Hub genes and pathways related to caries-free dental biofilm: clinical metatranscriptomic study.

OBJECTIVE: This study aimed to evaluate the microbial functional profile of biofilms related to caries-free (CF, n = 6) and caries-arrested (CI, n = 3) compared to caries-active (CA, n = 5) individuals. MATERIALS AND METHODS: A metatranscriptomic was performed in supragingival biofilm from different clinical conditions related to caries or health. Total RNA was extracted and cDNAs were obtained and sequenced (Illumina HiSeq3000). Trimmed data (SortMeRNA) were submitted to the SqueezeMeta pipeline in the co-assembly mode for functional analysis and further differential gene expression analysis (DESeq2) and weighted gene co-expression network analysis (WCGNA) to explore and identify gene modules related to these clinical conditions. RESULTS: A total of 5303 genes were found in the metatranscriptomic analysis. A co-expression network identified the most relevant modules strongly related to specific caries status. Correlation coefficients were calculated between the eigengene modules and the clinical conditions (CA, CI, and CF) discriminating multiple modules. CA and CI showed weak correlation coefficient strength across the modules, while the CF condition presented a very strong positive correlation coefficient (r = 0.9, p value = 4 × 10-9). Pearson's test was applied to further analyze the module membership and gene significance in CF conditions, and the most relevant were HSPA1s-K03283, Epr- K13277, and SLC1A-K05613. Gene Ontology (GO) shows important bioprocesses, such as two-component system, fructose and mannose metabolism, pentose and glucuronate interconversions, and flagellar assembly (p-adjust < 0.05). The ability to use different carbohydrates, integrate multiple signals, swarm, and bacteriocin production are significant metabolic advantages in the oral environment related to CF. CONCLUSIONS: A distinct functional health profile could be found in CF, where co-occurring genes can act in different pathways at the same time. Genes HSPA1s, Epr, and SLC1A may be appointed as potential biomarkers for caries-free biofilms. CLINICAL RELEVANCE: Potential biomarkers for caries-free biofilms could contribute to the knowledge of caries prevention and control.

Meta-analyses of host metagenomes from colorectal cancer patients reveal strong relationship between colorectal cancer-associated species.

Colorectal cancer (CRC) is one of the most common types of cancer, with many studies associating its development with changes in the gut microbiota. Recent developments in sequencing technologies and subsequent meta-analyses of gut metagenome provided a better understanding of species related to CRC tumorigenesis. Still, the importance of high-importance taxonomic singletons (i.e. species highly associated with a given condition but observed only in the minority of datasets) and the species interactions and co-occurrence across cohorts need further exploration. It has been shown that the gut metagenome presents a high functional redundancy, meaning that species interactions could mitigate the absence of any given species. In a CRC framework, this implies that species co-occurrence could play a role in tumorigenesis, even if CRC-associated species show low abundance. We propose to evaluate the prevalence of microbial species in tumor by initially analyzing each dataset individually and subsequently intersecting the results for differentially abundant species between CRC and healthy samples. We then identify metabolic pathways from these species based on KEGG orthologs, highlighting metabolic pathways associated with CRC. Our results indicate seven species with high prevalence across all projects and with high association to CRC, including the genus Bacteroides, Enterocloster and Prevotella. Finally, we show that CRC is also characterized by the co-occurrence of species that do not present significant differential abundance, but have been described in the literature as potential CRC biomarkers. These results indicate that between-species interactions could also play a role in CRC tumorigenesis.

Streptococcus mutans Gene Expression and Functional Profile in Root Caries: An RNA-Seq Study.

The literature is still scarce on studies describing Streptococcus mutans global gene expression under clinical conditions such as those found on complex biofilms from sound root surfaces (SRS) and carious root surfaces (RC). This study aimed to investigate the S. mutans gene expression and functional profile within the metatranscriptome of biofilms from SRS and from RC in an attempt to identify enriched functional signatures potentially associated with the healthy-to-disease transitioning process. Total RNA was extracted, and prepared libraries (SRS = 10 and RC = 9) were paired-end sequenced using the Illumina HiSeq2500. A read count assigned to each gene of the S. mutans UA159 strain was obtained. Differentially expressed genes (DEG) between SRS and RC were identified using the DESeq2 R package, and weighted gene co-expression network analysis (WGCNA) was performed to explore and identify functional modules related to SRS and RC. We found seventeen DEG between SRS and RC samples, with three overexpressed in RC and related to membrane protein, alanyl-tRNA synthetase, and GTP-binding protein, with the remaining ones overexpressed in SRS samples and related to hypothetical protein, transposon integrase, histidine kinase, putative transporter, bacteriocin immunity protein, response regulator, 6-phospho-beta-galactosidase, purine metabolism, and transcriptional regulator. Key-functional modules were identified for SRS and RC conditions based on WGCNA, being 139 hub genes found on SRS key-module and 17 genes on RC key-module. Functional analysis of S. mutans within the metatranscriptome of biofilms from sound root and from carious root revealed a similar pattern of gene expression, and only a few genes have been differentially expressed between biofilms from SRS and those from root carious lesions. However, S. mutans presented a greater functional abundance in the carious lesion samples. Some functional patterns related to sugar (starch, sucrose, fructose, mannose, and lactose) and heterofermentative metabolisms, to cell-wall biosynthesis, and to acid tolerance stress seem to be enriched on carious root surfaces, conferring ecological advantages to S. mutans. Altogether, the present data suggest that a functional signature may be associated with carious root lesions.

Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants.

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a Curated RNA-Seq Database for Cancer Research.

RNA-seq is gradually becoming the dominating technique employed to access the global gene expression in biological samples, allowing more flexible protocols and robust analysis. However, the nature of RNA-seq results imposes new data-handling challenges when it comes to computational analysis. With the increasing employment of machine learning (ML) techniques in biomedical sciences, databases that could provide curated data sets treated with state-of-the-art approaches already adapted to ML protocols, become essential for testing new algorithms. In this study, we present the Benchmarking of ARtificial intelligence Research: Curated RNA-seq Database (BARRA:CuRDa). BARRA:CuRDa was built exclusively for cancer research and is composed of 17 handpicked RNA-seq data sets for Homo sapiens that were gathered from the Gene Expression Omnibus, using rigorous filtering criteria. All data sets were individually submitted to sample quality analysis, removal of low-quality bases and artifacts from the experimental process, removal of ribosomal RNA, and estimation of transcript-level abundance. Moreover, all data sets were tested using standard approaches in the field, which allows them to be used as benchmark to new ML approaches. A feature selection analysis was also performed on each data set to investigate the biological accuracy of basic techniques. Results include genes already related to their specific tumoral tissue a large amount of long noncoding RNA and pseudogenes. BARRA:CuRDa is available at http://sbcb.inf.ufrgs.br/barracurda.

Multi-Approach Bioinformatics Analysis of Curated Omics Data Provides a Gene Expression Panorama for Multiple Cancer Types.

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

Perspectives and applications of machine learning for evolutionary developmental biology.

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.

Senescence; an endogenous anticancer mechanism.

Pre-malignant tumor cells enter a state of irreversible cell cycle arrest termed senescence (cellular senescence; CS). CS is a part of the aging program and involves multiple signaling cascades and transduction mechanisms. In general, senescence can be divided into replicative senescence and premature senescence. Replicative senescence (replicative CS) has been described for all metabolically active cells that undergo a spontaneous decline in growth rate. Notably, ectopic expression of telomerase holoenzyme (hTert) can prevent replicative CS. In cancer cells, premature senescence induced by oncogenes, named oncogene-induced senescence (oncogene induced CS; OIS), play an important role in preventing the development of cancer. Oncogene induced CS can be promoted by the loss of tumor suppressor genes, such as PTEN. Additionally, other interesting mechanisms, like selective microRNA expression, epigenetic modifications, or even stress conditions, are also able to activate the senescence program. Here, we will critically review the literature on the role of senescence in preventing the development of cancer and discuss the potential of senescence modulation for generating new molecular tools that could be explored as anticancer treatments.

Transcriptomics and systems biology analysis in identification of specific pathways involved in cacao resistance and susceptibility to witches' broom disease.

This study reports on expression analysis associated with molecular systems biology of cacao-Moniliophthora perniciosa interaction. Gene expression data were obtained for two cacao genotypes (TSH1188, resistant; Catongo, susceptible) challenged or not with the fungus M. perniciosa and collected at three time points through disease. Using expression analysis, we identified 154 and 227 genes that are differentially expressed in TSH1188 and Catongo, respectively. The expression of some of these genes was confirmed by RT-qPCR. Physical protein-protein interaction (PPPI) networks of Arabidopsis thaliana orthologous proteins corresponding to resistant and susceptible interactions were obtained followed by cluster and gene ontology analyses. The integrated analysis of gene expression and systems biology allowed designing a general scheme of major mechanisms associated with witches' broom disease resistance/susceptibility. In this sense, the TSH1188 cultivar shows strong production of ROS and elicitors at the beginning of the interaction with M. perniciosa followed by resistance signal propagation and ROS detoxification. On the other hand, the Catongo genotype displays defense mechanisms that include the synthesis of some defense molecules but without success in regards to elimination of the fungus. This phase is followed by the activation of protein metabolism which is achieved with the production of proteasome associated with autophagy as a precursor mechanism of PCD. This work also identifies candidate genes for further functional studies and for genetic mapping and marker assisted selection.

Aging as a consequence of intracellular water volume and density.

Aging is the result of a gradual failure of physiological and/or biochemical pathways that culminates with the death of the organism. Until now, the causative factors of aging are elusive, despite the increasing number of theories that try to explain how aging initiates. Interestingly, aging cells show an increase in intracellular water volume, but this fact is barely explored in aging studies. All cells have a crowded cytoplasm, where the high concentration and proximity of macromolecules create an environment that excludes many small molecules, including water. In this crowded environment, water can be found in two states termed low density water (LDW), which shows low reactivity and has an ice-like structure, and high density water (HDW) that has a disorganized structure and is highly reactive. LDW predominates in a macromolecular crowded environment, while HDW is found only in microenvironments within cytoplasm. In this sense, we hypothesized that the failure in the water homeostasis mechanisms with time changes the equilibrium between LDW and HDW, increasing the concentration of intracellular HDW. Being reactive, HDW leads to the generation of reactive oxygen species and disturbs the crowded cytoplasm environment, resulting in a diminished efficiency of metabolic reactions. Noteworthy, the cell becomes less prone to repair damage when the concentration of HDW increases with time, resulting in aging and finally death. Interestingly, some biological mechanisms (e.g., anhydrobiosis) reduce the concentration of intracellular water and prolong the life of cells and/or organisms. In this sense, anhydrobiosis and related biological mechanisms could be used as a platform to study new anti-aging therapies.