Lactic acid bacteria adjunct cultures exert a mitigation effect against spoilage microbiota in fresh cheese

Lactic acid bacteria (LAB) have a strong mitigation potential as adjunct cultures to inhibit undesirable bacteria in fermented foods. In fresh cheese with low salt concentration, spoilage and pathogenic bacteria can affect the shelf life with smear on the surface and packaging blowing. In this work, we studied the spoilage microbiota of an Italian fresh cheese to find tailor-made protective cultures for its shelf life improvement. On 14-tested LAB, three of them, namely Lacticaseibacillus rhamnosus LRH05, Latilactobacillus sakei LSK04, and Carnobacterium maltaromaticum CNB06 were the most effective in inhibiting Gram-negative bacteria. These cultures were assessed by the cultivation-dependent and DNA metabarcoding approach using in vitro experiments and industrial trials. Soft cheese with and without adjunct cultures were prepared and stored at 8 and 14 \ub0C until the end of the shelf life in modified atmosphere packaging. Data demonstrated that the use of adjunct cultures reduce and/or modulate the growth of spoilage microbiota at both temperatures. Particularly, during industrial experiments, C. maltaromaticum CNB06 and Lcb. rhamnosus RH05 lowered psychrotrophic bacteria of almost 3 Log CFU/g in a 5-week stored cheese. On the contrary, Llb. sakei LSK04 was able to colonize the cheese but it was not a good candidate for its inhibition capacity. The combined approach applied in this work allowed to evaluate the protective potential of LAB strains against Gram-negative communities

Performance and precision of double digestion RAD (ddRAD) genotyping in large multiplexed datasets of marine fish species

The development of Genotyping-By-Sequencing (GBS) technologies enables cost-effective analysis of large numbers of Single Nucleotide Polymorphisms (SNPs), especially in “non-model” species. Nevertheless, as such technologies enter a mature phase, biases and errors inherent to GBS are becoming evident. Here, we evaluated the performance of double digest Restriction enzyme Associated DNA (ddRAD) sequencing in SNP genotyping studies including high number of samples. Datasets of sequence data were generated from three marine teleost species (>5500 samples, >2.5 × 1012 bases in total), using a standardized protocol. A common bioinformatics pipeline based on STACKS was established, with and without the use of a reference genome. We performed analyses throughout the production and analysis of ddRAD data in order to explore (i) the loss of information due to heterogeneous raw read number across samples; (ii) the discrepancy between expected and observed tag length and coverage; (iii) the performances of reference based vs. de novo approaches; (iv) the sources of potential genotyping errors of the library preparation/bioinformatics protocol, by comparing technical replicates. Our results showed use of a reference genome and a posteriori genotype correction improved genotyping precision. Individual read coverage was a key variable for reproducibility; variance in sequencing depth between loci in the same individual was also identified as an important factor and found to correlate to tag length. A comparison of downstream analysis carried out with ddRAD vs single SNP allele specific assay genotypes provided information about the levels of genotyping imprecision that can have a significant impact on allele frequency estimations and population assignment. The results and insights presented here will help to select and improve approaches to the analysis of large datasets based on RAD-like methodologies

A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach

Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups’ separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon index: p-values: active UC vs HCs = 0.0005; active UC vs inactive UC = 0.0273; HCs vs inactive UC = 0.0260). In particular, patients with active UC showed the lowest values, followed by patients with inactive UC, and HCs. At species level, we found high levels of Bifidobacterium adolescentis and Haemophilus parainfluenzae in inactive UC and active UC, respectively. A specific microbiota profile was found for each group and was confirmed with sparse partial least squares discriminant analysis, a machine learning-supervised approach. The latter allowed us to observe a perfect class prediction and group separation using the complete information (full Operational Taxonomic Unit table), with a minimal loss in performance when using only 5% of features. A machine learning approach to 16S rRNA data identifies a bacterial signature characterizing different degrees of disease activity in UC. Follow-up studies will clarify whether such microbiota profiling are useful for diagnosis and management

Immediate early genes induced by H-Ras in thyroid cells

Expression of oncogenic v-H-Ras in the thyroid cell line FRTL-5 (FRTL-5(Ras)) results in uncontrolled proliferation, loss of thyroid-specific gene expression and tumorigenicity. Concomitant expression of constitutively activated MEK and Rac, two major H-Ras downstream effectors, in FRTL-5 (FRTL-5(MEK/Rac)) recapitulates H-Ras effects on proliferation and morphology. In contrast to FRTL-5(Ras), however, FRTL-5(MEK/Rac) cells remain differentiated and are not tumorigenic. To find H-Ras induced genes potentially responsible for tumorigenicity and loss of differentiation, we have used subtractive suppression hybridization (SSH), a PCR-based cDNA subtraction technique, between de-differentiated and tumorigenic FRTL-5(Ras) cells and differentiated and non-tumorigenic FRTL-5(MEK/Rac) cells. We examined 800 of the cDNA clones obtained after subtraction and verified their levels of expression in the two cell lines by reverse northern, identifying 337 H-Ras induced genes. By sequence analysis, we clustered 57 different genes. Among these, 39 were known genes (involved in diverse signal transduction processes regulating mitogenic activity, cell survival, cytoskeletal reorganization, stress response and invasion) while the remaining 18 clones were novel genes. Among the 57 H-Ras specific clones, we identified those genes whose expression is induced early by H-Ras. We suggest that these immediate-early genes may play a crucial role in H-Ras-mediated transformation in thyroid epithelial cells

Following food microbiome development during shelf-life in the Nextgen era.

Food microbial populations are complex and dynamic: they develop and change their composition all throughout shelf life, establishing tangled interaction that traditional culture-dependent methods cannot completely point out. Thus, in this field, Next generation sequencing has become more interesting since the study of microbial communities has reached a depth of analysis never seen before: species richness and evenness, the detection of rare bacterial populations, the analysis of the genetical responses to food environments are some information provided by Nextgen techniques. In this work an industrial ricotta cheese microbial community was monitored starting from raw materials to product expiry date: Illumina MiSeq 2x300bp 16S amplicon analysis was exploited with a simple protocol for library production, normalization and pooling, in parallel with culture-dependent analysis. On one hand raw material and the product were processed all throughout shelf-life using classic culture-dependent methods while, at the same time, RNA or DNA was extracted from the samples and sequenced with Nextgen approach. Microbial populations were different between the raw matters (whey and cream) and showed typical environmental contaminants, lactic acid bacteria and aerobic spores (Pseudomonas, Aeromonas, Streptococcus, Lactococcus, Kocuria, Hafnia). Most of the microbial populations in the ricotta cheese samples appeared to be composed of Bacillus and Paenibacillus. Both genera derived from spores germination and increased exponentially within two weeks of 8\ub0C storage, reaching 107 CFU/ml; within 20 days cheese pH decreased from 6,4-6,2 to 5,7-5,5, showing acidification due to active bacterial metabolism. With this approach we precisely understood what kind of bacteria were present in the tested products from raw matters to expiry date: these essential informations can be exploited in order to improve ricotta cheese chain production

Hypertrophic cardiomyopathy: two homozygous cases with "typical" hypertrophic cardiomyopathy and three new mutations in cases with progression to dilated cardiomyopathy

About 10% of cases of hypertrophic cardiomyopathy (HCM) evolve into dilated cardiomyopathy (DCM) with unknown causes. We studied 11 unrelated patients (pts) with HCM who progressed to DCM (group A) and 11 who showed "typical" HCM (group B). Mutational analysis of the beta-myosin heavy chain (MYH7), myosin-binding protein C (MYBPC3), and cardiac troponin T (TNNT2) genes demonstrated eight mutations affecting MYH7 or MYBPC3 gene, five of which were new mutations. In group A-pts, the first new mutation occurred in the myosin head-rod junction and the second occurred in the light chain-binding site. The third new mutation leads to a MYBPC3 lacking titin and myosin binding sites. In group B, two pts with severe HCM carried two homozygous MYBPC3 mutations and one with moderate hypertrophy was a compound heterozygous for MYBPC3 gene. We identified five unreported mutations, potentially "malignant" defects as for the associated phenotypes, but no specific mutations of HCM/DCM