System-level metabolic modeling facilitates unveiling metabolic signature in exceptional longevity.

Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system-level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome-wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI-60 cancer cell lines; it dramatically improved the performance of the modeling with an R2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long-lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age-related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.

Transcriptome integration analysis and specific diagnosis model construction for Hodgkin's lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma.

Transcriptome differences between Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), which are all derived from B cell, remained unclear. This study aimed to construct lymphoma-specific diagnostic models by screening lymphoma marker genes. Transcriptome data of HL, DLBCL, and MCL were obtained from public databases. Lymphoma marker genes were screened by comparing cases and controls as well as the intergroup differences among lymphomas. A total of 9 HL marker genes, 7 DLBCL marker genes, and 4 MCL marker genes were screened in this study. Most HL marker genes were upregulated, whereas DLBCL and MCL marker genes were downregulated compared to controls. The optimal HL-specific diagnostic model contains one marker gene (MYH2) with an AUC of 0.901. The optimal DLBCL-specific diagnostic model contains 7 marker genes (LIPF, CCDC144B, PRO2964, PHF1, SFTPA2, NTS, and HP) with an AUC of 0.951. The optimal MCL-specific diagnostic model contains 3 marker genes (IGLV3-19, IGKV4-1, and PRB3) with an AUC of 0.843. The present study reveals the transcriptome data-based differences between HL, DLBCL, and MCL, when combined with other clinical markers, may help the clinical diagnosis and prognosis.

Identification of hub ubiquitin ligase genes affecting Alzheimer's disease by analyzing transcriptome data from multiple brain regions.

The ubiquitin-proteasome system (UPS) plays crucial roles in numerous cellular functions. Dysfunction of the UPS shows certain correlations with the pathological changes in Alzheimer's disease (AD). This study aimed to explore the different impairments of the UPS in multiple brain regions and identify hub ubiquitin ligase (E3) genes in AD. The brain transcriptome, blood transcriptome and proteome data of AD were downloaded from a public database. The UPS genes were collected from the Ubiquitin and Ubiquitin-like Conjugation Database. The hub E3 genes were defined as the differentially expressed E3 genes shared by more than three brain regions. E3Miner and UbiBrowser were used to predict the substrate of hub E3. This study shows varied impairment of the UPS in different brain regions in AD. Furthermore, we identify seven hub E3 genes (CUL1, CUL3, EIF3I, NSMCE1, PAFAH1B1, RNF175, and UCHL1) that are downregulated in more than three brain regions. Three of these genes (CUL1, EIF3I, and NSMCE1) showed consistent low expression in blood. Most of these genes have been reported to promote AD, whereas the impact of RNF175 on AD is not yet reported. Further analysis revealed a potential regulatory mechanism by which hub E3 and its substrate genes may affect transcription functions and then exacerbate AD. This study identified seven hub E3 genes and their substrate genes affect transcription functions and then exacerbate AD. These findings may be helpful for the development of diagnostic biomarkers and therapeutic targets for AD.

Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases.

Considerable evidence suggests that metabolic abnormalities are associated with neurodegenerative diseases. This study aimed to conduct a systematic metabolic analysis of Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD). Human and mouse model microarray datasets were downloaded from the Gene Expression Omnibus database. The metabolic genes and pathways were collected from the Recon 3D human metabolic model. Drug and target information was obtained from the DrugBank database. This study identified ATP1A1, ATP6V1G2, GOT1, HPRT1, MAP2K1, PCMT1 and PLK2 as key metabolic genes that were downregulated in AD, PD and HD. We screened 57 drugs that target these genes, such as digoxin, ouabain and diazoxide. This study constructed multigene diagnostic models for AD, PD and HD by using metabolic gene expression profiles in blood, all models showed high accuracy (AUC > 0.8) both in the experimental and validation sets. Furthermore, analysis of animal models showed that there was almost no consistency among the metabolic changes between mouse models and human diseases. This study systematically revealed the metabolic damage among AD, PD, and HD and uncovered the differences between animal models and human diseases. This information may be helpful for understanding the metabolic mechanisms and drug development for neurodegenerative diseases.

Gain and loss events in the evolution of the apolipoprotein family in vertebrata.

BACKGROUND: Various apolipoproteins widely distributed among vertebrata play key roles in lipid metabolism and have a direct correlation with human diseases as diagnostic markers. However, the evolutionary progress of apolipoproteins in species remains unclear. Nine human apolipoproteins and well-annotated genome data of 30 species were used to identify 210 apolipoprotein family members distributed among species from fish to humans. Our study focused on the evolution of nine exchangeable apolipoproteins (ApoA-I/II/IV/V, ApoC-I~IV and ApoE) from Chondrichthyes, Holostei, Teleostei, Amphibia, Sauria (including Aves), Prototheria, Marsupialia and Eutheria. RESULTS: In this study, we reported the overall distribution and the frequent gain and loss evolutionary events of apolipoprotein family members in vertebrata. Phylogenetic trees of orthologous apolipoproteins indicated evident divergence between species evolution and apolipoprotein phylogeny. Successive gain and loss events were found by evaluating the presence and absence of apolipoproteins in the context of species evolution. For example, only ApoA-I and ApoA-IV occurred in cartilaginous fish as ancient apolipoproteins. ApoA-II, ApoE, and ApoC-I/ApoC-II were found in Holostei, Coelacanthiformes, and Teleostei, respectively, but the latter three apolipoproteins were absent from Aves. ApoC-I was also absent from Cetartiodactyla. The apolipoprotein ApoC-III emerged in terrestrial animals, and ApoC-IV first arose in Eutheria. The results indicate that the order of the emergence of apolipoproteins is most likely ApoA-I/ApoA-IV, ApoE, ApoA-II, ApoC-I/ApoC-II, ApoA-V, ApoC-III, and ApoC-IV. CONCLUSIONS: This study reveals not only the phylogeny of apolipoprotein family members in species from Chondrichthyes to Eutheria but also the occurrence and origin of new apolipoproteins. The broad perspective of gain and loss events and the evolutionary scenario of apolipoproteins across vertebrata provide a significant reference for the research of apolipoprotein function and related diseases.

Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets.

Although many of the genetic loci associated with breast cancer risk have been reported, there is a lack of systematic analysis of regulatory networks composed of different miRNAs and mRNAs on survival analysis in breast cancer. To reconstruct the microRNAs-genes regulatory network in breast cancer, we employed the expression data from The Cancer Genome Atlas (TCGA) related to five essential miRNAs including miR-21, miR-22, miR-210, miR-221, and miR-222, and their associated functional genomics data from the GEO database. Then, we performed an integration analysis to identify the essential target factors and interactions for the next survival analysis in breast cancer. Based on the results of our integrated analysis, we have identified significant common regulatory signatures including differentially expressed genes, enriched pathways, and transcriptional regulation such as interferon regulatory factors (IRFs) and signal transducer and activator of transcription 1 (STAT1). Finally, a reconstructed regulatory network of five miRNAs and 34 target factors was established and then applied to survival analysis in breast cancer. When we used expression data for individual miRNAs, only miR-21 and miR-22 were significantly associated with a survival change. However, we identified 45 significant miRNA-gene pairs that predict overall survival in breast cancer out of 170 one-on-one interactions in our reconstructed network covering all of five miRNAs, and several essential factors such as PSMB9, HLA-C, RARRES3, UBE2L6, and NMI. In our study, we reconstructed regulatory network of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets. These results may provide new insights into regulatory network-based precision medicine for breast cancer.

Low expression of aging-related NRXN3 is associated with Alzheimer disease: A systematic review and meta-analysis.

BACKGROUND: Alzheimer disease (AD) is a common neurodegenerative disorder with distinct pathological features, with aging considered the greatest risk factor. We explored how aging contributes to increased AD risk, and determined concurrent and coordinate changes (including genetic and phenotypic modifications) commonly exhibited in both normal aging and AD. METHODS: Using the Gene Expression Omnibus (GEO) database, we collected 1 healthy aging-related and 3 AD-related datasets of the hippocampal region. The normal aging dataset was divided into 3 age groups: young (20-40 years old), middle-aged (40-60 years old), and elderly (>60 years old). These datasets were used to analyze the differentially expressed genes (DEGs). The Gene Ontology (GO) terms, pathways, and function network analysis of these DEGs were analyzed. RESULTS: One thousand two hundred ninety-one DEGs were found to be shared in the natural aging groups and AD patients. Among the shared DEGs, ATP6V1E1, GNG3, NDUFV2, GOT1, USP14, and NAV2 have been previously found in both normal aging individuals and AD patients. Furthermore, using Java Enrichment of Pathways Extended to Topology (JEPETTO) analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we determined that changes in aging-related KEGG annotations may contribute to the aging-dependence of AD risk. Interestingly, NRXN3, the second most commonly deregulated gene identified in the present study, is known to carry a mutation in AD patients. According to functional network analysis, NRXN3 plays a critical role in synaptic functions involved in the cognitive decline associated with normal aging and AD. CONCLUSION: Our results indicate that the low expression of aging-related NRXN3 may increase AD risk, though the potential mechanism requires further clarification.

A strategy to find novel candidate anti-Alzheimer's disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants.

BACKGROUND: Alzheimer' disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs. METHODS: We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included. RESULTS: A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present. DISCUSSION: Natural compounds from TCM provide a broad prospect for the screening of anti-AD drugs. In this work, we established networks to systematically study the connections among natural compounds, approved drugs, TCM plants and AD target proteins with the goal of identifying promising drug candidates. We hope that our study will facilitate in-depth research for the treatment of AD in Chinese medicine.

Identification of metabolism-associated genes and pathways involved in different stages of clear cell renal cell carcinoma.

The lack of early diagnostic markers and novel therapeutic targets for clear cell renal cell carcinoma (ccRCC) negatively affects patient prognosis. Cancer metabolism is an attractive area for the understanding of the molecular mechanism of carcinogenesis. The present study attempted to identify metabolic changes from the view of the expression of metabolism-associated genes between control samples and those of ccRCC at different disease stages. Data concerning ccRCC gene expression obtained by RNA-sequencing was obtained from The Cancer Genome Atlas and data on metabolism-associated genes were extracted using the Recon2 model. Following analysis of differential gene expression, multiple differentially expressed metabolic genes at each tumor-node-metastasis disease stage were identified, compared with control non-disease samples: Metabolic genes (305) were differentially expressed in stage I disease, 323 in stage II disease, 355 in stage III disease and 363 in stage IV disease. Following enrichment analysis for differential metabolic genes, 22 metabolic pathways were identified to be dysregulated in multiple stages of ccRCC. Abnormalities in hormone, vitamin, glucose and lipid metabolism were present in the early stages of the disease, with dysregulation to reactive oxygen species detoxification and amino acid metabolism pathways occurring with advanced disease stages, particularly to valine, leucine, and isoleucine metabolism, which was substantially dysregulated in stage IV disease. The xenobiotic metabolism pathway, associated with multiple cytochrome P450 family genes, was dysregulated in each stage of the disease. This pathway is worthy of substantial attention since it may aid understanding of drug resistance in ccRCC. The results of the present study offer information to aid further research into early diagnostic biomarkers and therapeutic targets of ccRCC.

The interhemispheric CA1 circuit governs rapid generalisation but not fear memory.

Encoding specificity theory predicts most effective recall by the original conditions at encoding, while generalization endows recall flexibly under circumstances which deviate from the originals. The CA1 regions have been implicated in memory and generalization but whether and which locally separated mechanisms are involved is not clear. We report here that fear memory is quickly formed, but generalization develops gradually over 24 h. Generalization but not fear memory is impaired by inhibiting ipsilateral (ips) or contralateral (con) CA1, and by optogenetic silencing of the ipsCA1 projections onto conCA1. By contrast, in vivo fEPSP recordings reveal that ipsCA1-conCA1 synaptic efficacy is increased with delay over 24 h when generalization is formed but it is unchanged if generalization is disrupted. Direct excitation of ipsCA1-conCA1 synapses using chemogenetic hM3Dq facilitates generalization formation. Thus, rapid generalization is an active process dependent on bilateral CA1 regions, and encoded by gradual synaptic learning in ipsCA1-conCA1 circuit.

Folate Deficiency and Gene Polymorphisms of MTHFR, MTR and MTRR Elevate the Hyperhomocysteinemia Risk.

BACKGROUND: Hyperhomocysteinemia (HHcy) is an independent risk factor for cardiovascular diseases (CVDs). We aimed to investigate the joint effect of homocysteine metabolism gene polymorphisms, as well as the folate deficiency on the risk of HHcy in a Chinese hypertensive population. METHODS: This study enrolled 480 hypertensive patients aged 28 - 75 from six hospitals in different Chinese regions from 9/2005 - 12/2005. Known genotypes of methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C, methionine synthase (MTR) A2756G, and methionine synthase reductase (MTRR) A66G were detected by PCRRFLP methods. Serum Hcy was measured by high-performance liquid chromatography and serum folate was measured by chemiluminescent immunoassay. RESULTS: MTHFR C677T and MTR A2756G can independently elevate the risk of HHcy (TT vs. CC + CT, p < 0.001 and AG + GG vs. AA, p = 0.026, respectively), whereas MTHFR A1298C decreased HHcy risk (AC + CC vs. AA, p < 0.001) and showed a protective effect against HHcy risk. Importantly, the joint effect of these risk genotypes showed significantly higher odds of HHcy than non-risk genotypes, especially the patients with four risk genotypes. It is noteworthy that this deleterious effect was aggravated by folate deficiency. These findings were verified by generalized multifactor dimensionality reduction model (p = 0.001) and a cumulative effects model (p < 0.001). CONCLUSIONS: We have first demonstrated that the joint effect of homocysteine metabolism gene polymorphisms and folate deficiency lead to dramatic elevations in the HHcy risk.

Meta-Analysis of Parkinson's Disease and Alzheimer's Disease Revealed Commonly Impaired Pathways and Dysregulation of NRF2-Dependent Genes.

Many lines of evidence suggest that Parkinson's disease (PD) and Alzheimer's disease (AD) have common characteristics, such as mitochondrial dysfunction and oxidative stress. As the underlying molecular mechanisms are unclear, we perform a meta-analysis with 9 microarray datasets of PD studies and 7 of AD studies to explore it. Functional enrichment analysis revealed that PD and AD both showed dysfunction in the synaptic vesicle cycle, GABAergic synapses, phagosomes, oxidative phosphorylation, and TCA cycle pathways, and AD had more enriched genes. Comparing the differentially expressed genes between AD and PD, we identified 54 common genes shared by more than six tissues. Among them, 31 downregulated genes contained the antioxidant response element (ARE) consensus sequence bound by NRF2. NRF2 is a transcription factor, which protects cells against oxidative stress through coordinated upregulation of ARE-driven genes. To our surprise, although NRF2 was upregulated, its target genes were all downregulated. Further exploration found that MAFF was upregulated in all tissues and significantly negatively correlated with the 31 NRF2-dependent genes in diseased conditions. Previous studies have demonstrated over-expressed small MAFs can form homodimers and act as transcriptional repressors. Therefore, MAFF might play an important role in dysfunction of NRF2 regulatory network in PD and AD.

The identification and molecular mechanism of anti-stroke traditional Chinese medicinal compounds.

Stroke is a worldwide epidemic disease with high morbidity and mortality. The continuously exploration of anti-stroke medicines and molecular mechanism has a long way to go. In this study, in order to screen candidate anti-stroke compounds, more than 60000 compounds from traditional Chinese medicine (TCM) database were computationally analyzed then docked to the 15 known anti-stroke targets. 192 anti-stroke plants for clinical therapy and 51 current anti-stroke drugs were used to validate docking results. Totally 2355 candidate anti-stroke compounds were obtained. Among these compounds, 19 compounds are structurally identical with 16 existing drugs in which part of them have been used for anti-stroke treatment. Furthermore, these candidate compounds were significantly enriched in anti-stroke plants. Based on the above results, the compound-target-plant network was constructed. The network reveals the potential molecular mechanism of anti-stroke for these compounds. Most of candidate compounds and anti-stroke plants are tended to interact with target NOS3, PSD-95 and PDE5A. Finally, using ADMET filter, we identified 35 anti-stroke compounds with favorable properties. The 35 candidate anti-stroke compounds offer an opportunity to develop new anti-stroke drugs and will improve the research on molecular mechanism of anti-stroke.

Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets.

Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.

Folate Deficiency Was Associated with Increased Alanine Aminotransferase and Glutamyl Transpeptidase Concentrations in a Chinese Hypertensive Population: A Cross-Sectional Study.

Alanine aminotransferase (ALT), aspartate transaminase (AST), and glutamyl transpeptidase (GGT) were three key enzymes in the hepatic metabolism. This study aimed to investigate the effect of homocysteine (Hcy) metabolism gene polymorphisms and serum Hcy and folate level on the hepatic functions in a Chinese hypertensive population. A representative sample with 480 subjects aged 28-75 was enrolled in 2005.9-2005.12 from six hospitals in different Chinese regions. Serum ALT, AST and GGT were measured by using an automatic biochemistry analyzer. Serum Hcy was measured by high-performance liquid chromatography, and serum folate was measured by chemiluminescent immunoassay. Known genotypes were detected by PCR-RFLP methods. The results showed that the MTHFR C677T mutation was related a decreased serum AST level (r=-0.11, p=0.026), whereas the MTHFR A1298C mutation elevated serum AST level (r=0.11, p=0.032). Furthermore, multiple regression analysis showed that folate deficiency was associated with higher serum ALT (ß (SE): 0.13 (0.06), p=0.031) and GGT level (ß (SE): 0.18 (0.07), p=0.011). However, serum Hcy level may not affect the hepatic functions. Our data suggested that hepatic functions were affected by MTHFR gene polymorphisms and serum folate level. Further studies are needed to confirm these correlations in a larger population.

Integrated analysis of ischemic stroke datasets revealed sex and age difference in anti-stroke targets.

Ischemic stroke is a common neurological disorder and the burden in the world is growing. This study aims to explore the effect of sex and age difference on ischemic stroke using integrated microarray datasets. The results showed a dramatic difference in whole gene expression profiles and influenced pathways between males and females, and also in the old and young individuals. Furthermore, compared with old males, old female patients showed more serious biological function damage. However, females showed less affected pathways than males in young subjects. Functional interaction networks showed these differential expression genes were mostly related to immune and inflammation-related functions. In addition, we found ARG1 and MMP9 were up-regulated in total and all subgroups. Importantly, IL1A, ILAB, IL6 and TNF and other anti-stroke target genes were up-regulated in males. However, these anti-stroke target genes showed low expression in females. This study found huge sex and age differences in ischemic stroke especially the opposite expression of anti-stroke target genes. Future studies are needed to uncover these pathological mechanisms, and to take appropriate pre-prevention, treatment and rehabilitation measures.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs.

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.

Independent Evolution of Winner Traits without Whole Genome Duplication in Dekkera Yeasts.

Dekkera yeasts have often been considered as alternative sources of ethanol production that could compete with S. cerevisiae. The two lineages of yeasts independently evolved traits that include high glucose and ethanol tolerance, aerobic fermentation, and a rapid ethanol fermentation rate. The Saccharomyces yeasts attained these traits mainly through whole genome duplication approximately 100 million years ago (Mya). However, the Dekkera yeasts, which were separated from S. cerevisiae approximately 200 Mya, did not undergo whole genome duplication (WGD) but still occupy a niche similar to S. cerevisiae. Upon analysis of two Dekkera yeasts and five closely related non-WGD yeasts, we found that a massive loss of cis-regulatory elements occurred in an ancestor of the Dekkera yeasts, which led to improved mitochondrial functions similar to the S. cerevisiae yeasts. The evolutionary analysis indicated that genes involved in the transcription and translation process exhibited faster evolution in the Dekkera yeasts. We detected 90 positively selected genes, suggesting that the Dekkera yeasts evolved an efficient translation system to facilitate adaptive evolution. Moreover, we identified that 12 vacuolar H+-ATPase (V-ATPase) function genes that were under positive selection, which assists in developing tolerance to high alcohol and high sugar stress. We also revealed that the enzyme PGK1 is responsible for the increased rate of glycolysis in the Dekkera yeasts. These results provide important insights to understand the independent adaptive evolution of the Dekkera yeasts and provide tools for genetic modification promoting industrial usage.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database.

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.