The association between phylogenetic lineage and the subclinical phenotype of pulmonary tuberculosis: A retrospective 2-cohort study.

BACKGROUND: Subclinical pulmonary tuberculosis (PTB) is an asymptomatic disease state between established TB infection and symptomatic (clinical) TB disease. It is present in 20-25% of PTB patients in high-income countries. Mycobacterium tuberculosis complex (MTBC) genetic heterogeneity, and differential host immunological responses, have been implicated in its pathogenesis. METHODS: To determine the association between MTBC lineage and PTB disease phenotype, we used two retrospective cohorts of PTB patients in Canada and two independent lineage attribution methods (DNA fingerprinting and genome sequencing). The first cohort, Cohort 1, consisted of consecutively diagnosed PTB patients between 2014 and 2020. The second, Cohort 2, consisted of newly-arrived foreign-born PTB patients who either were or were not referred for post-landing medical surveillance between 2004 and 2017. Univariable and multivariable logistic regression models were sequentially fitted to both cohorts, adjusting for age, sex, disease type, drug resistance and HIV. Evolution of radiographic features was correlated to lineage in Cohort 2. FINDINGS: Cohort 1 and 2 included 874 (209 subclinical) and 111 (44 subclinical) patients, respectively. In both cohorts, subclinical patients were more likely than clinical patients to have relapse/retreatment disease, be smear-negative, have longer times-to-culture positivity and to harbor an ancestral MTBC lineage (Indo-Oceanic or Mycobacterium africanum). Relapse/retreatment disease and ancestral MTBC lineage were independent predictors of subclinical disease (ORs and 95% CIs in Cohort 1, 1.85 [1.07,3.28], p < 0.029 and 2.30 [1.66,3.18], p < 0.001, respectively, and Cohort 2, 5.74 [1.37-24.06], p < 0.017 and 3.21 (1.29,7.97], p < 0.012, respectively). The geographic distribution of Indo-Oceanic strains causing subclinical disease was uneven. Non-progressive lung disease was more common in patients infected with ancestral than modern lineages in Cohort 2, 56.0% vs 25.4%, p < 0.005. INTERPRETATION: MTBC lineage is a strong predictor of PTB disease phenotype. The genetic drivers of this association, and the relative contribution of other explanatory variables, are unknown.

Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data.

Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs - two hosts in which infection was transmitted from one to the other - is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.

SARS-CoV-2 Outbreak Investigation Using Contact Tracing and Whole-Genome Sequencing in an Ontario Tertiary Care Hospital.

Genomic epidemiology can facilitate an understanding of evolutionary history and transmission dynamics of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak. We used next-generation sequencing techniques to study SARS-CoV-2 genomes isolated from patients and health care workers (HCWs) across five wards of a Canadian hospital with an ongoing SARS-CoV-2 outbreak. Using traditional contact tracing methods, we show transmission events between patients and HCWs, which were also supported by the SARS-CoV-2 lineage assignments. The outbreak predominantly involved SARS-CoV-2 B.1.564.1 across all five wards, but we also show evidence of community introductions of lineages B.1, B.1.1.32, and B.1.231, falsely assumed to be outbreak related. Altogether, our study exemplifies the value of using contact tracing in combination with genomic epidemiology to understand the transmission dynamics and genetic underpinnings of a SARS-CoV-2 outbreak. IMPORTANCE Our manuscript describes a SARS-CoV-2 outbreak investigation in an Ontario tertiary care hospital. We use traditional contract tracing paired with whole-genome sequencing to facilitate an understanding of the evolutionary history and transmission dynamics of this SARS-CoV-2 outbreak in a clinical setting. These advancements have enabled the incorporation of phylogenetics and genomic epidemiology into the understanding of clinical outbreaks. We show that genomic epidemiology can help to explore the genetic evolution of a pathogen in real time, enabling the identification of the index case and helping understand its transmission dynamics to develop better strategies to prevent future spread of SARS-CoV-2 in congregate, clinical settings such as hospitals.

Insight into the regulatory networks underlying the high lipid perennial ryegrass growth under different irradiances.

Under favourable conditions, perennial ryegrass (Lolium perenne) engineered to accumulated high lipid (HL) carbon sink in their leaves was previously shown to also enhance photosynthesis and growth. The greater aboveground biomass was found to be diminished in a dense canopy compared to spaced pots. Besides, the underlying genetic regulatory network linking between leaf lipid sinks and these physiological changes remains unknown. In this study, we demonstrated that the growth advantage was not displayed in HL Lolium grown in spaced pots under low lights. Under standard lights, analysis of differentiating transcripts in HL Lolium reveals that the plants had elevated transcripts involved in lipid metabolism, light capturing, photosynthesis, and sugar signalling while reduced expression of genes participating in sugar biosynthesis and transportation. The plants also had altered several transcripts involved in mitochondrial oxidative respiration and redox potential. Many of the above upregulated or downregulated transcript levels were found to be complemented by growing the plants under low light. Overall, this study emphasizes the importance of carbon and energy homeostatic regulatory mechanisms to overall productivity of the HL Lolium through photosynthesis, most of which are significantly impacted by low irradiances.

Genomic epidemiology of Mycobacterium abscessus in a Canadian cystic fibrosis centre.

The Mycobacterium abscessus complex causes significant morbidity and mortality among patients with Cystic Fibrosis (CF). It has been hypothesized that these organisms are transmitted from patient to patient based on genomics. However, few studies incorporate epidemiologic data to confirm this hypothesis. We longitudinally sampled 27 CF and 7 non-CF patients attending a metropolitan hospital in Ontario, Canada from 2013 to 2018. Whole genome sequencing along with epidemiological data was used to evaluate the likelihood of transmission. Overall, the genetic diversity of M. abscessus was large, with a median pairwise distance (IQR) of 1,279 (143-134) SNVs between all Ontario M. abscessus isolates and 2,908 (21-3,204) single nucleotide variants (SNVs) between M. massiliense isolates. This reflects the global diversity of this pathogen, with Ontario isolates widely dispersed throughout global phylogenetic trees of each subspecies. Using a maximum distance of 25 SNVs as a threshold to identify possible transmission, we identified 23 (of 276 total) pairs of closely-related isolates. However, transmission was probable for only one pair based on both genomic and epidemiological data. This suggests that person-to-person transmission of M. abscessus among CF patients is indeed rare and reinforces the critical importance of epidemiological data for inferences of transmission.

Multi-site implementation of whole genome sequencing for hospital infection control: A prospective genomic epidemiological analysis.

Background: Current microbiological methods lack the resolution to accurately identify multidrug-resistant organism (MDRO) transmission, however, whole genome sequencing can identify highly-related patient isolates providing opportunities for precision infection control interventions. We investigated the feasibility and potential impact of a prospective multi-centre genomics workflow for hospital infection control. Methods: We conducted a prospective genomics implementation study across eight Australian hospitals over 15 months (2017,2018), collecting all clinical and screening isolates from inpatients with vanA VRE, MRSA, ESBL Escherichia coli (ESBL-Ec), or ESBL Klebsiella pneumoniae (ESBL-Kp). Genomic and epidemiologic data were integrated to assess MDRO transmission. Findings: In total, 2275 isolates were included from 1970 patients, predominantly ESBL-Ec (40·8%) followed by MRSA (35·6%), vanA VRE (15·2%), and ESBL-Kp (8·3%).Overall, hospital and genomic epidemiology showed 607 patients (30·8%) acquired their MDRO in hospital, including the majority of vanA VRE (266 patients, 86·4%), with lower proportions of ESBL-Ec (186 patients, 23·0%), ESBL-Kp (42 patients, 26·3%), and MRSA (113 patients, 16·3%). Complex patient movements meant the majority of MDRO transmissions would remain undetected without genomic data.The genomics implementation had major impacts, identifying unexpected MDRO transmissions prompting new infection control interventions, and contributing to vanA VRE becoming a notifiable condition. We identified barriers to implementation and recommend strategies for mitigation. Interpretation: Implementation of a multi-centre genomics-informed infection control workflow is feasible and identifies many unrecognised MDRO transmissions. This provides critical opportunities for interventions to improve patient safety in hospitals. Funding: Melbourne Genomics Health Alliance (supported by State Government of Victoria, Australia), and National Health and Medical Research Council (Australia).

What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?

BACKGROUND: To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission. METHODS: We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission. RESULTS: Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes. CONCLUSIONS: We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.

Estimation of the generation interval using pairwise relative transmission probabilities.

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

A novel TFL1 gene induces flowering in the mast seeding alpine snow tussock, Chionochloa pallens (Poaceae).

Masting, the synchronous, highly variable flowering across years by a population of perennial plants, has been reported to be precipitated by various factors including nitrogen levels, drought conditions, and spring and summer temperatures. However, the molecular mechanism leading to the initiation of flowering in masting plants in particular years remains largely unknown, despite the potential impact of climate change on masting phenology. We studied genes controlling flowering in the alpine snow tussock Chionochloa pallens (Poaceae), a strongly masting perennial grass. We used a range of in situ and manipulated plants to obtain leaf samples from tillers (shoots) which subsequently remained vegetative or flowered. Here, we show that a novel orthologue of TERMINAL FLOWER 1 (TFL1; normally a repressor of flowering in other species) promotes the induction of flowering in C. pallens (hence Anti-TFL1), a conclusion supported by structural, functional and expression analyses. Global transcriptomic analysis indicated differential expression of CpTPS1, CpGA20ox1, CpREF6 and CpHDA6, emphasizing the role of endogenous cues and epigenetic regulation in terms of responsiveness of plants to initiate flowering. Our molecular-based study provides insights into the cellular mechanism of flowering in masting plants and will supplement ecological and statistical models to predict how masting will respond to global climate change.

Pilot study of a combined genomic and epidemiologic surveillance program for hospital-acquired multidrug-resistant pathogens across multiple hospital networks in Australia.

OBJECTIVES: To conduct a pilot study implementing combined genomic and epidemiologic surveillance for hospital-acquired multidrug-resistant organisms (MDROs) to predict transmission between patients and to estimate the local burden of MDRO transmission. DESIGN: Pilot prospective multicenter surveillance study. SETTING: The study was conducted in 8 university hospitals (2,800 beds total) in Melbourne, Australia (population 4.8 million), including 4 acute-care, 1 specialist cancer care, and 3 subacute-care hospitals. METHODS: All clinical and screening isolates from hospital inpatients (April 24 to June 18, 2017) were collected for 6 MDROs: vanA VRE, MRSA, ESBL Escherichia coli (ESBL-Ec) and Klebsiella pneumoniae (ESBL-Kp), and carbapenem-resistant Pseudomonas aeruginosa (CRPa) and Acinetobacter baumannii (CRAb). Isolates were analyzed and reported as routine by hospital laboratories, underwent whole-genome sequencing at the central laboratory, and were analyzed using open-source bioinformatic tools. MDRO burden and transmission were assessed using combined genomic and epidemiologic data. RESULTS: In total, 408 isolates were collected from 358 patients; 47.5% were screening isolates. ESBL-Ec was most common (52.5%), then MRSA (21.6%), vanA VRE (15.7%), and ESBL-Kp (7.6%). Most MDROs (88.3%) were isolated from patients with recent healthcare exposure.Combining genomics and epidemiology identified that at least 27.1% of MDROs were likely acquired in a hospital; most of these transmission events would not have been detected without genomics. The highest proportion of transmission occurred with vanA VRE (88.4% of patients). CONCLUSIONS: Genomic and epidemiologic data from multiple institutions can feasibly be combined prospectively, providing substantial insights into the burden and distribution of MDROs, including in-hospital transmission. This analysis enables infection control teams to target interventions more effectively.

Reporting practices for genomic epidemiology of tuberculosis: a systematic review of the literature using STROME-ID guidelines as a benchmark.

BACKGROUND: Pathogen genomics have become increasingly important in infectious disease epidemiology and public health. The Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID) guidelines were developed to outline a minimum set of criteria that should be reported in genomic epidemiology studies to facilitate assessment of study quality. We evaluate such reporting practices, using tuberculosis as an example. METHODS: For this systematic review, we initially searched MEDLINE, Embase Classic, and Embase on May 3, 2017, using the search terms "tuberculosis" and "genom* sequencing". We updated this initial search on April 23, 2019, and also included a search of bioRxiv at this time. We included studies in English, French, or Spanish that recruited patients with microbiologically confirmed tuberculosis and used whole genome sequencing for typing of strains. Non-human studies, conference abstracts, and literature reviews were excluded. For each included study, the number and proportion of fulfilled STROME-ID criteria were recorded by two reviewers. A comparison of the mean proportion of fulfilled STROME-ID criteria before and after publication of the STROME-ID guidelines (in 2014) was done using a two-tailed t test. Quasi-Poisson regression and tobit regression were used to examine associations between study characteristics and the number and proportion of fulfilled STROME-ID criteria. This study was registered with PROSPERO, CRD42017064395. FINDINGS: 976 titles and abstracts were identified by our primary search, with an additional 16 studies identified in bioRxiv. 114 full texts (published between 2009 and 2019) were eligible for inclusion. The mean proportion of STROME-ID criteria fulfilled was 50% (SD 12; range 16-75). The proportion of criteria fulfilled was similar before and after STROME-ID publication (51% [SD 11] vs 46% [14], p=0·26). The number of criteria reported (among those applicable to all studies) was not associated with impact factor, h-index, country of affiliation of senior author, or sample size of isolates. Similarly, the proportion of criteria fulfilled was not associated with these characteristics, with the exception of a sample size of isolates of 277 or more (the highest quartile). In terms of reproducibility, 100 (88%) studies reported which bioinformatic tools were used, but only 33 (33%) reported corresponding version numbers. Sequencing data were available for 86 (75%) studies. INTERPRETATION: The reporting of STROME-ID criteria in genomic epidemiology studies of tuberculosis between 2009 and 2019 was low, with implications for assessment of study quality. The considerable proportion of studies without bioinformatics version numbers or sequencing data available highlights a key concern for reproducibility.

Molecular control of the floral transition in the mast seeding plant Celmisia lyallii (Asteraceae).

Mast flowering (or masting) is synchronous, highly variable flowering among years in populations of perennial plants. Despite having widespread consequences for seed consumers, endangered fauna and human health, masting is hard to predict. While observational studies show links to various weather patterns in different plant species, the mechanism(s) underpinning the regulation of masting is still not fully explained. We studied floral induction in Celmisia lyallii (Asteraceae), a mast flowering herbaceous alpine perennial, comparing gene expression in flowering and nonflowering plants. We performed translocation experiments to induce the floral transition in C. lyallii plants followed by both global and targeted expression analysis of flowering-pathway genes. Differential expression analysis showed elevated expression of ClSOC1 and ClmiR172 (promoters of flowering) in leaves of plants that subsequently flowered, in contrast to elevated expression of ClAFT and ClTOE1 (repressors of flowering) in leaves of plants that did not flower. The warm summer conditions that promoted flowering led to differential regulation of age and hormonal pathway genes, including ClmiR172 and ClGA20ox2, known to repress the expression of floral repressors and permit flowering. Upregulated expression of epigenetic modifiers of floral promoters also suggests that plants may maintain a novel "summer memory" across years to induce flowering. These results provide a basic mechanistic understanding of floral induction in masting plants and evidence of their ability to imprint various environmental cues to synchronize flowering, allowing us to better predict masting events under climate change.

Molecular Epidemiology of Mycobacterium tuberculosis To Describe the Transmission Dynamics Among Inuit Residing in Iqaluit Nunavut Using Whole-Genome Sequencing.

BACKGROUND: In the last decade, tuberculosis (TB) incidence among Inuit in the Canadian Arctic has been rising. Our aim was to better understand the transmission dynamics of TB in this remote region of Canada using whole-genome sequencing. METHODS: Isolates from patients who had culture-positive pulmonary TB in Iqaluit, Nunavut, between 2009 and 2015 underwent whole-genome sequencing (WGS). The number of transmission events between cases within clusters was calculated using a threshold of a ≤3 single nucleotide polymorphism (SNP) difference between isolates and then combined with detailed epidemiological data using a reproducible novel algorithm. Social network analysis of epidemiological data was used to support the WGS data analysis. RESULTS: During the study period, 140 Mycobacterium tuberculosis isolates from 135 cases were sequenced. Four clusters were identified, all from Euro-American lineage. One cluster represented 62% of all cases that were sequenced over the entire study period. In this cluster, 2 large chains of transmission were associated with 3 superspreading events in a homeless shelter. One of the superspreading events was linked to a nonsanctioned gambling house that resulted in further transmission. Shelter to nonshelter transmission was also confirmed. An algorithm developed for the determination of transmission events demonstrated very good reproducibility (κ score .98, 95% confidence interval, .97-1.0). CONCLUSIONS: Our study suggests that socioeconomic factors, namely residing in a homeless shelter and spending time in a gambling house, combined with the superspreading event effect may have been significant factors explaining the rise in cases in this predominantly Inuit Arctic community.

Using Genetic Distance from Archived Samples for the Prediction of Antibiotic Resistance in Escherichia coli.

The rising rates of antibiotic resistance increasingly compromise empirical treatment. Knowing the antibiotic susceptibility of a pathogen's close genetic relative(s) may improve empirical antibiotic selection. Using genomic and phenotypic data for Escherichia coli isolates from three separate clinically derived databases, we evaluated multiple genomic methods and statistical models for predicting antibiotic susceptibility, focusing on potentially rapidly available information, such as lineage or genetic distance from archived isolates. We applied these methods to derive and validate the prediction of antibiotic susceptibility to common antibiotics. We evaluated 968 separate episodes of suspected and confirmed infection with Escherichia coli from three geographically and temporally separated databases in Ontario, Canada, from 2010 to 2018. Across all approaches, model performance (area under the curve [AUC]) ranges for predicting antibiotic susceptibility were the greatest for ciprofloxacin (AUC, 0.76 to 0.97) and the lowest for trimethoprim-sulfamethoxazole (AUC, 0.51 to 0.80). When a model predicted that an isolate was susceptible, the resulting (posttest) probabilities of susceptibility were sufficient to warrant empirical therapy for most antibiotics (mean, 92%). An approach combining multiple models could permit the use of narrower-spectrum oral agents in 2 out of every 3 patients while maintaining high treatment adequacy (∼90%). Methods based on genetic relatedness to archived samples of E. coli could be used to predict antibiotic resistance and improve antibiotic selection.

Estimating the relative probability of direct transmission between infectious disease patients.

BACKGROUND: Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. METHODS: Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. RESULTS: In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. CONCLUSIONS: Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Previously undetected super-spreading of Mycobacterium tuberculosis revealed by deep sequencing.

Tuberculosis disproportionately affects the Canadian Inuit. To address this, it is imperative we understand transmission dynamics in this population. We investigate whether 'deep' sequencing can provide additional resolution compared to standard sequencing, using a well-characterized outbreak from the Arctic (2011-2012, 50 cases). Samples were sequenced to ~500-1000x and reads were aligned to a novel local reference genome generated with PacBio SMRT sequencing. Consensus and heterogeneous variants were identified and compared across genomes. In contrast with previous genomic analyses using ~50x depth, deep sequencing allowed us to identify a novel super-spreader who likely transmitted to up to 17 other cases during the outbreak (35% of the remaining cases that year). It is increasingly evident that within-host diversity should be incorporated into transmission analyses; deep sequencing may facilitate more accurate detection of super-spreaders and transmission clusters. This has implications not only for TB, but all genomic studies of transmission - regardless of pathogen.

In Canada, tuberculosis disproportionately affects the Inuit, a group of indigenous people inhabiting the Arctic regions. Canada is aiming to eliminate tuberculosis among the Inuit by 2030. One way to help stop transmission and prevent future outbreaks is to trace how and where the disease spreads using DNA sequencing. This information can then be used by public health organizations to identify possible interventions. Typically, the DNA of the bacterium that causes tuberculosis ­ Mycobacterium tuberculosis, or Mtb for short ­ is sequenced 50­100 times and a consensus DNA sequence is then generated for each patient from this data. These consensus DNA sequences are then compared to help piece together who infected whom. Recently, scientists have realized that the bacteria a person is infected with may have different DNA sequences due to people being infected with more than one bacterium or the bacterium developing variations in its genome after the infection. However, current DNA sequencing practices may miss these differences, making it harder to trace how the disease spreads. Now, Lee et al. show that sequencing the DNA of Mtb from an infected person 500­1000 times (i.e. ∼10-20 times more than usual) makes it easier to detect genetic differences and determine how tuberculosis spreads. This approach, also known as 'deep sequencing', was used to analyze DNA samples of Mtb collected from about 50 people during an outbreak of tuberculosis in 2011-2012, which had previously undergone standard DNA sequencing. This deep sequencing approach identified a 'super-spreading event' where one person had likely transmitted tuberculosis to up to 17 others during the outbreak. Lee et al. found that most of these people had visited the same 'gathering houses' which are social venues in the community. Implementing targeted public health interventions at these sites may help stop future outbreaks. To fully understand how useful this method will be for tracking the spread of tuberculosis, deep and routine sequencing will need to be compared against each other in different settings and outbreaks. Furthermore, the approach used in this study may be useful for tracking the transmission of other infectious diseases.

Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing.

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called 'genomic neighbour typing' for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

Identification of flowering-time genes in mast flowering plants using De Novo transcriptomic analysis.

Mast flowering is synchronised highly variable flowering by a population of perennial plants over a wide geographical area. High seeding years are seen as a threat to native and endangered species due to high predator density caused by the abundance of seed. An understanding of the molecular pathways that influence masting behaviour in plants could provide better prediction of a forthcoming masting season and enable conservation strategies to be deployed. The goal of this study was to identify candidate flowering genes that might be involved in regulating mast flowering. To achieve this, high-throughput large-scale RNA-sequencing was performed on two masting plant species, Celmisia lyallii (Asteraceae), and Chionochloa pallens (Poaceae) to develop a reference transcriptome for functional and molecular analysis. An average total of 33 million 150 base-paired reads, for both species, were assembled using the Trinity pipeline, resulting in 151,803 and 348,649 transcripts respectively for C. lyallii and C. pallens. For both species, about 56% of the unigenes were annotated with gene descriptions to known proteins followed by Gene Ontology analysis, categorising them on the basis of putative biological processes, molecular function, and cellular localization. A total of 543 transcripts from C. lyallii and 470 transcripts from C. pallens were also mapped to unique flowering-time proteins identified in Arabidopsis thaliana, suggesting the conservation of the flowering network in these wild alpine plants growing in natural field conditions. Expression analysis of several selected homologous flowering-pathway genes showed seasonal and photoperiodic variations. These genes can further be analysed to understand why seasonal cues, such as the increasing photoperiod in spring, that triggers the annual flowering of most plants, are insufficient to always trigger flowering in masting plants and to uncover the molecular basis of how additional cues (such as temperature during the previous growing seasons) then determines flowering in mast years.