Neurofilament Light Chain as a Biomarker of Hereditary Transthyretin-Mediated Amyloidosis.

OBJECTIVE: To identify changes in the proteome associated with onset and progression of hereditary transthyretin-mediated (hATTR) amyloidosis, also known as ATTRv amyloidosis, we performed an observational, case-controlled study that compared proteomes of patients with ATTRv amyloidosis and healthy controls. METHODS: Plasma levels of >1,000 proteins were measured in patients with ATTRv amyloidosis with polyneuropathy who received either placebo or patisiran in a Phase 3 study of patisiran (APOLLO), and in healthy controls. The effect of patisiran on the time profile of each protein was determined by linear mixed model at 0, 9, and 18 months. Neurofilament light chain (NfL) was further assessed with an orthogonal quantitative approach. RESULTS: Levels of 66 proteins were significantly changed with patisiran vs placebo, with NfL change most significant (p < 10-20). Analysis of changes in protein levels demonstrated that the proteome of patients treated with patisiran trended toward that of healthy controls at 18 months. Healthy controls' NfL levels were 4-fold lower than in patients with ATTRv amyloidosis with polyneuropathy (16.3 pg/mL vs 69.4 pg/mL, effect -53.1 pg/mL [95% confidence interval -60.5 to -45.9]). NfL levels at 18 months increased with placebo (99.5 pg/mL vs 63.2 pg/mL, effect 36.3 pg/mL [16.5-56.1]) and decreased with patisiran treatment (48.8 pg/mL vs 72.1 pg/mL, effect -23.3 pg/mL [-33.4 to -13.1]) from baseline. At 18 months, improvement in modified Neuropathy Impairment Score +7 score after patisiran treatment significantly correlated with reduced NfL (R = 0.43 [0.29-0.55]). CONCLUSIONS: Findings suggest that NfL may serve as a biomarker of nerve damage and polyneuropathy in ATTRv amyloidosis, enable earlier diagnosis of patients with ATTRv amyloidosis, and facilitate monitoring of disease progression. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that NfL levels may enable earlier diagnosis of polyneuropathy in patients with ATTRv amyloidosis and facilitate monitoring of disease progression.

Hepatic gap junctions amplify alcohol liver injury by propagating cGAS-mediated IRF3 activation.

Alcohol-related liver disease (ALD) accounts for the majority of cirrhosis and liver-related deaths worldwide. Activation of IFN-regulatory factor (IRF3) initiates alcohol-induced hepatocyte apoptosis, which fuels a robust secondary inflammatory response that drives ALD. The dominant molecular mechanism by which alcohol activates IRF3 and the pathways that amplify inflammatory signals in ALD remains unknown. Here we show that cytoplasmic sensor cyclic guanosine monophosphate-adenosine monophosphate (AMP) synthase (cGAS) drives IRF3 activation in both alcohol-injured hepatocytes and the neighboring parenchyma via a gap junction intercellular communication pathway. Hepatic RNA-seq analysis of patients with a wide spectrum of ALD revealed that expression of the cGAS-IRF3 pathway correlated positively with disease severity. Alcohol-fed mice demonstrated increased hepatic expression of the cGAS-IRF3 pathway. Mice genetically deficient in cGAS and IRF3 were protected against ALD. Ablation of cGAS in hepatocytes only phenocopied this hepatoprotection, highlighting the critical role of hepatocytes in fueling the cGAS-IRF3 response to alcohol. We identified connexin 32 (Cx32), the predominant hepatic gap junction, as a critical regulator of spreading cGAS-driven IRF3 activation through the liver parenchyma. Disruption of Cx32 in ALD impaired IRF3-stimulated gene expression, resulting in decreased hepatic injury despite an increase in hepatic steatosis. Taken together, these results identify cGAS and Cx32 as key factors in ALD pathogenesis and as potential therapeutic targets for hepatoprotection.

Gut Microbiota-Derived Tryptophan Metabolites Modulate Inflammatory Response in Hepatocytes and Macrophages.

The gut microbiota plays a significant role in the progression of fatty liver disease; however, the mediators and their mechanisms remain to be elucidated. Comparing metabolite profile differences between germ-free and conventionally raised mice against differences between mice fed a low- and high-fat diet (HFD), we identified tryptamine and indole-3-acetate (I3A) as metabolites that depend on the microbiota and are depleted under a HFD. Both metabolites reduced fatty-acid- and LPS-stimulated production of pro-inflammatory cytokines in macrophages and inhibited the migration of cells toward a chemokine, with I3A exhibiting greater potency. In hepatocytes, I3A attenuated inflammatory responses under lipid loading and reduced the expression of fatty acid synthase and sterol regulatory element-binding protein-1c. These effects were abrogated in the presence of an aryl-hydrocarbon receptor (AhR) antagonist, indicating that the effects are AhR dependent. Our results suggest that gut microbiota could influence inflammatory responses in the liver through metabolites engaging host receptors.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Peritransplant Energy Changes and Their Correlation to Outcome After Human Liver Transplantation.

BACKGROUND: The ongoing shortage of donor livers for transplantation and the increased use of marginal livers necessitate the development of accurate pretransplant tests of viability. Considering the importance energy status during transplantation, we aimed to correlate peritransplant energy cofactors to posttransplant outcome and subsequently model this in an ex vivo setting. METHODS: Sequential biopsies were taken from 19 donor livers postpreservation, as well as 30 minutes after portal venous reperfusion and hepatic arterial reperfusion and analyzed by liquid chromatography-mass spectrometry for energetic cofactors (adenosine triphosphate [ATP]/adenosine diphosphate [ADP]/adenosine monophosphate [AMP], nicotinamide adenine dinucleotide /NAD, nicotinamide adenine dinucleotide phosphate / nicotinamide adenine dinucleotide phosphate , flavin adenine dinucleotide , glutathione disulfide/glutathione). Energy status was correlated to posttransplant outcome. In addition, 4 discarded human donation after circulatory death livers were subjected to ex vivo reperfusion, modeling reperfusion injury and were similarly analyzed for energetic cofactors. RESULTS: A rapid shift toward higher energy adenine nucleotides was observed following clinical reperfusion, with a 2.45-, 3.17- and 2.12-fold increase in ATP:ADP, ATP:AMP and energy charge after portal venous reperfusion, respectively. Seven of the 19 grafts developed early allograft dysfunction. Correlation with peritransplant cofactors revealed a significant difference in EC between early allograft dysfunction and normal functioning grafts (0.09 vs 0.31, P < 0.05). In the simulated reperfusion model, a similar trend in adenine nucleotide changes was observed. CONCLUSIONS: A preserved energy status appears critical in the peritransplant period. Levels of adenine nucleotides change rapidly after reperfusion and ratios of ATP/ADP/AMP after reperfusion are significantly correlated to graft function. Using these markers as a viability test in combination with ex vivo reperfusion may provide a useful predictor of outcome that incorporates donor, preservation, and reperfusion factors.

Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics.

Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

A novel model for ex situ reperfusion of the human liver following subnormothermic machine perfusion.

Machine perfusion-based organ preservation techniques are prudently transitioning into clinical practice. Although experimental data is compelling, the outcomes in the highly variable clinical donation-transplantation setting are unpredictable. Here, we offer an intermediate tool for pre-clinical assessment of human donor livers. We present a model for ex situ reperfusion of discarded human livers and report on its application in three human livers that have undergone subnormothermic (21°C) machine perfusion as an experimental preservation method. During reperfusion, the livers macroscopically reperfused in the first 15 minutes, and remained visually well-perfused for 3 hours of ex situ reperfusion. Bile production and oxygen consumption were observed throughout ex situ reperfusion. ATP levels increased 4.25-fold during SNMP. Between the end of SNMP and the end of reperfusion ATP levels dropped 45%. ALT levels in blood increased rapidly in the first 30 minutes and ALT release continued to taper off towards the end of perfusion. Release of CRP, TNF-α, IL-1ß, and IL-12, IFN-γ was sustained during reperfusion. These findings support the use of this model for the evaluation of novel human liver preservation techniques.

Metabolic profiling during ex vivo machine perfusion of the human liver.

As donor organ shortages persist, functional machine perfusion is under investigation to improve preservation of the donor liver. The transplantation of donation after circulatory death (DCD) livers is limited by poor outcomes, but its application may be expanded by ex vivo repair and assessment of the organ before transplantation. Here we employed subnormothermic (21 °C) machine perfusion of discarded human livers combined with metabolomics to gain insight into metabolic recovery during machine perfusion. Improvements in energetic cofactors and redox shifts were observed, as well as reversal of ischemia-induced alterations in selected pathways, including lactate metabolism and increased TCA cycle intermediates. We next evaluated whether DCD livers with steatotic and severe ischemic injury could be discriminated from 'transplantable' DCD livers. Metabolomic profiling was able to cluster livers with similar metabolic patterns based on the degree of injury. Moreover, perfusion parameters combined with differences in metabolic factors suggest variable mechanisms that result in poor energy recovery in injured livers. We conclude that machine perfusion combined with metabolomics has significant potential as a clinical instrument for the assessment of preserved livers.

Functional human liver preservation and recovery by means of subnormothermic machine perfusion.

There is currently a severe shortage of liver grafts available for transplantation. Novel organ preservation techniques are needed to expand the pool of donor livers. Machine perfusion of donor liver grafts is an alternative to traditional cold storage of livers and holds much promise as a modality to expand the donor organ pool. We have recently described the potential benefit of subnormothermic machine perfusion of human livers. Machine perfused livers showed improving function and restoration of tissue ATP levels. Additionally, machine perfusion of liver grafts at subnormothermic temperatures allows for objective assessment of the functionality and suitability of a liver for transplantation. In these ways a great many livers that were previously discarded due to their suboptimal quality can be rescued via the restorative effects of machine perfusion and utilized for transplantation. Here we describe this technique of subnormothermic machine perfusion in detail. Human liver grafts allocated for research are perfused via the hepatic artery and portal vein with an acellular oxygenated perfusate at 21 °C.

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity.

BACKGROUND: A substrate cycle is a set of metabolic reactions, arranged in a loop, which does not result in net consumption or production of the metabolites. The cycle operates by transforming a cofactor, e.g. oxidizing a reducing equivalent. Substrate cycles have been found experimentally in many parts of metabolism; however, their physiological roles remain unclear. As genome-scale metabolic models become increasingly available, there is now the opportunity to comprehensively catalogue substrate cycles, and gain additional insight into this potentially important motif of metabolic networks. RESULTS: We present a method to identify substrate cycles in the context of metabolic modules, which facilitates functional analysis. This method utilizes elementary flux mode (EFM) analysis to find potential substrate cycles in the form of cyclical EFMs, and combines this analysis with network partition based on retroactive (cyclical) interactions between reactions. In addition to providing functional context, partitioning the network into modules allowed exhaustive EFM calculations on smaller, tractable subnetworks that are enriched in metabolic cycles. Applied to a large-scale model of human liver metabolism (HepatoNet1), our method found not only well-known substrate cycles involving ATP hydrolysis, but also potentially novel substrate cycles involving the transformation of other cofactors. A key characteristic of the substrate cycles identified in this study is that the lengths are relatively short (2-13 reactions), comparable to many experimentally observed substrate cycles. CONCLUSIONS: EFM computation for large scale networks remains computationally intractable for exhaustive substrate cycle enumeration. Our algorithm utilizes a 'divide and conquer' strategy where EFM analysis is performed on systematically identified network modules that are designed to be enriched in cyclical interactions. We find that several substrate cycles uncovered using our approach are not identified when the network is partitioned in a more generic manner based solely on connectivity rather than cycles, demonstrating the value of targeting motif searches to sub-networks replete with a topological feature that resembles the desired motif itself.

A novel low-volume two-chamber microfabricated platform for evaluating drug metabolism and toxicity.

To evaluate drug and metabolite efficacy on a target organ, it is essential to include metabolic function of hepatocytes, and to evaluate metabolite influence on both hepatocytes and the target of interest. Herein, we have developed a two-chamber microfabricated device separated by a membrane enabling communication between hepatocytes and cancer cells. The microscale environment created enables cell co-culture in a low media-to-cell ratio leading to higher metabolite formation and rapid accumulation, which is lost in traditional plate cultures or other interconnected models due to higher culture volumes. We demonstrate the efficacy of this system by metabolism of tegafur by hepatocytes resulting in cancer cell toxicity.

Prediction and quantification of bioactive microbiota metabolites in the mouse gut.

Metabolites produced by the intestinal microbiota are potentially important physiological modulators. Here we present a metabolomics strategy that models microbiota metabolism as a reaction network and utilizes pathway analysis to facilitate identification and characterization of microbiota metabolites. Of the 2,409 reactions in the model, ~53% do not occur in the host, and thus represent functions dependent on the microbiota. The largest group of such reactions involves amino-acid metabolism. Focusing on aromatic amino acids, we predict metabolic products that can be derived from these sources, while discriminating between microbiota- and host-dependent derivatives. We confirm the presence of 26 out of 49 predicted metabolites, and quantify their levels in the caecum of control and germ-free mice using two independent mass spectrometry methods. We further investigate the bioactivity of the confirmed metabolites, and identify two microbiota-generated metabolites (5-hydroxy-L-tryptophan and salicylate) as activators of the aryl hydrocarbon receptor.

Analysis of transcription factor network underlying 3T3-L1 adipocyte differentiation.

Lipid accumulation in adipocytes reflects a balance between enzymatic pathways leading to the formation and breakdown of esterified lipids, primarily triglycerides. This balance is extremely important, as both high and low lipid levels in adipocytes can have deleterious consequences. The enzymes responsible for lipid synthesis and breakdown (lipogenesis and lipolysis, respectively) are regulated through the coordinated actions of several transcription factors (TFs). In this study, we examined the dynamics of several key transcription factors (TFs) - PPARγ, C/EBPß, CREB, NFAT, FoxO1, and SREBP-1c - during adipogenic differentiation (week 1) and ensuing lipid accumulation. The activation profiles of these TFs at different times following induction of adipogenic differentiation were quantified using 3T3-L1 reporter cell lines constructed to secrete the Gaussia luciferase enzyme upon binding of a TF to its DNA binding element. The dynamics of the TFs was also modeled using a combination of logical gates and ordinary differential equations, where the logical gates were used to explore different combinations of activating inputs for PPARγ, C/EBPß, and SREBP-1c. Comparisons of the experimental profiles and model simulations suggest that SREBP-1c could be independently activated by either insulin or PPARγ, whereas PPARγ activation required both C/EBPß as well as a putative ligand. Parameter estimation and sensitivity analysis indicate that feedback activation of SREBP-1c by PPARγ is negligible in comparison to activation of SREBP-1c by insulin. On the other hand, the production of an activating ligand could quantitatively contribute to a sustained elevation in PPARγ activity.

Endogenous two-photon fluorescence imaging elucidates metabolic changes related to enhanced glycolysis and glutamine consumption in precancerous epithelial tissues.

Alterations in the balance between different metabolic pathways used to meet cellular bioenergetic and biosynthetic demands are considered hallmarks of cancer. Optical imaging relying on endogenous fluorescence has been used as a noninvasive approach to assess tissue metabolic changes during cancer development. However, quantitative correlations of optical assessments with variations in the concentration of relevant metabolites or in the specific metabolic pathways that are involved have been lacking. In this study, we use high-resolution, depth-resolved imaging, relying entirely on endogenous two-photon excited fluorescence in combination with invasive biochemical assays and mass spectrometry to demonstrate the sensitivity and quantitative nature of optical redox ratio tissue assessments. We identify significant differences in the optical redox ratio of live, engineered normal and precancerous squamous epithelial tissues. We establish that while decreases in the optical redox ratio are associated with enhanced levels of glycolysis relative to oxidative phosphorylation, increases in glutamine consumption to support energy production are associated with increased optical redox ratio values. Such mechanistic insights in the origins of optical metabolic assessments are critical for exploiting fully the potential of such noninvasive approaches to monitor and understand important metabolic changes that occur in live tissues at the onset of cancer or in response to treatment.

Microbiome-derived tryptophan metabolites and their aryl hydrocarbon receptor-dependent agonist and antagonist activities.

The tryptophan metabolites indole, indole-3-acetate, and tryptamine were identified in mouse cecal extracts and fecal pellets by mass spectrometry. The aryl hydrocarbon receptor (AHR) agonist and antagonist activities of these microbiota-derived compounds were investigated in CaCo-2 intestinal cells as a model for understanding their interactions with colonic tissue, which is highly aryl hydrocarbon (Ah)-responsive. Activation of Ah-responsive genes demonstrated that tryptamine and indole 3-acetate were AHR agonists, whereas indole was an AHR antagonist that inhibited TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin)-induced CYP1A1 expression. In contrast, the tryptophan metabolites exhibited minimal anti-inflammatory activities, whereas TCDD decreased phorbol ester-induced CXCR4 [chemokine (C-X-C motif) receptor 4] gene expression, and this response was AHR dependent. These results demonstrate that the tryptophan metabolites indole, tryptamine, and indole-3-acetate modulate AHR-mediated responses in CaCo-2 cells, and concentrations of indole that exhibit AHR antagonist activity (100-250 µM) are detected in the intestinal microbiome.

Lysyl oxidase-mediated collagen crosslinks may be assessed as markers of functional properties of tendon tissue formation.

Mechanical property elaboration of engineered tissues is often assumed on the basis of gene and protein characterizations, rather than mechanical testing. However, we recently demonstrated that mechanical properties are not consistently correlated with matrix content and organization during embryonic tissue development. Based on this, mechanical properties should be assessed independently during natural or engineered tissue formation. Unfortunately, mechanical testing is destructive, and thus alternative means of assessing these properties are desirable. In this study, we examined lysyl oxidase (LOX)-mediated crosslinks as markers for mechanical properties during embryonic tendon formation and the potential to detect them non-destructively. We used tandem mass spectrometry (LC-MS/MS) to quantify changes in hydroxylysyl pyridinoline (HP) and lysyl pyridinoline (LP) crosslink density in embryonic chick tendon as a function of developmental stage. In addition, we assessed a multiphoton imaging approach that exploits the natural fluorescence of HP and LP. With both techniques, we quantified crosslink density in normal and LOX-inhibited tendons, and correlated measurements with mechanical properties. HP and LP crosslink density varied as a function of developmental stage, with HP-to-dry mass ratio correlating highly to elastic modulus, even when enzymatic crosslink formation was inhibited. Multiphoton optical imaging corroborated LC-MS/MS data, identifying significant reductions in crosslink density from LOX inhibition. Taken together, crosslink density may be useful as a marker of tissue mechanical properties that could be assessed with imaging non-destructively and perhaps non-invasively. These outcomes could have significant scientific and clinical implications, enabling continuous and long-term monitoring of mechanical properties of collagen-crosslinked tissues or engineered constructs.

Quantitative metabolic imaging using endogenous fluorescence to detect stem cell differentiation.

The non-invasive high-resolution spatial mapping of cell metabolism within tissues could provide substantial advancements in assessing the efficacy of stem cell therapy and understanding tissue development. Here, using two-photon excited fluorescence microscopy, we elucidate the relationships among endogenous cell fluorescence, cell redox state, and the differentiation of human mesenchymal stem cells into adipogenic and osteoblastic lineages. Using liquid chromatography/mass spectrometry and quantitative PCR, we evaluate the sensitivity of an optical redox ratio of FAD/(NADH + FAD) to metabolic changes associated with stem cell differentiation. Furthermore, we probe the underlying physiological mechanisms, which relate a decrease in the redox ratio to the onset of differentiation. Because traditional assessments of stem cells and engineered tissues are destructive, time consuming, and logistically intensive, the development and validation of a non-invasive, label-free approach to defining the spatiotemporal patterns of cell differentiation can offer a powerful tool for rapid, high-content characterization of cell and tissue cultures.

Metabolic flux-based modularity using shortest retroactive distances.

BACKGROUND: Graph-based modularity analysis has emerged as an important tool to study the functional organization of biological networks. However, few methods are available to study state-dependent changes in network modularity using biological activity data. We develop a weighting scheme, based on metabolic flux data, to adjust the interaction distances in a reaction-centric graph model of a metabolic network. The weighting scheme was combined with a hierarchical module assignment algorithm featuring the preservation of metabolic cycles to examine the effects of cellular differentiation and enzyme inhibitions on the functional organization of adipocyte metabolism. RESULTS: Our analysis found that the differences between various metabolic states primarily involved the assignment of two specific reactions in fatty acid synthesis and glycerogenesis. Our analysis also identified cyclical interactions between reactions that are robust with respect to metabolic state, suggesting possible co-regulation. Comparisons based on cyclical interaction distances between reaction pairs suggest that the modular organization of adipocyte metabolism is stable with respect to the inhibition of an enzyme, whereas a major physiological change such as cellular differentiation leads to a more substantial reorganization. CONCLUSION: Taken together, our results support the notion that network modularity is influenced by both the connectivity of the network's components as well as the relative engagements of the connections.

Identification of biochemical network modules based on shortest retroactive distances.

Modularity analysis offers a route to better understand the organization of cellular biochemical networks as well as to derive practically useful, simplified models of these complex systems. While there is general agreement regarding the qualitative properties of a biochemical module, there is no clear consensus on the quantitative criteria that may be used to systematically derive these modules. In this work, we investigate cyclical interactions as the defining characteristic of a biochemical module. We utilize a round trip distance metric, termed Shortest Retroactive Distance (ShReD), to characterize the retroactive connectivity between any two reactions in a biochemical network and to group together network components that mutually influence each other. We evaluate the metric on two types of networks that feature feedback interactions: (i) epidermal growth factor receptor (EGFR) signaling and (ii) liver metabolism supporting drug transformation. For both networks, the ShReD partitions found hierarchically arranged modules that confirm biological intuition. In addition, the partitions also revealed modules that are less intuitive. In particular, ShReD-based partition of the metabolic network identified a 'redox' module that couples reactions of glucose, pyruvate, lipid and drug metabolism through shared production and consumption of NADPH. Our results suggest that retroactive interactions arising from feedback loops and metabolic cycles significantly contribute to the modularity of biochemical networks. For metabolic networks, cofactors play an important role as allosteric effectors that mediate the retroactive interactions.