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Microbes constitute the most prevalent life form on Earth, yet their remarkable diversity remains mostly unrecognized. Microbial diversity in vertebrate models presents a significant challenge for investigating host–microbiome interactions. The model organism Caenorhabditis elegans has many advantages for delineating the effects of host genetics on microbial composition. In the wild, the C. elegans gut contains various microbial species, while in the laboratory it is usually a host for a single bacterial species. There is a potential host–microbe interaction between microbial metabolites, drugs, and C. elegans phenotypes. This mini-review aims to summarize the current understanding regarding the microbiome in C. elegans. Examples using C. elegans to study host–microbe–metabolite interactions are discussed.

We present an analysis of Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project I and II and extend the Hubble diagram from optical to near-infrared wavelengths ( uBgVriYJH ). We calculate the Hubble constant, H _0 , using various distance calibrators: Cepheids, the tip of the red giant branch (TRGB), and surface brightness fluctuations (SBFs). Combining all methods of calibration, we derive H _0 = 71.76 ± 0.58 (stat) ± 1.19 (sys) km s ^−1 Mpc ^−1 from the B band and H _0 = 73.22 ± 0.68 (stat) ± 1.28 (sys) km s ^−1 Mpc ^−1 from the H band. By assigning equal weight to the Cepheid, TRGB, and SBF calibrators, we derive the systematic errors required for consistency in the first rung of the distance ladder, resulting in a systematic error of 1.2 ∼ 1.3 km s ^−1 Mpc ^−1 in H _0 . As a result, relative to the statistics-only uncertainty, the tension between the late-time H _0 we derive by combining the various distance calibrators and the early-time H _0 from the cosmic microwave background is reduced. The highest precision in SN Ia luminosity is found in the Y band (0.12 ± 0.01 mag), as defined by the intrinsic scatter ( σ _int ). We revisit SN Ia Hubble residual-host mass correlations and recover previous results that these correlations do not change significantly between the optical and near-infrared wavelengths. Finally, SNe Ia that explode beyond 10 kpc from their host centers exhibit smaller dispersion in their luminosity, confirming our earlier findings. A reduced effect of dust in the outskirts of hosts may be responsible for this effect.

Abstract Background Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient’s biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance. Methods We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions. Results In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling. Conclusions RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

Abstract Background Tacrolimus is given post-kidney transplant to suppress the immune system, and the amount of drug in the body is measured frequently. Higher variability over time may be indicative of poor drug adherence, leading to more adverse events. It is important to account for the variation in Tacrolimus, not just the average change over time. Methods Using data from the University of Colorado, we compare methods of assessing how the variability in Tacrolimus influences the hazard of de novo Donor Specific Antibodies (dnDSA), an early warning sign of graft failure. We compare multiple joint models in terms of fit and predictive ability. We explain that the models that account for the individual-specific variability over time have the best predictive performance. These models allowed each patient to have an individual-specific random error term in the longitudinal Tacrolimus model, and linked this to the hazard of dnDSA model. Results The hazard for the variance and coefficient of variation (CV) loading parameter were greater than 1, indicating that higher variability of Tacrolimus had a higher hazard of dnDSA. Introducing the individual-specific variability improved the fit, leading to more accurate predictions about the individual-specific time-to-dnDSA. Conclusions We showed that the individual’s variability in Tacrolimus is an important metric in predicting long-term adverse events in kidney transplantation. This is an important step in personalizing the dosage of TAC post-transplant to improve outcomes post-transplant.

Cytotoxic T lymphocytes (CTLs) play an integral role in the adaptive immune response by killing infected cells. Antigen presenting cells (APCs), such as dendritic cells, present pathogenic peptides to the T cell receptor on the CTL surface and co-stimulatory signals required for complete activation. Activated CTLs secrete lytic granules containing enzymes that trigger target cell death at the CTL-target contact, also known as the immune synapse (IS). The actin and microtubule cytoskeletons are instrumental in the killing of CTL targets. Lytic granules are transported along microtubules to the IS, where granule secretion is facilitated by actin depletion and recovery. Furthermore, actomyosin contractility promotes target cell death by mediating mechanical force exertion at the IS. Recent studies have shown that inflammatory cytokines produced by APCs, such as interleukin-12 (IL-12), act as a third signal for CTL activation and enhance CTL proliferation and effector function. However, the biophysical mechanisms mediating such enhanced effector function remain unclear. We hypothesized that the third signal for CTL activation, IL-12, modulates cytoskeletal dynamics and force exertion at the IS, thus potentiating CTL effector function. Here, we used live cell total internal reflection fluorescence (TIRF) microscopy to study actomyosin and microtubule dynamics at the IS of murine primary CTLs activated in the presence of peptide-MHC and co-stimulation alone (two signals), or additionally with IL-12 (three signals). We found that three signal-activated CTLs have altered actin flows, myosin dynamics and microtubule growth rates as compared to two signal-activated CTLs. We further showed that lytic granules in three-signal activated CTLs are less clustered and have lower velocities than in two-signal activated CTLs. Finally, we used traction force microscopy to show that three signal-activated CTLs exert greater traction forces than two signal-activated CTLs. Our results demonstrate that activation of CTLs in the presence of IL-12 leads to differential modulation of the cytoskeleton, thereby augmenting the mechanical response of CTLs to their targets. This indicates a potential physical mechanism via which the third signal can enhance the CTL response.

We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carnegie Supernova Project-II. These spectra, obtained with the FIRE spectrograph on the 6.5 m Magellan Baade telescope, have a spectral range of 0.8–2.5 μ m. Using this sample, we explore the NIR spectral diversity of SNe Ia and construct a template of spectral time series as a function of the light-curve-shape parameter, color stretch s _BV . Principal component analysis is applied to characterize the diversity of the spectral features and reduce data dimensionality to a smaller subspace. Gaussian process regression is then used to model the subspace dependence on phase and light-curve shape and the associated uncertainty. Our template is able to predict spectral variations that are correlated with s _BV , such as the hallmark NIR features: Mg ii at early times and the H -band break after peak. Using this template reduces the systematic uncertainties in K -corrections by ∼90% compared to those from the Hsiao template. These uncertainties, defined as the mean K -correction differences computed with the color-matched template and observed spectra, are on the level of 4 × 10 ^−4 mag on average. This template can serve as the baseline spectral energy distribution for light-curve fitters and can identify peculiar spectral features that might point to compelling physics. The results presented here will substantially improve future SN Ia cosmological experiments, for both nearby and distant samples.

Autosomal dominant polycystic kidney disease (ADPKD) is a leading genetic cause of end-stage renal disease with limited treatment options. Here the authors discover and characterize a microRNA inhibitor as a potential treatment for ADPKD.

Abstract Background Tacrolimus (TAC) is an immunosuppressant drug given to kidney transplant recipients post-transplant to prevent antibody formation and kidney rejection. The optimal therapeutic dose for TAC is poorly defined and therapy requires frequent monitoring of drug trough levels. Analyzing the association between TAC levels over time and the development of potentially harmful de novo donor specific antibodies (dnDSA) is complex because TAC levels are subject to measurement error and dnDSA is assessed at discrete times, so it is an interval censored time-to-event outcome. Methods Using data from the University of Colorado Transplant Center, we investigated the association between TAC and dnDSA using a shared random effects (intercept and slope) model with longitudinal and interval censored survival sub-models (JM) and compared it with the more traditional interval censored survival model with a time-varying covariate (TVC). We carried out simulations to compare bias, level and power for the association parameter in the TVC and JM under varying conditions of measurement error and interval censoring. In addition, using Markov Chain Monte Carlo (MCMC) methods allowed us to calculate clinically relevant quantities along with credible intervals (CrI). Results The shared random effects model was a better fit and showed both the average TAC and the slope of TAC were associated with risk of dnDSA. The simulation studies demonstrated that, in the presence of heavy interval censoring and high measurement error, the TVC survival model underestimates the association between the survival and longitudinal measurement and has inflated type I error and considerably less power to detect associations. Conclusions To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.

Southern Leaf Blight (SLB), Northern Leaf Blight (NLB), and Gray Leaf Spot (GLS) caused by Cochliobolus heterostrophus, Setosphaeria turcica, and Cercospora zeae-maydis respectively, are among the most important diseases of corn worldwide. Previously, moderately high and significantly positive genetic correlations between resistance levels to each of these diseases were identified in a panel of 253 diverse maize inbred lines. The goal of this study was to identify loci underlying disease resistance in some of the most multiple disease resistant (MDR) lines by the creation of chromosome segment substitution line (CSSL) populations in multiple disease susceptible (MDS) backgrounds. Four MDR lines (NC304, NC344, Ki3, NC262) were used as donor parents and two MDS lines (Oh7B, H100) were used as recurrent parents to produce eight BC3F4:5 CSSL populations comprising 1,611 lines in total. Each population was genotyped and assessed for each disease in replicated trials in two environments. Moderate to high heritabilities on an entry mean basis were observed (0.32 to 0.83). Several lines in each population were significantly more resistant than the MDS parental lines for each disease. Multiple quantitative trait loci (QTL) for disease resistance were detected for each disease in most of the populations. Seventeen QTL were associated with variation in resistance to more than one disease (SLB/NLB: 2; SLB/GLS: 7; NLB/GLS: 2 and 6 to all three diseases). For most populations and most disease combinations, significant correlations were observed between disease scores and also between marker effects for each disease. The number of lines that were resistant to more than one disease was significantly higher than would be expected by chance. Using the results from individual QTL analyses, a composite statistic based on Mahalanobis distance (Md) was used to identify joint marker associations with multiple diseases. Across all populations and diseases, 246 markers had significant Md values. However further analysis revealed that most of these associations were due to strong QTL effects on a single disease. Together, these findings reinforce our previous conclusions that loci associated with resistance to different diseases are clustered in the genome more often than would be expected by chance. Nevertheless true MDR loci which have significant effects on more than one disease are still much rarer than loci with single disease effects.

Molecular mechanisms underlying the negative health effects of shift work are poorly understood, which remains a barrier to developing intervention strategies to protect the long-term health of shift workers. We evaluated genome-wide differences in DNA methylation (measured in blood) between 111 actively employed female nightshift and 86 actively employed female dayshift workers from the Seattle metropolitan area. We also explored the effect of chronotype (i.e., measure of preference for activity earlier or later in the day) on DNA methylation among 110 of the female nightshift workers and an additional group of 131 male nightshift workers. Methylation data were generated using the Illumina Infinium HumanMethylation450 BeadChip (450K) Array. After applying the latest methylation data processing methods, we compared methylation levels at 361,210 CpG loci between the groups using linear regression models adjusted for potential confounders and applied the false-discovery rate (FDR) ≤ 0.05 to account for multiple comparisons. No statistically significant associations at the genome-wide level were observed with shift work or chronotype, though based on raw P values and absolute effect sizes, there were suggestive associations in genes that have been previously linked with cancer (e.g., BACH2, JRK, RPS6KA2) and type-2 diabetes (e.g., KCNQ1). Given that our study was underpowered to detect moderate effects, examining these suggestive results in well-powered independent studies or in pooled data sets may improve our understanding of the pathways underlying the negative health effects of shift work and the influence of personal factors such as chronotype. Such an approach may help identify potential interventions that can be used to protect the long-term health of shift workers.

Autosomal dominant polycystic kidney disease (ADPKD) is a life-threatening genetic disease that leads to renal failure. Here Hajarniset al. show that miR-17 modulates cyst progression in ADPKD through metabolic reprogramming of mitochondria and its inhibition slows cyst development and improves renal functions.

Genetic variation in the 6p21 chromosomal region, including human leukocyte antigen (HLA) genes and tumor necrosis factor (TNF), has been linked to both etiology and clinical outcomes of lymphomas. We estimated the effects of HLA class I (A, B, and C), class II DRB1 alleles, and the ancestral haplotype (AH) 8.1 (HLAA*01-B*08-DRB1*03-TNF-308A) on overall survival (OS) among patients with diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) in a population-based study of non-Hodgkin lymphoma. During a median followup of 89 months, 31% (52 of 166) DLBCL and 28% (46 of 165) FL patients died. Using multivariate Cox regression models, we observed statistically significant associations between genetic variants and survival: HLA-Cw*07:01 was associated with poorer OS among DLBCL patients (Hazard ratio [HR] = 1.76, 95% confidence interval [CI] = 1.01–3.05); HLA-A*01:01 was associated with poorer OS (HR = 2.23, 95% CI = 1.24–4.01), and HLA-DRB1*13 (HR = 0.12, 95% CI = 0.02–0.90) and HLA-B Bw4 (HR = 0.36, 95% CI = 0.20–0.63) with better OS among FL patients. These results support a role for HLA in the prognosis of DLBCL and FL and represent a promising class of prognostic factors that warrants further evaluation.

Non-Hodgkin lymphoma (NHL) represents a diverse group of hematological malignancies, of which follicular lymphoma (FL) is a prevalent subtype. A previous genome-wide association study has established a marker, rs10484561 in the human leukocyte antigen (HLA) class II region on 6p21.32 associated with increased FL risk. Here, in a three-stage genome-wide association study, starting with a genome-wide scan of 379 FL cases and 791 controls followed by validation in 1,049 cases and 5,790 controls, we identified a second independent FL-associated locus on 6p21.32, rs2647012 (OR(combined) = 0.64, P(combined) = 2 × 10(-21)) located 962 bp away from rs10484561 (r(2)

Genetic variations in human leukocyte antigens (HLA) are critical in host responses to infections, transplantation, and immunological diseases. We previously identified associations with non-Hodgkin lymphoma (NHL) and the HLA-DRB1*01:01 allele and extended ancestral haplotype (AH) 8.1 (HLA-A*01-B*08-DR*03-TNF-308A). To illuminate how HLA alleles and haplotypes may influence NHL etiology, we examined potential interactions between HLA-DRB1*01:01 and AH 8.1, and a wide range of NHL risk factors among 685 NHL cases and 646 controls from a United States population-based case-control study. We calculated odds ratios and 95% confidence intervals by HLA allele or haplotype status, adjusted for sex, age, race and study center for NHL and two major subtypes using polychotomous unconditional logistic regression models. The previously reported elevation in NHL risk associated with exposures to termite treatment and polychlorinated biphenyls were restricted to individuals who did not possess HLA-DRB1*01:01. Previous associations for NHL and DLBCL with decreased sun exposure, higher BMI, and autoimmune conditions were statistically significant only among those with AH 8.1, and null among those without AH 8.1. Our results suggest that NHL risk factors vary in their association based on HLA-DRB1*01:01 and AH 8.1 status. Our results further suggest that certain NHL risk factors may act through a common mechanism to alter NHL risk. Finally, control participants with either HLA-DRB1*01:01 or AH 8.1 reported having a family history of NHL twice as likely as those who did not have either allele or haplotype, providing the first empirical evidence that HLA associations may explain some of the well-established relationship between family history and NHL risk.

BACKGROUND:A promoter polymorphism in the pro-inflammatory cytokine tumor necrosis factor (TNF) (TNF G-308A) is associated with increased non-Hodgkin lymphoma (NHL) risk. The protein product, TNF-alpha, activates the nuclear factor kappa beta (NF-kappaB) transcription factor, and is critical for inflammatory and apoptotic responses in cancer progression. We hypothesized that the TNF and NF-kappaB pathways are important for NHL and that gene variations across the pathways may alter NHL risk. METHODOLOGY/PRINCIPAL FINDINGS:We genotyped 500 tag single nucleotide polymorphisms (SNPs) from 48 candidate gene regions (defined as 20 kb 5', 10 kb 3') in the TNF and TNF receptor superfamilies and the NF-kappaB and related transcription factors, in 1946 NHL cases and 1808 controls pooled from three independent population-based case-control studies. We obtained a gene region-level summary of association by computing the minimum p-value ("minP test"). We used logistic regression to compute odds ratios and 95% confidence intervals for NHL and four major NHL subtypes in relation to SNP genotypes and haplotypes. For NHL, the tail strength statistic supported an overall relationship between the TNF/NF-kappaB pathway and NHL (p = 0.02). We confirmed the association between TNF/LTA on chromosome 6p21.3 with NHL and found the LTA rs2844484 SNP most significantly and specifically associated with the major subtype, diffuse large B-cell lymphoma (DLBCL) (p-trend = 0.001). We also implicated for the first time, variants in NFKBIL1 on chromosome 6p21.3, associated with NHL. Other gene regions identified as statistically significantly associated with NHL included FAS, IRF4, TNFSF13B, TANK, TNFSF7 and TNFRSF13C. Accordingly, the single most significant SNPs associated with NHL were FAS rs4934436 (p-trend = 0.0024), IRF4 rs12211228 (p-trend = 0.0026), TNFSF13B rs2582869 (p-trend = 0.0055), TANK rs1921310 (p-trend = 0.0025), TNFSF7 rs16994592 (p-trend = 0.0024), and TNFRSF13C rs6002551 (p-trend = 0.0074). All associations were consistent in each study with no apparent specificity for NHL subtype. CONCLUSIONS/SIGNIFICANCE:Our results provide consistent evidence that variation in the TNF superfamily of genes and specifically within chromosome 6p21.3 impacts lymphomagenesis. Further characterization of these susceptibility loci and identification of functional variants are warranted.

There continues to be strong interest in the possibility that working non-day hours is associated with an increased risk of cancer, most notably breast and prostate cancer. There is now a substantial body of scientific evidence regarding this question. The findings to date from numerous epidemiologic studies, employing both case–control and cohort designs, support such an association although there are some inconsistencies, particularly in the definition of “shift work”. Most studies find an association but a few do not. One alternative to the comparison of individual studies is a formal meta-analysis that combines data from several studies and treats those data as one large study. Although meta-analyses can be informative, they are subject to a number of important limitations that must be considered in interpreting the results and drawing conclusions. Perhaps the most difficult aspect of conducting a meta-analysis is the degree of similarity among studies, both in design and execution. Especially important in this regard is the assessment of exposure. Ijaz et al (1) recently published the results of a new meta-analysis designed to evaluate the epidemiological studies of night shift work and breast cancer risk. Although this appears to be a carefully done study, it is also a good example of the inherent limitations of meta-analyses and the effects of such shortcomings on the final disease risk estimates. Of primary concern is that the studies included in Ijaz et al’s meta-analysis employed very different methods to define the exposure, such that no two studies defined their primary exposure variable(s) in exactly the same way. Each study is different regarding the definition and use of factors that can characterize non-day work. These include: (i) shift system (eg, rotating or permanent, start, and end times, etc.); (ii) years on shift; and (iii) shift intensity (time-off between shifts) (2). These more detailed aspects of non-day shiftwork will influence circadian rhythms, melatonin suppression, and sleep deprivation; all of which are hypothesized to be etiologic factors for breast and perhaps other types of cancer as well. Thus, it is vitally important that indicators of exposure to factors associated with non-day work be as accurate and reproducible as possible. This would greatly improve the ability to compare results across studies, and future studies would be much more uniform in exposure assessment. Current approaches can result in substantial degrees of exposure misclassification. This occurs in two different ways. One is general misclassification, often referred to as non-differential. This tends to reduce the risk estimates towards the null, therefore reducing the magnitude of the exposure– disease association. If the misclassification is strong enough, it can even mask true associations. The other is differential exposure misclassification, which can bias results in either direction. For example, it is not difficult to imagine that the probability of whether an identified subject would or would not participate in a given study could depend on their work schedule; day workers are more difficult to reach by, for example, telephone and therefore controls over-represent night workers compared to the general population. “Shift work” as assessed in some of the case–control studies that used personal interviews was not subject to any significant recall bias because it was derived from detailed occupational histories, which are accurately remembered by most people. It should also be kept in mind that several of the case–control studies used existing occupational records for workers and so no interview was conducted at all; and, in almost all of the case–control studies that did use interviews, those interviews were conducted before the International Agency for Research on Cancer (IARC) evaluation of October 2007 and thereby before the light-at-night hypothesis was widely publicized. The original reason for examining the non-day shift was as a surrogate for melatonin suppression and “circadian disruption” from electric light at night. Non-day workers were presumed to have more of this exposure than day workers. However, all women in the modern world are exposed to electric light to some extent during the period from dusk to dawn. So in all the epidemiological studies of shift work, the comparison group of day-working women also have a history of exposure to light-at-night. Therefore, if light-at-night does increase the risk of breast cancer, then the shift work studies only capture a small portion of the societal impact. A major limitation of meta-analyses is that internal validity largely depends on the quality of the primary contributing studies; in other words, a meta-analysis cannot overcome the methodological limitations ­inherent to the design of each individual study. The conclusions of Ijaz et al (1) depend crucially upon their subjective assessments of “quality”, and those assessments are not compelling for many of the studies. Their methods for defining “long-term” are also problematic. Other investigators who have recently published meta-analyses on the same topic (3, 4) came to different conclusions regarding the quality of several of the individual studies and the overall result. Finally, a recent case–control study not included in these meta-analyses also gives support to the association between long-term, non-day work and breast cancer risk (5). Given the vastly different exposure metrics used in each study, heterogeneity in results should be expected. Despite the dissimilarity of study design and an unknown but possibly substantial degree of non-differential exposure misclassification, Ijaz et al (1) do observe a 5% significantly increased risk of breast cancer per each five years of non-day work [risk ratio (RR) 1.05, 95% confidence interval (95% CI) 1.01–1.10] based on 12 studies. The authors assert that the cohort studies in their meta-analysis found little or no association, yet the case–control studies did; this, they suggest, is due to recall bias in the case–control studies, although as noted above recall bias almost certainly did not occur in those case–control studies. In addition, two of the three cohort studies included in the meta-analysis clearly report significantly elevated risks for long-term exposure, which is at odds with the authors’ assertion of a meaningful difference between the case–control (N=9) and cohort (N=3) results.