Spatially organized multicellular immune hubs in human colorectal cancer.

Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd individuals. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across tumors from affected individuals and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T cell-attracting chemokines. By identifying interacting cellular programs, we reveal the logic underlying spatially organized immune-malignant cell networks.

FOXA1 upregulation promotes enhancer and transcriptional reprogramming in endocrine-resistant breast cancer.

Forkhead box A1 (FOXA1) is a pioneer factor that facilitates chromatin binding and function of lineage-specific and oncogenic transcription factors. Hyperactive FOXA1 signaling due to gene amplification or overexpression has been reported in estrogen receptor-positive (ER+) endocrine-resistant metastatic breast cancer. However, the molecular mechanisms by which FOXA1 up-regulation promotes these processes and the key downstream targets of the FOXA1 oncogenic network remain elusive. Here, we demonstrate that FOXA1 overexpression in ER+ breast cancer cells drives genome-wide enhancer reprogramming to activate prometastatic transcriptional programs. Up-regulated FOXA1 employs superenhancers (SEs) to synchronize transcriptional reprogramming in endocrine-resistant breast cancer cells, reflecting an early embryonic development process. We identify the hypoxia-inducible transcription factor hypoxia-inducible factor-2α (HIF-2α) as the top high FOXA1-induced SE target, mediating the impact of high FOXA1 in activating prometastatic gene sets and pathways associated with poor clinical outcome. Using clinical ER+/HER2- metastatic breast cancer datasets, we show that the aberrant FOXA1/HIF-2α transcriptional axis is largely nonconcurrent with the ESR1 mutations, suggesting different mechanisms of endocrine resistance and treatment strategies. We further demonstrate the selective efficacy of an HIF-2α antagonist, currently in clinical trials for advanced kidney cancer and recurrent glioblastoma, in reducing the clonogenicity, migration, and invasion of endocrine-resistant breast cancer cells expressing high FOXA1. Our study has uncovered high FOXA1-induced enhancer reprogramming and HIF-2α-dependent transcriptional programs as vulnerable targets for treating endocrine-resistant and metastatic breast cancer.

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.

The Combination of Whole Cell Lipidomics Analysis and Single Cell Confocal Imaging of Fluidity and Micropolarity Provides Insight into Stress-Induced Lipid Turnover in Subcellular Organelles of Pancreatic Beta Cells.

Modern omics techniques reveal molecular structures and cellular networks of tissues and cells in unprecedented detail. Recent advances in single cell analysis have further revolutionized all disciplines in cellular and molecular biology. These methods have also been employed in current investigations on the structure and function of insulin secreting beta cells under normal and pathological conditions that lead to an impaired glucose tolerance and type 2 diabetes. Proteomic and transcriptomic analyses have pointed to significant alterations in protein expression and function in beta cells exposed to diabetes like conditions (e.g., high glucose and/or saturated fatty acids levels). These nutritional overload stressful conditions are often defined as glucolipotoxic due to the progressive damage they cause to the cells. Our recent studies on the rat insulinoma-derived INS-1E beta cell line point to differential effects of such conditions in the phospholipid bilayers in beta cells. This review focuses on confocal microscopy-based detection of these profound alterations in the plasma membrane and membranes of insulin granules and lipid droplets in single beta cells under such nutritional load conditions.

Real time quantitative analysis of lipid storage and lipolysis pathways by confocal spectral imaging of intracellular micropolarity.

Organisms store fatty acids in triacylglycerols in the form of lipid droplets, or hydrolyze triacylglycerols in response to energetic demands via activation of lipolytic or storage pathways. These pathways are complex sets of sequential reactions that are finely regulated in different cell types. Here we present a high spatial and temporal resolution-based method for the quantification of the turnover of fatty acids into triglycerides in live cells without introducing sample preparation artifacts. We performed confocal spectral imaging of intracellular micropolarity in cultured insulin secreting beta cells to detect micropolarity variations as they occur in time and at different pixels of microscope images. Acquired data are then analyzed in the framework of the spectral phasors technique. The method furnishes a metabolic parameter, which quantitatively assesses fatty acids - triacylglycerols turnover and the activation of lipolysis and storage pathways. Moreover, it provides a polarity profile, which represents the contribution of hyperpolar, polar and non-polar classes of lipids. These three different classes can be visualized on the image at a submicrometer resolution, revealing the spatial localization of lipids in cells under physiological and pathological settings. This new method allows for a fine-tuned, real-time visualization of the turnover of fatty acids into triglycerides in live cells with submicrometric resolution. It also detects imbalances between lipid storage and usage, which may lead to metabolic disorders within living cells and organisms.

Comparative transcriptomics across the prokaryotic tree of life.

Whole-transcriptome sequencing studies from recent years revealed an unexpected complexity in transcriptomes of bacteria and archaea, including abundant non-coding RNAs, cis-antisense transcription and regulatory untranslated regions (UTRs). Understanding the functional relevance of the plethora of non-coding RNAs in a given organism is challenging, especially since some of these RNAs were attributed to 'transcriptional noise'. To allow the search for conserved transcriptomic elements we produced comparative transcriptome maps for multiple species across the microbial tree of life. These transcriptome maps are detailed in annotations, comparable by gene families, and BLAST-searchable by user provided sequences. Our transcriptome collection includes 18 model organisms spanning 10 phyla/subphyla of bacteria and archaea that were sequenced using standardized RNA-seq methods. The utility of the comparative approach, as implemented in our web server, is demonstrated by highlighting genes with exceptionally long 5'UTRs across species, which correspond to many known riboswitches and further suggest novel putative regulatory elements. Our study provides a standardized reference transcriptome to major clinically and environmentally important microbial phyla. The viewer is available at http://exploration.weizmann.ac.il/TCOL, setting a framework for comparative studies of the microbial non-coding genome.

Beta cell response to nutrient overload involves phospholipid remodelling and lipid peroxidation.

AIMS/HYPOTHESIS: Membrane phospholipids are the major intracellular source for fatty acid-derived mediators, which regulate myriad cell functions. We showed previously that high glucose levels triggered the hydrolysis of polyunsaturated fatty acids from beta cell phospholipids. These fatty acids were subjected to free radical-catalysed peroxidation to generate the bioactive aldehyde 4-hydroxy-2E-nonenal (4-HNE). The latter activated the nuclear peroxisome proliferator-activated receptor-δ (PPARδ), which in turn augmented glucose-stimulated insulin secretion. The present study aimed at investigating the combined effects of glucose and fatty acid overload on phospholipid turnover and the subsequent generation of lipid mediators, which affect insulin secretion and beta cell viability. METHODS: INS-1E cells were incubated with increasing glucose concentrations (5-25 mmol/l) without or with palmitic acid (PA; 50-500 µmol/l) and taken for fatty acid-based lipidomic analysis and functional assays. Rat isolated islets of Langerhans were used similarly. RESULTS: PA was incorporated into membrane phospholipids in a concentration- and time-dependent manner; incorporation was highest at 25 mmol/l glucose. This was coupled to a rapid exchange with saturated, mono-unsaturated and polyunsaturated fatty acids. Importantly, released arachidonic acid and linoleic acid were subjected to peroxidation, resulting in the generation of 4-HNE, which further augmented insulin secretion by activating PPARδ in beta cells. However, this adaptive increase in insulin secretion was abolished at high glucose and PA levels, which induced endoplasmic reticulum stress, apoptosis and cell death. CONCLUSIONS/INTERPRETATION: These findings highlight a key role for phospholipid remodelling and fatty acid peroxidation in mediating adaptive and cytotoxic interactions induced by nutrient overload in beta cells.

CoPAP: Coevolution of presence-absence patterns.

Evolutionary analysis of phyletic patterns (phylogenetic profiles) is widely used in biology, representing presence or absence of characters such as genes, restriction sites, introns, indels and methylation sites. The phyletic pattern observed in extant genomes is the result of ancestral gain and loss events along the phylogenetic tree. Here we present CoPAP (coevolution of presence-absence patterns), a user-friendly web server, which performs accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization. We developed a platform for comparing different algorithms that detect coevolution, which includes simulated data with pairs of coevolving sites and independent sites. Using these simulated data we demonstrate that CoPAP performance is higher than alternative methods. We exemplify CoPAP utility by analyzing coevolution among thousands of bacterial genes across 681 genomes. Clusters of coevolving genes that were detected using our method largely coincide with known biosynthesis pathways and cellular modules, thus exhibiting the capability of CoPAP to infer biologically meaningful interactions. CoPAP is freely available for use at http://copap.tau.ac.il/.

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/.

Elucidating the Transcriptional States of Spermatogenesis-Joint Analysis of Germline and Supporting Cell, Mice and Human, Normal and Perturbed, Bulk and Single-Cell RNA-Seq.

In studying the molecular underpinning of spermatogenesis, we expect to understand the fundamental biological processes better and potentially identify genes that may lead to novel diagnostic and therapeutic strategies toward precision medicine in male infertility. In this review, we emphasized our perspective that the path forward necessitates integrative studies that rely on complementary approaches and types of data. To comprehensively analyze spermatogenesis, this review proposes four axes of integration. First, spanning the analysis of spermatogenesis in the healthy state alongside pathologies. Second, the experimental analysis of model systems (in which we can deploy treatments and perturbations) alongside human data. Third, the phenotype is measured alongside its underlying molecular profiles using known markers augmented with unbiased profiles. Finally, the testicular cells are studied as ecosystems, analyzing the germ cells alongside the states observed in the supporting somatic cells. Recently, the study of spermatogenesis has been advancing using single-cell RNA sequencing, where scientists have uncovered the unique stages of germ cell development in mice, revealing new regulators of spermatogenesis and previously unknown cell subtypes in the testis. An in-depth analysis of meiotic and postmeiotic stages led to the discovery of marker genes for spermatogonia, Sertoli and Leydig cells and further elucidated all the other germline and somatic cells in the testis microenvironment in normal and pathogenic conditions. The outcome of an integrative analysis of spermatogenesis using advanced molecular profiling technologies such as scRNA-seq has already propelled our biological understanding, with additional studies expected to have clinical implications for the study of male fertility. By uncovering new genes and pathways involved in abnormal spermatogenesis, we may gain insights into subfertility or sterility.

PD1 ligand functionality a biomarker of response to anti PD1 treatment in patients with HNSCC.

Therapies targeting the PD-1/PD-L1 pathway have transformed head and neck squamous cell carcinoma (HNSCC) treatment. However, predicting the response to anti-PD-1 therapy remains a clinical challenge. This study evaluated the functional binding of PD-1 ligands in 29 HNSCC patients and compared it to the standard PD-L1 Combined Positive Score (CPS). The assessment of PD-1 ligands' functionality advances the current ability to predict the response of HNSCC patients to anti-PD-1 therapy.

A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features.

Although metastatic disease is the leading cause of cancer-related deaths, its tumor microenvironment remains poorly characterized due to technical and biospecimen limitations. In this study, we assembled a multi-modal spatial and cellular map of 67 tumor biopsies from 60 patients with metastatic breast cancer across diverse clinicopathological features and nine anatomic sites with detailed clinical annotations. We combined single-cell or single-nucleus RNA sequencing for all biopsies with a panel of four spatial expression assays (Slide-seq, MERFISH, ExSeq and CODEX) and H&E staining of consecutive serial sections from up to 15 of these biopsies. We leveraged the coupled measurements to provide reference points for the utility and integration of different experimental techniques and used them to assess variability in cell type composition and expression as well as emerging spatial expression characteristics across clinicopathological and methodological diversity. Finally, we assessed spatial expression and co-localization features of macrophage populations, characterized three distinct spatial phenotypes of epithelial-to-mesenchymal transition and identified expression programs associated with local T cell infiltration versus exclusion, showcasing the potential of clinically relevant discovery in such maps.

Uncovering the co-evolutionary network among prokaryotic genes.

MOTIVATION: Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes. RESULTS: We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-evolutionary interactions substantially differed among COGs. Over 40% were found to co-evolve with at least one partner. We partitioned the network of co-evolutionary relations into clusters and uncovered multiple modular assemblies of genes with clearly defined functions. Finally, we measured the extent to which co-evolutionary relations coincide with other cellular relations such as genomic proximity, gene fusion propensity, co-expression, protein-protein interactions and metabolic connections. Our results show that co-evolutionary relations only partially overlap with these other types of networks. Our results suggest that the inferred co-evolutionary network in prokaryotes is highly informative towards revealing functional relations among genes, often showing signals that cannot be extracted from other network types. AVAILABILITY AND IMPLEMENTATION: Available under GPL license as open source. CONTACT: talp@post.tau.ac.il. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional binding of PD1 ligands predicts response to anti-PD1 treatment in patients with cancer.

Accurate predictive biomarkers of response to immune checkpoint inhibitors (ICIs) are required for better stratifying patients with cancer to ICI treatments. Here, we present a new concept for a bioassay to predict the response to anti-PD1 therapies, which is based on measuring the binding functionality of PDL1 and PDL2 to their receptor, PD1. In detail, we developed a cell-based reporting system, called the immuno-checkpoint artificial reporter with overexpression of PD1 (IcAR-PD1) and evaluated the functionality of PDL1 and PDL2 binding in tumor cell lines, patient-derived xenografts, and fixed-tissue tumor samples obtained from patients with cancer. In a retrospective clinical study, we found that the functionality of PDL1 and PDL2 predicts response to anti-PD1 and that the functionality of PDL1 binding is a more effective predictor than PDL1 protein expression alone. Our findings suggest that assessing the functionality of ligand binding is superior to staining of protein expression for predicting response to ICIs.

High FOXA1 levels induce ER transcriptional reprogramming, a pro-metastatic secretome, and metastasis in endocrine-resistant breast cancer.

Aberrant activation of the forkhead protein FOXA1 is observed in advanced hormone-related cancers. However, the key mediators of high FOXA1 signaling remain elusive. We demonstrate that ectopic high FOXA1 (H-FOXA1) expression promotes estrogen receptor-positive (ER+) breast cancer (BC) metastasis in a xenograft mouse model. Mechanistically, H-FOXA1 reprograms ER-chromatin binding to elicit a core gene signature (CGS) enriched in ER+ endocrine-resistant (EndoR) cells. We identify Secretome14, a CGS subset encoding ER-dependent cancer secretory proteins, as a strong predictor for poor outcomes of ER+ BC. It is elevated in ER+ metastases vs. primary tumors, irrespective of ESR1 mutations. Genomic ER binding near Secretome14 genes is also increased in mutant ER-expressing or mitogen-treated ER+ BC cells and in ER+ metastatic vs. primary tumors, suggesting a convergent pathway including high growth factor receptor signaling in activating pro-metastatic secretome genes. Our findings uncover H-FOXA1-induced ER reprogramming that drives EndoR and metastasis partly via an H-FOXA1/ER-dependent secretome.

Mutated HRAS Activates YAP1-AXL Signaling to Drive Metastasis of Head and Neck Cancer.

The survival rate for patients with head and neck cancer (HNC) diagnosed with cervical lymph node (cLN) or distant metastasis is low. Genomic alterations in the HRAS oncogene are associated with advanced tumor stage and metastasis in HNC. Elucidation of the molecular mechanisms by which mutated HRAS (HRASmut) facilitates HNC metastasis could lead to improved treatment options for patients. Here, we examined metastasis driven by mutant HRAS in vitro and in vivo using HRASmut human HNC cell lines, patient-derived xenografts, and a novel HRASmut syngeneic model. Genetic and pharmacological manipulations indicated that HRASmut was sufficient to drive invasion in vitro and metastasis in vivo. Targeted proteomic analysis showed that HRASmut promoted AXL expression via suppressing the Hippo pathway and stabilizing YAP1 activity. Pharmacological blockade of HRAS signaling with the farnesyltransferase inhibitor tipifarnib activated the Hippo pathway and reduced the nuclear export of YAP1, thus suppressing YAP1-mediated AXL expression and metastasis. AXL was required for HRASmut cells to migrate and invade in vitro and to form regional cLN and lung metastases in vivo. In addition, AXL-depleted HRASmut tumors displayed reduced lymphatic and vascular angiogenesis in the primary tumor. Tipifarnib treatment also regulated AXL expression and attenuated VEGFA and VEGFC expression, thus regulating tumor-induced vascular formation and metastasis. Our results indicate that YAP1 and AXL are crucial factors for HRASmut-induced metastasis and that tipifarnib treatment can limit the metastasis of HNC tumors with HRAS mutations by enhancing YAP1 cytoplasmic sequestration and downregulating AXL expression. SIGNIFICANCE: Mutant HRAS drives metastasis of head and neck cancer by switching off the Hippo pathway to activate the YAP1-AXL axis and to stimulate lymphovascular angiogenesis.

The complexity hypothesis revisited: connectivity rather than function constitutes a barrier to horizontal gene transfer.

Horizontal gene transfer (HGT) is a prevalent and a highly important phenomenon in microbial species evolution. One of the important challenges in HGT research is to better understand the factors that determine the tendency of genes to be successfully transferred and retained in evolution (i.e., transferability). It was previously observed that transferability of genes depends on the cellular process in which they are involved where genes involved in transcription or translation are less likely to be transferred than metabolic genes. It was further shown that gene connectivity in the protein-protein interaction network affects HGT. These two factors were shown to be correlated, and their influence on HGT is collectively termed the "Complexity Hypothesis". In this study, we used a stochastic mapping method utilizing advanced likelihood-based evolutionary models to quantify gene family acquisition events by HGT. We applied our methodology to an extensive across-species genome-wide dataset that enabled us to estimate the overall extent of transfer events in evolution and to study the trends and barriers to gene transferability. Focusing on the biological function and the connectivity of genes, we obtained novel insights regarding the "complexity hypothesis." Specifically, we aimed to disentangle the relationships between protein connectivity, cellular function, and transferability and to quantify the relative contribution of each of these factors in determining transferability. We show that the biological function of a gene family is an insignificant factor in the determination of transferability when proteins with similar levels of connectivity are compared. In contrast, we found that connectivity is an important and a statistically significant factor in determining transferability when proteins with a similar function are compared.

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation.

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities.

Multidimensional Molecular Profiling of Metastatic Triple-Negative Breast Cancer and Immune Checkpoint Inhibitor Benefit.

PURPOSE: In metastatic triple-negative breast cancer (mTNBC), consistent biomarkers of immune checkpoint inhibitor (ICI) therapy benefit remain elusive. We evaluated the immune, genomic, and transcriptomic landscape of mTNBC in patients treated with ICIs. METHODS: We identified 29 patients with mTNBC treated with pembrolizumab or atezolizumab, either alone (n = 9) or in combination with chemotherapy (n = 14) or targeted therapy (n = 6), who had tumor tissue and/or blood available before ICI therapy for whole-exome sequencing. RNA sequencing and CIBERSORTx-inferred immune population analyses were performed (n = 20). Immune cell populations and programmed death-ligand 1 expression were assessed using multiplexed immunofluorescence (n = 18). Clonal trajectories were evaluated via serial tumor/circulating tumor DNA whole-exome sequencing (n = 4). Association of biomarkers with progression-free survival and overall survival (OS) was assessed. RESULTS: Progression-free survival and OS were longer in patients with high programmed death-ligand 1 expression and tumor mutational burden. Patients with longer survival also had a higher relative inferred fraction of CD8+ T cells, activated CD4+ memory T cells, M1 macrophages, and follicular helper T cells and enrichment of inflammatory gene expression pathways. A mutational signature of defective repair of DNA damage by homologous recombination was enriched in patients with both shorter OS and primary resistance. Exploratory analysis of clonal evolution among four patients treated with programmed cell death protein 1 blockade and a tyrosine kinase inhibitor suggested that clonal stability post-treatment was associated with short time to progression. CONCLUSION: This study identified potential biomarkers of response to ICIs among patients with mTNBC: high tumor mutational burden; presence of CD8+, CD4 memory T cells, follicular helper T cells, and M1 macrophages; and inflammatory gene expression pathways. Pretreatment deficiencies in the homologous recombination DNA damage repair pathway and the absence of or minimal clonal evolution post-treatment may be associated with worse outcomes.

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.