LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Cleaning the Medicago Microarray Database to Improve Gene Function Analysis

Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases.

Comparison of the transcriptional profile in the decidua of early-onset and late-onset pre-eclampsia

AIM

To compare early-onset pre-eclampsia (EOPE) and late-onset pre-eclampsia (LOPE) and provide insight into the pathophysiology of pre-eclampsia (PE).

METHODS

Our recent work compared the transcriptomics in decidua of EOPE, LOPE and normal pregnancies (NP).

RESULTS

We found there are a significant number of genes uniquely expressed in the decidua of EOPE and LOPE comparing with NP. Moreover, EOPE and LOPE have their distinct profiles. Unique EOPE-associated genes were mainly involved in apoptosis related pathways such as 'apoptosis' and 'Ras signaling pathway'. PIK3CB and BCL-2 are the core regulatory genes in EOPE decidua, their abnormal expression caused decidual abnormal apoptosis which is relevant to the pathogenesis of EOPE. Whereas, LOPE is a more complicated entity which has more special LOPE-associated genes involved in decidua differentiation, especially in 'gap junction pathway', 'vascular smooth muscle contraction' and 'long-term depression'. PIK3CB, FLT1, CBLC and ITGA7 are the core regulatory genes differentially expressed in EOPE decidua comparing with LOPE.

CONCLUSION

In brief, the different decidual transcriptomics of EOPE and LOPE may correlate with their different etiology. These findings highlight the complex pathophysiology of PE and provide potential targets for a new treatment strategy in patients with PE.

LeGOO: An Expertized Knowledge Database for the Model Legume Medicago truncatula

Medicago truncatula was proposed, about three decades ago, as a model legume to study the Rhizobium-legume symbiosis. It has now been adopted to study a wide range of biological questions, including various developmental processes (in particular root, symbiotic nodule and seed development), symbiotic (nitrogen-fixing and arbuscular mycorrhizal endosymbioses) and pathogenic interactions, as well as responses to abiotic stress. With a number of tools and resources set up in M. truncatula for omics, genetics and reverse genetics approaches, massive amounts of data have been produced, as well as four genome sequence releases. Many of these data were generated with heterogeneous tools, notably for transcriptomics studies, and are consequently difficult to integrate. This issue is addressed by the LeGOO (for Legume Graph-Oriented Organizer) knowledge base (https://www.legoo.org), which finds the correspondence between the multiple identifiers of the same gene. Furthermore, an important goal of LeGOO is to collect and represent biological information from peer-reviewed publications, whatever the technical approaches used to obtain this information. The information is modeled in a graph-oriented database, which enables flexible representation, with currently over 200,000 relations retrieved from 298 publications. LeGOO also provides the user with mining tools, including links to the Mt5.0 genome browser and associated information (on gene functional annotation, expression, methylome, natural diversity and available insertion mutants), as well as tools to navigate through different model species. LeGOO is, therefore, an innovative database that will be useful to the Medicago and legume community to better exploit the wealth of data produced on this model species.

Exploration of the effects of a degS mutant on the growth of Vibrio cholerae and the global regulatory function of degS by RNA sequencing

Background

DegS is a periplasmic serine protease that is considered to be the initiator of the σ^E stress response pathway, and this protein plays an important role in the regulation of the stress response in E. coli. However, knowledge of the biological function and global regulatory network of DegS in Vibrio cholerae remains limited. In this study, we aimed to characterize the molecular functions and further investigate the regulatory network of degS in V. cholerae.

Methods

A deletion mutant of degS was constructed in the V. cholerae HN375 strain. Bacterial colony morphology was observed by a plate-based growth experiment, and bacterial growth ability was observed by a growth curve experiment. High-throughput RNA sequencing (RNA-Seq) technology was used to analyze the differential transcriptomic profiles between the wild-type and degS mutant strains. Gene ontology (GO), pathway analysis and Gene-Act-network analysis were performed to explore the main functions of the differentially expressed genes. Quantitative real-time PCR (qRT-PCR) was performed to validate the reliability and accuracy of the RNA-Seq analysis. The complementation experiments were used to test the roles of degS and ropS in the small colony degS mutant phenotype.

Results

When degS was deleted, the degS mutant exhibited smaller colonies on various media and slower growth than the wild-type strain. A total of 423 differentially expressed genes were identified, including 187 genes that were upregulated in the degS mutant compared to the wild-type strain and 236 genes that were relatively downregulated. GO categories and pathway analysis showed that many differentially expressed genes were associated with various cellular metabolic pathways and the cell cycle. Furthermore, Gene-Act network analysis showed that many differentially expressed genes were involved in cellular metabolic pathways and bacterial chemotaxis. The cAMP-CRP-RpoS signaling pathway and the LuxPQ signal transduction system were also affected by the degS mutant. The expression patterns of nine randomly selected differentially expressed genes were consistent between the qRT-PCR and RNA-seq results. The complementation experiments showed that the small colony degS mutant phenotype could be partially restored by complementation with the pBAD24-degS or pBAD24-rpoS plasmid.

Discussion

These results suggest that the degS gene is important for normal growth of V. cholerae. Some of the differentially expressed genes were involved in various cellular metabolic processes and the cell cycle, which may be associated with bacterial growth. Several new degS-related regulatory networks were identified. In addition, our results suggested that the cAMP-CRP-RpoS signaling pathway may be involved in the small colony degS mutant phenotype. Overall, we believe that these transcriptomic data will serve as useful genetic resources for research on the functions of degS in V. cholerae.

Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants

MOTIVATION

Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown.

RESULTS

We identify and systematically evaluate determinants of performance-including network properties, experimental design choices and data processing-by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets.

AVAILABILITY AND IMPLEMENTATION

Code is available at http://github.com/bagherilab/networkinference/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Cationic liposomes induce cytotoxicity in HepG2 via regulation of lipid metabolism based on whole-transcriptome sequencing analysis

Backgroud

Cationic liposomes (CLs) can be used as non-viral vectors in gene transfer and drug delivery. However, the underlying molecular mechanism of its cytotoxicity has not been well elucidated yet.

Methods

We herein report a systems biology approach based on whole-transcriptome sequencing coupled with computational method to identify the predominant genes and pathways involved in the cytotoxicity of CLs in HepG2 cell line.

Results

Firstly, we validated the concentration-dependent cytotoxicity of CLs with an IC₅₀ of 120 μg/ml in HepG2 exposed for 24 h. Subsequently, we used whole-transcriptome sequencing to identify 220 (77 up- and 143 down-regulated) differentially expressed genes (DEGs). Gene ontology (GO) and pathway analysis showed that these DEGs were mainly related to cholesterol, steroid, lipid biosynthetic and metabolic processes. Additionally, “key regulatory” genes were identified using gene act, pathway act and co-expression network analysis, and expression levels of 11 interested altered genes were confirmed by quantitative real time PCR. Interestingly, no cell cycle arrest was observed through flow cytometry.

Conclusions

These data are expected to provide deep insights into the molecular mechanism of CLs cytotoxicity.

Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

Our study exploits time—the relatively unexplored fourth dimension of gene regulatory networks (GRNs)—to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. We introduce several conceptual innovations to the analysis of time-series data in the area of predictive GRNs. Our resulting network now provides the “transcriptional logic” for transcription factor perturbations aimed at improving N-use efficiency, an important issue for global food production in marginal soils and for sustainable agriculture. More broadly, the combination of the time-based approaches we develop and deploy can be applied to uncover the temporal “transcriptional logic” for any response system in biology, agriculture, or medicine.

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and ¹⁵NO₃⁻ uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.

Applications of Bayesian network models in predicting types of hematological malignancies

Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

Functional Genomics and Seed Development in Medicago truncatula: An Overview

The study of seed development in the model species Medicago truncatula has made a significant contribution to our understanding of this process in crop legumes. Thanks to the availability of comprehensive proteomics and transcriptomics databases, coupled with exhaustive mutant collections, the roles of several regulatory genes in development and maturation are beginning to be deciphered and functionally validated. Advances in next-generation sequencing and the availability of a genomic sequence have made feasible high-density SNP genotyping, allowing the identification of markers tightly linked to traits of agronomic interest. A further major advance is to be expected from the integration of omics resources in functional network construction, which has been used recently to identify "hub" genes central to important traits.

The G-Box Transcriptional Regulatory Code in Arabidopsis

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

Nod factors potentiate auxin signaling for transcriptional regulation and lateral root formation in Medicago truncatula

Nodulation (Nod) factors (NFs) are symbiotic molecules produced by rhizobia that are essential for establishment of the rhizobium-legume endosymbiosis. Purified NFs can stimulate lateral root formation (LRF) in Medicago truncatula, but little is known about the molecular mechanisms involved. Using a combination of reporter constructs, pharmacological and genetic approaches, we show that NFs act on early steps of LRF in M. truncatula, independently of the ethylene signaling pathway and of the cytokinin receptor MtCRE1, but in interaction with auxin. We conducted a whole-genome transcriptomic study upon NF and/or auxin treatments, using a lateral root inducible system adapted for M. truncatula. This revealed a large overlap between NF and auxin signaling and, more interestingly, synergistic interactions between these molecules. Three groups showing interaction effects were defined: group 1 contained more than 1500 genes responding specifically to the combinatorial treatment of NFs and auxin; group 2 comprised auxin-regulated genes whose expression was enhanced or antagonized by NFs; and in group 3 the expression of NF regulated genes was antagonized by auxin. Groups 1 and 2 were enriched in signaling and metabolic functions, which highlights important crosstalk between NF and auxin signaling for both developmental and symbiotic processes.

Root adaptations to soils with low fertility and aluminium toxicity

Background Plants depend on their root systems to acquire the water and nutrients necessary for their survival in nature, and for their yield and nutritional quality in agriculture. Root systems are complex and a variety of root phenes have been identified as contributors to adaptation to soils with low fertility and aluminium (Al) toxicity. Phenotypic characterization of root adaptations to infertile soils is enabling plant breeders to develop improved cultivars that not only yield more, but also contribute to yield stability and nutritional security in the face of climate variability. Scope In this review the adaptive responses of root systems to soils with low fertility and Al toxicity are described. After a brief introduction, the purpose and focus of the review are outlined. This is followed by a description of the adaptive responses of roots to low supply of mineral nutrients [with an emphasis on low availability of nitrogen (N) and phosphorus (P) and on toxic levels of Al]. We describe progress in developing germplasm adapted to soils with low fertility or Al toxicity using selected examples from ongoing breeding programmes on food (maize, common bean) and forage/feed (Brachiaria spp.) crops. A number of root architectural, morphological, anatomical and metabolic phenes contribute to the superior performance and yield on soils with low fertility and Al toxicity. Major advances have been made in identifying root phenes in improving adaptation to low N (maize), low P (common bean) or high Al [maize, common bean, species and hybrids of brachiariagrass, bulbous canarygrass (Phalaris aquatica) and lucerne (Medicago sativa)]. Conclusions Advanced root phenotyping tools will allow dissection of root responses into specific root phenes that will aid both conventional and molecular breeders to develop superior cultivars. These new cultivars will play a key role in sustainable intensification of crop-livestock systems, particularly in smallholder systems of the tropics. Development of these new cultivars adapted to soils with low fertility and Al toxicity is needed to improve global food and nutritional security and environmental sustainability.

Quantitative Resistance to Verticillium Wilt in Medicago truncatula Involves Eradication of the Fungus from Roots and Is Associated with Transcriptional Responses Related to Innate Immunity

Resistance mechanisms to Verticillium wilt are well-studied in tomato, cotton, and Arabidopsis, but much less in legume plants. Because legume plants establish nitrogen-fixing symbioses in their roots, resistance to root-attacking pathogens merits particular attention. The interaction between the soil-borne pathogen Verticillium alfalfae and the model legume Medicago truncatula was investigated using a resistant (A17) and a susceptible (F83005.5) line. As shown by histological analyses, colonization by the pathogen was initiated similarly in both lines. Later on, the resistant line A17 eliminated the fungus, whereas the susceptible F83005.5 became heavily colonized. Resistance in line A17 does not involve homologs of the well-characterized tomato Ve1 and V. dahliae Ave1 genes. A transcriptomic study of early root responses during initial colonization (i.e., until 24 h post-inoculation) similarly was performed. Compared to the susceptible line, line A17 displayed already a significantly higher basal expression of defense-related genes prior to inoculation, and responded to infection with up-regulation of only a small number of genes. Although fungal colonization was still low at this stage, the susceptible line F83005.5 exhibited a disorganized response involving a large number of genes from different functional classes. The involvement of distinct phytohormone signaling pathways in resistance as suggested by gene expression patterns was supported by experiments with plant hormone pretreatment before fungal inoculation. Gene co-expression network analysis highlighted five main modules in the resistant line, whereas no structured gene expression was found in the susceptible line. One module was particularly associated to the inoculation response in A17. It contains the majority of differentially expressed genes, genes associated with PAMP perception and hormone signaling, and transcription factors. An in silico analysis showed that a high number of these genes also respond to other soil-borne pathogens in M. truncatula, suggesting a core of transcriptional response to root pathogens. Taken together, the results suggest that resistance in M. truncatula line A17 might be due to innate immunity combining preformed defense and PAMP-triggered defense mechanisms, and putative involvement of abscisic acid.

Comparative transcriptome profiling of longissimus muscle tissues from Qianhua Mutton Merino and Small Tail Han sheep

The Qianhua Mutton Merino (QHMM) is a new sheep (Ovis aries) variety with better meat performance compared with the traditional local variety Small Tail Han (STH) sheep. We aimed to evaluate the transcriptome regulators associated with muscle growth and development between the QHMM and STH. We used RNA-Seq to obtain the transcriptome profiles of the longissimus muscle from the QHMM and STH. The results showed that 960 genes were differentially expressed (405 were up-regulated and 555 were down-regulated). Among these, 463 differently expressed genes (DEGs) were probably associated with muscle growth and development and were involved in biological processes such as skeletal muscle tissue development and muscle cell differentiation; molecular functions such as catalytic activity and oxidoreductase activity; cellular components such as mitochondrion and sarcoplasmic reticulum; and pathways such as metabolic pathways and citrate cycle. From the potential genes, a gene-act-network and co-expression-network closely related to muscle growth and development were identified and established. Finally, the expressions of nine genes were validated by real-time PCR. The results suggested that some DEGs, including MRFs, GXP1 and STAC3, play crucial roles in muscle growth and development processes. This genome-wide transcriptome analysis of QHMM and STH muscle is reported for the first time.

Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis

Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties.

A Snapshot of Functional Genetic Studies in Medicago truncatula

In the current context of food security, increase of plant protein production in a sustainable manner represents one of the major challenges of agronomic research, which could be partially resolved by increased cultivation of legume crops. Medicago truncatula is now a well-established model for legume genomic and genetic studies. With the establishment of genomics tools and mutant populations in M. truncatula, it has become an important resource to answer some of the basic biological questions related to plant development and stress tolerance. This review has an objective to overview a decade of genetic studies in this model plant from generation of mutant populations to nowadays. To date, the three biological fields, which have been extensively studied in M. truncatula, are the symbiotic nitrogen fixation, the seed development, and the abiotic stress tolerance, due to their significant agronomic impacts. In this review, we summarize functional genetic studies related to these three major biological fields. We integrated analyses of a nearly exhaustive list of genes into their biological contexts in order to provide an overview of the forefront research advances in this important legume model plant.

Biological Network Inference from Microarray Data, Current Solutions, and Assessments

Currently in bioinformatics and systems biology there is a growing interest for the analysis of associations among biological molecules at a network level. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. The current scenario is characterized by a growing number of algorithms for the inference, while few attention has been posed on the determination of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard datasets. Here we survey some selected inference algorithms and we compare current assessments. We also present a systematic listing of freely available inference and assessment tools for easy reference. Finally we outline some possible future directions of research, such as the use of a prior knowledge into the assessment process.

Detecting causality from nonlinear dynamics with short-term time series

Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or "Cross Map Smoothness" (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method.

Global transcriptome-wide analysis of CIK cells identify distinct roles of IL-2 and IL-15 in acquisition of cytotoxic capacity against tumor

Background

Cytokine-induced killer (CIK) cells are an emerging approach of cancer treatment. Our previous study have shown that CIK cells stimulated with combination of IL-2 and IL-15 displayed improved proliferation capacity and tumor cytotoxicity. However, the mechanisms of CIK cell proliferation and acquisition of cytolytic function against tumor induced by IL-2 and IL-15 have not been well elucidated yet.

Methods

CIK_IL-2 and CIK_IL-15 were generated from peripheral blood mononuclear cells primed with IFN-γ, and stimulated with IL-2 and IL-15 in combination with OKT3 respectively. RNA-seq was performed to identify differentially expressed genes, and gene ontology and pathways based analysis were used to identify the distinct roles of IL-2 and IL-15 in CIK preparation.

Results

The results indicated that CIK_IL-15 showed improved cell proliferation capacity compared to CIK_IL-2. However, CIK_IL-2 has exhibited greater tumor cytotoxic effect than CIK_IL-15. Employing deep sequencing, we sequenced mRNA transcripts in CIK_IL-2 and CIK_IL-15. A total of 374 differentially expressed genes (DEGs) were identified including 175 up-regulated genes in CIK_IL-15 and 199 up-regulated genes in CIK_IL-2. Among DEGs in CIK_IL-15, Wnt signaling and cell adhesion were significant GO terms and pathways which related with their functions. In CIK_IL-2, type I interferon signaling and cytokine-cytokine receptor interaction were significant GO terms and pathways. We found that the up-regulation of Wnt 4 and PDGFD may contribute to enhanced cell proliferation capacity of CIK_IL-15, while inhibitory signal from interaction between CTLA4 and CD80 may be responsible for the weak proliferation capacity of CIK_IL-2. Moreover, up-regulated expressions of CD40LG and IRF7 may make for improved tumor cytolytic function of CIK_IL-2 through type I interferon signaling.

Conclusions

Through our findings, we have preliminarily elucidated the cells proliferation and acquisition of tumor cytotoxicity mechanism of CIK_IL-15 and CIK_IL-2. Better understanding of these mechanisms will help to generate novel CIK cells with greater proliferation potential and improved tumor cytolytic function.