RESUMO
The expansion and intensification of livestock production is predicted to promote the emergence of pathogens. As pathogens sometimes jump between species, this can affect the health of humans as well as livestock. Here, we investigate how livestock microbiota can act as a source of these emerging pathogens through analysis of Streptococcus suis, a ubiquitous component of the respiratory microbiota of pigs that is also a major cause of disease on pig farms and an important zoonotic pathogen. Combining molecular dating, phylogeography, and comparative genomic analyses of a large collection of isolates, we find that several pathogenic lineages of S. suis emerged in the 19th and 20th centuries, during an early period of growth in pig farming. These lineages have since spread between countries and continents, mirroring trade in live pigs. They are distinguished by the presence of three genomic islands with putative roles in metabolism and cell adhesion, and an ongoing reduction in genome size, which may reflect their recent shift to a more pathogenic ecology. Reconstructions of the evolutionary histories of these islands reveal constraints on pathogen emergence that could inform control strategies, with pathogenic lineages consistently emerging from one subpopulation of S. suis and acquiring genes through horizontal transfer from other pathogenic lineages. These results shed light on the capacity of the microbiota to rapidly evolve to exploit changes in their host population and suggest that the impact of changes in farming on the pathogenicity and zoonotic potential of S. suis is yet to be fully realized.
Assuntos
Infecções Estreptocócicas , Streptococcus suis , Doenças dos Suínos , Animais , Humanos , Suínos , Infecções Estreptocócicas/veterinária , Fazendas , Doenças dos Suínos/epidemiologia , Virulência/genética , Streptococcus suis/genética , GadoRESUMO
Mutation rates vary both within and between bacterial species, and understanding what drives this variation is essential for understanding the evolutionary dynamics of bacterial populations. In this study, we investigate two factors that are predicted to influence the mutation rate: ecology and genome size. We conducted mutation accumulation experiments on eight strains of the emerging zoonotic pathogen Streptococcus suis. Natural variation within this species allows us to compare tonsil carriage and invasive disease isolates, from both more and less pathogenic populations, with a wide range of genome sizes. We find that invasive disease isolates have repeatedly evolved mutation rates that are higher than those of closely related carriage isolates, regardless of variation in genome size. Independent of this variation in overall rate, we also observe a stronger bias towards G/C to A/T mutations in isolates from more pathogenic populations, whose genomes tend to be smaller and more AT-rich. Our results suggest that ecology is a stronger correlate of mutation rate than genome size over these timescales, and that transitions to invasive disease are consistently accompanied by rapid increases in mutation rate. These results shed light on the impact that ecology can have on the adaptive potential of bacterial pathogens.
Assuntos
Adaptação Biológica/genética , Doenças Transmissíveis Emergentes/microbiologia , Taxa de Mutação , Infecções Estreptocócicas/microbiologia , Streptococcus suis/genética , Zoonoses/microbiologia , Animais , Ecologia , Streptococcus suis/isolamento & purificação , Streptococcus suis/patogenicidade , Virulência/genéticaRESUMO
Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods' development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.
Assuntos
Infecções por HIV/epidemiologia , Infecções por HIV/virologia , HIV-1/genética , África Subsaariana/epidemiologia , Simulação por Computador , Epidemias , Feminino , Infecções por HIV/prevenção & controle , Infecções por HIV/transmissão , Humanos , Incidência , Masculino , FilogeniaRESUMO
Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies are unexplored in CUP cohorts. Here we describe CUPiD, a machine learning classifier for accurate TOO predictions across 29 tumour classes using circulating cell-free DNA (cfDNA) methylation patterns. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 84.6% and TOO accuracy of 96.8%. In an additional cohort of 41 patients with CUP CUPiD predictions were made in 32/41 (78.0%) cases, with 88.5% of the predictions clinically consistent with a subsequent or suspected primary tumour diagnosis, when available (23/26 patients). Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.
Assuntos
Ácidos Nucleicos Livres , Neoplasias Primárias Desconhecidas , Humanos , Ácidos Nucleicos Livres/genética , Neoplasias Primárias Desconhecidas/genética , Biomarcadores Tumorais/genética , Metilação de DNA , Biópsia LíquidaRESUMO
Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.
Assuntos
Carcinoma Pulmonar de Células não Pequenas , Heterogeneidade Genética , Neoplasias Pulmonares , Camundongos Endogâmicos NOD , Camundongos SCID , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Humanos , Animais , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Camundongos , Feminino , Sequenciamento do Exoma , Genômica/métodos , Masculino , Ensaios Antitumorais Modelo de Xenoenxerto , Xenoenxertos , Modelos Animais de Doenças , Idoso , Pessoa de Meia-IdadeRESUMO
Small cell lung cancer (SCLC) has a 5-year survival rate of <7%. Rapid emergence of acquired resistance to standard platinum-etoposide chemotherapy is common and improved therapies are required for this recalcitrant tumour. We exploit six paired pre-treatment and post-chemotherapy circulating tumour cell patient-derived explant (CDX) models from donors with extensive stage SCLC to investigate changes at disease progression after chemotherapy. Soluble guanylate cyclase (sGC) is recurrently upregulated in post-chemotherapy progression CDX models, which correlates with acquired chemoresistance. Expression and activation of sGC is regulated by Notch and nitric oxide (NO) signalling with downstream activation of protein kinase G. Genetic targeting of sGC or pharmacological inhibition of NO synthase re-sensitizes a chemoresistant CDX progression model in vivo, revealing this pathway as a mediator of chemoresistance and potential vulnerability of relapsed SCLC.
Assuntos
Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Etoposídeo/uso terapêutico , Neoplasias Pulmonares/metabolismo , Carcinoma de Pequenas Células do Pulmão/metabolismo , Guanilil Ciclase Solúvel/metabolismo , Animais , Proteínas Quinases Dependentes de GMP Cíclico/metabolismo , Modelos Animais de Doenças , Progressão da Doença , Resistencia a Medicamentos Antineoplásicos/genética , Inibidores Enzimáticos/uso terapêutico , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Camundongos , Células Neoplásicas Circulantes/metabolismo , Óxido Nítrico/metabolismo , Óxido Nítrico Sintase Tipo II/antagonistas & inibidores , Receptores Notch/metabolismo , Transdução de Sinais/genética , Carcinoma de Pequenas Células do Pulmão/tratamento farmacológico , Carcinoma de Pequenas Células do Pulmão/patologia , Guanilil Ciclase Solúvel/genéticaRESUMO
Identifying the germline genes involved in immunoglobulin rearrangements is an essential first step in the analysis of antibody repertoires. Based on our prior work in analysing diverse recombinant viruses, we present IgSCUEAL (Immunoglobulin Subtype Classification Using Evolutionary ALgorithms), a phylogenetic approach to assign V and J regions of immunoglobulin sequences to their corresponding germline alleles, with D regions assigned using a simple pairwise alignment algorithm. We also develop an interactive web application for viewing the results, allowing the user to explore the frequency distribution of sequence assignments and CDR3 region length statistics, which is useful for summarizing repertoires, as well as a detailed viewer of rearrangements and region alignments for individual query sequences. We demonstrate the accuracy and utility of our method compared with sequence similarity-based approaches and other non-phylogenetic model-based approaches, using both simulated data and a set of evaluation datasets of human immunoglobulin heavy chain sequences. IgSCUEAL demonstrates the highest accuracy of V and J assignment amongst existing approaches, even when the reassorted sequence is highly mutated, and can successfully cluster sequences on the basis of shared V/J germline alleles.