Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes Mellitus.

Diabetes Care. 2017 Jan 11;:

Authors: Mayer G, Heerspink HJ, Aschauer C, Heinzel A, Heinze G, Kainz A, Sunzenauer J, Perco P, de Zeeuw D, Rossing P, Pena M, Oberbauer R, SYSKID Consortium

Abstract
OBJECTIVE: Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large group of patients with type 2 diabetes mellitus and CKD at various stages.
RESEARCH DESIGN AND METHODS: We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serum samples from 1,765 patients recruited into two large clinical trials. eGFR decline was predicted based on molecular markers, clinical risk factors (including baseline eGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data.
RESULTS: The variability of annual eGFR loss explained by the biomarkers, indicated by the adjusted R(2) value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m(2), respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R(2) to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all P < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-the-large: -1.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively).
CONCLUSIONS: A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over a wide range of baseline eGFR values in patients with type 2 diabetes mellitus and CKD.

PMID: 28077457 [PubMed - as supplied by publisher]

Cell assemblies at multiple time scales with arbitrary lag constellations.

Elife. 2017 Jan 11;6:

Authors: Russo E, Durstewitz D

Abstract
Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

PMID: 28074777 [PubMed - in process]

Advanced Boolean modeling of biological networks applied to systems pharmacology.

Bioinformatics. 2017 Jan 10;:

Authors: Irurzun-Arana I, Pastor JM, Trocóniz IF, Gómez-Mantilla JD

Abstract
MOTIVATION: Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.
RESULTS: In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets.
AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/SPIDDOR/SPIDDOR CONTACT: itzirurzun@alumni.unav.es, itroconiz@unav.esSupplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28073755 [PubMed - as supplied by publisher]

Gene editing and genetic engineering approaches for advanced probiotics: A Review.

Crit Rev Food Sci Nutr. 2017 Jan 10;:0

Authors: Yadav R, Kumar V, Baweja M, Shukla P

Abstract
The applications of probiotics are significant and thus resulted in need of genome analysis of probiotic strains. Various omics methods and systems biology approaches enables us to understand and optimize the metabolic processes. These techniques have increased the researcher's attention towards gut microbiome and provided a new source for the revelation of uncharacterized biosynthetic pathways which enables novel metabolic engineering approaches. In recent years, the broad and quantitative analysis of modified strains relies on systems biology tools such as in silico design which are commonly used methods for improving strain performance. The genetic manipulation of probiotic microorganisms is crucial for defining their role in intestinal microbiota and exploring their beneficial properties. This review describes an overview of gene editing and system biology approaches, highlighting the advent of omics methods which allows the study of new routes for studying probiotic bacteria. We have also summarized gene editing tools like TALEN, ZFNs and CRISPR-Cas that edits or cleave the specific target DNA. Furthermore, in this review an overview of proposed design of advanced customized probiotic is also hypothesized to improvise the probiotics.

PMID: 28071925 [PubMed - as supplied by publisher]

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.

Sci Rep. 2017 Jan 10;7:40164

Authors: Iwata M, Sawada R, Iwata H, Kotera M, Yamanishi Y

Abstract
The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

PMID: 28071740 [PubMed - in process]

A review of connectivity map and computational approaches in pharmacogenomics.

Brief Bioinform. 2017 Jan 09;:

Authors: Musa A, Ghoraie LS, Zhang SD, Galzko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F

Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

PMID: 28069634 [PubMed - as supplied by publisher]

Multi-level evaluation of Escherichia coli polyphosphate related mutants using global transcriptomic, proteomic and phenomic analyses.

Biochim Biophys Acta. 2017 Jan 06;:

Authors: Varas M, Valdivieso C, Mauriaca C, Ortíz-Severín J, Paradela A, Poblete-Castro I, Cabrera R, Chávez FP

Abstract
BACKGROUND: Polyphosphate (polyP) is a linear biopolymer found in all living cells. In bacteria, mutants lacking polyphosphate kinase 1 (PPK1), the enzyme responsible for synthesis of most polyP, have many structural and functional defects. However, little is known about the causes of these pleiotropic alterations. The link between ppk1 deletion and those numerous phenotypes observed can be the result of complex molecular interactions that can be elucidated via a systems biology approach.
METHODS: By integrating different omics levels (transcriptome, proteome and phenome), we described the functioning of various metabolic pathways among Escherichia coli polyphosphate mutant strains (Δppk1, Δppx, and ΔpolyP). Bioinformatic analyses reveal the complex metabolic and regulatory bases of the phenotypes unique to polyP mutants.
RESULTS: Our results suggest that during polyP deficiency (Δppk1 mutant), metabolic pathways needed for energy supply are up-regulated, including fermentation, aerobic and anaerobic respiration. Transcriptomic and q-proteomic contrasting changes between Δppk1 and Δppx mutant strains were observed in those central metabolic pathways and confirmed by using Phenotypic microarrays. In addition, our results suggest a regulatory connection between polyP, second messenger metabolism, alternative Sigma/Anti-Sigma factors and type-II toxin-antitoxin (TA) systems.
CONCLUSIONS: We suggest a broader role for polyP via regulation of ATP-dependent proteolysis of type II toxin-antitoxin system and alternative Sigma/Anti-Sigma factors, that could explain the multiple structural and functional deficiencies described due to alteration of polyP metabolism.
GENERAL SIGNIFICANCE: Understanding the interplay of polyP in bacterial metabolism using a systems biology approach can help to improve design of novel antimicrobials toward pathogens.

PMID: 28069396 [PubMed - as supplied by publisher]

Drug target identification at the crossroad of neuronal apoptosis and survival.

Expert Opin Drug Discov. 2017 Jan 08;:

Authors: Maino B, Paparone S, Severini C, Ciotti MT, D'Agata V, Calissano P, Cavallaro S

Abstract
INTRODUCTION: Inappropriate activation of apoptosis may contribute to neurodegeneration, a multifaceted process that results in various chronic disorders, including Alzheimer's and Parkinson's diseases. Several in vitro and in vivo studies demonstrated that neuronal apoptosis is a multi-pathway cell-death program that requires RNA synthesis. Thus, transcriptionally activated genes whose products induce cell death can be triggered by different stimuli and antagonized by neurotrophic factors. Systems biology is now unveiling the series of intracellular signaling pathways and key drug targets at the intersection of neuronal apoptosis and survival. Areas Covered: This review introduces a genomic approach that can be used to elucidate the systems biology of neuronal apoptosis and survival, and to rationally select drug targets, no longer oriented to emulate the action of growth factors at the membrane receptor level, but rather to modulate their downstream signals. Expert opinion: The advent of genomics is offering an unprecedented opportunity to explore how the delicate balance between apoptosis and survival-inducing signals triggers a transcriptional program. Characterization of this program can be useful to identify potential pharmacological targets for existing drugs. Such knowledge might pave the way towards an innovative pharmacology.

PMID: 28067072 [PubMed - as supplied by publisher]

TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data.

Bioinformatics. 2017 Jan 08;:

Authors: Motamedian E, Mohammadi M, Shojaosadati SA, Heydari M

Abstract
MOTIVATION: Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations.
RESULTS: TRFBA considers the expression levels of genes as a new continuous variable and introduces two new linear constraints. The first constraint limits the rate of reaction(s) supported by a metabolic gene using a constant parameter (C) that converts the expression levels to the upper bounds of the reactions. Considering the concept of constraint-based modeling, the second set of constraints correlates the expression level of each target gene with that of its regulating genes. A set of constraints and binary variables was also added to prevent the second set of constraints from overlapping. TRFBA was implemented on Escherichia coli and Saccharomyces cerevisiae models to estimate growth rates under various environmental and genetic perturbations. The error sensitivity to the algorithm parameter was evaluated to find the best value of C. The results indicate a significant improvement in the quantitative prediction of growth in comparison with previously presented algorithms. The robustness of the algorithm to change in the expression data and the regulatory network was tested to evaluate the effect of noisy and incomplete data. Furthermore, the use of added constraints for perturbations without their gene expression profile demonstrates that these constraints can be applied to improve the growth prediction of FBA.
AVAILABILITY AND IMPLEMENTATION: TRFBA is implemented in Matlab software and requires COBRA toolbox. Source code is freely available at http://sbme.modares.ac.ir CONTACT: : motamedian@modares.ac.irSupplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28065897 [PubMed - as supplied by publisher]

Eugenol Specialty Chemical Production in Transgenic Poplar (Populus tremula × P. alba) Field Trials.

Plant Biotechnol J. 2017 Jan 08;:

Authors: Lu D, Yuan X, Kim SJ, Marques JV, Chakravarthy PP, Moinuddin SG, Luchterhand R, Herman B, Davin LB, Lewis NG

Abstract
A foundational study assessed effects of biochemical pathway introduction into poplar to produce eugenol, chavicol, p-anol, isoeugenol, and their sequestered storage products, from potentially available substrates, coniferyl and p-coumaryl alcohols. At the onset, it was unknown whether significant carbon flux to monolignols vs other phenylpropanoid (acetate) pathway metabolites would be kinetically favored. Various transgenic poplar lines generated eugenol and chavicol glucosides in ca. 0.45% (~0.35 and ~0.1%, respectively) of dry weight foliage tissue in field trials, as well as their corresponding aglycones in trace amounts. There were only traces of any of these metabolites in branch tissues, even after ~4 year field trials. Levels of bioproduct accumulation in foliage plateaued, even at the lowest introduced gene expression levels, suggesting limited monolignol substrate availability. Nevertheless, this level still allows foliage collection for platform chemical production, with the remaining (stem) biomass available for wood, pulp/paper and bioenergy product purposes. Several transformed lines displayed unexpected precocious flowering after 4 year field trial growth. This necessitated terminating (felling) these particular plants, as USDA APHIS prohibits the possibility of their interacting (cross-pollination, etc.) with wild type (native plant) lines. In future, additional biotechnological approaches can be employed (e.g. gene editing) to produce sterile plant lines, to avoid such complications. While increased gene expression did not increase target bioproduct accumulation, the exciting possibility now exists of significantly increasing their amounts (e.g. 10-40 fold plus) in foliage and stems via systematic deployment of numerous "omics", systems biology, synthetic biology, and metabolic flux modeling approaches. This article is protected by copyright. All rights reserved.

PMID: 28064439 [PubMed - as supplied by publisher]

Frontiers of high-throughput metabolomics.

Curr Opin Chem Biol. 2017 Jan 05;36:15-23

Authors: Zampieri M, Sekar K, Zamboni N, Sauer U

Abstract
Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.

PMID: 28064089 [PubMed - as supplied by publisher]

Metabolomics: A potential way to know the role of vitamin D on multiple sclerosis.

J Pharm Biomed Anal. 2016 Dec 29;136:22-31

Authors: Luque-Córdoba D, Luque de Castro MD

Abstract
The literature about the influence of vitamin D on multiple sclerosis (MS) is very controversial, possibly as a result of the way through which the research on the subject has been conducted. The studies developed so far have been focused exclusively on gene expression: the effect of a given vitamin D metabolite on target receptors. The influence of the vitamin D status (either natural or after supplementation) on MS has been studied by measurement of the 25 monohydroxylated metabolite (also known as circulating form), despite the 1,25 dihydroxylated metabolite is considered the active form. In the light of the multiple metabolic pathways in which both forms of vitamin D (D2 and D3) are involved, monitoring of the metabolites is crucial to know the activity of the target enzymes as a function of both the state of the MS patient and the clinical treatment applied. The study of metabolomics aspects is here proposed to clarify the present controversy. In "omics" terms, our proposal is to take profit from up-stream information-thus is, from metabolomics to genomics-with a potential subsequent step to systems biology, if required.

PMID: 28063332 [PubMed - as supplied by publisher]

Glioblastoma on a microfluidic chip: Generating pseudopalisades and enhancing aggressiveness through blood vessel obstruction events.

Neuro Oncol. 2017 Jan 06;:

Authors: Ayuso JM, Monge R, Martínez-González A, Virumbrales-Muñoz M, Llamazares GA, Berganzo J, Hernández-Laín A, Santolaria J, Doblaré M, Hubert C, Rich JN, Sánchez-Gómez P, Pérez-García VM, Ochoa I, Fernández LJ

Abstract
BACKGROUND: Glioblastoma (GBM) is one of the most lethal tumor types. Hypercellular regions, named pseudopalisades, are characteristic in these tumors and have been hypothesized to be waves of migrating glioblastoma cells. These "waves" of cells are thought to be induced by oxygen and nutrient depletion caused by tumor-induced blood vessel occlusion. Although the universal presence of these structures in GBM tumors suggests that they may play an instrumental role in GBM's spread and invasion, the recreation of these structures in vitro has remained challenging.
METHODS: Here we present a new microfluidic model of GBM that mimics the dynamics of pseudopalisade formation. To do this, we embedded U-251 MG cells within a collagen hydrogel in a custom-designed microfluidic device. By controlling the medium flow through lateral microchannels, we can mimic and control blood-vessel obstruction events associated with this disease.
RESULTS: Through the use of this new system, we show that nutrient and oxygen starvation triggers a strong migratory process leading to pseudopalisade generation in vitro. These results validate the hypothesis of pseudopalisade formation and show an excellent agreement with a systems-biology model based on a hypoxia-driven phenomenon.
CONCLUSIONS: This paper shows the potential of microfluidic devices as advanced artificial systems capable of modeling in vivo nutrient and oxygen gradients during tumor evolution.

PMID: 28062831 [PubMed - as supplied by publisher]

Integrated Cellular and Plasma Proteomics of Contrasting B-cell Cancers Reveals Common, Unique and Systemic Signatures.

Mol Cell Proteomics. 2017 Jan 04;:

Authors: Johnston HE, Carter MJ, Cox KL, Dunscombe M, Manousopoulou A, Townsend PA, Garbis SD, Cragg MS

Abstract
Approximately 800,000 leukaemia and lymphoma cases are diagnosed worldwide each year. Burkitt's lymphoma (BL) and chronic lymphocytic leukaemia (CLL), are examples of contrasting B-cell cancers; BL is a highly aggressive lymphoid tumour, frequently affecting children, whilst CLL typically presents as an indolent, slow-progressing leukaemia affecting the elderly. The B-cell-specific over-expression of the myc and tcl1 oncogenes in mice induce spontaneous malignancies modelling BL and CLL, respectively. Quantitative mass spectrometry proteomics and isobaric labelling were employed to examine the biology underpinning contrasting Eμ-myc and Eμ-TCL1 B-cell tumours. Additionally, the plasma proteome was evaluated using sub-proteome enrichment to interrogate biomarker emergence and the systemic effects of tumour burden. Over 10,000 proteins were identified (q<0.01) of which 8270 cellular and 2095 plasma proteins were quantitatively profiled. A common B-cell tumour signature of 695 over-expressed proteins highlighted ribosome biogenesis, cell-cycle promotion and chromosome segregation. Eμ-myc tumours over-expressed several methylating enzymes and under-expressed many cytoskeletal components. Eμ-TCL1 tumours specifically over-expressed ER stress response proteins and signalling components in addition to both subunits of the interleukin-5 (IL5) receptor. IL5 treatment promoted Eμ-TCL1 tumour proliferation, suggesting an amplification of IL5-induced AKT signalling by TCL1. Tumour plasma contained a substantial tumour lysis signature, most prominent in Eμ-myc plasma, whilst Eμ-TCL1 plasma contained signatures of immune-response, inflammation and microenvironment interactions, with putative biomarkers in early-stage cancer. These findings provide a detailed characterisation of contrasting B-cell tumour models, identifying common and specific tumour mechanisms. Integrated plasma proteomics allowed the dissection of a systemic response and a tumour lysis signature present in early- and late-stage cancers, respectively. Overall, this study suggests common B-cell cancer signatures exist and illustrates the potential of the further evaluation of B-cell cancer subtypes by integrative proteomics.

PMID: 28062796 [PubMed - as supplied by publisher]

Coordinated regulation of acid resistance in Escherichia coli.

BMC Syst Biol. 2017 Jan 06;11(1):1

Authors: Aquino P, Honda B, Jaini S, Lyubetskaya A, Hosur K, Chiu JG, Ekladious I, Hu D, Jin L, Sayeg MK, Stettner AI, Wang J, Wong BG, Wong WS, Alexander SL, Ba C, Bensussen SI, Bernstein DB, Braff D, Cha S, Cheng DI, Cho JH, Chou K, Chuang J, Gastler DE, Grasso DJ, Greifenberger JS, Guo C, Hawes AK, Israni DV, Jain SR, Kim J, Lei J, Li H, Li D, Li Q, Mancuso CP, Mao N, Masud SF, Meisel CL, Mi J, Nykyforchyn CS, Park M, Peterson HM, Ramirez AK, Reynolds DS, Rim NG, Saffie JC, Su H, Su WR, Su Y, Sun M, Thommes MM, Tu T, Varongchayakul N, Wagner TE, Weinberg BH, Yang R, Yaroslavsky A, Yoon C, Zhao Y, Zollinger AJ, Stringer AM, Foster JW, Wade J, Raman S, Broude N, Wong WW, Galagan JE

Abstract
BACKGROUND: Enteric Escherichia coli survives the highly acidic environment of the stomach through multiple acid resistance (AR) mechanisms. The most effective system, AR2, decarboxylates externally-derived glutamate to remove cytoplasmic protons and excrete GABA. The first described system, AR1, does not require an external amino acid. Its mechanism has not been determined. The regulation of the multiple AR systems and their coordination with broader cellular metabolism has not been fully explored.
RESULTS: We utilized a combination of ChIP-Seq and gene expression analysis to experimentally map the regulatory interactions of four TFs: nac, ntrC, ompR, and csiR. Our data identified all previously in vivo confirmed direct interactions and revealed several others previously inferred from gene expression data. Our data demonstrate that nac and csiR directly modulate AR, and leads to a regulatory network model in which all four TFs participate in coordinating acid resistance, glutamate metabolism, and nitrogen metabolism. This model predicts a novel mechanism for AR1 by which the decarboxylation enzymes of AR2 are used with internally derived glutamate. This hypothesis makes several testable predictions that we confirmed experimentally.
CONCLUSIONS: Our data suggest that the regulatory network underlying AR is complex and deeply interconnected with the regulation of GABA and glutamate metabolism, nitrogen metabolism. These connections underlie and experimentally validated model of AR1 in which the decarboxylation enzymes of AR2 are used with internally derived glutamate.

PMID: 28061857 [PubMed - in process]

Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types.

BMC Genomics. 2017 Jan 05;18(1):30

Authors: Giotti B, Joshi A, Freeman TC

Abstract
BACKGROUND: Cell division is central to the physiology and pathology of all eukaryotic organisms. The molecular machinery underpinning the cell cycle has been studied extensively in a number of species and core aspects of it have been found to be highly conserved. Similarly, the transcriptional changes associated with this pathway have been studied in different organisms and different cell types. In each case hundreds of genes have been reported to be regulated, however there seems to be little consensus in the genes identified across different studies. In a recent comparison of transcriptomic studies of the cell cycle in different human cell types, only 96 cell cycle genes were reported to be the same across all studies examined.
RESULTS: Here we perform a systematic re-examination of published human cell cycle expression data by using a network-based approach to identify groups of genes with a similar expression profile and therefore function. Two clusters in particular, containing 298 transcripts, showed patterns of expression consistent with cell cycle occurrence across the four human cell types assessed.
CONCLUSIONS: Our analysis shows that there is a far greater conservation of cell cycle-associated gene expression across human cell types than reported previously, which can be separated into two distinct transcriptional networks associated with the G1/S-S and G2-M phases of the cell cycle. This work also highlights the benefits of performing a re-analysis on combined datasets.

PMID: 28056781 [PubMed - in process]

Mouse Models for Drug Discovery. Can New Tools and Technology Improve Translational Power?

ILAR J. 2016 Dec;57(2):178-185

Authors: Zuberi A, Lutz C

Abstract
The use of mouse models in biomedical research and preclinical drug evaluation is on the rise. The advent of new molecular genome-altering technologies such as CRISPR/Cas9 allows for genetic mutations to be introduced into the germ line of a mouse faster and less expensively than previous methods. In addition, the rapid progress in the development and use of somatic transgenesis using viral vectors, as well as manipulations of gene expression with siRNAs and antisense oligonucleotides, allow for even greater exploration into genomics and systems biology. These technological advances come at a time when cost reductions in genome sequencing have led to the identification of pathogenic mutations in patient populations, providing unprecedented opportunities in the use of mice to model human disease. The ease of genetic engineering in mice also offers a potential paradigm shift in resource sharing and the speed by which models are made available in the public domain. Predictively, the knowledge alone that a model can be quickly remade will provide relief to resources encumbered by licensing and Material Transfer Agreements. For decades, mouse strains have provided an exquisite experimental tool to study the pathophysiology of the disease and assess therapeutic options in a genetically defined system. However, a major limitation of the mouse has been the limited genetic diversity associated with common laboratory mice. This has been overcome with the recent development of the Collaborative Cross and Diversity Outbred mice. These strains provide new tools capable of replicating genetic diversity to that approaching the diversity found in human populations. The Collaborative Cross and Diversity Outbred strains thus provide a means to observe and characterize toxicity or efficacy of new therapeutic drugs for a given population. The combination of traditional and contemporary mouse genome editing tools, along with the addition of genetic diversity in new modeling systems, are synergistic and serve to make the mouse a better model for biomedical research, enhancing the potential for preclinical drug discovery and personalized medicine.

PMID: 28053071 [PubMed - in process]

Modeling Dynamics and Function of Bone Marrow Cells in Mouse Liver Regeneration.

Cell Rep. 2017 Jan 03;18(1):107-121

Authors: Pedone E, Olteanu VA, Marucci L, Muñoz-Martin MI, Youssef SA, de Bruin A, Cosma MP

Abstract
In rodents and humans, the liver can efficiently restore its mass after hepatectomy. This is largely attributed to the proliferation and cell cycle re-entry of hepatocytes. On the other hand, bone marrow cells (BMCs) migrate into the liver after resection. Here, we find that a block of BMC recruitment into the liver severely impairs its regeneration after the surgery. Mobilized hematopoietic stem and progenitor cells (HSPCs) in the resected liver can fuse with hepatocytes, and the hybrids proliferate earlier than the hepatocytes. Genetic ablation of the hybrids severely impairs hepatocyte proliferation and liver mass regeneration. Mathematical modeling reveals a key role of bone marrow (BM)-derived hybrids to drive proliferation in the regeneration process, and predicts regeneration efficiency in experimentally non-testable conditions. In conclusion, BM-derived hybrids are essential to trigger efficient liver regeneration after hepatectomy.

PMID: 28052241 [PubMed - in process]

Seeing the Forest for the Trees.

Circ Res. 2016 Nov 11;119(11):1170-1172

Authors: O'Rourke B, Liu T, Foster DB

PMID: 28051783 [PubMed - in process]

Widespread Historical Contingency in Influenza Viruses.

Genetics. 2017 Jan;205(1):409-420

Authors: Nshogozabahizi JC, Dench J, Aris-Brosou S

Abstract
In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.

PMID: 28049709 [PubMed - in process]