A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

Protocol for the Australian Type 1 Diabetes National Screening Pilot: Assessing the feasibility and acceptability of three general population screening models in children.

AIM: One third of Australian children diagnosed with type 1 diabetes present with life-threatening diabetic ketoacidosis (DKA) at diagnosis. Screening for early-stage, presymptomatic type 1 diabetes, with ongoing follow-up, can substantially reduce this risk (<5% risk). Several screening models are being trialled internationally, without consensus on the optimal approach. This pilot study aims to assess three models for a routine, population-wide screening programme in Australia. METHODS: An implementation science-guided pilot study to evaluate the feasibility, acceptability and costs of three screening models in children will be conducted between July 2022 and June 2024. These models are as follows: (1) Genetic risk-stratified screening using newborn heel prick dried bloodspots, followed by autoantibody testing from 11 months of age; (2) genetic risk-stratified screening of infant (6-12 months) saliva followed by autoantibody testing from 10 months of age; and (3) autoantibody screening using capillary dried bloodspots collected from children aged 2, 6 or 10 years. Cohorts for each model will be recruited from targeted geographic areas across Australia involving ≥2 states per cohort, with a recruitment target of up to 3000 children per cohort (total up to 9000 children). The primary outcome is screening uptake for each cohort. Secondary outcomes include programme feasibility, costs, parental anxiety, risk perception, satisfaction, well-being and quality of life, and health professional attitudes and satisfaction. CONCLUSIONS: This pilot is the first direct comparison of three screening implementation models for general population screening. Findings will provide evidence to inform a potential national screening programme for Australian children. TRIAL REGISTRATION: ACTRN12622000381785.

SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

Addressing methodological issues in a study of impulsivity and vulnerability for transition to alcohol use disorder.

BACKGROUND: Heightened behavioral impulsivity has been advocated as a preexisting risk factor for the development of alcohol use disorder (AUD). Nonetheless, studies investigating impulsivity in adolescent/young adult at-risk drinkers-who are at increased risk of developing AUD-report mixed findings. This may be due to methodological limitations related to definitions of at-risk drinking, the retrospective assessment of alcohol intake, and/or the relatively modest sample size of some studies. METHODS: Healthy individuals (N = 814, Mage  = 22.50) completed online surveys and a measure of choice impulsivity. Of these, a number of participants also undertook an online measure of response inhibition (n = 627, Mage  = 22.66), and a further subgroup submitted real-time alcohol consumption information for a period of 21 days using an app (n = 543, Mage  = 22.96). Differences in behavioral impulsivity were assessed as a function of various at-risk alcohol intake categories. Hierarchical multiple regression was employed to determine whether impulsivity predicted alcohol use in the form of a continuous index comprising variables related to intake and consequences of use. RESULTS: Significantly greater impulsivity was not evident in heavy, standard binge, high binge, harmful, or hazardous alcohol drinkers as compared to controls, regardless of the criteria employed to categorize these at-risk drinkers. Neither choice impulsivity nor reduced response inhibition significantly predicted the alcohol use index. CONCLUSIONS: While results could be attributed to the online nature of this research, it is possible that more sensitive measures of behavioral impulsivity are required when assessing nondependent drinkers.

Changes in pancreatic exocrine function in young at-risk children followed to islet autoimmunity and type 1 diabetes in the ENDIA study.

BACKGROUNDS: We aimed to monitor pancreatic exocrine function longitudinally in relation to the development of islet autoimmunity (IA) and type 1 diabetes (T1D) in at-risk children with a first-degree relative with T1D, who were followed prospectively in the Environmental Determinants of Islet Autoimmunity (ENDIA) study. METHODS: Fecal elastase-1 (FE-1) concentration was measured longitudinally in 85 ENDIA children from median age 1.0 (IQR 0.7,1.3) year. Twenty-eight of 85 children (progressors) developed persistent islet autoantibodies at median age of 1.5 (IQR 1.1,2.5) years, of whom 11 went on to develop clinical diabetes. The other 57 islet autoantibody-negative children (non-progressors) followed similarly were age and gender-matched with the progressors. An adjusted linear mixed model compared FE-1 concentrations in progressors and non-progressors. RESULTS: Baseline FE-1 did not differ between progressors and non-progressors, or by HLA DR type or proband status. FE-1 decreased over time in progressors in comparison to non-progressors (Wald statistic 5.46, P = .02); in some progressors the fall in FE-1 preceded the onset of IA. CONCLUSIONS: Pancreatic exocrine function decreases in the majority of young at-risk children who progress to IA and T1D.

Higher frequency of vertebrate-infecting viruses in the gut of infants born to mothers with type 1 diabetes.

BACKGROUND: Microbial exposures in utero and early life shape the infant microbiome, which can profoundly impact on health. Compared to the bacterial microbiome, very little is known about the virome. We set out to characterize longitudinal changes in the gut virome of healthy infants born to mothers with or without type 1 diabetes using comprehensive virome capture sequencing. METHODS: Healthy infants were selected from Environmental Determinants of Islet Autoimmunity (ENDIA), a prospective cohort of Australian children with a first-degree relative with type 1 diabetes, followed from pregnancy. Fecal specimens were collected three-monthly in the first year of life. RESULTS: Among 25 infants (44% born to mothers with type 1 diabetes) at least one virus was detected in 65% (65/100) of samples and 96% (24/25) of infants during the first year of life. In total, 26 genera of viruses were identified and >150 viruses were differentially abundant between the gut of infants with a mother with type 1 diabetes vs without. Positivity for any virus was associated with maternal type 1 diabetes and older infant age. Enterovirus was associated with older infant age and maternal smoking. CONCLUSIONS: We demonstrate a distinct gut virome profile in infants of mothers with type 1 diabetes, which may influence health outcomes later in life. Higher prevalence and greater number of viruses observed compared to previous studies suggests significant underrepresentation in existing virome datasets, arising most likely from less sensitive techniques used in data acquisition.

Predictors of Adverse Alcohol Use Consequences Among Tertiary Students.

BACKGROUND: The alcohol consumption patterns of young adults are of concern. Critically, tertiary students consume greater quantities of alcohol, are at increased risk of injury/harm, and have higher rates of alcohol use disorders as compared to their nonuniversity enrolled peers. The Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ) is one of several tools utilized to explore adverse alcohol-related outcomes among tertiary students. Alcohol intake behavior, assessed via retrospective summary measures, has been linked to BYAACQ score. It is unclear, however, how drinking assessed in real time, in conjunction with variables such as age of drinking onset, might predict severity of adverse alcohol consequences as captured by the BYAACQ. METHODS: The psychometric properties of the BYAACQ were explored using a large Australian sample of tertiary students (N = 893). A subsample (n = 504) provided alcohol intake information in real time (21 days; event and notification contingent) via a smartphone app (CNLab-A) plus details related to age of drinking onset, drug use, parental alcohol/drug use, and anxiety/depression symptomology. RESULTS: Average BYAACQ score was 7.53 (SD = 5.37). Classical and item response theory analyses revealed inconsistencies related to dimensionality, progressive item severity, and male/female differential item functioning. Current drinking-namely, frequency of intake and quantity per drinking occasion-plus age of drinking onset predicted BYAACQ score after controlling for age, other drug use, and depression symptomology. CONCLUSIONS: The BYAACQ is a sound tool for use with Australian samples. Information related to current drinking, age of drinking onset, and drug use is useful for predicting severity of alcohol use consequences. These markers might enable tertiary institutions to better target students who could benefit from prevention/intervention programs.

Investigating reproducibility and tracking provenance - A genomic workflow case study.

BACKGROUND: Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows. RESULTS: We have implemented a complex but widely deployed bioinformatics workflow using three representative approaches to workflow definition and execution. Through implementation, we identified assumptions implicit in these approaches that ultimately produce insufficient documentation of workflow requirements resulting in failed execution of the workflow. This study proposes a set of recommendations that aims to mitigate these assumptions and guides the scientific community to accomplish reproducible science, hence addressing reproducibility crisis. CONCLUSIONS: Reproducing, adapting or even repeating a bioinformatics workflow in any environment requires substantial technical knowledge of the workflow execution environment, resolving analysis assumptions and rigorous compliance with reproducibility requirements. Towards these goals, we propose conclusive recommendations that along with an explicit declaration of workflow specification would result in enhanced reproducibility of computational genomic analyses.

The Australasian Diabetes Data Network: first national audit of children and adolescents with type 1 diabetes.

OBJECTIVES: To assess glycaemic control, anthropometry and insulin regimens in a national sample of Australian children and adolescents with type 1 diabetes. DESIGN: Cross-sectional analysis of de-identified, prospectively collected data from the Australasian Diabetes Data Network (ADDN) registry. SETTING: Five paediatric diabetes centres in New South Wales, Queensland, South Australia, Victoria and Western Australia. PARTICIPANTS: Children and adolescents (aged 18 years or under) with type 1 diabetes of at least 12 months' duration for whom data were added to the ADDN registry during 2015. MAIN OUTCOME MEASURES: Glycaemic control was assessed by measuring haemoglobin A1c (HbA1c) levels. Body mass index standard deviation scores (BMI-SDS) were calculated according to the CDC-2000 reference; overweight and obesity were defined by International Obesity Task Force guidelines. Insulin regimens were classified as twice-daily injections (BD), multiple daily injections (MDI; at least three injection times per day), or continuous subcutaneous insulin infusion (CSII). RESULTS: The mean age of the 3279 participants was 12.8 years (SD, 3.7), mean diabetes duration was 5.7 years (SD, 3.7), and mean HbA1c level 67 mmol/mol (SD, 15); only 27% achieved the national HbA1c target of less than 58 mmol/mol. The mean HbA1c level was lower in children under 6 (63 mmol/mol) than in adolescents (14-18 years; 69 mmol/mol). Mean BMI-SDS for all participants was 0.6 (SD, 0.9); 33% of the participants were overweight or obese. 44% were treated with CSII, 38% with MDI, 18% with BD. CONCLUSIONS: Most Australian children and adolescents with type 1 diabetes are not meeting the recognised HbA1c target. The prevalence of overweight and obesity is high. There is an urgent need to identify barriers to achieving optimal glycaemic control in this population.

Environmental Determinants of Islet Autoimmunity (ENDIA) longitudinal prospective pregnancy to childhood cohort study of Australian children at risk of type 1 diabetes: parental demographics and birth information.

INTRODUCTION: The Environmental Determinants of Islet Autoimmunity (ENDIA) Study is an ongoing Australian prospective cohort study investigating how modifiable prenatal and early-life exposures drive the development of islet autoimmunity and type 1 diabetes (T1D) in children. In this profile, we describe the cohort's parental demographics, maternal and neonatal outcomes and human leukocyte antigen (HLA) genotypes. RESEARCH DESIGN AND METHODS: Inclusion criteria were an unborn child, or infant aged less than 6 months, with a first-degree relative (FDR) with T1D. The primary outcome was persistent islet autoimmunity, with children followed until a T1D diagnosis or 10 years of age. Demographic data were collected at enrollment. Lifestyle, clinical and anthropometric data were collected at each visit during pregnancy and clinical pregnancy and birth data were verified against medical case notes. Data were compared between mothers with and without T1D. HLA genotyping was performed on the ENDIA child and all available FDRs. RESULTS: The final cohort comprised 1473 infants born to 1214 gestational mothers across 1453 pregnancies, with 80% enrolled during pregnancy. The distribution of familial T1D probands was 62% maternal, 28% paternal and 11% sibling. The frequency of high-risk HLA genotypes was highest in T1D probands, followed by ENDIA infants, and lowest among unaffected family members. Mothers with T1D had higher rates of pregnancy complications and perinatal intervention, and larger babies of shorter gestation. Parent demographics were comparable to the Australian population for age, parity and obesity. A greater percentage of ENDIA parents were Australian born, lived in a major city and had higher socioeconomic advantage and education. CONCLUSIONS: This comprehensive profile provides the context for understanding ENDIA's scope, methodology, unique strengths and limitations. Now fully recruited, ENDIA will provide unique insights into the roles of early-life factors in the development of islet autoimmunity and T1D in the Australian environment. TRIAL REGISTRATION NUMBER: ACTRN12613000794707.

A GDPR-Compliant Dynamic Consent Mobile Application for the Australasian Type-1 Diabetes Data Network.

Australia has a high prevalence of diabetes, with approximately 1.2 million Australians diagnosed with the disease. In 2012, the Australasian Diabetes Data Network (ADDN) was established with funding from the Juvenile Diabetes Research Foundation (JDRF). ADDN is a national diabetes registry which captures longitudinal information about patients with type-1 diabetes (T1D). Currently, the ADDN data are directly contributed from 42 paediatric and 17 adult diabetes centres across Australia and New Zealand, i.e., where the data are pre-existing in hospital systems and not manually entered into ADDN. The historical data in ADDN have been de-identified, and patients are initially afforded the opportunity to opt-out of being involved in the registry; however, moving forward, there is an increased demand from the clinical research community to utilise fully identifying data. This raises additional demands on the registry in terms of security, privacy, and the nature of patient consent. General Data Protection Regulation (GDPR) is an increasingly important mechanism allowing individuals to have the right to know about their health data and what those data are being used for. This paper presents a mobile application being designed to support the ADDN data collection and usage processes and aligning them with GDPR. The app utilises Dynamic Consent-an informed specific consent model, which allows participants to view and modify their research-driven consent decisions through an interactive interface. It focuses specifically on supporting dynamic opt-in consent to both the registry and to associated sub-projects requesting access to and use of the patient data for research purposes.

A surge in serum mucosal cytokines associated with seroconversion in children at risk for type 1 diabetes.

AIMS/INTRODUCTION: Autoantibodies to pancreatic islet antigens identify young children at high risk of type 1 diabetes. On a background of genetic susceptibility, islet autoimmunity is thought to be driven by environmental factors, of which enteric viruses are prime candidates. We sought evidence for enteric pathology in children genetically at-risk for type 1 diabetes followed from birth who had developed islet autoantibodies ("seroconverted"), by measuring mucosa-associated cytokines in their sera. MATERIALS AND METHODS: Sera were collected 3 monthly from birth from children with a first-degree type 1 diabetes relative, in the Environmental Determinants of Islet Autoimmunity (ENDIA) study. Children who seroconverted were matched for sex, age, and sample availability with seronegative children. Luminex xMap technology was used to measure serum cytokines. RESULTS: Of eight children who seroconverted, for whom serum samples were available at least 6 months before and after seroconversion, the serum concentrations of mucosa-associated cytokines IL-21, IL-22, IL-25, and IL-10, the Th17-related cytokines IL-17F and IL-23, as well as IL-33, IFN-γ, and IL-4, peaked from a low baseline in seven around the time of seroconversion and in one preceding seroconversion. These changes were not detected in eight sex- and age-matched seronegative controls, or in a separate cohort of 11 unmatched seronegative children. CONCLUSIONS: In a cohort of children at risk for type 1 diabetes followed from birth, a transient, systemic increase in mucosa-associated cytokines around the time of seroconversion lends support to the view that mucosal infection, e.g., by an enteric virus, may drive the development of islet autoimmunity.

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort.

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D.Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case's onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood.Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation.KEY MESSAGESThe ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.

Evaluating untimed and timed abridged versions of Raven's Advanced Progressive Matrices.

INTRODUCTION: Raven's Advanced Progressive Matrices (APM) are frequently utilized in clinical and experimental settings to index intellectual capacity. As the APM is a relatively long assessment, abridged versions of the test have been proposed. The psychometric properties of an untimed 12-item APM have received some consideration in the literature, but validity explorations have been limited. Moreover, both reliability and validity of a timed 12-item APM have not previously been examined. METHOD: We considered the psychometric properties of untimed (Study 1; N = 608; Mage = 27.89, SD = 11.68) and timed (Study 2; N = 479; Mage = 20.93, SD = 3.12) versions of a brief online 12-item form of the APM. RESULTS: Confirmatory factor analyses established both versions of the tests are unidimensional. Item response theory analyses revealed that, in each case, the 12 items are characterized by distinct differences in difficulty, discrimination, and guessing. Differential item functioning showed few male/female or native English/non-native English performance differences. Test-retest reliability was .65 (Study 1) to .69 (Study 2). Both tests had medium-to-large correlations with the Wechsler Abbreviated Scale of Intelligence (2nd ed.) Perceptual Reasoning Index (r = .50, Study 1; r = .56, Study 2) and Full-Scale IQ (r = .34, Study 1; r = .41, Study 2). CONCLUSION: In sum, results suggest both untimed and timed online versions of the brief APM are psychometrically sound. As test duration was found to be highly variable for the untimed version, the timed form might be a more suitable choice when it is likely to form part of a longer battery of tests. Nonetheless, classical test and item response theory analyses, plus validity considerations, suggest the untimed version might be the superior abridged form.

Mental Health During Late Pregnancy and Postpartum in Mothers With and Without Type 1 Diabetes: The ENDIA Study.

OBJECTIVE: Pregnancy and type 1 diabetes are each associated with increased anxiety and depression, but the combined impact on well-being is unresolved. We compared the mental health of women with and without type 1 diabetes during pregnancy and postpartum and examined the relationship between mental health and glycemic control. RESEARCH DESIGN AND METHODS: Participants were women enrolled from 2016 to 2020 in the Environmental Determinants of Islet Autoimmunity (ENDIA) study, a pregnancy to birth prospective cohort following children with a first-degree relative with type 1 diabetes. Edinburgh Postnatal Depression Scale (EPDS) and Perceived Stress Scale (PSS) were completed during the third trimester (T3) (median [interquartile range] 34 [32, 36] weeks) and postpartum (14 [13, 16] weeks) by 737 women (800 pregnancies) with (n = 518) and without (n = 282) type 1 diabetes. RESULTS: EPDS and PSS scores did not differ between women with and without type 1 diabetes during T3 and postpartum. EPDS scores were marginally higher in T3: predicted mean (95% CI) 5.7 (5.4, 6.1) than postpartum: 5.3 (5.0, 5.6), independent of type 1 diabetes status (P = 0.01). HbA1c levels in type 1 diabetes were 6.3% [5.8, 6.9%] in T3 and did not correlate with EPDS or PSS scores. Reported use of psychotropic medications was similar in women with (n = 44 of 518 [8%]) and without type 1 diabetes (n = 17 of 282 [6%]), as was their amount of physical activity. CONCLUSIONS: Overall, mental health in late pregnancy and postpartum did not differ between women with and without type 1 diabetes, and mental health scores were not correlated with glycemic control.

Women with type 1 diabetes exhibit a progressive increase in gut Saccharomyces cerevisiae in pregnancy associated with evidence of gut inflammation.

AIMS: Studies of the gut microbiome have focused on its bacterial composition. We aimed to characterize the gut fungal microbiome (mycobiome) across pregnancy in women with and without type 1 diabetes. METHODS: Faecal samples (n = 162) were collected from 70 pregnant women (45 with and 25 without type 1 diabetes) across all trimesters. Fungi were analysed by internal transcribed spacer 1 amplicon sequencing. Markers of intestinal inflammation (faecal calprotectin) and intestinal epithelial integrity (serum intestinal fatty acid binding protein; I-FABP), and serum antibodies to Saccharomyces cerevisiae (ASCA) were measured. RESULTS: Women with type 1 diabetes had decreased fungal alpha diversity by the third trimester, associated with an increased abundance of Saccharomyces cerevisiae that was inversely related to the abundance of the anti-inflammatory butyrate-producing bacterium Faecalibacterium prausnitzii. Women with type 1 diabetes had higher concentrations of calprotectin, I-FABP and ASCA. CONCLUSIONS: Women with type 1 diabetes exhibit a shift in the gut mycobiome across pregnancy associated with evidence of gut inflammation and impaired intestinal barrier function. The relevance of these findings to the higher rate of pregnancy complications in type 1 diabetes warrants further study.

Type 1 diabetes in pregnancy is associated with distinct changes in the composition and function of the gut microbiome.

BACKGROUND: The gut microbiome changes in response to a range of environmental conditions, life events and disease states. Pregnancy is a natural life event that involves major physiological adaptation yet studies of the microbiome in pregnancy are limited and their findings inconsistent. Pregnancy with type 1 diabetes (T1D) is associated with increased maternal and fetal risks but the gut microbiome in this context has not been characterized. By whole metagenome sequencing (WMS), we defined the taxonomic composition and function of the gut bacterial microbiome across 70 pregnancies, 36 in women with T1D. RESULTS: Women with and without T1D exhibited compositional and functional changes in the gut microbiome across pregnancy. Profiles in women with T1D were distinct, with an increase in bacteria that produce lipopolysaccharides and a decrease in those that produce short-chain fatty acids, especially in the third trimester. In addition, women with T1D had elevated concentrations of fecal calprotectin, a marker of intestinal inflammation, and serum intestinal fatty acid-binding protein (I-FABP), a marker of intestinal epithelial damage. CONCLUSIONS: Women with T1D exhibit a shift towards a more pro-inflammatory gut microbiome during pregnancy, associated with evidence of intestinal inflammation. These changes could contribute to the increased risk of pregnancy complications in women with T1D and are potentially modifiable by dietary means. Video abstract.

Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli.

The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.