Graphical AbstractHighlights d Geobacter nanowires are made up of micrometer-long polymerization of cytochrome OmcS d All hemes are closely stacked (<4-6 Å ), providing a continuous path for electron flow d We show that these are the same filaments that were earlier thought as type IV pili d This structure explains the molecular basis for electron conduction in protein wires SUMMARY Long-range (>10 mm) transport of electrons along networks of Geobacter sulfurreducens protein filaments, known as microbial nanowires, has been invoked to explain a wide range of globally important redox phenomena. These nanowires were previously thought to be type IV pili composed of PilA protein.Here, we report a 3.7 Å resolution cryoelectron microscopy structure, which surprisingly reveals that, rather than PilA, G. sulfurreducens nanowires are assembled by micrometer-long polymerization of the hexaheme cytochrome OmcS, with hemes packed within $3.5-6 Å of each other. The inter-subunit interfaces show unique structural elements such as inter-subunit parallel-stacked hemes and axial coordination of heme by histidines from neighboring subunits. Wild-type OmcS filaments show 100-fold greater conductivity than other filaments from a DomcS strain, highlighting the importance of OmcS to conductivity in these nanowires. This structure explains the remarkable capacity of soil bacteria to transport electrons to remote electron acceptors for respiration and energy sharing.

Increasing antibiotic resistance in multidrug-resistant (MDR) Gram-negative bacteria (MDR-GNB) presents significant health problems worldwide, since the vital available and effective antibiotics, including; broad-spectrum penicillins, fluoroquinolones, aminoglycosides, and β-lactams, such as; carbapenems, monobactam, and cephalosporins; often fail to fight MDR Gram-negative pathogens as well as the absence of new antibiotics that can defeat these "superbugs". All of these has prompted the reconsideration of old drugs such as polymyxins that were reckoned too toxic for clinical use. Only two polymyxins, polymyxin E (colistin) and polymyxin B, are currently commercially available. Colistin has re-emerged as a last-hope treatment in the mid-1990s against MDR Gram-negative pathogens due to the development of extensively drug-resistant GNB. Unfortunately, rapid global resistance towards colistin has emerged following its resurgence. Different mechanisms of colistin resistance have been characterized, including intrinsic, mutational, and transferable mechanisms. In this review, we intend to discuss the progress over the last two decades in understanding the alternative colistin mechanisms of action and different strategies used by bacteria to develop resistance against colistin, besides providing an update about what is previously recognized and what is novel concerning colistin resistance.

Background The global dissemination of colistin resistance encoded by mcr-1 has been attributed to extensive use of colistin in livestock, threatening colistin efficacy in medicine. The emergence of mcr-1 in common pathogens, such as Escherichia coli, is of particular concern. China banned the use of colistin in animal feed from May 1, 2017. We investigated subsequent changes in mcr-1 prevalence in animals, humans, food, and the environment, and the genomic epidemiology of mcr-1-positive E coli (MCRPEC).Methods Sampling was done before (October to December, 2016) and after (October to December, 2017, and 2018, respectively) the colistin ban. 3675 non-duplicate pig faecal samples were collected from 14 provinces (66 farms) in China to measure intervention-related changes in mcr-1 prevalence. 15 193 samples were collected from pigs, healthy human volunteers, patients colonised or infected with Enterobacteriaceae who were admitted to hospital, food and the environment in Guangzhou, to characterise source-specific mcr-1 prevalence and the wider ecological effect of the ban. From these samples, 688 MCRPEC were analysed with whole genome sequencing, plasmid conjugation, and S1 pulsed-field gel electrophoresis with Southern blots to characterise associated genomic changes. FindingsAfter the ban, mcr-1 prevalence decreased significantly in national pig farms, from 308 (45%) of 684 samples in 2016 to 274 (19%) of 1416 samples in 2018 (p<0•0001). A similar decrease occurred in samples from most sources in Guangzhou (959 [19%] of 5003 samples in 2016; 238 [5%] of 4489 samples in 2018; p<0•0001). The population structure of MCRPEC was diverse (23 sequence clusters); sequence type 10 clonal complex isolates were predominant (247 [36%] of 688). MCRPEC causing infection in patients admitted to hospital were genetically more distinct and appeared less affected by the ban. mcr-1 was predominantly found on plasmids (632 [92%] of 688). Common mcr-1 plasmid types included IncX4, IncI2, and IncHI2 (502 [76%] of 656); significant increases in IncI2-associated mcr-1 and a distinct lineage of mcr-1-associated IncHI2 were observed post ban. Changes in the frequency of mcr-1-associated flanking sequences (ISApl1-negative MCRPEC), 63 core genome single nucleotide polymorphisms, and 30 accessory genes were also significantly different after the ban (Benjamini-Hochberg-adjusted p<0•05), consistent with rapid genetic adaptation in response to changing selection pressures. Interpretation A rapid, ecosystem-wide, decline in mcr-1 was observed after the use of colistin in animal feed was banned, with associated genetic changes in MCRPEC. Withdrawal of antimicrobials from animal feed should be an important One Health measure contributing to the wider control of antimicrobial resistance globally.

BackgroundMobilized resistance to colistin is evolving rapidly and its global dissemination poses a severe threat to human health and safety. Transferable colistin resistance gene, mcr-3, first identified in Shandong, China, has already been found in several countries in multidrug-resistant human infections. Here we track the spread of mcr-3 within 13 provinces in China and provide a complete characterization of its evolution, structure and function.MethodsA total of 6497 non-duplicate samples were collected from thirteen provinces in China, from 2016 to 2017 and then screened for the presence of mcr-3 gene by PCR amplification. mcr-3-positive isolates were analyzed for antibiotic resistance and by southern blot hybridization, transfer analysis and plasmid typing. We then examined the molecular evolution of MCR-3 through phylogenetic analysis. Furthermore, we also characterized the structure and function of MCR-3 through circular dichroism analyses, inductively coupled plasma mass spectrometry (ICP-MS), liquid chromatography mass spectrometry (LC/MS), confocal microscopy and chemical rescue tests.Findings49 samples (49/6497 = 0.75%) were mcr-3 positive, comprising 40 samples (40/4144 = 0.97%) from 2017 and 9 samples (9/2353 = 0.38%) from 2016. Overall, mcr-3-positive isolates were distributed in animals and humans in 8 of the 13 provinces. Three mcr-3-positive IncP-type and one mcr-1-bearing IncHI2-like plasmids were identified and characterized. MCR-3 clusters with PEA transferases from Aeromonas and other bacteria and forms a phylogenetic entity that is distinct from the MCR-1/2/P(M) family, the largest group of transferable colistin resistance determinants. Despite that the two domains of MCR-3 not being exchangeable with their counterparts in MCR-1/2, structure-guided functional mapping of MCR-3 defines a conserved PE-lipid recognizing cavity prerequisite for its enzymatic catalysis and its resultant phenotypic resistance to colistin. We therefore propose that MCR-3 uses a possible “ping-pong” mechanism to transfer the moiety of PEA from its donor PE to the 1(or 4′)-phosphate of lipid A via an adduct of MCR-3-bound PEA. Additionally, the expression of MCR-3 in E. coli prevents the colistin-triggered formation of reactive oxygen species (ROS) and interferes bacterial growth and viability.InterpretationOur results provide an evolutionary, structural and functional definition of MCR-3 and its epidemiology in China, paving the way for smarter policies, better surveillance and effective treatments.

Background: Ultrasound (US) examination is helpful in the differential diagnosis of thyroid nodules (malignant vs. benign), but its accuracy relies heavily on examiner experience. Therefore, the aim of this study was to develop a less subjective diagnostic model aided by machine learning. Methods: A total of 2064 thyroid nodules (2032 patients, 695 male; M age = 45.25-13.49 years) met all of the following inclusion criteria: (i) hemi-or total thyroidectomy, (ii) maximum nodule diameter 2.5 cm, (iii) examination by conventional US and real-time elastography within one month before surgery, and (iv) no previous thyroid surgery or percutaneous thermotherapy. Models were developed using 60% of randomly selected samples based on nine commonly used algorithms, and validated using the remaining 40% of cases. All models function with a validation data set that has a pretest probability of malignancy of 10%. The models were refined with machine learning that consisted of 1000 repetitions of derivatization and validation, and compared to diagnosis by an experienced radiologist. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Results: A random forest algorithm led to the best diagnostic model, which performed better than radiologist diagnosis based on conventional US only (AUC = 0.924 [confidence interval (CI) 0.895-0.953] vs. 0.834 [CI 0.815-0.853]) and based on both conventional US and real-time elastography (AUC = 0.938 [CI 0.914-0.961] vs. 0.843 [CI 0.829-0.857]). Conclusions: Machine-learning algorithms based on US examinations, particularly the random forest classifier, may diagnose malignant thyroid nodules better than radiologists.