Correction to: FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

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FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

BACKGROUND: Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. RESULTS: Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). CONCLUSION: FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

Subcellular stoichiogenomics reveal cell evolution and electrostatic interaction mechanisms in cytoskeleton.

BACKGROUND: Eukaryotic cells contain a huge variety of internally specialized subcellular compartments. Stoichiogenomics aims to reveal patterns of elements usage in biological macromolecules. However, the stoichiogenomic characteristics and how they adapt to various subcellular microenvironments are still unknown. RESULTS: Here we first updated the definition of stoichiogenomics. Then we applied it to subcellular research, and detected distinctive nitrogen content of nuclear and hydrogen, sulfur content of extracellular proteomes. Specially, we found that acidic amino acids (AAs) content of cytoskeletal proteins is the highest. The increased charged AAs are mainly caused by the eukaryotic originated cytoskeletal proteins. Functional subdivision of the cytoskeleton showed that activation, binding/association, and complexes are the three largest functional categories. Electrostatic interaction analysis showed an increased electrostatic interaction between both primary sequences and PPI interfaces of 3D structures, in the cytoskeleton. CONCLUSIONS: This study creates a blueprint of subcellular stoichiogenomic characteristics, and explains that charged AAs of the cytoskeleton increased greatly in evolution, which offer material basis for the eukaryotic cytoskeletal proteins to act in two ways of electrostatic interactions, and further perform their activation, binding/association and complex formation.

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.

Inferring therapeutic targets from heterogeneous data: HKDC1 is a novel potential therapeutic target for cancer.

MOTIVATION: The discovery of therapeutic targets is important for cancer treatment. Although dozens of targets have been used in cancer therapies, cancer remains a serious disease with a high mortality rate. Owing to the expansion of cancer-related data, we now have the opportunity to infer therapeutic targets using computational biology methods. RESULTS: Here, we describe a method, termed anticancer activity enrichment analysis, used to determine genes that could be used as therapeutic targets. The results show that these genes have high likelihoods of being developed into clinical targets (>60%). Combined with gene expression data, we predicted 50 candidate targets for lung cancer, of which 19 of the top 20 genes are targeted by approved drugs or drugs used in clinical trials. A hexokinase family member, hexokinase domain-containing protein 1 (HKDC1), is the only one of the top 20 genes that has not been targeted by either an approved drug or one being used in clinical trials. Further investigations indicate that HKDC1 is a novel potential therapeutic target for lung cancer. CONCLUSION: We developed a protocol to identify potential therapeutic targets from heterogeneous data. We suggest that HKDC1 is a novel potential therapeutic target for lung cancer. CONTACT: huangjf@mail.kiz.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Repeated functional convergent effects of NaV1.7 on acid insensitivity in hibernating mammals.

Hibernating mammals need to be insensitive to acid in order to cope with conditions of high CO2; however, the molecular basis of acid tolerance remains largely unknown. The African naked mole-rat (Heterocephalus glaber) and hibernating mammals share similar environments and physiological features. In the naked mole-rat, acid insensitivity has been shown to be conferred by the functional motif of the sodium ion channel NaV1.7. There is now an opportunity to evaluate acid insensitivity in other taxa. In this study, we tested for functional convergence of NaV1.7 in 71 species of mammals, including 22 species that hibernate. Our analyses revealed a functional convergence of amino acid sequences, which occurred at least six times independently in mammals that hibernate. Evolutionary analyses determined that the convergence results from both parallel and divergent evolution of residues in the functional motif. Our findings not only identify the functional molecules responsible for acid insensitivity in hibernating mammals, but also open new avenues to elucidate the molecular underpinnings of acid insensitivity in mammals.

Specific differences and novel key regulatory genes of sex in influencing exceptional longevity phenotypes.

BACKGROUND AND AIMS: Although the life expectancy of women systematically and robustly exceeds that of men, specific differences and molecular mechanisms of sex in influencing longevity phenotypes remain largely unknown. Therefore, we performed transcriptome sequencing of peripheral blood samples to explore regulatory mechanisms of healthy longevity by incorporating sex data. METHODS: We selected 34 exceptional longevity (age: 98.26 ± 2.45 years) and 16 controls (age: 52.81 ± 9.78) without advanced outcomes from 1363 longevity and 692 controls recruited from Nanning of Guangxi for RNA sequencing 1. The transcriptome sequencing 1 data of 50 samples were compared by longevity and sex to screen differentially expressed genes (DEGs). Then, 121 aging samples (40-110 years old) without advanced outcomes from 355 longevity and 294 controls recruited from Dongxing of Guangxi were selected for RNA sequencing 2. The genes associated with aging from the transcriptome sequencing 2 of 121 aging samples were filtered out. Finally, the gender-related longevity candidate genes and their possible metabolic pathways were verified by cell model of aging and a real-time polymerase chain reaction (RT-PCR). RESULTS: Metabolism differs between male and female and plays a key role in longevity. Moreover, the principal findings of this study revealed a novel key gene, UGT2B11, that plays an important role in regulating lipid metabolism through the peroxisome proliferator activated receptor gamma (PPARG) signalling pathway and ultimately improving lifespan, particularly in females. CONCLUSION: The findings suggest specific differences in metabolism affecting exceptional longevity phenotypes between the sexes and offer novel therapeutic targets to extend lifespan by regulating lipid homeostasis.

Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging.

The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.

Hearing aid for vertebrates via multiple episodic adaptive events on prestin genes.

Auditory detection is essential for survival and reproduction of vertebrates, yet the genetic changes underlying the evolution and diversity of hearing are poorly documented. Recent discoveries concerning prestin, which is responsible for cochlear amplification by electromotility, provide an opportunity to redress this situation. We identify prestin genes from the genomes of 14 vertebrates, including three fishes, one amphibian, one lizard, one bird, and eight mammals. An evolutionary analysis of these sequences and 34 previously known prestin genes reveals for the first time that this hearing gene was under positive selection in the most recent common ancestor (MRCA) of tetrapods. This discovery might document the genetic basis of enhanced high sound sensibility in tetrapods. An investigation of the adaptive gain and evolution of electromotility, an important evolutionary innovation for the highest hearing ability of mammals, detects evidence for positive selections on the MRCA of mammals, therians, and placentals, respectively. It is suggested that electromotility determined by prestin might initially appear in the MRCA of mammals, and its functional improvements might occur in the MRCA of therian and placental mammals. Our patch clamp experiments further support this hypothesis, revealing the functional divergence of voltage-dependent nonlinear capacitance of prestin from platypus, opossum, and gerbil. Moreover, structure-based cdocking analyses detect positively selected amino acids in the MRCA of placental mammals that are key residues in sulfate anion transport. This study provides new insights into the adaptation and functional diversity of hearing sensitivity in vertebrates by evolutionary and functional analysis of the hearing gene prestin.

CDRUG: a web server for predicting anticancer activity of chemical compounds.

Cancer is the leading cause of death worldwide. Screening anticancer candidates from tens of millions of chemical compounds is expensive and time-consuming. A rapid and user-friendly web server, known as CDRUG, is described here to predict the anticancer activity of chemical compounds. In CDRUG, a hybrid score was developed to measure the similarity of different compounds. The performance analysis shows that CDRUG has the area under curve of 0.878, indicating that CDRUG is effective to distinguish active and inactive compounds.

Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis.

Glioblastoma (GBM) is one of the most aggressive forms of cancer. Although IDH1 mutation indicates a good prognosis and a potential target for treatment, most GBMs are IDH1 wild-type. Identifying additional molecular markers would help to generate personalized therapies and improve patient outcomes. Here, we used our recently developed metabolic modeling method (genome-wide precision metabolic modeling, GPMM) to investigate the metabolic profiles of GBM, aiming to identify additional novel molecular markers for this disease. We systematically analyzed the metabolic reaction profiles of 149 GBM samples lacking IDH1 mutation. Forty-eight reactions showing significant association with prognosis were identified. Further analysis indicated that the purine recycling, nucleotide interconversion, and folate metabolism pathways were the most robust modules related to prognosis. Considering the three pathways, we then identified the most significant GBM type for a better prognosis, namely N+P-. This type presented high nucleotide interconversion (N+) and low purine recycling (P-). N+P--type exhibited a significantly better outcome (log-rank p = 4.7 × 10-7) than that of N-P+. GBM patients with the N+P--type had a median survival time of 19.6 months and lived 65% longer than other GBM patients. Our results highlighted a novel molecular type of GBM, which showed relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, and therefore exhibits potential in GBM prognostic assessment and personalized therapy.

MOfinder: a novel algorithm for detecting overlapping modules from protein-protein interaction network.

Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.

Specific Gain and Loss of Co-Expression Modules in Long-Lived Individuals Indicate a Role of circRNAs in Human Longevity.

Deep RNA sequencing of 164 blood samples collected from long-lived families was performed to investigate the expression patterns of circular RNAs (circRNAs). Unlike that observed in previous studies, circRNA expression in long-lived elderly individuals (98.3 ± 3.4 year) did not exhibit an age-accumulating pattern. Based on weighted circRNA co-expression network analysis, we found that longevous elders specifically gained eight but lost seven conserved circRNA-circRNA co-expression modules (c-CCMs) compared with normal elder controls (spouses of offspring of long-lived individuals, age = 59.3 ± 5.8 year). Further analysis showed that these modules were associated with healthy aging-related pathways. These results together suggest an important role of circRNAs in regulating human lifespan extension.

Multiple time-series expression trajectories imply dynamic functional changes during cellular senescence.

Cellular senescence is a dynamic process driven by epigenetic and genetic changes. Although some transcriptomic signatures of senescent cells have been discovered, how these senescence-related signals change over time remains largely unclear. Here, we profiled the transcriptome dynamics of human dermal fibroblast (HDF) cells in successive stages of growth from proliferation to senescence. Based on time-series expression profile analysis, we discovered four trajectories (C1, C2, C3, C4) that are dynamically expressed as senescence progresses. While some genes were continuously up-regulated (C4) or down-regulated (C2) with aging, other genes did not change linearly with cell proliferation, but remained stable until entering the senescent state (C1, C3). Further analysis revealed that the four modes were enriched in different biological pathways, including regulation of cellular senescence. These findings provide a new perspective on understanding the dynamic regulatory mechanism of cellular senescence.

Mechanism of Thymus Rejuvenation in Elderly Macaques.

Senile thymus atrophy is an important factor leading to decreased immune function. Repairing the atrophic thymus tissue structure, rebuilding immune function, and replenishing the number of exogenous stem cells may be ideal methods. In this study, bone marrow mesenchymal stem cells were intravenously infused into elderly macaques. We found that thymus volume was substantially increased, some thymus tissue regeneration was observed, the degree of thymus tissue fibrosis decreased, collagen fiber deposition decreased, cortical and medulla structures emerged gradually, the number of apoptotic cells decreased significantly, and the expression of apoptosis-related proteins decreased. For the effects of stem cell therapy on aging-related genes, we performed transcriptomic analysis of thymus tissue. The results show the expression pattern of the tissue transcriptome tended to be similar to the thymus expression pattern in young macaques compared with the elderly group, reverse aging-related proteins. Based on the results, it is suggested that stem cell therapy is an ideal method to prevent or reverse the aging of the thymus.

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

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.

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.

CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation.

BACKGROUND: The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods. RESULTS: The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA). CONCLUSIONS: The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (http://159.226.149.45/other1/CMASA/CMASA.htm).

Identification of DNA N6-methyladenine sites by integration of sequence features.

BACKGROUND: An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N6-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources and time, especially given the rapid accumulation of sequencing data. RESULTS: In this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron-ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets. CONCLUSIONS: p6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https://github.com/Konglab404/p6mA.