Systems biology and OMIC data integration to understand gastrointestinal cancers.

Gastrointestinal (GI) cancers are a set of diverse diseases affecting many parts/ organs. The five most frequent GI cancer types are esophageal, gastric cancer (GC), liver cancer, pancreatic cancer, and colorectal cancer (CRC); together, they give rise to 5 million new cases and cause the death of 3.5 million people annually. We provide information about molecular changes crucial to tumorigenesis and the behavior and prognosis. During the formation of cancer cells, the genomic changes are microsatellite instability with multiple chromosomal arrangements in GC and CRC. The genomically stable subtype is observed in GC and pancreatic cancer. Besides these genomic subtypes, CRC has epigenetic modification (hypermethylation) associated with a poor prognosis. The pathway information highlights the functions shared by GI cancers such as apoptosis; focal adhesion; and the p21-activated kinase, phosphoinositide 3-kinase/Akt, transforming growth factor beta, and Toll-like receptor signaling pathways. These pathways show survival, cell proliferation, and cell motility. In addition, the immune response and inflammation are also essential elements in the shared functions. We also retrieved information on protein-protein interaction from the STRING database, and found that proteins Akt1, catenin beta 1 (CTNNB1), E1A binding protein P300, tumor protein p53 (TP53), and TP53 binding protein 1 (TP53BP1) are central nodes in the network. The protein expression of these genes is associated with overall survival in some GI cancers. The low TP53BP1 expression in CRC, high EP300 expression in esophageal cancer, and increased expression of Akt1/TP53 or low CTNNB1 expression in GC are associated with a poor prognosis. The Kaplan Meier plotter database also confirmed the association between expression of the five central genes and GC survival rates. In conclusion, GI cancers are very diverse at the molecular level. However, the shared mutations and protein pathways might be used to understand better and reveal diagnostic/prognostic or drug targets.

Antimicrobial photodynamic therapy (aPDT) with curcumin controls intradermal infection by Staphylococcus aureus in mice with type 1 diabetes mellitus: a pilot study.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the main pathogens that cause infections in diabetic individuals. In this paper, we report the outcomes of our investigation on the intradermal application of antimicrobial photodynamic therapy (PDT) with curcumin in an infection induced by MRSA ATCC 43300 strain in the ear of mice with Type 1 Diabetes Mellitus (T1DM). A solution containing 100 µg of curcumin was photoactivated ex vivo with a LED light (450 nm) delivering a fluency of 13.5 J/cm3. This solution was administered in the ear intradermally, at the same inoculum site as the MRSA ATCC 43300 strain (PDT Group). This study also included the use of two control groups (both infected): One was treated with saline and the other was treated with non-photoactivated curcumin. The animals were euthanized 24 h after these treatments and samples of draining lymph node and treated ear were collected for examination. The PDT group showed lower bacterial load in the draining lymph node when compared to the saline and curcumin groups (p-value <0.05) 24 h after treatment. In addition to bacterial load, the PDT group presented a higher concentration of nitrates and nitrites in the draining lymph node when compared to the saline and curcumin groups (p-value <0.001). Examining the infectious site, despite apparently having similar inflammatory cell recruitment compared with the control groups, the PDT group showed a profile with less intense activity in the myeloperoxidase expression when compared to the saline group (p-value <0.001). Additionally, the detected concentration of cytokines such as IL-1ß, IL-12, and IL-10 was significantly lower in the PDT group when compared to the saline group (p-value <0.01; p-value <0.05; p-value <0.05, respectively), thus presenting a less intense inflammatory response during infection resolution. Our pilot study showed for the first time the therapeutic potential of PDT using curcumin when administered intradermally in the treatment of infections caused by S. aureus in mice with T1DM.

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression.

BACKGROUND: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. METHODS: We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples. RESULTS: This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1. CONCLUSION: The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.

Update of microbial genome programs for bacteria and archaea.

Since the Haemophilus influenzae genome sequence was completed in 1995, 172 other prokaryotic genomes have been completely sequenced, while 508 projects are underway. Besides pathogens, organisms important in several other fields, such as biotechnology and bioremediation, have also been sequenced. Institutions choose the organisms they wish to sequence according to the importance that these species represent to them, the availability of the microbes, and based on the similarity of a species of interest with others that have been sequenced previously. Improvements in sequencing techniques and in associated methodologies have been achieved; however, scientists need to continue working on the development of this field. In Brazil, a multicentered, centrally coordinated and research-focused network was adopted and successfully used for the sequencing of several important organisms. We analyzed the current status of microbial genomes, the trends for criteria used to choose new sequencing projects, the future of microbial sequencing, and the Brazilian genome network.