Machine learning classification of archaea and bacteria identifies novel predictive genomic features.

Background: Archaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria ( ) and archaea ( ).

TumFlow: An AI Model for Predicting New Anticancer Molecules.

Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imposes significant limitations. However, the advent of Artificial Intelligence (AI) has opened up new possibilities for simulating and evaluating numerous drug candidates, thereby mitigating the requisite time and resources.

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.

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.

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.

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 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.

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).

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.

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.

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.

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 variants and transcription factors associated with an aggressive phenotype, in which the truncated TGH isoform correlates with malignancy.

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 (), 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.

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.

The 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.

GMIEC: a shiny application for the identification of gene-targeted drugs for precision medicine.

Background: Precision medicine is a medical approach that takes into account individual genetic variability and often requires Next Generation Sequencing data in order to predict new treatments. Here we present GMIEC, Genomic Modules Identification et Characterization for genomics medicine, an application that is able to identify specific drugs at the level of single patient integrating multi-omics data such as RNA-sequencing, copy-number variation, methylation, Chromatin Immuno-Precipitation and Exome/Whole Genome sequencing. It is also possible to include clinical data related to each patient.

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.

Abrogation of esophageal carcinoma development in miR-31 knockout rats.

MicroRNA-31 (miR-31) is overexpressed in esophageal squamous cell carcinoma (ESCC), a deadly disease associated with dietary Zn deficiency and inflammation. In a Zn deficiency-promoted rat ESCC model with miR-31 up-regulation, cancer-associated inflammation, and a high ESCC burden following -nitrosomethylbenzylamine (NMBA) exposure, systemic antimiR-31 delivery reduced ESCC incidence from 85 to 45% ( = 0.038) and miR-31 gene knockout abrogated development of ESCC ( = 1 × 10).