CoDP: predicting the impact of unclassified genetic variants in MSH6 by the combination of different properties of the protein

Background

Lynch syndrome is a hereditary cancer predisposition syndrome caused by a mutation in one of the DNA mismatch repair (MMR) genes. About 24% of the mutations identified in Lynch syndrome are missense substitutions and the frequency of missense variants in MSH6 is the highest amongst these MMR genes. Because of this high frequency, the genetic testing was not effectively used in MSH6 so far. We, therefore, developed CoDP (Combination of the Different Properties), a bioinformatics tool to predict the impact of missense variants in MSH6.

Methods

We integrated the prediction results of three methods, namely MAPP, PolyPhen-2 and SIFT. Two other structural properties, namely solvent accessibility and the change in the number of heavy atoms of amino acids in the MSH6 protein, were further combined explicitly. MSH6 germline missense variants classified by their associated clinical and molecular data were used to fit the parameters for the logistic regression model and to assess the prediction. The performance of CoDP was compared with those of other conventional tools, namely MAPP, SIFT, PolyPhen-2 and PON-MMR.

Results

A total of 294 germline missense variants were collected from the variant databases and literature. Of them, 34 variants were available for the parameter training and the prediction performance test. We integrated the prediction results of MAPP, PolyPhen-2 and SIFT, and two other structural properties, namely solvent accessibility and the change in the number of heavy atoms of amino acids in the MSH6 protein, were further combined explicitly. Variants data classified by their associated clinical and molecular data were used to fit the parameters for the logistic regression model and to assess the prediction. The values of the positive predictive value (PPV), the negative predictive value (NPV), sensitivity, specificity and accuracy of the tools were compared on the whole data set. PPV of CoDP was 93.3% (14/15), NPV was 94.7% (18/19), specificity was 94.7% (18/19), sensitivity was 93.3% (14/15) and accuracy was 94.1% (32/34). Area under the curve of CoDP was 0.954, that of MAPP for MSH6 was 0.919, of SIFT was 0.864 and of PolyPhen-2 HumVar was 0.819. The power to distinguish between pathogenic and non-pathogenic variants of these methods was tested by Wilcoxon rank sum test (p < 8.9 × 10^-6 for CoDP, p < 3.3 × 10^-5 for MAPP, p < 3.1 × 10^-4 for SIFT and p < 1.2 × 10^-3 for PolyPhen-2 HumVar), and CoDP was shown to outperform other conventional methods.

Conclusion

In this paper, we provide a human curated data set for MSH6 missense variants, and CoDP, the prediction tool, which achieved better accuracy for predicting the impact of missense variants in MSH6 than any other known tools. CoDP is available at http://cib.cf.ocha.ac.jp/CoDP/.     

Priors in Whole-Genome Regression: The Bayesian Alphabet Returns

Whole-genome enabled prediction of complex traits has received enormous attention in animal and plant breeding and is making inroads into human and even Drosophila genetics. The term “Bayesian alphabet” denotes a growing number of letters of the alphabet used to denote various Bayesian linear regressions that differ in the priors adopted, while sharing the same sampling model. We explore the role of the prior distribution in whole-genome regression models for dissecting complex traits in what is now a standard situation with genomic data where the number of unknown parameters (p) typically exceeds sample size (n). Members of the alphabet aim to confront this overparameterization in various manners, but it is shown here that the prior is always influential, unless n ≫ p. This happens because parameters are not likelihood identified, so Bayesian learning is imperfect. Since inferences are not devoid of the influence of the prior, claims about genetic architecture from these methods should be taken with caution. However, all such procedures may deliver reasonable predictions of complex traits, provided that some parameters (“tuning knobs”) are assessed via a properly conducted cross-validation. It is concluded that members of the alphabet have a room in whole-genome prediction of phenotypes, but have somewhat doubtful inferential value, at least when sample size is such that n ≪ p.     

Stress response of biomolecules (carbohydrate,protein and lipid profiles) in fish Channa punctatus inhabiting river polluted by Thermal Power Plant effluent

Qualitative and quantitative assessment of heavy metals in the Thermal Power Plant effluent was performed to study the impact of their toxic effects on various biomarkers (carbohydrate, protein and lipid profiles). Heavy metals present in the water were in the order Fe > Cu > Zn > Mn > Ni > Co > Cr. Fe and Ni exceeded and Cr was equal to the USA standards set by UNEPGEMS. Glycogen in liver (p < 0.001) and muscle (p < 0.01) depleted significantly. Insignificant (p < 0.05) decline in blood glucose (−21.0%) and significant (p < 0.05) elevation in both total protein and globulin in serum, liver and muscle was noted. Albumin decreased significantly (p < 0.01) in serum but showed significant (p < 0.05) increase in liver and muscle. Thus A:G ratio fell in serum and rose in liver and muscle. Similarly lipid profile also gets altered where significant elevation in serum total lipid (p < 0.01), total cholesterol (p < 0.01), phospholipid (p < 0.05), triglycerides (p < 0.001), LDL (p < 0.01) was observed but significant (p < 0.05) decline in VLDL was recorded. These biomarkers suggested that fish become hypoglycemic, hyperlipidemic and hypercholesterolemic. Heavy metals also provoked immune response as evident from the rise in globulin. In conclusion the Thermal Power Plant wastewater containing heavy metals induced stress, making fish weak and vulnerable to diseases.     

The cardiovascular effects of amodiaquine and structurally related antimalarials: An individual patient data meta-analysis

BackgroundAmodiaquine is a 4-aminoquinoline antimalarial similar to chloroquine that is used extensively for the treatment and prevention of malaria. Data on the cardiovascular effects of amodiaquine are scarce, although transient effects on cardiac electrophysiology (electrocardiographic QT interval prolongation and sinus bradycardia) have been observed. We conducted an individual patient data meta-analysis to characterise the cardiovascular effects of amodiaquine and thereby support development of risk minimisation measures to improve the safety of this important antimalarial.Methods and findingsStudies of amodiaquine for the treatment or prevention of malaria were identified from a systematic review. Heart rates and QT intervals with study-specific heart rate correction (QTcS) were compared within studies and individual patient data pooled for multivariable linear mixed effects regression.The meta-analysis included 2,681 patients from 4 randomised controlled trials evaluating artemisinin-based combination therapies (ACTs) containing amodiaquine (n = 725), lumefantrine (n = 499), piperaquine (n = 716), and pyronaridine (n = 566), as well as monotherapy with chloroquine (n = 175) for uncomplicated malaria. Amodiaquine prolonged QTcS (mean = 16.9 ms, 95% CI: 15.0 to 18.8) less than chloroquine (21.9 ms, 18.3 to 25.6, p = 0.0069) and piperaquine (19.2 ms, 15.8 to 20.5, p = 0.0495), but more than lumefantrine (5.6 ms, 2.9 to 8.2, p < 0.001) and pyronaridine (−1.2 ms, −3.6 to +1.3, p < 0.001). In individuals aged ≥12 years, amodiaquine reduced heart rate (mean reduction = 15.2 beats per minute [bpm], 95% CI: 13.4 to 17.0) more than piperaquine (10.5 bpm, 7.7 to 13.3, p = 0.0013), lumefantrine (9.3 bpm, 6.4 to 12.2, p < 0.001), pyronaridine (6.6 bpm, 4.0 to 9.3, p < 0.001), and chloroquine (5.9 bpm, 3.2 to 8.5, p < 0.001) and was associated with a higher risk of potentially symptomatic sinus bradycardia (≤50 bpm) than lumefantrine (risk difference: 14.8%, 95% CI: 5.4 to 24.3, p = 0.0021) and chloroquine (risk difference: 8.0%, 95% CI: 4.0 to 12.0, p < 0.001). The effect of amodiaquine on the heart rate of children aged <12 years compared with other antimalarials was not clinically significant. Study limitations include the unavailability of individual patient-level adverse event data for most included participants, but no serious complications were documented.ConclusionsWhile caution is advised in the use of amodiaquine in patients aged ≥12 years with concomitant use of heart rate–reducing medications, serious cardiac conduction disorders, or risk factors for torsade de pointes, there have been no serious cardiovascular events reported after amodiaquine in widespread use over 7 decades. Amodiaquine and structurally related antimalarials in the World Health Organization (WHO)-recommended dose regimens alone or in ACTs are safe for the treatment and prevention of malaria.

In this meta-analysis, Xin Hui Supanee Chan and colleagues investigate the cardiovascular effects of amodiaquine and structurally-related antimalarials using individual patient data from trials.     

A Proteomic Approach to Investigating Gene Cluster Expression and Secondary Metabolite Functionality in Aspergillus fumigatus

A combined proteomics and metabolomics approach was utilised to advance the identification and characterisation of secondary metabolites in Aspergillus fumigatus. Here, implementation of a shotgun proteomic strategy led to the identification of non-redundant mycelial proteins (n = 414) from A. fumigatus including proteins typically under-represented in 2-D proteome maps: proteins with multiple transmembrane regions, hydrophobic proteins and proteins with extremes of molecular mass and pI. Indirect identification of secondary metabolite cluster expression was also achieved, with proteins (n = 18) from LaeA-regulated clusters detected, including GliT encoded within the gliotoxin biosynthetic cluster. Biochemical analysis then revealed that gliotoxin significantly attenuates H₂O₂-induced oxidative stress in A. fumigatus (p>0.0001), confirming observations from proteomics data. A complementary 2-D/LC-MS/MS approach further elucidated significantly increased abundance (p<0.05) of proliferating cell nuclear antigen (PCNA), NADH-quinone oxidoreductase and the gliotoxin oxidoreductase GliT, along with significantly attenuated abundance (p<0.05) of a heat shock protein, an oxidative stress protein and an autolysis-associated chitinase, when gliotoxin and H₂O₂ were present, compared to H₂O₂ alone. Moreover, gliotoxin exposure significantly reduced the abundance of selected proteins (p<0.05) involved in de novo purine biosynthesis. Significantly elevated abundance (p<0.05) of a key enzyme, xanthine-guanine phosphoribosyl transferase Xpt1, utilised in purine salvage, was observed in the presence of H₂O₂ and gliotoxin. This work provides new insights into the A. fumigatus proteome and experimental strategies, plus mechanistic data pertaining to gliotoxin functionality in the organism.     

The Association between Meditation Practice and Job Performance: A Cross-Sectional Study

Many previous studies have shown that meditation practice has a positive impact on cognitive and non-cognitive functioning, which are related to job performance. Thus, the aims of this study were to (1) estimate the prevalence of meditation practice, (2) identify the characteristics of individuals who practice meditation, and (3) examine the association between meditation practice and job performance. Two population-based, cross-sectional surveys were conducted. In study 1, we examined the prevalence of meditation practice and the characteristics of the persons practicing meditation; in Study 2, we examined the association between meditation practice and job performance. The outcome variables included work engagement, subjective job performance, and job satisfaction. The Utrecht Work Engagement Scale was used to assess work engagement, the World Health Organization Health and Work Performance Questionnaire (HPQ) was used to measure subjective job performance, and a scale developed by the Japanese government was used to assess job satisfaction. Hierarchical multiple regression analyses were used in Study 2. Demographic characteristics and behavioral risk factors were included as covariates in the analyses. The results of Study 1 indicated that 3.9% of persons surveyed (n = 30,665) practiced meditation; these individuals were younger and had a higher education, higher household income, higher stress level, and lower body mass index than those who did not practice meditation. The results of Study 2 (n = 1,470) indicated that meditation practice was significantly predictive of work engagement (β = 0.112, p < .001), subjective job performance (β = 0.116, p < .001), and job satisfaction (β = 0.079, p = .002), even after adjusting for covariates (β = 0.083, p < .001; β = 0.104, p < .001; β = 0.060, p = .015, respectively). The results indicate that meditation practice may positively influence job performance, including job satisfaction, subjective job performance, and work engagement.     

Serum levels of appetite-regulating hormones and pro-inflammatory cytokines are ameliorated by a CLA diet and endurance exercise in rats fed a high-fat diet

[Purpose]

This study examined whether conjugated linoleic acid (CLA) supplementation and endurance exercise affect appetite-regulating hormones and pro-inflammatory cytokines in rats.

[Methods]

Seven-week-old male Sprague-Dawley rats were divided randomly into the high-fat diet sedentary group (HS, n=8), the 1.0% CLA supplemented high-fat diet sedentary group (CS, n=8), and the 1.0% CLA supplemented high-fat diet exercise group (CE, n=8). Rats in the CE group swam 60 min/day, 5 days/week for 4 weeks.

[Results]

Leptin and insulin levels in the CS and CE groups were significantly lower than those in the HS group (p<0.001), whereas leptin (p<0.01) and insulin (p<0.05) levels decreased significantly in the CE compared to those in the CS group. Interleukin (IL)-1β (p<0.001) and IL-6 (p<0.01) levels in the CS and CE groups decreased significantly compared to those in the HS group. Leptin (IL-1β: r=0.835, p<0.001), IL-6 (r=0.607, p<0.05), insulin (IL-1β: r=0.797, p<0.01), and IL-6 (r=0.827, p<0.01) levels were positively related with pro-inflammatory cytokine levels.

[Conclusion]

Endurance exercise may play an important role during CLA supplementation of rats on a high-fat diet.     

Genomic prediction based on data from three layer lines: a comparison between linear methods

Background

The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction.

Methods

Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms.

Results

Genomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76).

Conclusions

Multi-line genomic prediction did not affect or slightly improved prediction accuracy for closely-related lines. For distantly-related lines, multi-line genomic prediction yielded similar or slightly lower accuracies than single-line genomic prediction. Bayesian variable selection and GBLUP generally gave similar accuracies. Overall, RRPCA yielded the greatest accuracies for two lines, suggesting that using PCA helps to alleviate the “n ≪ p” problem in genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0057-5) contains supplementary material, which is available to authorized users.     

Autophagic Protein Beclin 1 Serves as an Independent Positive Prognostic Biomarker for Non-Small Cell Lung Cancer

Beclin 1, a key regulator of autophagy, has been found to be aberrantly expressed in a variety of human malignancies. Herein, we employed immunohistochemistry (IHC) to detect the protein expression of Beclin 1 in non-small cell lung cancer (NSCLC) and paired normal adjacent lung tissues, and analyzed its clinicopathological/prognostic significance in NSCLC. Receiver operating characteristic (ROC) curve analysis was utilized to determine a cutoff point (>2 VS. ≤2) for Beclin 1 expression in a training set (n = 105). For validation, the ROC-derived cutoff value was subjected to analysis of the association of Beclin 1 with patients’ clinical characteristics and outcome in a testing set (n = 111) and the overall patient cohort (n = 216). Our data showed that Beclin 1 was significantly lower in NSCLC tissues compared with the adjacent normal tissues, negatively associating with tumor recurrence rate (65.8% VS 32.3%; p < 0.001). In the testing set and the overall patient cohort, low expression of Beclin 1 showed significantly inferior overall survival (OS) (p < 0.001) and progression-free survival (PFS) (p < 0.001) compared to high expression of Beclin 1. In the testing set and the overall patient cohort, the median duration of OS for patients with high and low expression of Beclin 1 was 108 VS. 24.5 months (p < 0.001) and 108 VS. 28 months (p < 0.001), respectively. Furthermore, low expression of Beclin 1 was also a poor prognostic factor within each stage of NSCLC patients. Multivariate analysis identified that Beclin 1 was an independent prognostic factor for NSCLC. Our findings in the present study provided evidence that Beclin 1 may thus emerge as an independent prognostic biomarker in this tumor entity in the future.     

GAD2 on chromosome 10p12 is a candidate gene for human obesity

The gene GAD2 encoding the glutamic acid decarboxylase enzyme (GAD65) is a positional candidate gene for obesity on Chromosome 10p11–12, a susceptibility locus for morbid obesity in four independent ethnic populations. GAD65 catalyzes the formation of γ-aminobutyric acid (GABA), which interacts with neuropeptide Y in the paraventricular nucleus to contribute to stimulate food intake. A case-control study (575 morbidly obese and 646 control subjects) analyzing GAD2 variants identified both a protective haplotype, including the most frequent alleles of single nucleotide polymorphisms (SNPs) +61450 C>A and +83897 T>A (OR = 0.81, 95% CI [0.681–0.972], p = 0.0049) and an at-risk SNP (−243 A>G) for morbid obesity (OR = 1.3, 95% CI [1.053–1.585], p = 0.014). Furthermore, familial-based analyses confirmed the association with the obesity of SNP +61450 C>A and +83897 T>A haplotype (χ² = 7.637, p = 0.02). In the murine insulinoma cell line βTC3, the G at-risk allele of SNP −243 A>G increased six times GAD2 promoter activity (p < 0.0001) and induced a 6-fold higher affinity for nuclear extracts. The −243 A>G SNP was associated with higher hunger scores (p = 0.007) and disinhibition scores (p = 0.028), as assessed by the Stunkard Three-Factor Eating Questionnaire. As GAD2 is highly expressed in pancreatic β cells, we analyzed GAD65 antibody level as a marker of β-cell activity and of insulin secretion. In the control group, −243 A>G, +61450 C>A, and +83897 T>A SNPs were associated with lower GAD65 autoantibody levels (p values of 0.003, 0.047, and 0.006, respectively). SNP +83897 T>A was associated with lower fasting insulin and insulin secretion, as assessed by the HOMA-B% homeostasis model of β-cell function (p = 0.009 and 0.01, respectively). These data support the hypothesis of the orexigenic effect of GABA in humans and of a contribution of genes involved in GABA metabolism in the modulation of food intake and in the development of morbid obesity.     

Genome-Wide Prediction of Traits with Different Genetic Architecture Through Efficient Variable Selection

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.     

Preconditioning 2D Integer Data for Fast Convex Hull Computations

In order to accelerate computing the convex hull on a set of n points, a heuristic procedure is often applied to reduce the number of points to a set of s points, s ≤ n, which also contains the same hull. We present an algorithm to precondition 2D data with integer coordinates bounded by a box of size p × q before building a 2D convex hull, with three distinct advantages. First, we prove that under the condition min(p, q) ≤ n the algorithm executes in time within O(n); second, no explicit sorting of data is required; and third, the reduced set of s points forms a simple polygonal chain and thus can be directly pipelined into an O(n) time convex hull algorithm. This paper empirically evaluates and quantifies the speed up gained by preconditioning a set of points by a method based on the proposed algorithm before using common convex hull algorithms to build the final hull. A speedup factor of at least four is consistently found from experiments on various datasets when the condition min(p, q) ≤ n holds; the smaller the ratio min(p, q)/n is in the dataset, the greater the speedup factor achieved.     

Clinical Risk Scoring Models for Prediction of Acute Kidney Injury after Living Donor Liver Transplantation: A Retrospective Observational Study

Acute kidney injury (AKI) is a frequent complication of liver transplantation and is associated with increased mortality. We identified the incidence and modifiable risk factors for AKI after living-donor liver transplantation (LDLT) and constructed risk scoring models for AKI prediction. We retrospectively reviewed 538 cases of LDLT. Multivariate logistic regression analysis was used to evaluate risk factors for the prediction of AKI as defined by the RIFLE criteria (RIFLE = risk, injury, failure, loss, end stage). Three risk scoring models were developed in the retrospective cohort by including all variables that were significant in univariate analysis, or variables that were significant in multivariate analysis by backward or forward stepwise variable selection. The risk models were validated by way of cross-validation. The incidence of AKI was 27.3% (147/538) and 6.3% (34/538) required postoperative renal replacement therapy. Independent risk factors for AKI by multivariate analysis of forward stepwise variable selection included: body-mass index >27.5 kg/m² [odds ratio (OR) 2.46, 95% confidence interval (CI) 1.32–4.55], serum albumin <3.5 mg/dl (OR 1.76, 95%CI 1.05–2.94), MELD (model for end-stage liver disease) score >20 (OR 2.01, 95%CI 1.17–3.44), operation time >600 min (OR 1.81, 95%CI 1.07–3.06), warm ischemic time >40 min (OR 2.61, 95%CI 1.55–4.38), postreperfusion syndrome (OR 2.96, 95%CI 1.55–4.38), mean blood glucose during the day of surgery >150 mg/dl (OR 1.66, 95%CI 1.01–2.70), cryoprecipitate > 6 units (OR 4.96, 95%CI 2.84–8.64), blood loss/body weight >60 ml/kg (OR 4.05, 95%CI 2.28–7.21), and calcineurin inhibitor use without combined mycophenolate mofetil (OR 1.87, 95%CI 1.14–3.06). Our risk models performed better than did a previously reported score by Utsumi et al. in our study cohort. Doses of calcineurin inhibitor should be reduced by combined use of mycophenolate mofetil to decrease postoperative AKI. Prospective randomized trials are required to address whether artificial modification of hypoalbuminemia, hyperglycemia and postreperfusion syndrome would decrease postoperative AKI in LDLT.     

Circulating Mitochondrial DNA in Patients in the ICU as a Marker of Mortality: Derivation and Validation

Background

Mitochondrial DNA (mtDNA) is a critical activator of inflammation and the innate immune system. However, mtDNA level has not been tested for its role as a biomarker in the intensive care unit (ICU). We hypothesized that circulating cell-free mtDNA levels would be associated with mortality and improve risk prediction in ICU patients.

Methods and Findings

Analyses of mtDNA levels were performed on blood samples obtained from two prospective observational cohort studies of ICU patients (the Brigham and Women''s Hospital Registry of Critical Illness [BWH RoCI, n = 200] and Molecular Epidemiology of Acute Respiratory Distress Syndrome [ME ARDS, n = 243]). mtDNA levels in plasma were assessed by measuring the copy number of the NADH dehydrogenase 1 gene using quantitative real-time PCR. Medical ICU patients with an elevated mtDNA level (≥3,200 copies/µl plasma) had increased odds of dying within 28 d of ICU admission in both the BWH RoCI (odds ratio [OR] 7.5, 95% CI 3.6–15.8, p = 1×10⁻⁷) and ME ARDS (OR 8.4, 95% CI 2.9–24.2, p = 9×10⁻⁵) cohorts, while no evidence for association was noted in non-medical ICU patients. The addition of an elevated mtDNA level improved the net reclassification index (NRI) of 28-d mortality among medical ICU patients when added to clinical models in both the BWH RoCI (NRI 79%, standard error 14%, p<1×10⁻⁴) and ME ARDS (NRI 55%, standard error 20%, p = 0.007) cohorts. In the BWH RoCI cohort, those with an elevated mtDNA level had an increased risk of death, even in analyses limited to patients with sepsis or acute respiratory distress syndrome. Study limitations include the lack of data elucidating the concise pathological roles of mtDNA in the patients, and the limited numbers of measurements for some of biomarkers.

Conclusions

Increased mtDNA levels are associated with ICU mortality, and inclusion of mtDNA level improves risk prediction in medical ICU patients. Our data suggest that mtDNA could serve as a viable plasma biomarker in medical ICU patients. Please see later in the article for the Editors'' Summary     

The Quality of Methods Reporting in Parasitology Experiments

There is a growing concern both inside and outside the scientific community over the lack of reproducibility of experiments. The depth and detail of reported methods are critical to the reproducibility of findings, but also for making it possible to compare and integrate data from different studies. In this study, we evaluated in detail the methods reporting in a comprehensive set of trypanosomiasis experiments that should enable valid reproduction, integration and comparison of research findings. We evaluated a subset of other parasitic (Leishmania, Toxoplasma, Plasmodium, Trichuris and Schistosoma) and non-parasitic (Mycobacterium) experimental infections in order to compare the quality of method reporting more generally. A systematic review using PubMed (2000–2012) of all publications describing gene expression in cells and animals infected with Trypanosoma spp was undertaken based on PRISMA guidelines; 23 papers were identified and included. We defined a checklist of essential parameters that should be reported and have scored the number of those parameters that are reported for each publication. Bibliometric parameters (impact factor, citations and h-index) were used to look for association between Journal and Author status and the quality of method reporting. Trichuriasis experiments achieved the highest scores and included the only paper to score 100% in all criteria. The mean of scores achieved by Trypanosoma articles through the checklist was 65.5% (range 32–90%). Bibliometric parameters were not correlated with the quality of method reporting (Spearman''s rank correlation coefficient <−0.5; p>0.05). Our results indicate that the quality of methods reporting in experimental parasitology is a cause for concern and it has not improved over time, despite there being evidence that most of the assessed parameters do influence the results. We propose that our set of parameters be used as guidelines to improve the quality of the reporting of experimental infection models as a pre-requisite for integrating and comparing sets of data.     

Low Birth Weight Is a Risk Factor for Severe Retinopathy of Prematurity Depending on Gestational Age

Objective

To evaluate the impact of low birth weight as a risk factor for retinopathy of prematurity (ROP) that will require treatment in correlation with gestational age at birth (GA).

Study design

In total, 2941 infants born <32 weeks GA were eligible from five cohorts of preterm infants previously collected for analysis in WINROP (Weight IGF-I Neonatal ROP) from the following locations: Sweden (EXPRESS) (n = 426), North America (n = 1772), Boston (n = 338), Lund (n = 52), and Gothenburg (n = 353). Data regarding GA at birth, birth weight (BW), gender, and need for ROP treatment were retrieved. Birth weight standard deviation scores (BWSDS) were calculated with Swedish as well as Canadian reference models. Small for gestational age (SGA) was defined as BWSDS less than −2.0 SDS using the Swedish reference and as BW below the 10^th percentile using the Canadian reference charts.

Results

Univariate analysis showed that low GA (p<0.001), low BW (p<0.001), male gender (p<0.05), low BWSDS_Canada (p<0.001), and SGA_Canada (p<0.01) were risk factors for ROP that will require treatment. In multivariable logistic regression analysis, low GA (p<0.0001), male gender (p<0.01 and p<0.05), and an interaction term of BWSDS*GA group (p<0.001), regardless of reference chart, were risk factors. Low BWSDS was less important as a risk factor in infants born at GA <26 weeks compared with infants born at GA ≥26 weeks calculated with both reference charts (BWSDS_Sweden, OR = 0.80 vs 0.56; and BWSDS_Canada, OR = 0.72 vs 0.41).

Conclusions

Low BWSDS as a risk factor for vision-threatening ROP is dependent on the infant''s degree of immaturity. In more mature infants (GA ≥26 weeks), low BWSDS becomes a major risk factor for developing ROP that will require treatment. These results persist even when calculating BW deficit with different well-established approaches.     

Associations of parental depression during adolescence with cognitive development in later life in China: A population-based cohort study

BackgroundPrior research has underscored negative impacts of perinatal parental depression on offspring cognitive performance in early childhood. However, little is known about the effects of parental depression during adolescence on offspring cognitive development.Methods and findingsThis study used longitudinal data from the nationally representative China Family Panel Studies (CFPS). The sample included 2,281 adolescents aged 10–15 years (the median age was 13 years with an interquartile range between 11 and 14 years) in 2012 when their parents were surveyed for depression symptoms with the 20-item Center for Epidemiologic Studies Depression Scale (CES-D). The sample was approximately balanced by sex, with 1,088 females (47.7%). We examined the associations of parental depression in 2012 with offspring cognitive performance (measured by mathematics, vocabulary, immediate word recall, delayed word recall, and number series tests) in subsequent years (i.e., 2014, 2016, and 2018) using linear regression models, adjusting for various offspring (i.e., age, sex, and birth order), parent (i.e., parents’ education level, age, whether living with the offspring, and employment status), and household characteristics (i.e., place of residence, household income, and the number of offspring). We found parental depression during adolescence to be significantly associated with worse cognitive performance in subsequent years, in both crude and adjusted models. For example, in the crude models, adolescents whose mothers had depression symptoms in 2012 scored 1.0 point lower (95% confidence interval [CI]: −1.2 to −0.8, p < 0.001) in mathematics in 2014 compared to those whose mothers did not have depression symptoms; after covariate adjustment, this difference marginally reduced to 0.8 points (95% CI: −1.0 to −0.5, p < 0.001); the associations remained robust after further adjusting for offspring earlier cognitive ability in toddlerhood (−1.2, 95% CI: −1.6, −0.9, p < 0.001), offspring cognitive ability in 2012 (−0.6, 95% CI: −0.8, −0.3, p < 0.001), offspring depression status (−0.7, 95% CI: −1.0, −0.5, p < 0.001), and parents’ cognitive ability (−0.8, 95% CI: −1.2, −0.3, p < 0.001). In line with the neuroplasticity theory, we observed stronger associations between maternal depression and mathematical/vocabulary scores among the younger adolescents (i.e., 10–11 years) than the older ones (i.e., 12–15 years). For example, the association between maternal depression and 2014 vocabulary scores was estimated to be −2.1 (95% CI: −2.6, −1.6, p < 0.001) in those aged 10–11 years, compared to −1.2 (95% CI: −1.6, −0.8, p < 0.001) in those aged 12–15 years with a difference of 0.9 (95% CI: 0.2, 1.6, p = 0.010). We also observed a stronger association of greater depression severity with worse mathematical scores. The primary limitations of this study were the relatively high attrition rate and residual confounding.ConclusionsIn this study, we observed that parental depression during adolescence was associated with adverse offspring cognitive development assessed up to 6 years later. These findings highlight the intergenerational association between depression in parents and cognitive development across the early life course into adolescence.

In this cohort study, Zhihui Li and colleagues explore associations between parental depression and offspring cognitive development up to six years later.     

Riboflavin status modifies the effects of methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) polymorphisms on homocysteine

Methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR), riboflavin-dependent enzymes, participate in homocysteine metabolism. Reported effects of riboflavin status on the association between the MTHFR 677C>T polymorphism and homocysteine vary, and the effects of the MTRR 66A>G or MTRR 524C>T polymorphisms on homocysteine are unclear. We tested the hypothesis that the effects of the MTHFR 677C>T, MTRR 66A>G and MTRR 524C>T polymorphisms on fasting plasma total homocysteine (tHcy) depend on riboflavin status (erythrocyte glutathionine reductase activation coefficient, optimum: <1.2; marginally deficient: 1.2–1.4; deficient: ≥1.4) in 771 adults aged 18–75 years. MTHFR 677T allele carriers with middle or low tertile plasma folate (<14.7 nmol/L) had 8.2 % higher tHcy compared to the 677CC genotype (p < 0.01). This effect was eliminated when riboflavin status was optimal (p for interaction: 0.048). In the lowest cobalamin quartile (≤273 pmol/L), riboflavin status modifies the relationship between the MTRR 66 A>G polymorphism and tHcy (p for interaction: 0.034). tHcy was 6.6 % higher in MTRR 66G allele carriers compared to the 66AA genotype with marginally deficient or optimal riboflavin status, but there was no difference when riboflavin status was deficient (p for interaction: 0.059). tHcy was 13.7 % higher in MTRR 524T allele carriers compared to the 524CC genotype when cobalamin status was low (p < 0.01), but no difference was observed when we stratified by riboflavin status. The effect of the MTHFR 677C>T polymorphism on tHcy depends on riboflavin status, that of the MTRR 66A>G polymorphism on cobalamin and riboflavin status and that of the MTRR 524C>T polymorphism on cobalamin status.     

Genome-Wide Regression and Prediction with the BGLR Statistical Package

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.