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CTX-M β-lactamases are considered a paradigm in the evolution of a resistance mechanism. Incorporation of different chromosomal blaCTX-M related genes from different species of Kluyvera has derived in different CTX-M clusters. In silico analyses have shown that this event has occurred at least nine times; in CTX-M-1 cluster (3), CTX-M-2 and CTX-M-9 clusters (2 each), and CTX-M-8 and CTX-M-25 clusters (1 each). This has been mainly produced by the participation of genetic mobilization units such as insertion sequences (ISEcp1 or ISCR1) and the later incorporation in hierarchical structures associated with multifaceted genetic structures including complex class 1 integrons and transposons. The capture of these blaCTX-M genes from the environment by highly mobilizable structures could have been a random event. Moreover, after incorporation within these structures, β-lactam selective force such as that exerted by cefotaxime and ceftazidime has fueled mutational events underscoring diversification of different clusters. Nevertheless, more variants of CTX-M enzymes, including those not inhibited by β-lactamase inhibitors such as clavulanic acid (IR-CTX-M variants), only obtained under in in vitro experiments, are still waiting to emerge in the clinical setting. Penetration and the later global spread of CTX-M producing organisms have been produced with the participation of the so-called "epidemic resistance plasmids" often carried in multi-drug resistant and virulent high-risk clones. All these facts but also the incorporation and co-selection of emerging resistance determinants within CTX-M producing bacteria, such as those encoding carbapenemases, depict the currently complex pandemic scenario of multi-drug resistant isolates. ; José María González-Alba is supported by fellow research contracts from the European Commission funded project PAR, HEALTH-2009-241476). The content and the scientific background of the manuscript were obtained in part from the European Commission funded projects (COBRA, LSHM-CT-2003-503335; TROCAR, HEALTH-F3-2008-223031; and PAR, HEALTH-2009-241476), the Spanish Ministry of Science and Innovation through Instituto de Salud Carlos III (research grant FIS-PI-080624) and the CIBERESP (research network in Epidemiology and Public Health, CB06/02/0053), and the Regional Government of Madrid [DeRemicrobiana Network (CAM.S-SAL-0246-2006)]. ; Peer reviewed ; Peer Reviewed

Antibiotics and antibiotic resistance determinants, natural molecules closely related to bacterial physiology and consistent with an ancient origin, are not only present in antibiotic-producing bacteria. Throughput sequencing technologies have revealed an unexpected reservoir of antibiotic resistance in the environment. These data suggest that co-evolution between antibiotic and antibiotic resistance genes has occurred since the beginning of time. This evolutionary race has probably been slow because of highly regulated processes and low antibiotic concentrations. Therefore to understand this global problem, a new variable must be introduced, that the antibiotic resistance is a natural event, inherent to life. However, the industrial production of natural and synthetic antibiotics has dramatically accelerated this race, selecting some of the many resistance genes present in nature and contributing to their diversification. One of the best models available to understand the biological impact of selection and diversification are β-lactamases. They constitute the most widespread mechanism of resistance, at least among pathogenic bacteria, with more than 1000 enzymes identified in the literature. In the last years, there has been growing concern about the description, spread, and diversification of β-lactamases with carbapenemase activity and AmpC-type in plasmids. Phylogenies of these enzymes help the understanding of the evolutionary forces driving their selection. Moreover, understanding the adaptive potential of β-lactamases contribute to exploration the evolutionary antagonists trajectories through the design of more efficient synthetic molecules. In this review, we attempt to analyze the antibiotic resistance problem from intrinsic and environmental resistomes to the adaptive potential of resistance genes and the driving forces involved in their diversification, in order to provide a global perspective of the resistance problem. ; Juan-Carlos Galán was supported by research grants from Ministerio de Sanidad y Consumo (Instituto de Salud Carlos III, FIS-PI-12/00567) and HEALTH-F3-2011-282004 (EVOTAR) and HEALTH-F3-2010-241476 (PAR) from European Commission. Fernando González was supported by project BFU2011-24112 from MINECO (Spanish Government). Rafael Cantón was supported by national grant (PS09/00825) and LSHMCT-2008-223031 and FP7-HEALTH-2010-28251 from the European Union. Juan-Carlos Galán and Fernando González-Candelas belong to CIBERESP, Network for Biomedical Research in Epidemiology and Public Health (CB06/02/0053). Rafael Cantón also belongs to REIPI, Spanish Network for Research in Infectious Diseases (RD12/0015). ; Peer reviewed ; Peer Reviewed

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time, and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses, hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10,320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20%, 50%, and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modelling exercise exemplifies the application of membrane computing for designing appropriate interventions in epidemic situations. ; MC and FB were sponsored by the Projects COV20_00067 of the Program SARS-COV-2 and COVID-19 infection of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación of Spain, CB06/02/0053 of the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), and the Regional Government of Madrid (InGeMICS-B2017/BMD-3691). For JCG, this study was partially founded by the Autonomous Community of Madrid, Spain (COVID-19 Grant, 2020) and the Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain. For AM, this study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), the government of Valencia (project Prometeo/2018/A/133) and cofinanced by the European Regional Development Fund (ERDF). ; No

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations. ; MC and FB were sponsored by the Projects COV20_00067 of the Program SARS-COV-2 and COVID-19 infection of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación of Spain, CB06/02/0053 of the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), and the Regional Government of Madrid (InGeMICS-B2017/BMD-3691). For JCG, this study was partially founded by the Autonomous Community of Madrid, Spain (COVID-19 Grant, 2020) and the Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain. For AM, this study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), the government of Valencia (project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). ; Peer reviewed

Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested "membrane-surrounded entities" able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes. ; IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples—just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power. ; This work was supported by the European Commission, Seven Framework Program (EVOTAR; FP7-HEALTH-282004) to F. Baquero, T. Coque, V. Fernandez-Lanza, and M. Campos; the Instituto de Salud Carlos III of Spain (Plan Estatal de IDi 2013-2016, grant PI15-00818 and FIS18-1942; CIBERESP, grant CB06/02/0053, and the EU Joint Programming Initiative JPIAMR2016-AC16/00036 to F. Baquero; the Regional Government of Madrid (InGEMICS-C; S2017/BMD-3691) to T. Coque and F. Baquero; and SAF2015-65878-R (MINECO, Spain) and PrometeoII/2014/065 (Generalitat Valenciana, Spain) to A. Moya (all cofinanced by the European Development Regional Fund [ERDF] "A Way to Achieve Europe"). ; Peer reviewed

The COVID-19 pandemic has brought to light the need for fast and sensitive detection methods to prevent the spread of pathogens. The scientific community is making a great effort to design new molecular detection methods suitable for fast point-of-care applications. In this regard, a variety of approaches have been developed or optimized, including isothermal amplification of viral nucleic acids, CRISPR-mediated target recognition, and read-out systems based on nanomaterials. Herein, we present CASCADE (CRISPR/CAS-based Colorimetric nucleic Acid DEtection), a sensing system for fast and specific naked-eye detection of SARS-CoV-2 RNA. In this approach, viral RNA is recognized by the LwaCas13a CRISPR protein, which activates its collateral RNase activity. Upon target recognition, Cas13a cleaves ssRNA oligonucleotides conjugated to gold nanoparticles (AuNPs), thus inducing their colloidal aggregation, which can be easily visualized. After an exhaustive optimization of functionalized AuNPs, CASCADE can detect picomolar concentrations of SARS-CoV-2 RNA. This sensitivity is further increased to low femtomolar (3 fM) and even attomolar (40 aM) ranges when CASCADE is coupled to RPA or NASBA isothermal nucleic acid amplification, respectively. We finally demonstrate that CASCADE succeeds in detecting SARS-CoV-2 in clinical samples from nasopharyngeal swabs. In conclusion, CASCADE is a fast and versatile RNA biosensor that can be coupled to different isothermal nucleic acid amplification methods for naked-eye diagnosis of infectious diseases. ; This work was supported by the Spanish Ministry of Economy and Competitiveness (SAF2017-87305-R, PID2020-119352RB-I00), Instituto de Salud Carlos III (FONDO-COVID19:COV20/00144 and COV20/00122) and Madrid Regional Government (NANOCOV-CM and IND2017/IND7809). C.E-N, M. L-V and C.R-D thank Madrid Regional Government for the pre-doctoral Grants (PEJD-2017-PRE/BMD-3730, PEJD-2018-PRE/IND-9584 and PEJD-2017-PRE/IND-4438). P.M.R thanks the Ministry of Economy, Industry and ...

Introduction: We present the protocol of a large population-based case-control study of 5 common tumors in Spain (MCC-Spain) that evaluates environmental exposures and genetic factors. Methods: Between 2008-2013, 10,183 persons aged 20-85 years were enrolled in 23 hospitals and primary care centres in 12 Spanish provinces including 1,115 cases of a new diagnosis of prostate cancer, 1,750 of breast cancer, 2,171 of colorectal cancer, 492 of gastro-oesophageal cancer, 554 cases of chronic lymphocytic leukaemia (CLL) and 4,101 population-based controls matched by frequency to cases by age, sex and region of residence. Participation rates ranged from 57% (stomach cancer) to 87% (CLL cases) and from 30% to 77% in controls. Participants completed a face-to-face computerized interview on sociodemographic factors, environmental exposures, occupation, medication, lifestyle, and personal and family medical history. In addition, participants completed a self-administered food-frequency questionnaire and telephone interviews. Blood samples were collected from 76% of participants while saliva samples were collected in CLL cases and participants refusing blood extractions. Clinical information was recorded for cases and paraffin blocks and/or fresh tumor samples are available in most collaborating hospitals. Genotyping was done through an exome array enriched with genetic markers in specific pathways. Multiple analyses are planned to assess the association of environmental, personal and genetic risk factors for each tumor and to identify pleiotropic effects. Discussion: This study, conducted within the Spanish Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), is a unique initiative to evaluate etiological factors for common cancers and will promote cancer research and prevention in Spain ; The study was partially funded by the "Accion Transversal del Cancer", approved on the Spanish Ministry Council on the 11th October 2007, by the Instituto de Salud Carlos III-FEDER (PI08/1770, PI08/0533, PI08/1359, PS09/00773, PS09/01286, PS09/01903, PS09/02078, PS09/01662, PI11/01403, PI11/01889, PI11/00226, PI11/01810, PI11/02213, PI12/00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150), by the Fundación Marqués de Valdecilla (API 10/09), by the ICGC International Cancer Genome Consortium CLL, by the Junta de Castilla y León (LE22A10-2), by the Consejería de Salud of the Junta de Andalucía (PI-0571), by the Conselleria de Sanitat of the Generalitat Valenciana (AP 061/10), by the Recercaixa (2010ACUP 00310), by the Regional Government of the Basque Country by European Commission grants FOOD-CT- 2006-036224-HIWATE, by the Spanish Association Against Cancer (AECC) Scientific Foundation, by the The Catalan Government DURSI grant 2009SGR1489