Metabolic Network Modularity in Archaea Depends on Growth Conditions

Network modularity is an important structural feature in metabolic networks. A previous study suggested that the variability in natural habitat promotes metabolic network modularity in bacteria. However, since many factors influence the structure of the metabolic network, this phenomenon might be limited and there may be other explanations for the change in metabolic network modularity. Therefore, we focus on archaea because they belong to another domain of prokaryotes and show variability in growth conditions (e.g., trophic requirement and optimal growth temperature), but not in habitats because of their specialized growth conditions (e.g., high growth temperature). The relationship between biological features and metabolic network modularity is examined in detail. We first show the absence of a relationship between network modularity and habitat variability in archaea, as archaeal habitats are more limited than bacterial habitats. Although this finding implies the need for further studies regarding the differences in network modularity, it does not contradict previous work. Further investigations reveal alternative explanations. Specifically, growth conditions, trophic requirement, and optimal growth temperature, in particular, affect metabolic network modularity. We have discussed the mechanisms for the growth condition-dependant changes in network modularity. Our findings suggest different explanations for the changes in network modularity and provide new insights into adaptation and evolution in metabolic networks, despite several limitations of data analysis

MIANN models of networks of biochemical reactions, ecosystems, and U.S. Supreme Court with Balaban-Markov indices

[Abstract] We can use Artificial Neural Networks (ANNs) and graph Topological Indices (TIs) to seek structure-property relationship. Balabans’ J index is one of the classic TIs for chemo-informatics studies. We used here Markov chains to generalize the J index and apply it to bioinformatics, systems biology, and social sciences. We seek new ANN models to show the discrimination power of the new indices at node level in three proof-of-concept experiments. First, we calculated more than 1,000,000 values of the new Balaban-Markov centralities Jk(i) and other indices for all nodes in >100 complex networks. In the three experiments, we found new MIANN models with >80% of Specificity (Sp) and Sensitivity (Sn) in train and validation series for Metabolic Reactions of Networks (MRNs) for 42 organisms (bacteria, yeast, nematode and plants), 73 Biological Interaction Webs or Networks (BINs), and 43 sub-networks of U.S. Supreme court citations in different decades from 1791 to 2005. This work may open a new route for the application of TIs to unravel hidden structure-property relationships in complex bio-molecular, ecological, and social networks

Working Together: Using protein networks of bacterial species to compare essentiality, centrality, and conservation in Escherichia coli.

Proteins in Escherichia coli were compared in terms of essentiality, centrality, and conservation. The hypotheses of this study are: for proteins in Escherichia coli, (1) there is a positive, measureable correlation between protein conservation and essentiality, (2) there is a positive relationship between conservation and degree centrality, and (3) essentiality and centrality also have a positive correlation. The third hypothesis was supported by a moderate correlation, the first with a weak correlation, and the second hypotheis was not supported. When proteins that did not map to orthologous groups and proteins that had no interactions were removed, the relationship between essentality and conservation increased to a strong relationship. This was due to the effect of proteins that did not map to orthologus groups and suggests that protein orthology represented by clusters of orthologus groups does not accurately dipict protein conservation among the species studied

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Energy metabolism in mobile, wild-sampled sharks inferred by plasma lipids

Evaluating how predators metabolize energy is increasingly useful for conservation physiology, as it can provide information on their current nutritional condition. However, obtaining metabolic information from mobile marine predators is inherently challenging owing to their relative rarity, cryptic nature and often wide-ranging underwater movements. Here, we investigate aspects of energy metabolism in four free-ranging shark species (n = 281; blacktip, bull, nurse, and tiger) by measuring three metabolic parameters [plasma triglycerides (TAG), free fatty acids (FFA) and cholesterol (CHOL)] via non-lethal biopsy sampling. Plasma TAG, FFA and total CHOL concentrations (in millimoles per litre) varied inter-specifically and with season, year, and shark length varied within a species. The TAG were highest in the plasma of less active

Limitations of a Metabolic Network-Based Reverse Ecology Method for Inferring Host–Pathogen Interactions

Background Host–pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. Results We re-evaluated the importance of the reverse ecology method for evaluating host–pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host–pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. Conclusion These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host–pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host–pathogen interactions

Tilbake til det grunnleggende : forenkling av mikrobielle samfunn for å tolke komplekse interaksjoner

Microbes are everywhere and contribute to many essential processes relevant for planet Earth, ranging from biogeochemical cycles to complex human behavior. The means to achieve these colossal tasks for such small and, at first glance, simple organisms rely on their ability to assemble in heterogeneous communities in which populations with different taxonomies and functions coexist and complement each other. Some microbes are of particular interest for human civilization and have long been used for everyday tasks, such as the production of bread and wine. More recently, large-scale industrial and civil projects have taken advantage of the transformative capabilities of microbial communities, with key examples being biogas reactors, mining and wastewater treatment. Decades of classical microbiology, based on pure culture isolates and their physiological characterization, have built the foundations of modern microbial ecology. Molecular analysis of microbes and microbial communities has generated an understanding that for many microbial populations cultivation is hard to achieve and that breaking a community apart impacts its function. These limitations have driven the development of technical tools that bring us directly in contact with communities in their natural environment. In the mid 2000’s the recently established “omics” techniques were quickly adapted to their “meta-omics” version, enabling direct analysis of the microbial samples without culture. Every class of molecules (DNA, RNA, protein, metabolite, etc.) can now theoretically be analyzed from the entire community within a given sample. Metagenomics uses community DNA to build the phylogenetic picture and the genetic potential, whereas metatranscriptomics and metaproteomics employ RNA and proteins respectively to inquire the gene expression of the community. Finally, meta-metabolomics can close the loop and describe the metabolic activity of the microbes. Here, we combined the four aforementioned major meta-omics disciplines in a gene- and population-centric perspective to re-iterate the same Aristotelian question underlying microbial ecology: how is it possible that the whole is more than the sum of its parts? Along the detailed answers provided by the individual communities in various environments, we also tried to learn something about biology itself. We first addressed in a saccharolytic and methane-producing minimalistic consortium (SEM1b), the strain-specific interplay engaged in (hemi)cellulose degradation, explaining the ubiquity of Coprothermobacter proteolyticus in biogas reactors. We showed through the genetic potential of the C. proteolyticus-affiliated COPR1 population, the putative acquisition via horizontal gene transfer of a gene cassette for hemicellulose degradation. Moreover, we showed how the gene expression of these COPR1 genes were both coherent with the release of hemicellulose by another population of the community (RCLO1) and synced with the gene expression of the orthologous genes of an already known hemicellulolytic population (CLOS1). Conclusively, we demonstrated how the same purified COPR1 protein (Glycosyl Hydrolases 16) showed endoglucanase activity on several hemicellulose substrates. Secondly, we explored the combined application of absolute omics-based quantification of RNA and proteins using SEM1b as a benchmark community, due to its lower complexity (less than 12 populations) and relatively resolved biology. We subsequently demonstrated that the uncultured bacterial populations in SEM1b followed the expected protein-to-RNA ratio (102-104) of previously analyzed cultured bacteria in exponential phase. In contrast, an archaeon population from SEM1b showed values in the range 103-105, the same as what has been reported for eukaryotes (yeast and human) in the literature. In addition, we modeled the linearity (k) between genome-centric transcriptomes and proteomes over time and used it to predict the essential metabolic populations of the SEM1b community through converging and parallel k-trends, which was subsequently confirmed via classical pathway analysis. Finally, we estimated the translation and the protein degradation rates, coming to the conclusion that some of the processes in the cell that require a rapid tuning (e.g. metabolism and motility) are regulated (also) post-transcriptionally. Thirdly we sought to apply our approach of collapsing complex datasets into simplistic metrics in order to identify underlying community trends, onto a more complex and “real-world” microbiome. To do this, we resolved more than one year of weekly sampling from a lipid-accumulating community (Shif-LAO) that inhabits a wastewater treatment in Shifflange (Luxembourg), and showed an extreme genetic redundancy and turnover in contrast to a more conservative trend in functions. Moreover, we demonstrated how the time patterns (e.g. seasonality) in both gene count and gene expression are linked with the physico-chemical parameters associated with the corresponding samples. Furthermore, we built the static reaction network underlying the whole community over the complete dataset (51 temporal samples). From this, we characterized the sub-network for lipid accumulation, and showed that its more expressed nodes were defined by resource competition between different taxa (deduced via inverse taxonomic richness and gene expression over time). In contrast, the nitrogen metabolism sub-network instead exhibited a dominant taxon and a keystone ammonia oxidizing monooxygenase, the first enzyme of ammonia oxidation, which may lead to the production of nitrous gas (a powerful greenhouse gas). Overall, our results presented in this thesis build a comprehensive repertoire of interactions in microbial communities ranging from a simplistic (10’s of populations) consortium to a natural complex microbiome (100’s of populations). These were ultimately uncovered using an array of techniques, including unsupervised gene expression clustering, pathway analysis, reaction networks, co-expression networks, eigengenes and linearity trends between transcriptome and proteome. Moreover, we learnt that to achieve a full understanding of microbial ecology and detailed interactions, we need to integrate all the meta-omics layers quantified with absolute measurements. However, when scaling these approaches to real-world communities the massive amounts of generated data brings new challenges and necessitates simplifying strategies to reduce complexity and extrapolate ecological trends.Mikroorganismer er overalt og de bidrar til mange essensielle prosesser som er viktige for planeten vår, alt fra biokjemiske sykluser til kompleks menneskelig oppførsel. Midlene disse små, og ved første øyekast enkle organismene bruker for å oppnå så betydelige oppgaver på, ligger i deres evne til å forenes i et heterogent samfunn der ulike populasjoner med en forskjellig taksonomi og funksjoner sameksisterer og utfyller hverandre. Noen mikrobielle samfunn er av særlig interesse for oss mennesker, og har i lang tid blitt utnyttet i hverdagslige gjøremål, slik som produksjon av brød og vin. I senere tid har også stor-skala industri og kommunale anlegg, for eksempel biogass reaktorer og renseanlegg, dratt nytte av mikrobesamfunns evne til å transformere. Tiår med klassisk mikrobiologi, basert på dyrking og fysiologisk karakterisering av renkulturer har bygget grunnlaget for moderne mikrobiell økologi. Molekylære analyser av mikrober og mikrobielle samfunn har resultert i forståelsen om at mange mikrobielle populasjoner er vanskelige å kultivere, og at en oppdeling av samfunnet vil påvirke dens funksjoner. Disse begrensningene har vært en drivkraft for utviklingen av tekniske verktøy som kan bringe oss i direkte kontakt med mikrobesamfunnet i deres naturlige miljø. I midten av 2000-talles ble de nylig etablerte «omikk»-teknikkene raskt adoptert til også å gjelde «meta-omikk», som muliggjør direkte analysering av mikrobielle samfunn uten kultivering. I dag kan i teorien hver molekylerære klasse (DNA, RNA, proteiner, metabolitter, osv.) bli analysert fra hele mikrobesamfunn i en bestemt prøve. I metagenomikk benyttes DNA-innholdet til å konstruere et fylogenetisk bilde av samfunnet og det genetiske potensiale, mens metatranskriptomikk og metaproteomikk bruker henholdsvis RNA og proteiner for å se på gen-uttrykket i samfunnet. Meta-metabolomikk kan slutte sirkelen ved å beskrive den metabolske aktiviteten til mikrobene. I arbeidet som ligger til grunn for denne avhandlingen, kombinerte vi fire av de nevnte fagfeltene innen meta-omikk i et gen- og populasjons-orientert perspektiv for å gjenta det samme Aristoteliske spørsmålet bak mikrobiell økologi: hvordan er det mulig at helheten er større enn summen av enkeltdelene? Sammen med de detaljerte svarene som ble gitt av de enkelte mikrobesamfunnene i ulike miljøer, forsøkte vi også å lære noe om biologi i seg selv. Først adresserte vi det stamme-spesifikke samspillet involvert i (hemi)cellulose degradering i et sakkarolytisk og metan-produserende minimalistisk konsortium (SEM1b), som belyser omfanget av Coprothermobacter proteolyticus i biogass reaktorer. Gjennom det genetiske potensiale til COPR1-populasjonen tilknyttet C. proteolyticus, viste vi den antatte ervervelsen, via horisontal gen-overføring, av en gen-kassett for nedbrytning av hemicellulose. Videre viste vi hvordan genuttrykket til disse COPR1-genene var i samsvar med frigivelsen av hemicellulose av en annen populasjon i samfunnet (RCLO1), og synkronisert med genuttrykket av de ortologe genene fra en allerede kjent hemicellulolytisk populasjon (CLOS1). Avslutningsvis demonstrerte vi hvordan det samme rensede COPR1-proteinet (glykosid-hydrolase 16) viste endoglukanase-aktivitet på flere hemicellulosesubstrater. På grunn av lavere kompleksitet (færre enn 12 populasjoner) og en relativt kjent biologi, benytte vi SEM1b videre som et referansesamfunn for å utforske den kombinerte anvendelsen av absolutt omikk-basert kvantifisering av RNA og proteiner. Vi demonstrerte deretter at de ukultiverte bakterie-populasjonene i SEM1b fulgte en protein-til-RNA ratio (102-104) som var forventet basert på tidligere analyser av bakteriekulturer i eksponentiell fase. I kontrast til dette viste en arkeonpopulasjon fra SEM1b verdier i området mellom 103-105, som er det samme som tidligere rapportert i litteraturen for eukaryote (gjær og menneske). I tillegg modellerte vi lineariteten (k) mellom genom-orienterte transkriptomer og proteomer over tid, og brukte dette til å forutsi de essensielle metabolsk populasjon i SEM1b-samfunnet gjennom konvergerende og parallelle k-trender, som senere ble bekreftet via klassiske analyser av metabolske synteseveier. Til slutt estimerte vi frekvensen av translasjon og protein degradering, hvorpå vi konkluderte med at noen av prosessene i en celle som krever rask innstilling (som for eksempel metabolisme og bevegelse) er regulert (også) post- transkripsjonelt. Til slutt ønsket vi å anvende vår tilnærming for å sette komplekse datasett inn i forenklede matriser for å identifisere underliggende trender i mikrosamfunnet, på et mer komplekst og virkelighetsnært mikrobiom. Til dette benyttet vi et mer enn ett år med ukentlige prøvetakninger fra en lipid-akkumulerende mikrobesamfunn (Shif-LAO) i et renseanlegg i Shifflange (Luxembourg), og avdekket en ekstrem genetisk redundans og turnover, i motsetning til en mer konservativ trend i funksjoner. Videre demonstrerte vi hvordan tidsavhengige mønstre (som for eksempel sesongvariasjoner) i både antall gener og genuttrykk er knyttet til fysisk-kjemiske parameter assosiert med de tilsvarende prøvene. I tillegg rekonstruerte vi det underliggende statiske reaksjonsnettverket til mikrobesamfunnet over hele datasettet (51 prøver over tid). Basert på dette, karakteriserte vi sub-nettverk for lipid-akkumulering, og demonstrerte at mer uttrykte noder var definert av konkurransen om ressurser mellom ulike taksonomiske grupper (antatt via reversert taksonomisk diversitet og genuttrykk over tid). I motsetning til dette, viste nettverket for nitrogen-metabolismen i stedet et dominerende taxon og en keystone ammoniakk-oksiderende monooxygenase, det første enzymet i ammoniakk oksidasjon, som fører til produksjonen av lystgass (en svært sterk klimagass). Resultatene presentert i denne doktorgradsavhandlingen bygger på et omfattende repertoar av interaksjoner i mikrobielle samfunn som spenner fra et forenklet konsortium (titalls populasjoner) til et naturlig komplekst mikrobiom (hundretalls populasjoner). Disse mikrobiomene ble til slutt kartlagt ved hjelp av en rekke teknikker, blant annet unsupervised gruppering av genutrykk, analyser av metabolisk synteseveier, nettverk av reaksjoner og co-uttrykte gener, eigengener og lineære trender mellom transkriptom og proteom. I tillegg erfarte vi at for å oppnå en full forståelse av mikrobiell økologi og detaljerte interaksjoner må vi integrere alle lagene av meta-omikk, kvantifisert med absolutte målinger. Når man oppskalering disse tilnærmingen til virkelige mikrobesamfunn, bringer imidlertid enorme mengder generert data til nye utfordringer som nødvendiggjør en forenkling av strategier for å redusere kompleksiteten og ekstrapolerer økologiske trender

On the Origin of Biomolecular Networks

Biomolecular networks have already found great utility in characterizing complex biological systems arising from pairwise interactions amongst biomolecules. Here, we explore the important and hitherto neglected role of information asymmetry in the genesis and evolution of such pairwise biomolecular interactions. Information asymmetry between sender and receiver genes is identified as a key feature distinguishing early biochemical reactions from abiotic chemistry, and a driver of network topology as biomolecular systems become more complex. In this context, we review how graph theoretical approaches can be applied not only for a better understanding of various proximate (mechanistic) relations, but also, ultimate (evolutionary) structures encoded in such networks from among all types of variations they induce. Among many possible variations, we emphasize particularly the essential role of gene duplication in terms of signaling game theory, whereby sender and receiver gene players accrue benefit from gene duplication, leading to a preferential attachment mode of network growth. The study of the resulting dynamics suggests many mathematical/computational problems, the majority of which are intractable yet yield to efficient approximation algorithms, when studied through an algebraic graph theoretic lens. We relegate for future work the role of other possible generalizations, additionally involving horizontal gene transfer, sexual recombination, endo-symbiosis, etc., which enrich the underlying graph theory even further