various novel features that differentiate it from existing tools: (i) FastML uses an indel-coding method, in which each gap, possibly spanning multiples sites, is coded as binary data. FastML then reconstructs ancestral indel states assuming a continuous time Markov process. FastML provides the most likely ancestral sequences, integrating both indels and characters; (ii) FastML accounts for uncertainty in ancestral states: it provides not only the posterior probabilities for each character and indel at each sequence position, but also a sample of ancestral sequences from this posterior distribution, and a list of the k-most likely ancestral sequences; (iii) FastML implements a large array of evolutionary models, which makes it generic and applicable for nucleotide, protein and codon sequences; and (iv) a graphical representation of the results is provided, including, for example, a graphical logo of the inferred ancestral sequences. The utility of FastML is demonstrated by reconstructing ancestral sequences of the Env protein from various HIV-1 subtypes. FastML is freely available for all academic users and is available online at http://fastml.tau.ac.il/.

BREX is a novel phage resistance system widespread in microbial genomes

The perpetual arms race between bacteria and phage has resulted in the evolution of efficient resistance systems that protect bacteria from phage infection. Such systems, which include the CRISPR‐Cas and restriction‐modification systems, have proven to be invaluable in the biotechnology and dairy industries. Here, we report on a six‐gene cassette in Bacillus cereus which, when integrated into the Bacillus subtilis genome, confers resistance to a broad range of phages, including both virulent and temperate ones. This cassette includes a putative Lon‐like protease, an alkaline phosphatase domain protein, a putative RNA‐binding protein, a DNA methylase, an ATPase‐domain protein, and a protein of unknown function. We denote this novel defense system BREX (Bacteriophage Exclusion) and show that it allows phage adsorption but blocks phage DNA replication. Furthermore, our results suggest that methylation on non‐palindromic TAGGAG motifs in the bacterial genome guides self/non‐self discrimination and is essential for the defensive function of the BREX system. However, unlike restriction‐modification systems, phage DNA does not appear to be cleaved or degraded by BREX, suggesting a novel mechanism of defense. Pan genomic analysis revealed that BREX and BREX‐like systems, including the distantly related Pgl system described in Streptomyces coelicolor, are widely distributed in ~10% of all sequenced microbial genomes and can be divided into six coherent subtypes in which the gene composition and order is conserved. Finally, we detected a phage family that evades the BREX defense, implying that anti‐BREX mechanisms may have evolved in some phages as part of their arms race with bacteria.

Genomic analysis of 38 Legionella species identifies large and diverse effector repertoires

Infection by the human pathogen Legionella pneumophila relies on the translocation of ∼300 virulence proteins, termed effectors, which manipulate host cell processes. However, almost no information exists regarding effectors in other Legionella pathogens. Here we sequenced, assembled and characterized the genomes of 38 Legionella species and predicted their effector repertoires using a previously validated machine learning approach. This analysis identified 5,885 predicted effectors. The effector repertoires of different Legionella species were found to be largely non-overlapping, and only seven core effectors were shared by all species studied. Species-specific effectors had atypically low GC content, suggesting exogenous acquisition, possibly from the natural protozoan hosts of these species. Furthermore, we detected numerous new conserved effector domains and discovered new domain combinations, which allowed the inference of as yet undescribed effector functions. The effector collection and network of domain architectures described here can serve as a roadmap for future studies of effector function and evolution.

Inference and Characterization of Horizontally Transferred Gene Families Using Stochastic Mapping

Macrogenomic events, in which genes are gained and lost, play a pivotal evolutionary role in microbial evolution. Nevertheless, probabilistic-evolutionary models describing such events and methods for their robust inference are considerably less developed than existing methodologies for analyzing site-specific sequence evolution. Here, we present a novel method for the inference of gains and losses of gene families. First, we develop probabilistic-evolutionary models describing the dynamics of gene-family content, which are more biologically realistic than previously suggested models. In our likelihood-based models, gains and losses are represented by transitions between presence and absence, given an underlying phylogeny. We employ a mixture-model approach in which we allow both the gain rate and the loss rate to vary among gene families. Second, we use these models together with the analytic implementation of stochastic mapping to infer branch-specific events. Our novel methodology allows us to infer and quantify horizontal gene transfer (HGT) events. This enables us to rank various gene families and lineages according to their propensity to undergo gains and losses. Applying our methodology to 4,873 gene families shows that: 1) the novel mixture models describe the observed variability in gene-family content among microbes significantly better than previous models; 2) The stochastic mapping approach enables accurate inference of gain and loss events based on simulations; 3) At least 34% of the gene families analyzed are inferred to have experienced HGT at least once during their evolution; and 4) Gene families that were inferred to experience HGT are both enriched and depleted with respect to specific functional categories.

A likelihood framework to analyse phyletic patterns

Probabilistic evolutionary models revolutionized our capability to extract biological insights from sequence data. While these models accurately describe the stochastic processes of site-specific substitutions, single-base substitutions represent only a fraction of all the events that shape genomes. Specifically, in microbes, events in which entire genes are gained (e.g. via horizontal gene transfer) and lost play a pivotal evolutionary role. In this research, we present a novel likelihood-based evolutionary model for gene gains and losses, and use it to analyse genome-wide patterns of the presence and absence of gene families. The model assumes a Markovian stochastic process, where gains and losses are represented by the transition between presence and absence, respectively, given an underlying phylogenetic tree. To account for differences in the rates of gain and loss of different gene families, we assume among-gene family rate variability, thus allowing for more accurate description of the data. Using the Bayesian approach, we estimated an evolutionary rate for each gene family. Simulation studies demonstrated that our methodology accurately infers these rates. Our methodology was applied to analyse a large corpus of data, consisting of 4873 gene families spanning 63 species and revealed novel insights regarding the evolutionary nature of genome-wide gain and loss dynamics.

Erratum: Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma (Cell Reports (2016) 15(4) (857–865) (S2211124716303643) (10.1016/j.celrep.2016.03.075))

Marios Giannakis, Xinmeng Jasmine Mu, Sachet A. Shukla, Zhi Rong Qian, Ofir Cohen, Reiko Nishihara, Samira Bahl, Yin Cao, Ali Amin-Mansour, Mai Yamauchi, Yasutaka Sukawa, Chip Stewart, Mara Rosenberg, Kosuke Mima, Kentaro Inamura, Katsuhiko Nosho, Jonathan A. Nowak, Michael S. Lawrence, Edward L. Giovannucci, Andrew T. Chan, Kimmie Ng, Jeffrey A. Meyerhardt, Eliezer M. Van Allen, Gad Getz, Stacey B. Gabriel, Eric S. Lander, Catherine J. Wu, Charles S. Fuchs, Shuji Ogino,* and Levi A. Garraway* *Correspondence: shuji_ogino@dfci.harvard.edu (S.O.), levi_garraway@dfci.harvard.edu (L.A.G.) http://dx.doi.org/10.1016/j.celrep.2016.10.009

Large-Scale Parsimony Analysis of Metazoan Indels in Protein-Coding Genes

Insertions and deletions (indels) are considered to be rare evolutionary events, the analysis of which may resolve controversial phylogenetic relationships. Indeed, indel characters are often assumed to be less homoplastic than amino acid and nucleotide substitutions and, consequently, more reliable markers for phylogenetic reconstruction. In this study, we analyzed indels from over 1,000 metazoan orthologous genes. We studied the impact of different species sampling, ortholog data sets, lengths of included indels, and indel-coding methods on the resulting metazoan tree. Our results show that, similar to sequence substitutions, indels are homoplastic characters, and their analysis is sensitive to the long-branch attraction artifact. Furthermore, improving the taxon sampling and choosing a closely related outgroup greatly impact the phylogenetic inference. Our indel-based inferences support the Ecdysozoa hypothesis over the Coelomata hypothesis and suggest that sponges are a sister clade to other animals.

Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants

Tumor-specific genomic information has the potential to guide therapeutic strategies and revolutionize patient treatment. Currently, this approach is limited by an abundance of disease-associated mutants whose biological functions and impacts on therapeutic response are uncharacterized. To begin to address this limitation, we functionally characterized nearly all (99.84%) missense mutants of MAPK1/ERK2, an essential effector of oncogenic RAS and RAF. Using this approach, we discovered rare gain- and loss-of-function ERK2 mutants found in human tumors, revealing that, in the context of this assay, mutational frequency alone cannot identify all functionally impactful mutants. Gain-of-function ERK2 mutants induced variable responses to RAF-, MEK-, and ERK-directed therapies, providing a reference for future treatment decisions. Tumor-associated mutations spatially clustered in two ERK2 effector-recruitment domains yet produced mutants with opposite phenotypes. This approach articulates an allele-characterization framework that can be scaled to meet the goals of genome-guided oncology.