Tuning of CHO secretional machinery improve activity of secreted therapeutic sulfatase 150-fold.

Rare diseases are, despite their name, collectively common and millions of people are affected daily of conditions where treatment often is unavailable. Sulfatases are a large family of activating enzymes related to several of these diseases. Heritable genetic variations in sulfatases may lead to impaired activity and a reduced macromolecular breakdown within the lysosome, with several severe and lethal conditions as a consequence. While therapeutic options are scarce, treatment for some sulfatase deficiencies by recombinant enzyme replacement are available. The recombinant production of such sulfatases suffers greatly from both low product activity and yield, further limiting accessibility for patient groups. To mitigate the low product activity, we have investigated cellular properties through computational evaluation of cultures with varying media conditions and comparison of two CHO clones with different levels of one active sulfatase variant. Transcriptome analysis identified 18 genes in secretory pathways correlating with increased sulfatase production. Experimental validation by upregulation of a set of three key genes improved the specific enzymatic activity at varying degree up to 150-fold in another sulfatase variant, broadcasting general production benefits. We also identified a correlation between product mRNA levels and sulfatase activity that generated an increase in sulfatase activity when expressed with a weaker promoter. Furthermore, we suggest that our proposed workflow for resolving bottlenecks in cellular machineries, to be useful for improvements of cell factories for other biologics as well.

The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease.

The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials.

Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes.

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/ß-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

Postmortem Cortical Transcriptomics of Lewy Body Dementia Reveal Mitochondrial Dysfunction and Lack of Neuroinflammation.

OBJECTIVE: Prevalence of Lewy body dementias (LBD) is second only to Alzheimer's disease (AD) among people with neurodegenerative dementia. LBD cause earlier mortality, more intense neuropsychiatric symptoms, more caregivers' burden, and higher costs than AD. The molecular mechanisms underlying LBD are largely unknown. As advancing molecular level mechanistic understanding is essential for identifying reliable peripheral biomarkers and novel therapeutic targets for LBD, the authors aimed to identify differentially expressed genes (DEG), and dysfunctional molecular networks in postmortem LBD brains. METHODS: The authors investigated the transcriptomics of postmortem anterior cingulate and dorsolateral prefrontal cortices of people with pathology-verified LBD using next-generation RNA-sequencing. The authors verified the identified DEG using high-throughput quantitative polymerase chain reactions. Functional implications of identified DEG and the consequent metabolic reprogramming were evaluated by Ingenuity pathway analyses, genome-scale metabolic modeling, reporter metabolite analyses, and in silico gene silencing. RESULTS: The authors identified and verified 12 novel DEGs (MPO, SELE, CTSG, ALPI, ABCA13, GALNT6, SST, RBM3, CSF3, SLC4A1, OXTR, and RAB44) in LBD brains with genome-wide statistical significance. The authors documented statistically significant down-regulation of several cytokine genes. Identified dysfunctional molecular networks highlighted the contributions of mitochondrial dysfunction, oxidative stress, and immunosenescence toward neurodegeneration in LBD. CONCLUSION: Our findings support that chronic microglial activation and neuroinflammation, well-documented in AD, are notably absent in LBD. The lack of neuroinflammation in LBD brains was corroborated by statistically significant down-regulation of several inflammatory markers. Identified DEGs, especially down-regulated inflammatory markers, may aid distinguishing LBD from AD, and their biomarker potential warrant further investigation.

TCSBN: a database of tissue and cancer specific biological networks.

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.

FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction.

MOTIVATION: A fundamental computational problem in the area of metabolic engineering is finding metabolic pathways between a pair of source and target metabolites efficiently. We present an approach, namely FogLight, for searching metabolic networks utilizing Boolean (AND-OR) operations represented in matrix notation to efficiently reduce the search space. This enables the enumeration of all pathways between metabolites that are too distant for the application of brute-force methods. RESULTS: Benchmarking tests run with FogLight show that it can reduce the search space by up to 98%, after which the accelerated search for high accurate results is guaranteed. Using FogLight, several pathways between eight given pairs of metabolites are found of which the pathways from CO2 to ethanol are specifically discussed. Additionally, in comparison with three path-finding tools, namely PHT, FMM and RouteSearch, FogLight can find shorter and more pathways for attempted source-target metabolite pairs. CONTACT: szamani@aut.ac.ir, gholamreza.bidkhori@vtt.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer systems biology and modeling: microscopic scale and multiscale approaches.

Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.

Genome scale modeling in systems biology: algorithms and resources.

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.

MIGRENE: The Toolbox for Microbial and Individualized GEMs, Reactobiome and Community Network Modelling.

Understanding microbial metabolism is crucial for evaluating shifts in human host-microbiome interactions during periods of health and disease. However, the primary hurdle in the realm of constraint-based modeling and genome-scale metabolic models (GEMs) pertaining to host-microbiome interactions lays in the efficient utilization of metagenomic data for constructing GEMs that encompass unexplored and uncharacterized genomes. Challenges persist in effectively employing metagenomic data to address individualized microbial metabolisms to investigate host-microbiome interactions. To tackle this issue, we have created a computational framework designed for personalized microbiome metabolisms. This framework takes into account factors such as microbiome composition, metagenomic species profiles and microbial gene catalogues. Subsequently, it generates GEMs at the microbial level and individualized microbiome metabolisms, including reaction richness, reaction abundance, reactobiome, individualized reaction set enrichment (iRSE), and community models. Using the toolbox, our findings revealed a significant reduction in both reaction richness and GEM richness in individuals with liver cirrhosis. The study highlighted a potential link between the gut microbiota and liver cirrhosis, i.e., increased level of LPS, ammonia production and tyrosine metabolism on liver cirrhosis, emphasizing the importance of microbiome-related factors in liver health.

Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment.

The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes mellitus. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how dietary guidelines may improve drug efficiency through the gut microbiota.

Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer.

Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.

Candidate gene prioritization.

Candidate gene identification is typically labour intensive, involving laboratory experiments required to corroborate or disprove any hypothesis for a nominated candidate gene being considered the causative gene. The traditional approach to reduce the number of candidate genes entails fine-mapping studies using markers and pedigrees. Gene prioritization establishes the ranking of candidate genes based on their relevance to the biological process of interest, from which the most promising genes can be selected for further analysis. To date, many computational methods have focused on the prediction of candidate genes by analysis of their inherent sequence characteristics and similarity with respect to known disease genes, as well as their functional annotation. In the last decade, several computational tools for prioritizing candidate genes have been proposed. A large number of them are web-based tools, while others are standalone applications that install and run locally. This review attempts to take a close look at gene prioritization criteria, as well as candidate gene prioritization algorithms, and thus provide a comprehensive synopsis of the subject matter.

Genome-scale metabolic modelling of the human gut microbiome reveals changes in the glyoxylate and dicarboxylate metabolism in metabolic disorders.

The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders.

Acute kidney injury leading to CKD is associated with a persistence of metabolic dysfunction and hypertriglyceridemia.

Fibrosis is the pathophysiological hallmark of progressive chronic kidney disease (CKD). The kidney is a highly metabolically active organ, and it has been suggested that disruption in its metabolism leads to renal fibrosis. We developed a longitudinal mouse model of acute kidney injury leading to CKD and an in vitro model of epithelial to mesenchymal transition to study changes in metabolism, inflammation, and fibrosis. Using transcriptomics, metabolic modeling, and serum metabolomics, we observed sustained fatty acid metabolic dysfunction in the mouse model from early to late stages of CKD. Increased fatty acid biosynthesis and downregulation of catabolic pathways for triglycerides and diacylglycerides were associated with a marked increase in these lipids in the serum. We therefore suggest that the kidney may be the source of the abnormal lipid profile seen in patients with CKD, which may provide insights into the association between CKD and cardiovascular disease.

Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson's disease.

Parkinson's disease (PD) is the most common progressive neurological disorder compromising motor functions. However, nonmotor symptoms, such as gastrointestinal (GI) dysfunction, precede those affecting movement. Evidence of an early involvement of the GI tract and enteric nervous system highlights the need for better understanding of the role of gut microbiota in GI complications in PD. Here, we investigate the gut microbiome of patients with PD using metagenomics and serum metabolomics. We integrate these data using metabolic modeling and construct an integrative correlation network giving insight into key microbial species linked with disease severity, GI dysfunction, and age of patients with PD. Functional analysis reveals an increased microbial capability to degrade mucin and host glycans in PD. Personalized community-level metabolic modeling reveals the microbial contribution to folate deficiency and hyperhomocysteinemia observed in patients with PD. The metabolic modeling approach could be applied to uncover gut microbial metabolic contributions to PD pathophysiology.

Disease, Drugs and Dysbiosis: Understanding Microbial Signatures in Metabolic Disease and Medical Interventions.

Since the discovery of the potential role for the gut microbiota in health and disease, many studies have gone on to report its impact in various pathologies. These studies have fuelled interest in the microbiome as a potential new target for treating disease Here, we reviewed the key metabolic diseases, obesity, type 2 diabetes and atherosclerosis and the role of the microbiome in their pathogenesis. In particular, we will discuss disease associated microbial dysbiosis; the shift in the microbiome caused by medical interventions and the altered metabolite levels between diseases and interventions. The microbial dysbiosis seen was compared between diseases including Crohn's disease and ulcerative colitis, non-alcoholic fatty liver disease, liver cirrhosis and neurodegenerative diseases, Alzheimer's and Parkinson's. This review highlights the commonalities and differences in dysbiosis of the gut between diseases, along with metabolite levels in metabolic disease vs. the levels reported after an intervention. We identify the need for further analysis using systems biology approaches and discuss the potential need for treatments to consider their impact on the microbiome.

Low Shear Stress Increases Recombinant Protein Production and High Shear Stress Increases Apoptosis in Human Cells.

Human embryonic kidney cells HEK293 can be used for the production of therapeutic glycoproteins requiring human post-translational modifications. High cell density perfusion processes are advantageous for such production but are challenging due to the shear sensitivity of HEK293 cells. To understand the impact of hollow filter cell separation devices, cells were cultured in bioreactors operated with tangential flow filtration (TFF) or alternating tangential flow filtration (ATF) at various flow rates. The average theoretical velocity profile in these devices showed a lower shear stress for ATF by a factor 0.637 compared to TFF. This was experimentally validated and, furthermore, transcriptomic evaluation provided insights into the underlying cellular processes. High shear caused cellular stress leading to apoptosis by three pathways, i.e. endoplasmic reticulum stress, cytoskeleton reorganization, and extrinsic signaling pathways. Positive effects of mild shear stress were observed, with increased recombinant erythropoietin production and increased gene expression associated with transcription and protein phosphorylation.

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals.

Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer.

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.