Systems Biology-based Approach Research Articles

Influence of dietary pattern on anti-tuberculosis treatment outcomes in persons with dysglycemia: a Peruvian prospective cohort study.

Dietary patterns (DPs) are associated with overall nutritional status and may alter the clinical prognosis of tuberculosis. This interaction can be further intricated by dysglycemia (i.e., diabetes or prediabetes). Here, we identified DPs that are more common with tuberculosis-dysglycemia and depicted their association with tuberculosis treatment outcomes. A prospective cohort study of persons with tuberculosis and their contacts was conducted in Peru. A food frequency questionnaire and a multidimensional systems biology-based analytical approach were employed to identify DPs associated with these clinical groups. Potential independent associations between clinical features and DPs were analyzed. Three major DPs were identified. TB-dysglycemia cases more often had a high intake of carbohydrates (DP1). Furthermore, DP1 was found to be associated with an increased risk of unfavorable TB outcomes independent of other factors, including dysglycemia. Our findings suggest that the evaluation of nutritional status through DPs in comorbidities such as dysglycemia is a fundamental action to predict TB treatment outcomes. The mechanisms underlying the association between high intake of carbohydrates, dysglycemia, and unfavorable tuberculosis treatment outcomes warrant further investigation.

Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach

BackgroundHerbal medicine Sanqi (SQ), the dried root or stem of Panax notoginseng (PNS), has been reported to have anti-diabetic and anti-obesity effects and is usually administered as a decoction for Chinese medicine. Alternative to utilizing PNS pure compound for treatment, we are motivated to propose an unconventional scheme to investigate the functions of PNS mixture. However, studies providing a detailed overview of the transcriptomics-based signaling network in response to PNS are seldom available.MethodsTo explore the reasoning of PNS in treating metabolic disorders such as insulin resistance, we implemented a systems biology-based approach with RNA sequencing (RNA-seq) and miRNA sequencing data to elucidate key pathways, genes and miRNAs involved.ResultsFunctional enrichment analysis revealed PNS up-regulating oxidative stress-related pathways and down-regulating insulin and fatty acid metabolism. Superoxide dismutase 1 (SOD1), peroxiredoxin 1 (PRDX1), heme oxygenase-1 (Hmox1) and glutamate cysteine ligase (GCLc) mRNA and protein levels, as well as related miRNA levels, were measured in PNS treated rat pancreatic β cells (INS-1). PNS treatment up-regulated Hmox1, SOD1 and GCLc expression while down-regulating miR-24-3p and miR-139-5p to suppress oxidative stress. Furthermore, we verified the novel interactions between miR-139-5p and miR-24-3p with GCLc and SOD1.ConclusionThis work has demonstrated the mechanism of how PNS regulates cellular molecules in metabolic disorders. Therefore, combining omics data with a systems biology strategy could be a practical means to explore the potential function and molecular mechanisms of Chinese herbal medicine in the treatment of metabolic disorders.

A systems biology-based approach to screen key splicing factors in hepatocellular carcinoma.

A splicing factor is as an important upstream regulator of the alternative splicing process. Hence, it is considered to be a therapeutic target for hepatocellular carcinoma (HCC) tissues. In this study, a systems biology-based methodology was used to screen the essential splicing factors precisely and efficiently. A more comprehensive set of alternative splicing events, which were linked to patient survival, was constructed by performing the bivariate Cox regression and receiver operating characteristic (ROC) analyses. Then, the expression data was obtained from The Cancer Genome Altas (TCGA) data set and the three Gene Expression Omnibus (GEO) datasets. It was used to obtain the survival-related splicing factors, which showed a significantly differential expression in the tumor and normal tissues. Using the topological properties of the bipartite graph association network of the alternative splicing events and the splicing factors, we identified the five key splicing factors. Among them, four factors were found to play a prominent role in the development of HCC. The remaining factor was Survival Motor Neuron Domain Containing 1(SMNDC1), which showed a positive correlation with the immune cell infiltration, the biomarkers of immune cells, and the immune checkpoint genes. By performing quantitative real-time polymerase chain reaction analyses, we proved that SMNDC1 was overexpressed in tumor cells. Following the knockdown of its expression, the proliferation and the migration of HCC cells could be suppressed. These results confirmed that the screening method of this study was reliable and accurate. It provided new insights into the mechanism through which splicing factors elicit tumor development.

Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.

Systems Biology Analysis of Temporal Dynamics That Govern Endothelial Response to Cyclic Stretch.

Endothelial cells in vivo are subjected to a wide array of mechanical stimuli, such as cyclic stretch. Notably, a 10% stretch is associated with an atheroprotective endothelial phenotype, while a 20% stretch is associated with an atheroprone endothelial phenotype. Here, a systems biology-based approach is used to present a comprehensive overview of the functional responses and molecular regulatory networks that characterize the transition from an atheroprotective to an atheroprone phenotype in response to cyclic stretch. Using primary human umbilical vein endothelial cells (HUVECs), we determined the role of the equibiaxial cyclic stretch in vitro, with changes to the radius of the magnitudes of 10% and 20%, which are representative of physiological and pathological strain, respectively. Following the transcriptome analysis of next-generation sequencing data, we identified four key endothelial responses to pathological cyclic stretch: cell cycle regulation, inflammatory response, fatty acid metabolism, and mTOR signaling, driven by a regulatory network of eight transcription factors. Our study highlights the dynamic regulation of several key stretch-sensitive endothelial functions relevant to the induction of an atheroprone versus an atheroprotective phenotype and lays the foundation for further investigation into the mechanisms governing vascular pathology. This study has significant implications for the development of treatment modalities for vascular disease.

Asthma and the Missing Heritability Problem: Necessity for Multiomics Approaches in Determining Accurate Risk Profiles.

Asthma is ranked among the most common chronic conditions and has become a significant public health issue due to the recent and rapid increase in its prevalence. Investigations into the underlying genetic factors predict a heritable component for its incidence, estimated between 35% and 90% of causation. Despite the application of large-scale genome-wide association studies (GWAS) and admixture mapping approaches, the proportion of variants identified accounts for less than 15% of the observed heritability of the disease. The discrepancy between the predicted heritable component of disease and the proportion of heritability mapped to the currently identified susceptibility loci has been termed the ‘missing heritability problem.’ Here, we examine recent studies involving both the analysis of genetically encoded features that contribute to asthma and also the role of non-encoded heritable characteristics, including epigenetic, environmental, and developmental aspects of disease. The importance of vertical maternal microbiome transfer and the influence of maternal immune factors on fetal conditioning in the inheritance of disease are also discussed. In order to highlight the broad array of biological inputs that contribute to the sum of heritable risk factors associated with allergic disease incidence that, together, contribute to the induction of a pro-atopic state. Currently, there is a need to develop in-depth models of asthma risk factors to overcome the limitations encountered in the interpretation of GWAS results in isolation, which have resulted in the missing heritability problem. Hence, multiomics analyses need to be established considering genetic, epigenetic, and functional data to create a true systems biology-based approach for analyzing the regulatory pathways that underlie the inheritance of asthma and to develop accurate risk profiles for disease.

Systems biology analysis of pyrethroid biodegradation in bacteria and its effect on the cellular environment of pests and humans

Pyrethroid insecticides are widely used to manage crop and household pests. The extensive use of pyrethroids leads to pesticide resistance in insects and negatively affects human health. Microbial degradation of pyrethroids is an emerging method to minimize their off-target toxic effects on the environment. The main aim of this research work to compare the biomagnification of pyrethroids in different living systems in environment. This study performed a detailed analysis of pyrethroid biodegradation and toxicity using an in silico approach. Pyrethroid biodegradation was studied using a systems biology-based approach that correlated human, insect and microbial systems to achieve a comprehensive view of total environmental pyrethroid biomagnification and bioremediation. The topology of the pyrethroid model was determined using the hub nodes in cytoscape that improved the understanding of pyrethroid biodegradation and environmental losses. The results of the simulated model were used for the real-time analysis of metabolites, genes, RNA and enzymes in bacteria, insect and human cells. The model predicted the pyrethroid biochemistry and physiology in three living systems. The findings of this study elucidated the systems biology-based analysis of the effects of pyrethroids on bacterial, insect and human systems.

Elucidating the Mechanism of Action of the Attributed Immunomodulatory Role of Eltrombopag in Primary Immune Thrombocytopenia: An In Silico Approach.

Eltrombopag is a thrombopoietin receptor (MPL) agonist approved for the treatment of primary immune thrombocytopenia (ITP). Recent evidence shows that some patients may sustain platelet counts following eltrombopag discontinuation. The systemic immunomodulatory response that resolves ITP in some patients could result from an increase in platelet mass, caused either by the direct action of eltrombopag on megakaryocytes through MPL stimulation, or potential MPL-independent actions on other cell types. To uncover the possible mechanisms of action of eltrombopag, in silico analyses were performed, including a systems biology-based approach, a therapeutic performance mapping system, and structural analyses. Through manual curation of the available bibliography, 56 key proteins were identified and integrated into the ITP interactome analysis. Mathematical models (94.92% mean accuracy) were obtained to elucidate potential MPL-dependent pathways in non-megakaryocytic cell subtypes. In addition to the effects on megakaryocytes and platelet numbers, the results were consistent with MPL-mediated effects on other cells, which could involve interferon-gamma, transforming growth factor-beta, peroxisome proliferator-activated receptor-gamma, and forkhead box protein P3 pathways. Structural analyses indicated that effects on three apoptosis-related proteins (BCL2L1, BCL2, BAX) from the Bcl-2 family may be off-target effects of eltrombopag. In conclusion, this study proposes new hypotheses regarding the immunomodulatory functions of eltrombopag in patients with ITP.

Highlighting the Role of Universally Available and Innate Immune Cell Counts in Acute Ischemic Stroke: A Scoping Review

Stroke is one of the leading causes of adult disability and the second leading cause of death worldwide. The immune system actively participates in the pathobiological process of acute ischemic stroke (AIS), during the index event and the repair process. Research on neurovascular inflammation has created a renewed interest in the use of easily available biomarkers reflective of innate and adaptive immunological changes with potential diagnostic, prognostic, and therapeutic implications particularly in AIS. The current scoping review aimed to assess the significance the neutrophil to lymphocyte (NLR) in AIS and its related complications and explore their association with post-stroke recovery trajectory. The Arksey and O’Malley methodological framework was employed to review the published papers on the neutrophil–lymphocyte ratio (NLR) and AIS in late November 2020. Only studies published in English from 2000–2020 were included in this scoping review. Fifty-three published papers were reviewed. This review’s key finding is that a canonical inflammatory response occurs in the hyperacute, acute, subacute, and chronic stages of stroke. An excessive circulating innate immune cells (neutrophils) and reduced circulating adaptive immune cells (lymphocytes) are associated with poorer outcomes during the acute interventions as well as the recovery trajectory. This scoping review’s findings highlights the utility of a systems biology-based approach in stroke care.

Head to head evaluation of second generation ALK inhibitors brigatinib and alectinib as first-line treatment for ALK+ NSCLC using an in silico systems biology-based approach.

Around 3–7% of patients with non-small cell lung cancer (NSCLC), which represent 85% of diagnosed lung cancers, have a rearrangement in the ALK gene that produces an abnormal activity of the ALK protein cell signaling pathway. The developed ALK tyrosine kinase inhibitors (TKIs), such as crizotinib, ceritinib, alectinib, brigatinib and lorlatinb present good performance treating ALK+ NSCLC, although all patients invariably develop resistance due to ALK secondary mutations or bypass mechanisms. In the present study, we compare the potential differences between brigatinib and alectinib’s mechanisms of action as first-line treatment for ALK+ NSCLC in a systems biology-based in silico setting.Therapeutic performance mapping system (TPMS) technology was used to characterize the mechanisms of action of brigatinib and alectinib and the impact of potential resistances and drug interferences with concomitant treatments.The analyses indicate that brigatinib and alectinib affect cell growth, apoptosis and immune evasion through ALK inhibition. However, brigatinib seems to achieve a more diverse downstream effect due to a broader cancer-related kinase target spectrum. Brigatinib also shows a robust effect over invasiveness and central nervous system metastasis-related mechanisms, whereas alectinib seems to have a greater impact on the immune evasion mechanism.Based on this in silico head to head study, we conclude that brigatinib shows a predicted efficacy similar to alectinib and could be a good candidate in a first-line setting against ALK+ NSCLC. Future investigation involving clinical studies will be needed to confirm these findings. These in silico systems biology-based models could be applied for exploring other unanswered questions.

Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12035-020-02266-w.

Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.

Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway‐extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning‐based averaging of multiple pathway‐extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning‐based pathway‐extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.

Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

A systems approach to clinical oncology uses deep phenotyping to deliver personalized care.

Cancer encompasses a complex, heterogeneous and dynamic group of diseases that arise from perturbations to multiple biological networks within the body. A systems biology-based approach would help to decipher this complexity, to deeply characterize the pathophysiology of the disease and to stratify cancers into appropriate molecular subtypes to facilitate the development of personalized therapies. Technological advances made over the past decade have enabled multiscale, longitudinal measurements ('snapshots') of human biology, from single-cell analyses to whole-body monitoring. In this Perspective, we discuss some of these technologies and how they have (and will) contributed to our understanding of cancer biology as well as to the development of early diagnostics and personalized therapies. We argue that the integration of molecular profiling of cancerous tissues with deep, longitudinal profiling of the physiological state of an individual ('deep phenotyping') is key to understanding the prevention, initiation, progression and response to treatment of cancers. Systems biology-based approaches can provide an unprecedented trove of data for early detection of disease transitions, prediction of therapeutic responses and clinical outcomes, and for the design of personalized treatments.

Divergent lncRNA MYMLR regulates MYC by eliciting DNA looping and promoter-enhancer interaction.

Long non-coding RNAs (lncRNAs) function in a wide range of processes by diverse mechanisms, though their roles in regulation of oncogenes and/or tumor suppressors remain rather elusive. We performed a global search for lncRNAs affecting MYC activity using a systems biology-based approach with a K supercomputer and the GIMLET algorism based on local distance correlations. Consequently, MYMLR was identified and experimentally shown to maintain MYC transcriptional activity and cell cycle progression despite the low levels of expression. A proteomic search for MYMLR-binding proteins identified PCBP2, while it was also found that MYMLR places a 557-kb upstream enhancer region in the proximity of the MYC promoter in cooperation with PCBP2. These findings implicate a crucial role for MYMLR in regulation of the archetypical oncogene MYC and warrant future studies regarding the involvement of low copy number lncRNAs in regulation of other crucial oncogenes and tumor suppressor genes.

Proteomic changes driven by urban pollution suggest particulate matter as a deregulator of energy metabolism, mitochondrial activity, and oxidative pathways in the rat brain

The adverse effects of air pollution have been long studied in the lung and respiratory systems, but the molecular changes that this causes at the central nervous system level have yet to be fully investigated and understood. To explore the evolution with time of protein expression levels in the brain of rats exposed to particulate matter of different sizes, we carried out two-dimensional gel electrophoresis followed by determination of dysregulated proteins through Coomassie blue staining-based densities (SameSpots software) and subsequent protein identification using MALDI-based mass spectrometry. Expression differences in dysregulated proteins were found to be statistically significant with p-value <0.05. A systems biology-based approach was utilized to determine critical biochemical pathways involved in the rats' brain response. Our results suggest that rats' brains have a particulate matter size dependent-response, being the mitochondrial activity and the astrocyte function severely affected. Our proteomic study confirms the dysregulation of different biochemical pathways involving energy metabolism, mitochondrial activity, and oxidative pathways as some of the main effects of PM exposure on the rat brain. SignificanceRat brains exposed to particulate matter with origin in car engines are affected in two main areas: mitochondrial activity, by the dysregulation of many pathways linked to the respiratory chain, and neuronal and astrocytic function, which stimulates brain changes triggering tumorigenesis and neurodegeneration.

Understanding biological driver mutations and mechanism of resistance to EGFR-tyrosine kinase inhibitors in lung cancer patients using a novel translational platform.

e20555 Background: Lung cancer causes ~1.69 million deaths globally and 5-year survival rate is &lt; 20%. Surgery is preferred for patients with early stage NSCLC, however, in &gt; 50% cases lung cancers are diagnosed at an advanced stage. EGFR-TKIs, erlotinib and gefitinib have shown benefit in patients harboring sensitizing EGFR mutations, although 20-30% of NSCLC patients do not show good clinical response to EGFR-TKIs despite having the mutations. Several factors have been identified for de novo and acquired resistance to first generation EGFR-TKIs, including secondary mutations such as T790M, aberrant activation of bypass pathways such as amplification of c-MET, or other abnormalities in downstream pathways including KRAS mutations or loss of PTEN among others. There exist significant inter-patient variations in response profiles and mechanisms of resistance, which often confounds treatment selection on a personalized level, especially if first-line therapy fails. Methods: We have developed a patient tumor derived ex vivo platform technology using multi-disciplinary systems biology-based approach that recreates a patient’s tumor microenvironment by preserving the tumor-stromal architecture and immune contexture. Fresh tumor biopsies and 10-ml blood were collected from patients with IRB approval and patient consent. Tumors obtained from 21 lung cancer patients were grown ex vivo on our platform in presence of PBMC and autologous sera. The tumors were treated with standard of care chemotherapies and EGFR inhibitors, and drug efficacy on tumors were measured. Results: Of 18 tumor samples treated with EGFR-TKIs, 4 were predicted responders to gefitinib and 14 were predicted non-responders to EGFR-TKIs (erlotinib or gefitinib). Of the 14 non-responders, 7 were predicted responders to carboplatin/pemetrexed and/or carboplatin/docetaxel. Patients were followed, and clinical data was captured. Whole genome sequencing and mutational analysis was done on the tumors. Hits were validated by immunohistochemical assays. Data from molecular signatures of the tumors with functional, phenotypic assays, were used to delineate mechanism of resistance in individual lung cancer patients to EGFR-TKIs. Conclusions: We have developed an ex vivo, personalized platform, which enables prediction of treatment response on an individual level, and also aids in capturing resistance mechanisms for identifying actionable targets.

A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space

BackgroundWeaning is a period of marked physiological change. The introduction of solid foods and the changes in milk consumption are accompanied by significant gastrointestinal, immune, developmental, and microbial adaptations. Defining a reduced number of infections as the desired health benefit for infants around weaning, we identified in silico (i.e., by advanced public domain mining) infant gut microbes as potential deliverers of this benefit. We then investigated the requirements of these bacteria for exogenous metabolites as potential prebiotic feeds that were subsequently searched for in the natural product space.ResultsUsing public domain literature mining and an in silico reverse metabolic approach, we constructed probiotic-prebiotic-food associations, which can guide targeted feeding of immune health-beneficial microbes by weaning food; analyzed competition and synergy for (prebiotic) nutrients between selected microbes; and translated this information into designing an experimental complementary feed for infants enrolled in a pilot clinical trial (http://www.nourishtoflourish.auckland.ac.nz/).ConclusionsIn this study, we applied a benefit-oriented microbiome research strategy for enhanced early-life immune health. We extended from “classical” to molecular nutrition aiming to identify nutrients, bacteria, and mechanisms that point towards targeted feeding to improve immune health in infants around weaning. Here, we present the systems biology-based approach we used to inform us on the most promising prebiotic combinations known to support growth of beneficial gut bacteria (“probiotics”) in the infant gut, thereby favorably promoting development of the immune system.

Abstract 2464: Divergent lncRNA MYMLR regulates MYC by eliciting DNA looping and promoter-enhancer interaction

Abstract Long non-coding RNAs (lncRNAs), longer than 200 nucleotides in length, function in a wide range of processes by diverse mechanisms. lncRNAs are attracting research interest in the field of cancer research though their roles in regulation of oncogenes and/or tumor suppressors remain rather elusive. In order to search for MYC-modulating lncRNA, we performed a global search for lncRNAs affecting MYC activity using a systems biology-based approach with a K supercomputer and the GIMLET algorism based on local distance correlations. Consequently, MYMLR was identified and experimentally shown to maintain MYC transcriptional activity despite its low levels of expression and cell cycle progression. A proteomic search for MYMLR-binding proteins identified PCBP2, which transcriptionally regulates MYC expression in cooperation with MYMLR. Mechanistically, MYMLR places a 557-kb upstream enhancer region to make the MYC promoter and enhancer regions reside in the proximity. These findings implicate a crucial role for MYMLR in regulation of the archetypical oncogene MYC, and warrant future studies regarding the involvement of low copy number lncRNAs in regulation of other crucial oncogenes and tumor suppressor genes in cancer development. Citation Format: Taisuke Kajino, Teppei Shimamura, Shuyi Gong, Kiyoshi Yanagisawa, Masahiro Nakatochi, Sebastian Griesing, Yukako Shimada, Keiko Kano, Motoshi Suzuki, Satoru Miyano, Takashi Takahashi. Divergent lncRNA MYMLR regulates MYC by eliciting DNA looping and promoter-enhancer interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2464.