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Cancer prevention; Intestinal microbiology; Pancreatic tumours Prevenció del càncer; Microbiologia intestinal; Tumors pancreàtics Prevención de cáncer; Microbiología intestinal; Tumores pancreáticos Background Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. Objective To explore the faecal and salivary microbiota as potential diagnostic biomarkers. Methods We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase. Results Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. Conclusion Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible. We acknowledge funding from EMBL, CNIO, World Cancer Research (#15–0391), the European Research Council (ERC-AdG-669830 MicrobioS), the BMBF-funded Heidelberg CenterCentre for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (de.NBI #031A537B), Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III-FEDER, Spain (grant numbers PI15/01573, PI18/01347, FIS PI17/02303); Red Temática de Investigación Cooperativa en Cáncer, Spain (grant numbers RD12/0036/0034, RD12/0036/0050, RD12/0036/0073); III beca Carmen Delgado/Miguel Pérez-Mateo de AESPANC-ACANPAN; EU-6FP Integrated Project (#018771-MOLDIAG-PACA); EU-FP7-HEALTH (#259737-CANCERALIA). Funders had no involvement in the study design, patient enrolment, analysis, manuscript writing or reviewing.

Nature 528, 262–266 (2015); doi:10.1038/nature15766 In the Supplementary Information to this Letter, data from two previous studies were used in the meta-analysis. However, the unit conversions used to make the data comparable were inconsistent for two of the included phenotype measures. Although this error does not affect the data used to generate the conclusions of the Letter, it might affect follow-up studies using the glycated haemoglobin (HbA1c) and serum insulin phenotypes.

Characteristics of the patients. (DOCX 28 kb)

A colormap of genotypes distribution among optimally and non-optimally responding patients to first-line imatinib treatment at 12 months. Each square illustrates each genotyped SNP for each patient. Red squares = minor allele homozygotes; pink squares = heterozygotes; white squares = major allele homozygotes; gray square = not analyzed. Figure S2. Genotype frequencies of the rs460089 and rs460271 in patients with optimal and non-optimal response to imatinib at 12 months. 1 – Initial cohort of 83 patients; 2 – An independent group of added patients. Note – the graphs illustrate frequencies of genotypes of rs460089, which exactly reflect genotypes frequencies of rs460271. Figure S3. Genotype frequencies of a. rs13180043 (SLC22A5) and b. rs1050152 (SLC22A4, exon 9) in patients with optimal and non-optimal response to imatinib at 12 months. Note – the graph a. illustrates frequencies of genotypes of rs13180043, which exactly reflect genotypes frequencies of rs4646298, rs13180169, rs1310186, and rs13180295. Figure S4. Relative mRNA levels of SLC22A4 and SLC22A5 in tested cell lines. a. Graph shows expression in all eight cell lines. b. Graph shows expression of cell lines carrying rs460089-GG_rs2631365-TC or rs460089-GC_rs2631365-TC genotypes. (DOCX 687 kb)

Noninvasive RhD genotyping in eastern Croatia – first steps

Entomological evidence is most commonly used to estimate the postmortem interval, where it is crucial to identify species of insect feeding on the corpse. Analysis of human DNA isolated from insects enables identification of human remains: in cases of identification of corpse displaced from the crime scene, in cases where larvae had alternative food source, or in cases of questionable reliability of collected evidence. The aim of this study was to recover suitable human DNA from the gut of blowfly larvae (Calliphoridae) at different larval stage. Different decontamination, preservation and extraction methods were compared. Obtained DNA profiles were matched to cadaver reference sample, using commercial forensic kits (AmpFlSTR® Identifiler™ and AmpFlSTR® MiniFiler™). The results show that it is possible to obtain human STR profile sufficient for human identification from fly maggot gut at different larval stage, while several technical precautions should be considered.

Aiming to estimate frequency of various types of errors that may have occurred during the process of identification of victims of war in Croatia we compared genotypes of 911 parent-child pairs in the database of nearly 3500 relatives of missing people. By repeated analysis of mismatched genotypes we identified seven errors in the database. Out of these seven errors, falsely genotypes alleles were caused by human error, while the remaining five errors were caused by allelic dropout or electrophoretic aberrations. An important observation is that all three observed allelic dropouts occurred at D2S1338 locus which is the largest fragment amplified by the AmpFlSTR Identifiler kit. Larger alleles at this locus frequently amplify with lower efficiency, sometimes not reaching the detection level indicating that additional proofreading procedures should be performed when homozygous D2S1338 locus is being reported.

An out of locus allele was observed while analyzing the sample data generated using the AmpFlSTR® Identifiler™ PCR amplification kit. Since both loci adjacent to the observed allele, D16S539 and D2S1338, showed a single-peak homozygous pattern, it was neccessary to confirm the origin of the allele by direct sequencing.

Objective Changes in the gut microbiota are increasingly recognised to be involved in many diseases. This ecosystem is known to be shaped by many factors, including climate, geography, host nutrition, lifestyle and medication. Thus, knowledge of varying populations with different habits is important for a better understanding of the microbiome. Design We therefore conducted a metagenomic analysis of intestinal microbiota from Kazakh donors, recruiting 84 subjects, including male and female healthy subjects and metabolic syndrome (MetS) patients aged 25–75 years, from the Kazakh administrative centre, Astana. We characterise and describe these microbiomes, the first deep-sequencing cohort from Central Asia, in comparison with a global dataset (832 individuals from five countries on three continents), and explore correlations between microbiota, clinical and laboratory parameters as well as with nutritional data from Food Frequency Questionnaires. Results We observe that Kazakh microbiomes are relatively different from both European and East Asian counterparts, though similar to other Central Asian microbiomes, with the most striking difference being significantly more samples falling within the Prevotella-rich enterotype, potentially reflecting regional diet and lifestyle. We show that this enterotype designation remains stable within an individual over time in 82% of cases. We further observe gut microbiome features that distinguish MetS patients from controls (eg, significantly reduced Firmicutes to Bacteroidetes ratio, Bifidobacteria and Subdoligranulum, alongside increased Prevotella), though these overlap little with previously published reports and thus may reflect idiosyncrasies of the present cohort. Conclusion Taken together, this exploratory study describes gut microbiome data from an understudied population, providing a starting point for further comparative work on biogeography and research on widespread diseases., BMJ Open, 8 (7), ISSN:2044-6055