Transitioning Towards a Virtual Falls Prevention Program for Frail Seniors: Learning from the Experiences of Older Adults During the COVID-19 Pandemic.

Background: The literature to date is unable to clearly characterize the appropriateness of virtual care for falls prevention services from the patient perspective. In response to COVID-19, the Falls Prevention Program (FPP) at Sunnybrook Health Sciences Centre was modified to include virtual components. We set out to uncover the experiences of this unique older-adult patient population to inform FPP quality improvement and appropriate incorporation of technology post-pandemic. Methods: FPP patients during the COVID-19 pandemic (February 2020 - February 2022) and their primary caregivers met inclusion criteria. Out of 18 eligible patients, 10 consented to participate in 20-minute, semi-structured telephone interviews conducted and transcribed by the first author. Inductive coding followed by theme generation occurred through collaborative analysis. Results: The participants (n=10) were 60% female, mean age 84 years (SD 5.8), 60% living alone, and 70% university educated. We generated three main themes: 1) First Steps First, revealed a common desire for physical and mental support and the perceived essentials of a successful FPP highlighting the importance of program length and individualized attention; 2) Overcoming Obstacles, highlighted participants' experiences overcoming barriers with technology in the context of an isolating pandemic; and 3) Advancing Care Post-Pandemic, elaborated on the appropriateness of virtual care and delved into the importance of program personalization. Conclusion: The interviewed older adults revealed agreement on the FPP's necessity and the importance of increasing program length, one-on-one interaction, and program flexibility for unique patient needs. Incorporating virtual assessment prior to in-person exercises was largely favoured and should be considered as an appropriate use of technology post-pandemic.

Prevention and treatment of traumatic brain injury-related delirium: a systematic review.

BACKGROUND: Our systematic review examines the effectiveness and safety of non-pharmacologic and pharmacologic interventions in preventing or treating traumatic brain injury (TBI)-related delirium in acute care. METHODS: We searched four electronic databases (MEDLINE, EMBASE, CENTRAL/CDSR, and PsycINFO) to identify randomized controlled trials (RCTs), quasi-experimental, and observational studies. Eligible studies included adults with TBI, at least one comparator group, delirium as an outcome and took place in acute care. Two reviewers independently completed all study screening, data abstraction, and risk of bias assessment using the Cochrane risk of bias 2 tool for RCTs or risk of bias in non-randomized studies-of interventions tool for observational studies. We implemented the PROGRESS-Plus framework to describe social determinants of health (SDoH) reporting. RESULTS: We identified 20,022 citations, reviewed 301 in full text, and included eight studies in the descriptive synthesis. The mean age of study participants ranged from 32 to 62 years. 12.5% of included studies reported SDoH. Included studies had moderate-to-high risk of bias. Studies compared reorientation programs and an intervention bundle to usual care, but these interventions were not better than usual care in treating TBI-related delirium. Individual studies found that rosuvastatin and aripiprazole were more efficacious than placebo, and dexmedetomidine was more efficacious than propofol and haloperidol for preventing TBI-related delirium. No studies reported safety as the primary outcome. CONCLUSIONS: We identified efficacious pharmacologic interventions for preventing TBI-related delirium, but these studies were at moderate-to-high risk of bias, which limits our confidence in these findings. Future studies should incorporate safety outcomes, and a diverse study population, including older adults.

How the Invisible Becomes Visible: The Lived Experience of Economic Abuse in Heterosexual Relationships.

The invisible nature of economic abuse contributes to its pervasiveness. Through interviews with 14 women survivors in Canada, this study identifies the ways in which economic abuse is (in)visible to survivors. There were three major themes: "Constructing and maintaining the fairy-tale" describes how gender roles and ideas of love concealed abuse. "The normalization of financial problems in heterosexual relationships" examines how disagreements about money were normalized in ways that masked abuse. "Recognizing economic abuse" describes how breaking away from expectations was critical to recognition. These findings can aid in improving support to help survivors identify, avoid, and escape economic abuse.

Plasma proteome of growing tumors.

Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

Virtual fall program assessment for frail Canadian community-dwelling older adults: Examining equitable accessibility.

Objective: In response to COVID-19, the fall prevention program (FPP) at Sunnybrook Health Sciences Centre was modified to be delivered virtually. We compared patient populations assessed for the FPP virtually versus in-person to explore equitable accessibility. Methods: A retrospective chart review was performed. All patients assessed virtually from the beginning of the COVID-19 pandemic until the end of abstraction (April 25, 2022) were compared to a historic sample of patients assessed in-person beginning in January 2019. Demographics, measures of frailty, co-morbidity, and cognition were abstracted. Wilcoxon Rank Sum tests and Fisher's Exact tests were used for continuous and categorical variables, respectively. Results: Thirty patients were assessed virtually and compared to 30 in-person historic controls. Median age was 80 years (interquartile range 75-85), 82% were female, 70% were university educated, the median Clinical Frailty Score was 5 out of 9, and 87% used >5 medications. Once normalized, frailty scores showed no difference (p = 0.446). The virtual cohort showed significantly higher outdoor walking aid use (p = 0.015), reduced accuracy with clock drawing (p = 0.020), and nonsignificant trends toward using >10 medications, requiring assistance with >3 instrumental activities of daily living (IADLs), and higher treatment attendance. No significant differences were seen for time-to-treat (p = 0.423). Conclusion: Patients assessed virtually were similarly frail as the in-person controls but had increased use of walking aids, medications, IADL assistance, and cognitive impairment. In a Canadian context, frail and high socioeconomic status older adults continued to access treatment through virtual FPP assessments during the COVID-19 pandemic highlighting both the benefits of virtual care and potential inequity.

Diurnal and eating-associated microbial patterns revealed via high-frequency saliva sampling.

The oral microbiome is linked to oral and systemic health, but its fluctuation under frequent daily activities remains elusive. Here, we sampled saliva at 10- to 60-min intervals to track the high-resolution microbiome dynamics during the course of human activities. This dense time series data showed that eating activity markedly perturbed the salivary microbiota, with tongue-specific Campylobacter concisus and Oribacterium sinus and dental plaque-specific Lautropia mirabilis, Rothia aeria, and Neisseria oralis increased after every meal in a temporal order. The observation was reproducible in multiple subjects and across an 11-mo period. The microbiome composition showed significant diurnal oscillation patterns at different taxonomy levels with Prevotella/Alloprevotella increased at night and Bergeyella HMT 206/Haemophilus slowly increased during the daytime. We also identified microbial co-occurring patterns in saliva that are associated with the intricate biogeography of the oral microbiome. Microbial source tracking analysis showed that the contributions of distinct oral niches to the salivary microbiome were dynamically affected by daily activities, reflecting the role of saliva in exchanging microbes with other oral sites. Collectively, our study provides insights into the temporal microbiome variation in saliva and highlights the need to consider daily activities and diurnal factors in design of oral microbiome studies.

Tick Microbiomes in Neotropical Forest Fragments Are Best Explained by Tick-Associated and Environmental Factors Rather than Host Blood Source.

The composition of tick microbiomes varies both within and among tick species. Whether this variation is intrinsic (related to tick characteristics) or extrinsic (related to vertebrate host and habitat) is poorly understood but important, as microbiota can influence the reproductive success and vector competence of ticks. We aimed to uncover what intrinsic and extrinsic factors best explain the microbial composition and taxon richness of 11 species of neotropical ticks collected from eight species of small mammals in 18 forest fragments across central Panama. Microbial richness varied among tick species, life stages, and collection sites but was not related to host blood source. Microbiome composition was best explained by tick life stage, with bacterial assemblages of larvae being a subset of those of nymphs. Collection site explained most of the bacterial taxa with differential abundance across intrinsic and extrinsic factors. Francisella and Rickettsia were highly prevalent, but their proportional abundance differed greatly among tick species, and we found both positive and negative cooccurrence between members of these two genera. Other tick endosymbionts (e.g., Coxiella and Rickettsiella) were associated with specific tick species. In addition, we detected Anaplasma and Bartonella in several tick species. Our results indicate that the microbial composition and richness of neotropical ticks are principally related to intrinsic factors (tick species and life stage) and collection site. Taken together, our analysis informs how tick microbiomes are structured and can help anchor our understanding of tick microbiomes from tropical environments more broadly.IMPORTANCE Blood-feeding arthropod microbiomes often play important roles in disease transmission, yet the factors that structure tick microbial communities in the Neotropics are unknown. Utilizing ticks collected from live animals in neotropical forest fragments, this study teases apart the contributions of intrinsic and extrinsic tick-associated factors on tick microbial composition as well as which specific microbes contribute to differences across tick species, tick life stages, the mammals they fed on, and the locations from where they were sampled. Furthermore, this study provides revelations of how notable tick-associated bacterial genera are interacting with other tick-associated microbes as well as the forest animals they encounter.

Impact of Kidney Function on the Blood Proteome and on Protein Cardiovascular Risk Biomarkers in Patients With Stable Coronary Heart Disease.

Background Chronic kidney disease (CKD) confers increased cardiovascular risk, not fully explained by traditional factors. Proteins regulate biological processes and inform the risk of diseases. Thus, in 938 patients with stable coronary heart disease from the Heart and Soul cohort, we quantified 1054 plasma proteins using modified aptamers (SOMAscan) to: (1) discern how reduced glomerular filtration influences the circulating proteome, (2) learn of the importance of kidney function to the prognostic information contained in recently identified protein cardiovascular risk biomarkers, and (3) identify novel and even unique cardiovascular risk biomarkers among individuals with CKD. Methods and Results Plasma protein levels were correlated to estimated glomerular filtration rate (eGFR) using Spearman-rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the cardiovascular outcome (first among myocardial infarction, stroke, heart failure hospitalization, or mortality). Seven hundred and nine (67.3%) plasma proteins correlated with eGFR at P<0.05 (ρ 0.06-0.74); 218 (20.7%) proteins correlated with eGFR moderately or strongly (ρ 0.2-0.74). Among the previously identified 196 protein cardiovascular biomarkers, just 87 remained prognostic after correction for eGFR. Among patients with CKD (eGFR <60 mL/min per 1.73 m2), we identified 21 protein cardiovascular risk biomarkers of which 8 are unique to CKD. Conclusions CKD broadly alters the composition of the circulating proteome. We describe protein biomarkers capable of predicting cardiovascular risk independently of glomerular filtration, and those that are prognostic of cardiovascular risk specifically in patients with CKD and even unique to patients with CKD.

Mechanisms of Sodium-Glucose Cotransporter 2 Inhibition: Insights From Large-Scale Proteomics.

OBJECTIVE: To assess the effects of empagliflozin, a selective sodium-glucose cotransporter 2 (SGLT2) inhibitor, on broad biological systems through proteomics. RESEARCH DESIGN AND METHODS: Aptamer-based proteomics was used to quantify 3,713 proteins in 144 paired plasma samples obtained from 72 participants across the spectrum of glucose tolerance before and after 4 weeks of empagliflozin 25 mg/day. The biology of the plasma proteins significantly changed by empagliflozin (at false discovery rate-corrected P < 0.05) was discerned through Ingenuity Pathway Analysis. RESULTS: Empagliflozin significantly affected levels of 43 proteins, 6 related to cardiomyocyte function (fatty acid-binding protein 3 and 4 [FABPA], neurotrophic receptor tyrosine kinase, renin, thrombospondin 4, and leptin receptor), 5 to iron handling (ferritin heavy chain 1, transferrin receptor protein 1, neogenin, growth differentiation factor 2 [GDF2], and ß2-microglobulin), and 1 to sphingosine/ceramide metabolism (neutral ceramidase), a known pathway of cardiovascular disease. Among the protein changes achieving the strongest statistical significance, insulin-like binding factor protein-1 (IGFBP-1), transgelin-2, FABPA, GDF15, and sulphydryl oxidase 2 precursor were increased, while ferritin, thrombospondin 3, and Rearranged during Transfection (RET) were decreased by empagliflozin administration. CONCLUSIONS: SGLT2 inhibition is associated, directly or indirectly, with multiple biological effects, including changes in markers of cardiomyocyte contraction/relaxation, iron handling, and other metabolic and renal targets. The most significant differences were detected in protein species (GDF15, ferritin, IGFBP-1, and FABP) potentially related to the clinical and metabolic changes that were actually measured in the same patients. These novel results may inform further studies using targeted proteomics and a prospective design.

Plasma protein patterns as comprehensive indicators of health.

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Proteomic Profiles in Advanced Age-Related Macular Degeneration Using an Aptamer-Based Proteomic Technology.

PURPOSE: To explore top-ranked plasma proteins related to neovascular age-related macular degeneration (AMD) and geographic atrophy (GA), and explore pathways related to neovascular AMD and GA. METHODS: We conducted a pilot study of patients with neovascular AMD (n = 10), GA (n = 10), and age-matched cataract controls (n = 10) who were recruited into an AMD registry. We measured 4001 proteins in ethylenediaminetetraacetic acid plasma samples using an aptamer-based proteomic technology. Relative concentrations of each of 4001 proteins were log (base 2) transformed and compared between cases of neovascular AMD and GA versus controls using linear regression. Pathway analysis was conducted using pathways downloaded from Reactome. RESULTS: In this pilot study, higher levels of vinculin and lower levels of CD177 were found in patients with neovascular AMD compared with controls. Neuregulin-4 was higher and soluble intercellular adhesion molecule-1 was lower in patients with GA compared with controls. For neovascular AMD, cargo trafficking to the periciliary membrane, fibroblast growth factor receptor 3b ligand binding and activation, and vascular endothelial growth factor-related pathways were in the top ranked pathways. The top-ranked pathways for GA included several related to ErbB4 signaling. CONCLUSIONS: We found different proteins and different pathways associated with neovascular AMD and GA. Vinculin and some of the top-ranked pathways have been previously associated with AMD, whereas others have not been described. TRANSLATIONAL RELEVANCE: Biomarkers identified in plasma likely reflect systemic alterations in protein expression and may improve our understanding of the mechanisms leading to AMD.

Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib.

BACKGROUND: Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics. METHODS: A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death. RESULTS: Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk. CONCLUSIONS: Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264.

Composition of Micro-eukaryotes on the Skin of the Cascades Frog (Rana cascadae) and Patterns of Correlation between Skin Microbes and Batrachochytrium dendrobatidis.

Global amphibian decline linked to fungal pathogens has galvanized research on applied amphibian conservation. Skin-associated bacterial communities of amphibians have been shown to mediate fungal skin infections and the development of probiotic treatments with antifungal bacteria has become an emergent area of research. While exploring the role of protective bacteria has been a primary focus for amphibian conservation, we aim to expand and study the other microbes present in amphibian skin communities including fungi and other micro-eukaryotes. Here, we characterize skin-associated bacteria and micro-eukaryotic diversity found across life stages of Cascades frog (Rana cascadae) and their associated aquatic environments using culture independent 16S and 18S rRNA marker-gene sequencing. Individuals of various life stages of Cascades frogs were sampled from a population located in the Trinity Alps in Northern California during an epidemic of the chytrid fungus, Batrachochytrium dendrobatidis. We filtered the bacterial sequences against a published database of bacteria known to inhibit B. dendrobatidis in co-culture to estimate the proportion of the skin bacterial community that is likely to provide defense against B. dendrobatidis. Tadpoles had a significantly higher proportion of B. dendrobatidis-inhibitory bacterial sequence matches relative to subadult and adult Cascades frogs. We applied a network analysis to examine patterns of correlation between bacterial taxa and B. dendrobatidis, as well as micro-eukaryotic taxa and B. dendrobatidis. Combined with the published database of bacteria known to inhibit B. dendrobatidis, we used the network analysis to identify bacteria that negatively correlated with B. dendrobatidis and thus could be good probiotic candidates in the Cascades frog system.

Evaluating the impact of domestication and captivity on the horse gut microbiome.

The mammal gut microbiome, which includes host microbes and their respective genes, is now recognized as an essential second genome that provides critical functions to the host. In humans, studies have revealed that lifestyle strongly influences the composition and diversity of the gastrointestinal microbiome. We hypothesized that these trends in humans may be paralleled in mammals subjected to anthropogenic forces such as domestication and captivity, in which diets and natural life histories are often greatly modified. We investigated fecal microbiomes of Przewalski's horse (PH; Equus ferus przewalskii), the only horses alive today not successfully domesticated by humans, and herded, domestic horse (E. f. caballus) living in adjacent natural grasslands. We discovered PH fecal microbiomes hosted a distinct and more diverse community of bacteria compared to domestic horses, which is likely partly explained by different plant diets as revealed by trnL maker data. Within the PH population, four individuals were born in captivity in European zoos and hosted a strikingly low diversity of fecal microbiota compared to individuals born in natural reserves in France and Mongolia. These results suggest that anthropogenic forces can dramatically reshape equid gastrointestinal microbiomes, which has broader implications for the conservation management of endangered mammals.

Changes in microbial ecology after fecal microbiota transplantation for recurrent C. difficile infection affected by underlying inflammatory bowel disease.

BACKGROUND: Gut microbiota play a key role in maintaining homeostasis in the human gut. Alterations in the gut microbial ecosystem predispose to Clostridium difficile infection (CDI) and gut inflammatory disorders such as inflammatory bowel disease (IBD). Fecal microbiota transplantation (FMT) from a healthy donor can restore gut microbial diversity and pathogen colonization resistance; consequently, it is now being investigated for its ability to improve inflammatory gut conditions such as IBD. In this study, we investigated changes in gut microbiota following FMT in 38 patients with CDI with or without underlying IBD. RESULTS: There was a significant change in gut microbial composition towards the donor microbiota and an overall increase in microbial diversity consistent with previous studies after FMT. FMT was successful in treating CDI using a diverse set of donors, and varying degrees of donor stool engraftment suggesting that donor type and degree of engraftment are not drivers of a successful FMT treatment of CDI. However, patients with underlying IBD experienced an increased number of CDI relapses (during a 24-month follow-up) and a decreased growth of new taxa, as compared to the subjects without IBD. Moreover, the need for IBD therapy did not change following FMT. These results underscore the importance of the existing gut microbial landscape as a decisive factor to successfully treat CDI and potentially for improvement of the underlying pathophysiology in IBD. CONCLUSIONS: FMT leads to a significant change in microbial diversity in patients with recurrent CDI and complete resolution of symptoms. Stool donor type (related or unrelated) and degree of engraftment are not the key for successful treatment of CDI by FMT. However, CDI patients with IBD have higher proportion of the original community after FMT and lack of improvement of their IBD symptoms and increased episodes of CDI on long-term follow-up.

Normalization and microbial differential abundance strategies depend upon data characteristics.

BACKGROUND: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. RESULTS: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. CONCLUSIONS: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases.

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.

Parallel Mapping of Antibiotic Resistance Alleles in Escherichia coli.

Chemical genomics expands our understanding of microbial tolerance to inhibitory chemicals, but its scope is often limited by the throughput of genome-scale library construction and genotype-phenotype mapping. Here we report a method for rapid, parallel, and deep characterization of the response to antibiotics in Escherichia coli using a barcoded genome-scale library, next-generation sequencing, and streamlined bioinformatics software. The method provides quantitative growth data (over 200,000 measurements) and identifies contributing antimicrobial resistance and susceptibility alleles. Using multivariate analysis, we also find that subtle differences in the population responses resonate across multiple levels of functional hierarchy. Finally, we use machine learning to identify a unique allelic and proteomic fingerprint for each antibiotic. The method can be broadly applied to tolerance for any chemical from toxic metabolites to next-generation biofuels and antibiotics.