Genomics of an endemic cystic fibrosis Burkholderia multivorans strain reveals low within-patient evolution but high between-patient diversity

In many countries, Burkholderia multivorans is the most prevalent species within the Burkholderia cepacia complex (Bcc) found infecting the lungs of patients with cystic fibrosis (CF). Its positive identification is of immediate concern to the health of the patient as it is notoriously hard to eradicate using antibiotics and can cause necrosis of the lung tissues (cepacia syndrome). Infection control measures reduced the prevalence of B. cenocepacia in CF wards, but patients continue to acquire infections by B. multivorans from environmental sources. In most reported cases, the infecting strains are unique except in rare cases in which cross-infection is observed between patients. We report here an endemic strain of B. multivorans with sequence type ST-742 that has been infecting multiple patients, without evidence for cross-infection. We investigated the epidemiology and genomics of this ST-742 strain and show that it is microdiverse, as isolates between-patients exhibit numerous genomic differences, at scales that have not been observed previously when looking at evolutionary trajectories within-patients. Additionally, we found that the specific genomic background of a given strain may dictate the strategy of adaptation within the CF lung. Burkholderia multivorans is a member of the Burkholderia cepacia complex (Bcc), notorious for its pathogenicity in persons with cystic fibrosis. Epidemiological surveillance suggests that patients predominantly acquire B. multivorans from environmental sources, with rare cases of patient-to-patient transmission. Here we report on the genomic analysis of thirteen isolates from an endemic B. multivorans strain infecting four cystic fibrosis patients treated in different pediatric cystic fibrosis centers in Belgium, with no evidence of cross-infection. All isolates share an identical sequence type (ST-742) but whole genome analysis shows that they exhibit peculiar patterns of genomic diversity between patients. By combining short and long reads sequencing technologies, we highlight key differences in terms of small nucleotide polymorphisms indicative of low rates of adaptive evolution within patient, and well-defined, hundred Kbps-long segments of high enrichment in mutations between patients. In addition, we observed large structural genomic variations amongst the isolates which revealed different plasmid contents, active roles for transposase IS3 and IS5 in the deactivation of genes, and mobile prophage elements. Our study shows limited within-patient B. multivorans evolution and high between-patient strain diversity, indicating that an environmental microdiverse reservoir must be present for this endemic strain, in which active diversification is taking place. Furthermore, our analysis also reveals a set of 30 parallel adaptations across multiple patients, indicating that the specific genomic background of a given strain may dictate the route of adaptation within the cystic fibrosis lung

Neoadjuvant immunotherapy with nivolumab and ipilimumab induces major pathological responses in patients with head and neck squamous cell carcinoma

Surgery for locoregionally advanced head and neck squamous cell carcinoma (HNSCC) results in 30‒50% five-year overall survival. In IMCISION (NCT03003637), a non-randomized phase Ib/IIa trial, 32 HNSCC patients are treated with 2 doses (in weeks 1 and 3) of immune checkpoint blockade (ICB) using nivolumab (NIVO MONO, n = 6, phase Ib arm A) or nivolumab plus a single dose of ipilimumab (COMBO, n = 26, 6 in phase Ib arm B, and 20 in phase IIa) prior to surgery. Primary endpoints are feasibility to resect no later than week 6 (phase Ib) and primary tumor pathological response (phase IIa). Surgery is not delayed or suspended for any patient in phase Ib, meeting the primary endpoint. Grade 3‒4 immune-related adverse events are seen in 2 of 6 (33%) NIVO MONO and 10 of 26 (38%) total COMBO patients. Pathological response, defined as the %-change in primary tumor viable tumor cell percentage from baseline biopsy to on-treatment resection, is evaluable in 17/20 phase IIa patients and 29/32 total trial patients (6/6 NIVO MONO, 23/26 COMBO). We observe a major pathological response (MPR, 90‒100% response) in 35% of patients after COMBO ICB, both in phase IIa (6/17) and in the whole trial (8/23), meeting the phase IIa primary endpoint threshold of 10%. NIVO MONO’s MPR rate is 17% (1/6). None of the MPR patients develop recurrent HSNCC during 24.0 months median postsurgical follow-up. FDG-PET-based total lesion glycolysis identifies MPR patients prior to surgery. A baseline AID/APOBEC-associated mutational profile and an on-treatment decrease in hypoxia RNA signature are observed in MPR patients. Our data indicate that neoadjuvant COMBO ICB is feasible and encouragingly efficacious in HNSCC

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody–antigen complexes and membrane protein complexes, and the use of evolutionary and shape information

A Membrane Protein Complex Docking Benchmark

We report the first membrane protein-protein docking benchmark consisting of 37 targets of diverse functions and folds. The structures were chosen based on a set of parameters such as the availability of unbound structures, the modelling difficulty and their uniqueness. They have been cleaned and consistently numbered to facilitate their use in docking. Using this benchmark, we establish the baseline performance of HADDOCK, without any specific optimization for membrane proteins, for two scenarios: True interface-driven docking and ab-initio docking. Despite the fact that HADDOCK has been developed for soluble complexes, it shows promising docking performance for membrane systems, but there is clearly room for further optimisation. The resulting set of docking decoys, together with analysis scripts are made freely available. These can serve as a basis for the optimisation of membrane complex-specific scoring functions

A Membrane Protein Complex Docking Benchmark

We report the first membrane protein-protein docking benchmark consisting of 37 targets of diverse functions and folds. The structures were chosen based on a set of parameters such as the availability of unbound structures, the modelling difficulty and their uniqueness. They have been cleaned and consistently numbered to facilitate their use in docking. Using this benchmark, we establish the baseline performance of HADDOCK, without any specific optimization for membrane proteins, for two scenarios: True interface-driven docking and ab-initio docking. Despite the fact that HADDOCK has been developed for soluble complexes, it shows promising docking performance for membrane systems, but there is clearly room for further optimisation. The resulting set of docking decoys, together with analysis scripts are made freely available. These can serve as a basis for the optimisation of membrane complex-specific scoring functions

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody–antigen complexes and membrane protein complexes, and the use of evolutionary and shape information

Partial avoidance of female inflorescences of a dioecious fig by their mutualistic pollinating wasps

Every dioecious species of fig is pollinated by a specific wasp that only reproduces within the inflorescences of male trees. Pollinators usually die within the closed urn-shaped inflorescence (fig or syconium) they visit. Thus pollinators that enter female syconia allow seed production but die without reproducing. In a previous study, pollinators of one dioecious fig where male and female trees flower synchronously, Ficus hispida, did not exhibit differential attraction or choice between inflorescences of the two sexes. Here we show that Blastophaga psenes, the pollinator of another dioecious species of different lineage, the common fig (F. carica), significantly avoided female syconia, when we experimentally induce a situation of choice. Paradoxically, choosiness can be demonstrated in F. carica where usually wasps do not face a choice because male and female trees do not flower synchronously. We discuss how the mutualism may be stable despite this discrimination and hypothesize why the two species of fig-pollinators exhibit different behaviour on dioecious figs