Identifying Key Genes and Functionally Enriched Pathways in Osteoporotic Patients by Weighted Gene Co-Expression Network Analysis.

Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

Identifying similarities at metabolic pathways with a strategy of Enzymatic Step Sequences.

An easy and fast strategy to compare functionally the metabolic maps is described. The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm. To do this, the KGML files are retrieved, and directed graph representations are created; where the nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is the 'product' from one reaction and a 'substrate' for the next. Then, a set of initialization nodes are selected, and used as the root for the construction of the BFS tree. This tree is used as a guide to the construction of the ESS. From each leaf (terminal node), the path is traced backwards until it reaches the root metabolic map and with two or fewer neighbors in the graph. In a second step, the ESS are compared with a Dynamic Programing algorithm, considering an "ad hoc" substitution matrix, and minimizing the global score. The dissimilarity values between two EC numbers ranged from 0 to 1, where 0 indicates similar EC numbers, and 1 indicates different EC numbers. Finally, the alignment is evaluated by using the normalized entropy-based function, considering a threshold of ≤ 0.27 as significant.•The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm.•Nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is 'product' from one reaction and a 'substrate' for the next.•The ESS are compared with a Dynamic Programing algorithm, considering an "ad hoc" substitution matrix, and minimizing the global score.

Proteomic Analysis Reveals Differential Expression Profiles in Idiopathic Pulmonary Fibrosis Cell Lines.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, irreversible lung disorder of unknown cause. This disease is characterized by profibrotic activation of resident pulmonary fibroblasts resulting in aberrant deposition of extracellular matrix (ECM) proteins. However, although much is known about the pathophysiology of IPF, the cellular and molecular processes that occur and allow aberrant fibroblast activation remain an unmet need. To explore the differentially expressed proteins (DEPs) associated with aberrant activation of these fibroblasts, we used the IPF lung fibroblast cell lines LL97A (IPF-1) and LL29 (IPF-2), compared to the normal lung fibroblast cell line CCD19Lu (NL-1). Protein samples were quantified and identified using a label-free quantitative proteomic analysis approach by liquid chromatography-tandem mass spectrometry (LC-MS/MS). DEPs were identified after pairwise comparison, including all experimental groups. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) network construction were used to interpret the proteomic data. Eighty proteins expressed exclusively in the IPF-1 and IPF-2 clusters were identified. In addition, 19 proteins were identified up-regulated in IPF-1 and 10 in IPF-2; 10 proteins were down-regulated in IPF-1 and 2 in IPF-2 when compared to the NL-1 proteome. Using the search tool for retrieval of interacting genes/proteins (STRING) software, a PPI network was constructed between the DEPs and the 80 proteins expressed exclusively in the IPF-2 and IPF-1 clusters, containing 115 nodes and 136 edges. The 10 hub proteins present in the IPP network were identified using the CytoHubba plugin of the Cytoscape software. GO and KEGG pathway analyses showed that the hub proteins were mainly related to cell adhesion, integrin binding, and hematopoietic cell lineage. Our results provide relevant information on DEPs present in IPF lung fibroblast cell lines when compared to the normal lung fibroblast cell line that could play a key role during IPF pathogenesis.

De novo assembly and functional annotation of blood transcriptome of loggerhead turtle, and in silico characterization of peroxiredoxins and thioredoxins.

The aim of this study was to generate and analyze the atlas of the loggerhead turtle blood transcriptome by RNA-seq, as well as identify and characterize thioredoxin (Tnxs) and peroxiredoxin (Prdxs) antioxidant enzymes of the greatest interest in the control of peroxide levels and other biological functions. The transcriptome of loggerhead turtle was sequenced using the Illumina Hiseq 2000 platform and de novo assembly was performed using the Trinity pipeline. The assembly comprised 515,597 contigs with an N50 of 2,631 bp. Contigs were analyzed with CD-Hit obtaining 374,545 unigenes, of which 165,676 had ORFs encoding putative proteins longer than 100 amino acids. A total of 52,147 (31.5%) of these transcripts had significant homology matches in at least one of the five databases used. From the enrichment of GO terms, 180 proteins with antioxidant activity were identified, among these 28 Prdxs and 50 putative Tnxs. The putative proteins of loggerhead turtles encoded by the genes Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip were predicted and characterized in silico. When comparing Prdxs and Txns of loggerhead turtle with homologous human proteins, they showed 18 (9%), 52 (18%) 94 (43%), 36 (16%), 35 (33%) and 74 (19%) amino acid mutations respectively. However, they showed high conservation in active sites and structural motifs (98%), with few specific modifications. Of these, Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip presented 0, 25, 18, three, six and two deleterious changes. This study provides a high quality blood transcriptome and functional annotation of loggerhead sea turtles.

Fecal bacterial communities of wild black capuchin monkeys (Sapajus nigritus) from the Atlantic Forest biome in Southern Brazil are divergent from those of other non-human primates.

Gut microbiota are influenced by factors such as diet, habitat, and social contact, which directly affect the host's health. Studies related to gut microbiota in non-human primates are increasing worldwide. However, little remains known about the gut bacterial composition in wild Brazilian monkeys. Therefore, we studied the fecal microbiota composition of wild black capuchin monkey (Sapajus nigritus) (n=10) populations from two different Atlantic Forest biome fragments (five individuals per fragment) in south Brazil. The bacterial community was identified via the high-throughput sequencing and partial amplification of the 16S rRNA gene (V4 region) using an Ion Personal Genome Machine (PGMTM) System. In contrast to other studies involving monkey microbiota, which have generally reported the phyla Firmicutes and Bacteroidetes as predominant, black capuchin monkeys showed a high relative abundance of Proteobacteria ( χ ¯ = 80.54%), followed by Firmicutes ( χ ¯ = 12.14%), Actinobacteria ( χ ¯ = 4.60%), and Bacteriodetes ( χ ¯ = 1.31%). This observed particularity may have been influenced by anthropogenic actions related to the wild habitat and/or diet specific to the Brazilian biome's characteristics and/or monkey foraging behavior. Comparisons of species richness (Chao1) and diversity indices (Simpson and InvSimpson) showed no significant differences between the two groups of monkeys. Interestingly, PICRUSt2 analysis revealed that metabolic pathways present in the bacterial communities were associated with xenobiotic biodegradation and the biosynthesis of secondary metabolites, which may suggest positive effects on monkey health and conservation in this anthropogenic habitat. Infectious disease-associated microorganisms were also observed in the samples. The present study provides information about the bacterial population and metabolic functions present in fecal microbiota, which may contribute to a better understanding of the ecology and biology of black capuchin monkeys living in forest fragments within the Atlantic Forest biome in southern Brazil. Additionally, the present study demonstrates that the fecal bacterial communities of wild black capuchin monkeys in this area are divergent from those of other wild non-human primates.

Microbial and functional characterization of an allochthonous consortium applied to hydrogen production from Citrus Peel Waste in batch reactor in optimized conditions.

Energy recovery from lignocellulosic waste has been studied as an alternative to the problem of inappropriate waste disposal. The present study aimed at characterizing the microbial community and the functional activity of reactors applied to H2 production through lignocellulosic waste fermentation in optimized conditions. The latter were identified by means of Rotational Central Composite Design (RCCD), applied to optimize allochthonous inoculum concentration (2.32-5.68 gTVS/L of granular anaerobic sludge), pH (4.32-7.68) and Citrus Peel Waste (CPW) concentration (1.55-28.45 g/L). After validation, the conditions identified for optimal H2 production were 4 gSTV/L of allochthonous inoculum, 29.8 g/L of CPW (substrate) and initial pH of 8.98. In these conditions, 48.47 mmol/L of H2 was obtained, which is 3.64 times higher than the concentration in unoptimized conditions (13.31 mmol H2/L using 15 g/L of CPW, 2 gTVS/L of allochthonous inoculum, pH 7.0). Acetogenesis was the predominant pathway, and maximal concentrations of 3,731 mg/L of butyric acid and 3,516 mg/L of acetic acid were observed. Regarding the metataxonomic profile, Clostridium genus was dramatically favored in the optimized condition (79.78%) when compared to the allochthonous inoculum (0.43%). It was possible to identify several genes related to H2 (i.e dehydrogenases) and volatile fatty acids (VFA) production and with cellulose degradation, especially some CAZymes from the classes Auxiliary Activities, Glycoside Hydrolases and Glycosyl Transferase. By means of differential gene expression it was observed that cellulose degradation and acetic acid production pathways were overabundant in samples from the optimized reactors, highlighting endo-ß-1,4-glucanase/cellulose, endo-ß-1,4-xylanase, ß-glucosidase, ß-mannosidase, cellulose ß-1,4-cellobiosidase, cellobiohydrolase, and others, as main the functions.

Database limitations for studying the human gut microbiome.

BACKGROUND: In the last twenty years, new methodologies have made possible the gathering of large amounts of data concerning the genetic information and metabolic functions associated to the human gut microbiome. In spite of that, processing all this data available might not be the simplest of tasks, which could result in an excess of information awaiting proper annotation. This assessment intended on evaluating how well respected databases could describe a mock human gut microbiome. METHODS: In this work, we critically evaluate the output of the cross-reference between the Uniprot Knowledge Base (Uniprot KB) and the Kyoto Encyclopedia of Genes and Genomes Orthologs (KEGG Orthologs) or the evolutionary genealogy of genes: Non-supervised Orthologous groups (EggNOG) databases regarding a list of species that were previously found in the human gut microbiome. RESULTS: From a list which contemplates 131 species and 52 genera, 53 species and 40 genera had corresponding entries for KEGG Database and 82 species and 47 genera had corresponding entries for EggNOG Database. Moreover, we present the KEGG Orthologs (KOs) and EggNOG Orthologs (NOGs) entries associated to the search as their distribution over species and genera and lists of functions that appeared in many species or genera, the "core" functions of the human gut microbiome. We also present the relative abundance of KOs and NOGs throughout phyla and genera. Lastly, we expose a variance found between searches with different arguments on the database entries. Inferring functionality based on cross-referencing UniProt and KEGG or EggNOG can be lackluster due to the low number of annotated species in Uniprot and due to the lower number of functions affiliated to the majority of these species. Additionally, the EggNOG database showed greater performance for a cross-search with Uniprot about a mock human gut microbiome. Notwithstanding, efforts targeting cultivation, single-cell sequencing or the reconstruction of high-quality metagenome-assembled genomes (MAG) and their annotation are needed to allow the use of these databases for inferring functionality in human gut microbiome studies.

Proteomic Analyses Reveal New Insights on the Antimicrobial Mechanisms of Chitosan Biopolymers and Their Nanosized Particles against Escherichia coli.

The well-known antimicrobial effects of chitosan (CS) polymers make them a promising adjuvant in enhancing antibiotic effectiveness against human pathogens. However, molecular CS antimicrobial mechanisms remain unclear, despite the insights presented in the literature. Thus, the aim of the present study was to depict the molecular effects implicated in the interaction of low or medium molecular mass CS polymers and their nanoparticle-counterparts against Escherichia coli. The differential E. coli proteomes sensitized to either CS polymers or nanoparticles were investigated by nano liquid chromatography-mass spectrometry (micro-LC-MS/MS). A total of 127 proteins differentially expressed in CS-sensitized bacteria were predominantly involved in (i) structural functions associated to the stability of outer membrane, (ii) increment of protein biosynthesis due to high abundance of ribosomal proteins and (iii) activation of biosynthesis of amino acid and purine metabolism pathways. Antibacterial activity of CS polymers/nanoparticles seems to be triggered by the outer bacterial membrane disassembly, leading to increased protein biosynthesis by diverting the metabolic flux to amino acid and purine nucleotides supply. Understanding CS-antibacterial molecular effects can be valuable to optimize the use of CS-based nanomaterials in food decontamination, and may represent a breakthrough on CS nanocapsules-drug delivery devices for novel antibiotics, as the chitosan-disassembly of bacteria cell membranes can potentialize antibiotic effects.

NeVOmics: An Enrichment Tool for Gene Ontology and Functional Network Analysis and Visualization of Data from OMICs Technologies.

The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at https://github.com/bioinfproject/bioinfo/.

Molecular Characterization of the Human Stomach Microbiota in Gastric Cancer Patients.

Helicobacter pylori (Hp) is the primary cause of gastric cancer but we know little of its relative abundance and other microbes in the stomach, especially at the time of gastric cancer diagnosis. Here we characterized the taxonomic and derived functional profiles of gastric microbiota in two different sets of gastric cancer patients, and compared them with microbial profiles in other body sites. Paired non-malignant and tumor tissues were sampled from 160 gastric cancer patients with 80 from China and 80 from Mexico. The 16S rRNA gene V3-V4 region was sequenced using MiSeq platform for taxonomic profiles. PICRUSt was used to predict functional profiles. Human Microbiome Project was used for comparison. We showed that Hp is the most abundant member of gastric microbiota in both Chinese and Mexican samples (51 and 24%, respectively), followed by oral-associated bacteria. Taxonomic (phylum-level) profiles of stomach microbiota resembled oral microbiota, especially when the Helicobacter reads were removed. The functional profiles of stomach microbiota, however, were distinct from those found in other body sites and had higher inter-subject dissimilarity. Gastric microbiota composition did not differ by Hp colonization status or stomach anatomic sites, but did differ between paired non-malignant and tumor tissues in either Chinese or Mexican samples. Our study showed that Hp is the dominant member of the non-malignant gastric tissue microbiota in many gastric cancer patients. Our results provide insights on the gastric microbiota composition and function in gastric cancer patients, which may have important clinical implications.

Bioinformatics Tools for Proteomics Data Interpretation.

Biological systems function via intricate cellular processes and networks in which RNAs, metabolites, proteins and other cellular compounds have a precise role and are exquisitely regulated (Kumar and Mann, FEBS Lett 583(11):1703-1712, 2009). The development of high-throughput technologies, such as the Next Generation DNA Sequencing (NGS) and DNA microarrays for sequencing genomes or metagenomes, have triggered a dramatic increase in the last few years in the amount of information stored in the GenBank and UniProt Knowledgebase (UniProtKB). GenBank release 210, reported in October 2015, contains 202,237,081,559 nucleotides corresponding to 188,372,017 sequences, whilst there are only 1,222,635,267,498 nucleotides corresponding to 309,198,943 sequences from Whole Genome Shotgun (WGS) projects. In the case of UniProKB/Swiss-Prot, release 2015_12 (December 9, 2015) contains 196,219,159 amino acids that correspond to 550,116 entries. Meanwhile, UniProtKB/TrEMBL (release 2015_12 of December 9 2015) contains 1,838,851,8871 amino acids corresponding to 555,270,679 entries. Proteomics has also improved our knowledge of proteins that are being expressed in cells at a certain time of the cell cycle. It has also allowed the identification of molecules forming part of multiprotein complexes and an increasing number of posttranslational modifications (PTMs) that are present in proteins, as well as the variants of proteins expressed.

Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms.

In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case.

Isolation and characterization of drought-responsive genes from peanut roots by suppression subtractive hybridization

Background Peanut (Arachis hypogaea L.) is an important economic and oilseed crop. Long-term rainless conditions and seasonal droughts can limit peanut yields and were conducive to preharvest aflatoxin contamination. To elucidate the molecular mechanisms by which peanut responds and adapts to water limited conditions, we isolated and characterized several drought-induced genes from peanut roots using a suppression subtractive hybridization (SSH) technique. Results RNA was extracted from peanut roots subjected to a water stress treatment (45% field capacity) and from control plants (75% field capacity), and used to generate an SSH cDNA library. A total of 111 non-redundant sequences were obtained, with 80 unique transcripts showing homology to known genes and 31 clones with no similarity to either hypothetical or known proteins. GO and KEGG analyses of these differentially expressed ESTs indicated that drought-related responses in peanut could mainly be attributed to genes involved in cellular structure and metabolism. In addition, we examined the expression patterns of seven differentially expressed candidate genes using real-time reverse transcription-PCR (qRT-PCR) and confirmed that all were up-regulated in roots in response to drought stress, but to differing extents. Conclusions We successfully constructed an SSH cDNA library in peanut roots and identified several drought-related genes. Our results serve as a foundation for future studies into the elucidation of the drought stress response mechanisms of peanut.