Transcriptomics Studies Reveal Functions of Transglutaminase 2 in Breast Cancer Cells Using Membrane Permeable and Impermeable Inhibitors.

Transglutaminase 2 (TG2) performs many functions both under physiological and pathological conditions. In cancer, its expression is associated with aggressiveness, propensity to epithelial-mesenchymal transition, and metastasis. Since TG2 performs key functions both outside and inside the cell, using inhibitors with different membrane permeability we analyzed the changes in the transcriptome induced in two triple-negative cell lines (MDA-MB-436 and MDA-MB-231) with aggressive features. By characterizing pathways and gene networks, we were able to define the effects of TG2 inhibitors (AA9, membrane-permeable, and NCEG2, impermeable) in relation to the roles of the enzyme in the intra- and extracellular space within the context of breast cancer. The deregulated genes revealed p53 and integrin signaling to be the common pathways with some genes showing opposite changes in expression. In MDA-MB-436, AA9 induced apoptosis, modulated cadherin, Wnt, gastrin and cholecystokinin receptors (CCKR) mediated signaling, with RHOB and GNG2 playing significant roles, and affected the Warburg effect by decreasing glycolytic enzymes. In MDA-MB-231 cells, AA9 strongly impacted HIF-mediated hypoxia, including AKT and mTOR pathway. These effects suggest an anti-tumor activity by blocking intracellular TG2 functions. Conversely, the use of NCEG2 stimulated the expression of ATP synthase and proteins involved in DNA replication, indicating a potential promotion of cell proliferation through inhibition of extracellular TG2. To effectively utilize these molecules as an anti-tumor strategy, an appropriate delivery system should be evaluated to target specific functions and avoid adverse effects. Additionally, considering combinations with other pathway modulators is crucial.

State-of-the-art in transposable element modulation affected by drugs in malignant prostatic cancer cells.

Over recent years, the investigation of transposable elements (TEs) has granted researchers a deeper comprehension of their characteristics and functions, particularly regarding their significance in the mechanisms contributing to cancer development. This manuscript focuses on prostate carcinoma cell lines and offers a comprehensive review intended to scrutinize the associations and interactions between TEs and genes, as well as their response to treatment using various chemical drugs, emphasizing their involvement in cancer progression. We assembled a compendium of articles retrieved from the PubMed database to construct networks demonstrating correlations with genes and pharmaceuticals. In doing so, we linked the transposition of certain TE types to the expression of specific transcripts directly implicated in carcinogenesis. Additionally, we underline that treatment employing different drugs revealed unique patterns of TE reactivation. Our hypothesis gathers the current understanding and guides research toward evidence-based investigations, emphasizing the association between antiviral drugs, chemotherapy, and the reduced expression of TEs in patients affected by prostate cancer.

Uncovering Patterns in Dairy Cow Behaviour: A Deep Learning Approach with Tri-Axial Accelerometer Data.

The accurate detection of behavioural changes represents a promising method of detecting the early onset of disease in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows' behaviour from accelerometry data acquired by single sensors on the cows' left flanks and compared the results with those obtained through classical machine learning (ML) from the same raw data. Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect five main behaviours: standing still, moving, feeding, ruminating and resting. For each 8 s time interval, 15 metrics were calculated, obtaining a dataset of 211,720 observation units and 15 columns. The entire dataset was randomly split into training (80%) and testing (20%) datasets. The DL accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML models. The best predictive model was an 8-layer convolutional neural network (CNN) with an overall accuracy and F1 score equal to 0.96. The precision, sensitivity/recall and F1 score of single behaviours had the following ranges: 0.93-0.99. The CNN outperformed all the classical ML algorithms. The CNN used to monitor the cows' conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer.

Metabolic characterisation of transglutaminase 2 inhibitor effects in breast cancer cell lines.

Transglutaminase 2 (TG2), which mediates post-translational modifications of multiple intracellular enzymes, is involved in the pathogenesis and progression of cancer. We used 1 H-NMR metabolomics to study the effects of AA9, a novel TG2 inhibitor, on two breast cancer cell lines with distinct phenotypes, MCF-7 and MDA-MB-231. AA9 can promote apoptosis in both cell lines, but it is particularly effective in MD-MB-231, inhibiting transamidation reactions and decreasing cell migration and invasiveness. This metabolomics study provides evidence of a major effect of AA9 on MDA-MB-231 cells, impacting glutamate and aspartate metabolism, rather than on MCF-7 cells, characterised by choline and O-phosphocholine decrease. Interestingly, AA9 treatment induces myo-inositol alteration in both cell lines, indicating action on phosphatidylinositol metabolism, likely modulated by the G protein activity of TG2 on phospholipase C. Considering the metabolic deregulations that characterise various breast cancer subtypes, the existence of a metabolic pathway affected by AA9 further points to TG2 as a promising hot spot. The metabolomics approach provides a powerful tool to monitor the effectiveness of inhibitors and better understand the role of TG2 in cancer.

Comparative analysis of bats and rodents' genomes suggests a relation between non-LTR retrotransposons, cancer incidence, and ageing.

The presence in nature of species showing drastic differences in lifespan and cancer incidence has recently increased the interest of the scientific community. In particular, the adaptations and the genomic features underlying the evolution of cancer-resistant and long-lived organisms have recently focused on transposable elements (TEs). In this study, we compared the content and dynamics of TE activity in the genomes of four rodent and six bat species exhibiting different lifespans and cancer susceptibility. Mouse, rat, and guinea pig genomes (short-lived and cancer-prone organisms) were compared with that of naked mole rat (Heterocephalus glaber) which is a cancer-resistant organism and the rodent with the longest lifespan. The long-lived bats of the genera Myotis, Rhinolophus, Pteropus and Rousettus were instead compared with Molossus molossus, which is one of the organisms with the shortest lifespan among the order Chiroptera. Despite previous hypotheses stating a substantial tolerance of TEs in bats, we found that long-lived bats and the naked mole rat share a marked decrease of non-LTR retrotransposons (LINEs and SINEs) accumulation in recent evolutionary times.

Potential of Full-Fat Silkworm-Based Diets for Laying Quails: Performance and Egg Physical Quality.

The present research was conducted to determine the optimal inclusion level of full-fat silkworm chrysalis meal (SWM) into laying quails' diets, focusing on performance traits and egg physical quality. A total of 240 31-day-old female Japanese quails were randomly assigned to four dietary groups (12 replicates/treatment; 5 quails/replicate); quails were initially fed a standard commercial diet for pullets until 63 days of age. When oviposition started, the experimental groups received the following diets: a conventional corn and soybean-based diet (control diet-C) and three other diets, including 4%, 8%, or 12% of full-fat SWM (SWM4, SWM8, SWM12, respectively). Experimental diets were provided until quails reached 119 days of age. Birds displayed satisfactory productive performance throughout the trial. SWM12 and SWM8 had higher (p < 0.001) egg production but also a higher feed conversion ratio compared to C. At the end of the trial, the eggs edible portion increased, and shell weight decreased with increasing the SWM dietary inclusion level (p < 0.001). At the same time, SWM12 displayed an increase in albumen pH (p < 0.05), even though in the normal range for quail egg. Overall, full-fat SWM (up to 12%) can be considered a promising feed ingredient for laying quails, although higher inclusion levels (>8%) require special attention because SWM also contains anti-nutritional factors.

Machine Learning Algorithms Highlight tRNA Information Content and Chargaff's Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics.

Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.

Circulating microRNAs Suggest Networks Associated with Biological Functions in Aggressive Refractory Type 2 Celiac Disease.

Despite following a gluten-free diet, which is currently the only effective therapy for celiac disease, about 5% of patients can develop serious complications, which in the case of refractory type 2 could evolve towards intestinal lymphoma. In this study, we have identified a set of 15 microRNAs in serum discriminating between the two types of refractory disease. Upregulated miR-770-5p, miR-181b-2-3p, miR-1193, and miR-1226-3p could be useful for the better stratification of patients and the monitoring of disease development, while miR-490-3p was found to be dysregulated in patients with refractory type 1. Finally, by using bioinformatic tools applied to the analysis of the targets of dysregulated microRNAs, we have completed a more precise assessment of their functions. These mainly include the pathway of response to Transforming Growth Factor ß cell-cell signaling by Wnt; epigenetic regulation, especially novel networks associated with transcriptional and post-transcriptional alterations; and the well-known inflammatory profiles.

miRNAs Copy Number Variations Repertoire as Hallmark Indicator of Cancer Species Predisposition.

Aging is one of the hallmarks of multiple human diseases, including cancer. We hypothesized that variations in the number of copies (CNVs) of specific genes may protect some long-living organisms theoretically more susceptible to tumorigenesis from the onset of cancer. Based on the statistical comparison of gene copy numbers within the genomes of both cancer-prone and -resistant species, we identified novel gene targets linked to tumor predisposition, such as CD52, SAT1 and SUMO. Moreover, considering their genome-wide copy number landscape, we discovered that microRNAs (miRNAs) are among the most significant gene families enriched for cancer progression and predisposition. Through bioinformatics analyses, we identified several alterations in miRNAs copy number patterns, involving miR-221, miR-222, miR-21, miR-372, miR-30b, miR-30d and miR-31, among others. Therefore, our analyses provide the first evidence that an altered miRNAs copy number signature can statistically discriminate species more susceptible to cancer from those that are tumor resistant, paving the way for further investigations.

A retrotransposon storm marks clinical phenoconversion to late-onset Alzheimer's disease.

Recent reports have suggested that the reactivation of otherwise transcriptionally silent transposable elements (TEs) might induce brain degeneration, either by dysregulating the expression of genes and pathways implicated in cognitive decline and dementia or through the induction of immune-mediated neuroinflammation resulting in the elimination of neural and glial cells. In the work we present here, we test the hypothesis that differentially expressed TEs in blood could be used as biomarkers of cognitive decline and development of AD. To this aim, we used a sample of aging subjects (age > 70) that developed late-onset Alzheimer's disease (LOAD) over a relatively short period of time (12-48 months), for which blood was available before and after their phenoconversion, and a group of cognitive stable subjects as controls. We applied our developed and validated customized pipeline that allows the identification, characterization, and quantification of the differentially expressed (DE) TEs before and after the onset of manifest LOAD, through analyses of RNA-Seq data. We compared the level of DE TEs within more than 600,000 TE-mapping RNA transcripts from 25 individuals, whose specimens we obtained before and after their phenotypic conversion (phenoconversion) to LOAD, and discovered that 1790 TE transcripts showed significant expression differences between these two timepoints (logFC ± 1.5, logCMP > 5.3, nominal p value < 0.01). These DE transcripts mapped both over- and under-expressed TE elements. Occurring before the clinical phenoconversion, this TE storm features significant increases in DE transcripts of LINEs, LTRs, and SVAs, while those for SINEs are significantly depleted. These dysregulations end with signs of manifest LOAD. This set of highly DE transcripts generates a TE transcriptional profile that accurately discriminates the before and after phenoconversion states of these subjects. Our findings suggest that a storm of DE TEs occurs before phenoconversion from normal cognition to manifest LOAD in risk individuals compared to controls, and may provide useful blood-based biomarkers for heralding such a clinical transition, also suggesting that TEs can indeed participate in the complex process of neurodegeneration.

Domestication reprogrammed the budding yeast life cycle.

Domestication of plants and animals is the foundation for feeding the world human population but can profoundly alter the biology of the domesticated species. Here we investigated the effect of domestication on one of our prime model organisms, the yeast Saccharomyces cerevisiae, at a species-wide level. We tracked the capacity for sexual and asexual reproduction and the chronological life span across a global collection of 1,011 genome-sequenced yeast isolates and found a remarkable dichotomy between domesticated and wild strains. Domestication had systematically enhanced fermentative and reduced respiratory asexual growth, altered the tolerance to many stresses and abolished or impaired the sexual life cycle. The chronological life span remained largely unaffected by domestication and was instead dictated by clade-specific evolution. We traced the genetic origins of the yeast domestication syndrome using genome-wide association analysis and genetic engineering and disclosed causative effects of aneuploidy, gene presence/absence variations, copy number variations and single-nucleotide polymorphisms. Overall, we propose domestication to be the most dramatic event in budding yeast evolution, raising questions about how much domestication has distorted our understanding of the natural biology of this key model species.

Dysregulation of Transglutaminase type 2 through GATA3 defines aggressiveness and Doxorubicin sensitivity in breast cancer.

The role of transglutaminase type 2 in cell physiology is related to protein transamidation and signal transduction (affecting extracellular, intracellular and nuclear processes) aided by the expression of truncated isoforms and of two lncRNAs with regulatory functions. In breast cancer TG2 is associated with disease progression supporting motility, epithelial-mesenchymal transition, invasion and drug resistance. The aim of his work is to clarify these issues by emphasizing the interconnections among TGM2 variants and transcription factors associated with an aggressive phenotype, in which the truncated TGH isoform correlates with malignancy. TGM2 transcripts are upregulated by several drugs in MCF-7, but only Doxorubicin is effective in MDA-MB-231 cells. These differences reflect the expression of GATA3, as demonstrated by silencing, suggesting a link between this transcription factor and gene dysregulation. Of note, NC9, an irreversible inhibitor of enzymatic TG2 activities, emerges to control NF-ĸB and apoptosis in breast cancer cell lines, showing potential for combination therapies with Doxorubicin.

Inhibition of the lncRNA Coded within Transglutaminase 2 Gene Impacts Several Relevant Networks in MCF-7 Breast Cancer Cells.

Long non-coding RNAs are nucleotide molecules that regulate transcription in numerous cellular processes and are related to the occurrence of many diseases, including cancer. In this regard, we recently discovered a polyadenylated long non-coding RNA (named TG2-lncRNA) encoded within the first intron of the Transglutaminase type 2 gene (TGM2), which is related to tumour proliferation in human cancer cell lines. To better characterize this new biological player, we investigated the effects of its suppression in MCF-7 breast cancer cells, using siRNA treatment and RNA-sequencing. In this way, we found modifications in several networks associated to biological functions relevant for tumorigenesis (apoptosis, chronic inflammation, angiogenesis, immunomodulation, cell mobility, and epithelial-mesenchymal transition) that were originally attributed only to Transglutaminase type 2 protein but that could be regulated also by TG2-lncRNA. Moreover, our experiments strongly suggest the ability of TG2-lncRNA to directly interact with important transcription factors, such as RXRα and TP53, paving the way for several regulatory loops that can potentially influence the phenotypic behaviour of MCF-7 cells. These considerations imply the need to further investigate the relative relevance of the TG2 protein itself and/or other gene products as key regulators in the organization of breast cancer program.

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.

Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow's milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.

The Non-Coding RNA Journal Club: Highlights on Recent Papers-8.

We are glad to share with you our eighth Journal Club and to highlight some of the most interesting papers published recently [...].

Successful extraction of insect DNA from recent copal inclusions: limits and perspectives.

Insects entombed in copal, the sub-fossilized resin precursor of amber, represent a potential source of genetic data for extinct and extant, but endangered or elusive, species. Despite several studies demonstrated that it is not possible to recover endogenous DNA from insect inclusions, the preservation of biomolecules in fossilized resins samples is still under debate. In this study, we tested the possibility of obtaining endogenous ancient DNA (aDNA) molecules from insects preserved in copal, applying experimental protocols specifically designed for aDNA recovery. We were able to extract endogenous DNA molecules from one of the two samples analyzed, and to identify the taxonomic status of the specimen. Even if the sample was found well protected from external contaminants, the recovered DNA was low concentrated and extremely degraded, compared to the sample age. We conclude that it is possible to obtain genomic data from resin-entombed organisms, although we discourage aDNA analysis because of the destructive method of extraction protocols and the non-reproducibility of the results.

DNA sequence symmetries from randomness: the origin of the Chargaff's second parity rule.

Most living organisms rely on double-stranded DNA (dsDNA) to store their genetic information and perpetuate themselves. This biological information has been considered as the main target of evolution. However, here we show that symmetries and patterns in the dsDNA sequence can emerge from the physical peculiarities of the dsDNA molecule itself and the maximum entropy principle alone, rather than from biological or environmental evolutionary pressure. The randomness justifies the human codon biases and context-dependent mutation patterns in human populations. Thus, the DNA 'exceptional symmetries,' emerged from the randomness, have to be taken into account when looking for the DNA encoded information. Our results suggest that the double helix energy constraints and, more generally, the physical properties of the dsDNA are the hard drivers of the overall DNA sequence architecture, whereas the selective biological processes act as soft drivers, which only under extraordinary circumstances overtake the overall entropy content of the genome.