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Abstract Background Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context. Results We analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'. Conclusions Using JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations.

Viral infection makes us feel sick as the immune system alters systemic metabolism to better fight the pathogen. The extent of these changes is relative to the severity of disease. Whether blood glucose is subject to infection-induced modulation is mostly unknown. Here we show that strong, nonlethal infection restricts systemic glucose availability, which promotes the antiviral type I interferon (IFN-I) response. Following viral infection, we find that IFNγ produced by γδ T cells stimulates pancreatic β cells to increase glucose-induced insulin release. Subsequently, hyperinsulinemia lessens hepatic glucose output. Glucose restriction enhances IFN-I production by curtailing lactate-mediated inhibition of IRF3 and NF-κB signaling. Induced hyperglycemia constrained IFN-I production and increased mortality upon infection. Our findings identify glucose restriction as a physiological mechanism to bring the body into a heightened state of responsiveness to viral pathogens. This immune-endocrine circuit is disrupted in hyperglycemia, possibly explaining why patients with diabetes are more susceptible to viral infection., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Context: Atherosclerosis is a dominant cause of cardiovascular disease (CVD), including myocardial infarction and stroke., Objective: To investigate metabolic states that are associated with the development of atherosclerosis., Methods: Cross-sectional cohort study at a university hospital in the Netherlands. A total of 302 adult subjects with a body mass index (BMI) ≥ 27 kg/m2 were included. We integrated plasma metabolomics with clinical metadata to quantify the "atherogenic state" of each individual, providing a continuous spectrum of atherogenic states that ranges between nonatherogenic states to highly atherogenic states., Results: Analysis of groups of individuals with different clinical conditions-such as metabolically healthy individuals with obesity, and individuals with metabolic syndrome-confirmed the generalizability of this spectrum; revealed a wide variation of atherogenic states within each condition; and allowed identification of metabolites that are associated with the atherogenic state regardless of the particular condition, such as gamma-glutamyl-glutamic acid and homovanillic acid sulfate. The analysis further highlighted metabolic pathways such as catabolism of phenylalanine and tyrosine and biosynthesis of estrogens and phenylpropanoids. Using validation cohorts, we confirmed variation in atherogenic states in healthy subjects (before atherosclerosis plaques become visible), and showed that metabolites associated with the atherogenic state were also associated with future CVD., Conclusion: Our results provide a global view of atherosclerosis risk states using plasma metabolomics., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Secondary bacterial challenges during influenza virus infection "superinfection") cause excessive mortality and hospitalization. Here, we present a longitudinal study of bulk gene expression changes in murine lungs during superinfection, with an initial influenza A virus infection and a subsequent Streptococcus pneumoniae infection. In addition to the well-characterized impairment of the host response, we identified superinfection-specific alterations in the global transcriptional program that are linked to the host's ability to resist the pathogens. Particularly, whereas superinfected mice manifested an excessive rapid induction of the resistance-to-infection program, there was a substantial tissue-level rewiring of this program: upon superinfection, interferon-regulated genes were switched from positive to negative correlations with the host's resistance state, whereas genes of fatty acid metabolism switched from negative to positive correlations with resistance states. Thus, the transcriptional resistance state in superinfection is reprogrammed toward repressed interferon signaling and induced fatty acid metabolism. Our findings suggest new insights into a tissue-level remodeling of the host defense upon superinfection, providing promising targets for future therapeutic interventions., Importance: Secondary bacterial infections are the most frequent complications during influenza A virus (IAV) pandemic outbreaks, contributing to excessive morbidity and mortality in the human population. Most IAV-related deaths are attributed to Streptococcus pneumoniae (SP) infections, which usually begin within the first week of IAV infection in the respiratory tracts. Here, we focused on longitudinal transcriptional responses during a superinfection model consisting of an SP infection that follows an initial IAV infection, comparing superinfection to an IAV-only infection, an SP-only infection, and control treatments. Our longitudinal data allowed a fine analysis of gene expression changes during superinfection. For instance, we found that superinfected mice exhibited rapid gene expression induction or reduction within the first 12 h after encountering the second pathogen. Cell proliferation and immune response activation processes were upregulated, while endothelial processes, vasculogenesis, and angiogenesis were downregulated, providing promising targets for future therapeutic interventions. We further analyzed the longitudinal transcriptional responses in the context of a previously defined spectrum of the host's resistance state, revealing superinfection-specific reprogramming of resistance states, such as reprogramming of fatty acid metabolism and interferon signaling. The reprogrammed functions are compelling new targets for switching the pathogenic superinfection state into a single-infection state., Competing Interests: The authors declare no conflict of interest.

Background: Aspergillus fumigatus (Af) is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic background. The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus (Af) using an RNAseq approach in CC lines and hepatic gene expression., Methods: We studied 31 male mice from 25 CC lines at 8 weeks old; the mice were infected with Af. Liver tissues were extracted from these mice 5 days post-infection, and next-generation RNA-sequencing (RNAseq) was performed. The GENE-E analysis platform was used to generate a clustered heat map matrix., Results: Significant variation in body weight changes between CC lines was observed. Hepatic gene expression revealed 12 top prioritized candidate genes differentially expressed in resistant versus susceptible mice based on body weight changes. Interestingly, three candidate genes are located within genomic intervals of the previously mapped quantitative trait loci (QTL), including Gm16270 and Stox1 on chromosome 10 and Gm11033 on chromosome 8., Conclusions: Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af. As a next step, eQTL analysis will be performed for our RNA-Seq data. Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis., (© 2024 The Authors. Animal Models and Experimental Medicine published by John Wiley & Sons Australia, Ltd on behalf of The Chinese Association for Laboratory Animal Sciences.)

Sepsis remains a major cause of morbidity and mortality in both low- and high-income countries. Antibiotic therapy and supportive care have significantly improved survival following sepsis in the twentieth century, but further progress has been challenging. Immunotherapy trials for sepsis, mainly aimed at suppressing the immune response, from the 1990s and 2000s, have largely failed, in part owing to unresolved patient heterogeneity in the underlying immune disbalance. The past decade has brought the promise to break this blockade through technological developments based on omics-based technologies and systems medicine that can provide a much larger data space to describe in greater detail the immune endotypes in sepsis. Patient stratification opens new avenues towards precision medicine approaches that aim to apply immunotherapies to sepsis, on the basis of precise biomarkers and molecular mechanisms defining specific immune endotypes. This approach has the potential to lead to the establishment of immunotherapy as a successful pillar in the treatment of sepsis for future generations., (© 2024. Springer Nature America, Inc.)

Seasonal influenza results in 3 to 5 million cases of severe disease and 250,000 to 500,000 deaths annually. Macrophages have been implicated in both the resolution and progression of the disease, but the drivers of these outcomes are poorly understood. We probed mouse lung transcriptomic datasets using the Digital Cell Quantifier algorithm to predict immune cell subsets that correlated with mild or severe influenza A virus (IAV) infection outcomes. We identified a unique lung macrophage population that transcriptionally resembled small serosal cavity macrophages and whose presence correlated with mild disease. Until now, the study of serosal macrophage translocation in the context of viral infections has been neglected. Here, we show that pleural macrophages (PMs) migrate from the pleural cavity to the lung after infection with IAV. We found that the depletion of PMs increased morbidity and pulmonary inflammation. There were increased proinflammatory cytokines in the pleural cavity and an influx of neutrophils within the lung. Our results show that PMs are recruited to the lung during IAV infection and contribute to recovery from influenza. This study expands our knowledge of PM plasticity and identifies a source of lung macrophages independent of monocyte recruitment and local proliferation., Competing Interests: Competing interests statement:The authors declare no competing interest.

When challenged with an invading pathogen, the host-defense response is engaged to eliminate the pathogen (resistance) and to maintain health in the presence of the pathogen (disease tolerance). However, the identification of distinct molecular programs underpinning disease tolerance and resistance remained obscure. We exploited transcriptional and physiological monitoring across 33 mouse strains, during in vivo influenza virus infection, to identify two host-defense gene programs-one is associated with hallmarks of disease tolerance and the other with hallmarks of resistance. Both programs constitute generic responses in multiple mouse and human cell types. Our study describes the organizational principles of these programs and validates Arhgdia as a regulator of disease-tolerance states in epithelial cells. We further reveal that the baseline disease-tolerance state in peritoneal macrophages is associated with the pathophysiological response to injury and infection. Our framework provides a paradigm for the understanding of disease tolerance and resistance at the molecular level., Competing Interests: Declaration of interests Tel Aviv University has filed a patent application on markers of resistance and disease tolerance and uses thereof with I.G.-V., E.B., O.C., G.Y., and N.P.-Y. as inventors (PCT/IL2022/050880), which has been filed in the Israel PCT Receiving Office., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Cell therapy using induced pluripotent stem cell-derived neurons is considered a promising approach to regenerate the injured spinal cord (SC). However, the scar formed at the chronic phase is not a permissive microenvironment for cell or biomaterial engraftment or for tissue assembly. Engineering of a functional human neuronal network is now reported by mimicking the embryonic development of the SC in a 3D dynamic biomaterial-based microenvironment. Throughout the in vitro cultivation stage, the system's components have a synergistic effect, providing appropriate cues for SC neurogenesis. While the initial biomaterial supported efficient cell differentiation in 3D, the cells remodeled it to provide an inductive microenvironment for the assembly of functional SC implants. The engineered tissues are characterized for morphology and function, and their therapeutic potential is investigated, revealing improved structural and functional outcomes after acute and chronic SC injuries. Such technology is envisioned to be translated to the clinic to rewire human injured SC., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a large collection of metabolic, hemodynamic, and immune parameters, and then use the mapping to dissect phenotypic states. We find that the whole-body state is faithfully represented by a quantitative two-dimensional model. One component of the whole-body state represents 'metabolic syndrome' (MetS) - a conventional way to determine the cardiometabolic state. The second component is decoupled from the classical MetS, suggesting a novel 'non-classical MetS' that is characterized by dozens of parameters, including dysregulated lipoprotein parameters (e.g. low free cholesterol in small high-density lipoproteins) and attenuated cytokine responses of immune cells to ex vivo stimulations. Both components are associated with disease, but differ in their particular associations, thus opening new avenues for improved personalized diagnosis and treatment. These results provide a practical paradigm to describe whole-body states and to dissect complex disease within the ecosystem of the human body., Competing Interests: AF, Iv, Rt, KS, LJ, JR, AI, ID, BT, MN, NR, IG No competing interests declared, (© 2021, Frishberg et al.)

Motivation: Cell-to-cell variation has uncovered associations between cellular phenotypes. However, it remains challenging to address the cellular diversity of such associations., Results: Here, we do not rely on the conventional assumption that the same association holds throughout the entire cell population. Instead, we assume that associations may exist in a certain subset of the cells. We developed CEllular Niche Association (CENA) to reliably predict pairwise associations together with the cell subsets in which the associations are detected. CENA does not rely on predefined subsets but only requires that the cells of each predicted subset would share a certain characteristic state. CENA may therefore reveal dynamic modulation of dependencies along cellular trajectories of temporally evolving states. Using simulated data, we show the advantage of CENA over existing methods and its scalability to a large number of cells. Application of CENA to real biological data demonstrates dynamic changes in associations that would be otherwise masked., Availability and Implementation: CENA is available as an R package at Github: https://github.com/mayalevy/CENA and is accompanied by a complete set of documentations and instructions., Contact: iritgv@gmail.com., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-death due to early metastatic spread, in many cases primarily to the brain. Organ-specific pattern of spread of disease might be driven by the activity of a specific signaling pathway within the primary tumors. We aimed to identify an expression signature of genes and the relevant signaling associated with the development of brain metastasis (BM) after surgical resection of NSCLC., Methods: Rapidly frozen NSCLC surgical specimens were procured from tumor banks. RNA was extracted and analyzed by RNA-sequencing (Illumina HiSeq 2500). Clinical parameters and gene expression were examined for differentiating between patients with BM, patients with metastases to sites other than brain, and patients who did not develop metastatic disease at a clinically significant follow up. Principal component analysis and pathway enrichments studies were done., Results: A total of 91 patients were included in this study, 32 of which developed BM. Stage of disease at diagnosis (P=0.004) and level of differentiation (P=0.007) were significantly different between BM and control group. We identified a set of 22 genes which correlated specifically with BM, and not with metastasis to other sites. This set achieved 93.4% accuracy (95% CI: 86.2-97.5%), 96.6% specificity and 87.5% sensitivity of correctly identifying BM patients in a leave-one-out internal validation analysis. The oxidative phosphorylation pathway was strongly correlated with BM risk., Conclusions: Expression level of a small set of genes from primary tumors was found to predict BM development, distinctly from metastasis to other organs. These genes and the correlated oxidative phosphorylation pathway require further validation as potentially clinically useful predictors of BM and possibly as novel therapeutic targets for BM prevention., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tlcr-19-477). IK has a patent Gene signature prognostic of brain metastasis in NSCLC. 62/879,716 pending. ID has a patent Gene signature prognostic of brain metastasis in NSCLC. 62/879,716 pending. AO reports and honoraria from BI, MSD, Roche, AstraZeneca in Israel. JB has a patent Gene signature prognostic of brain metastasis in NSCLC. 62/879,716 pending and consultant fees from Roche, Boehringer Ingelheim, Novartis, BMS, Pfizer, AstraZeneca, Takada, MSD, VBL, Abbvie, Bayer, Lilly, grant support (to the institute) from MSD, Boehringer Ingelheim, AstraZeneca, Pfizer, Roche, Abbvie, BMS, Takeda. The other authors have no conflicts of interest to declare., (2020 Translational Lung Cancer Research. All rights reserved.)

Over the past decade, neural networks have become one of the cutting-edge methods in various research fields, outshining specifically in complex classification problems. In this paper, we propose two main contributions: first, we conduct a methodological study of neural network modeling for classifying biological traits based on structured gene expression data. Then, we suggest an innovative approach for utilizing deep learning visualization techniques in order to reveal the specific genes important for the correct classification of each trait within the trained models. Our data suggests that this approach have great potential for becoming a standard feature importance tool used in complex medical research problems, and that it can further be generalized to various structured data classification problems outside the biological domain., (Copyright © 2020 Wilentzik Müller and Gat-Viks.)

Host dependency factors that are required for influenza A virus infection may serve as therapeutic targets as the virus is less likely to bypass them under drug-mediated selection pressure. Previous attempts to identify host factors have produced largely divergent results, with few overlapping hits across different studies. Here, we perform a genome-wide CRISPR/Cas9 screen and devise a new approach, meta-analysis by information content (MAIC) to systematically combine our results with prior evidence for influenza host factors. MAIC out-performs other meta-analysis methods when using our CRISPR screen as validation data. We validate the host factors, WDR7, CCDC115 and TMEM199, demonstrating that these genes are essential for viral entry and regulation of V-type ATPase assembly. We also find that CMTR1, a human mRNA cap methyltransferase, is required for efficient viral cap snatching and regulation of a cell autonomous immune response, and provides synergistic protection with the influenza endonuclease inhibitor Xofluza.

The clinical diagnosis of acute infections in the emergency department is a challenging task due to the similarity in symptom presentation between virally and bacterially infected individuals, while the use of routine laboratory tests for pathogen identification is often time-consuming and may contain contaminants. We investigated the ability of various anemia-related parameters, including hemoglobin, red cell distribution width (RDW), and iron, to differentiate between viral and bacterial infection in a retrospective study of 3883 patients admitted to the emergency department with a confirmed viral (n = 1238) or bacterial (n = 2645) infection based on either laboratory tests or microbiological cultures. The ratio between hemoglobin to RDW was found to be significant in distinguishing between virally and bacterially infected patients and outperformed other anemia measurements. Moreover, the predictive value of the ratio was high even in patients presenting with low C-reactive protein values (< 21 mg/L). We followed the dynamics of hemoglobin, RDW, and the ratio between them up to 72 h post emergency department admission, and observed a consistent discrepancy between virally and bacterially infected patients over time. Additional analysis demonstrated higher levels of ferritin and lower levels of iron in bacterially infected compared with virally infected patients. The anemia measurements were associated with length of hospital stay, where all higher levels, except for RDW, corresponded to a shorter hospitalization period. We highlighted the importance of various anemia measurements as an additional host-biomarker to discern virally from bacterially infected patients.

Despite the importance of complex phenotypes, an in-depth understanding of the combined molecular and genetic effects on a phenotype has yet to be achieved. Here, we introduce InPhenotype, a novel computational approach for complex phenotype prediction, where gene-expression data and genotyping data are integrated to yield quantitative predictions of complex physiological traits. Unlike existing computational methods, InPhenotype makes it possible to model potential regulatory interactions between gene expression and genomic loci without compromising the continuous nature of the molecular data. We applied InPhenotype to synthetic data, exemplifying its utility for different data parameters, as well as its superiority compared to current methods in both prediction quality and the ability to detect regulatory interactions of genes and genomic loci. Finally, we show that InPhenotype can provide biological insights into both mouse and yeast datasets., (Copyright © 2019 by the Genetics Society of America.)

The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of "omic"-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8 + T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.

Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.

Despite incremental improvements in the field of tissue engineering, no technology is currently available for producing completely autologous implants where both the cells and the scaffolding material are generated from the patient, and thus do not provoke an immune response that may lead to implant rejection. Here, a new approach is introduced to efficiently engineer any tissue type, which its differentiation cues are known, from one small tissue biopsy. Pieces of omental tissues are extracted from patients and, while the cells are reprogrammed to become induced pluripotent stem cells, the extracellular matrix is processed into an immunologically matching, thermoresponsive hydrogel. Efficient cell differentiation within a large 3D hydrogel is reported, and, as a proof of concept, the generation of functional cardiac, cortical, spinal cord, and adipogenic tissue implants is demonstrated. This versatile bioengineering approach may assist to regenerate any tissue and organ with a minimal risk for immune rejection., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology., Methods: Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high-density single nucleotide polymorphic markers., Results: Statistical analysis revealed a significant ( P  < 0.05) variation of liver weight between the CC lines, with broad sense heritability ( H 2 ) of 0.32 and genetic coefficient of variation (CV G ) of 0.28. Subsequently, quantitative trait locus (QTL) mapping was performed, and results showed a significant QTL only for females on chromosome 8 at genomic interval 88.61-93.38 Mb (4.77 Mb). Three suggestive QTL were mapped at chromosomes 4, 12 and 13. The four QTL were designated as LWL 1- LWL 4 referring to liver weight loci 1-4 on chromosomes 8, 4, 12 and 13, respectively., Conclusion: To our knowledge, this report presents, for the first time, the utilization of the CC for mapping QTL associated with baseline liver weight in mice. Our findings demonstrate that liver weight is a complex trait controlled by multiple genetic factors that differ significantly between sexes., Competing Interests: None.

The influenza virus is a major cause of morbidity and mortality worldwide. Yet, both the impact of intracellular viral replication and the variation in host response across different cell types remain uncharacterized. Here we used single-cell RNA sequencing to investigate the heterogeneity in the response of lung tissue cells to in vivo influenza infection. Analysis of viral and host transcriptomes in the same single cell enabled us to resolve the cellular heterogeneity of bystander (exposed but uninfected) as compared with infected cells. We reveal that all major immune and non-immune cell types manifest substantial fractions of infected cells, albeit at low viral transcriptome loads relative to epithelial cells. We show that all cell types respond primarily with a robust generic transcriptional response, and we demonstrate novel markers specific for influenza-infected as opposed to bystander cells. These findings open new avenues for targeted therapy aimed exclusively at infected cells., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Major efforts are invested in the analysis of phenotypic variation in a population of individuals. While many of these studies focus on the genetic basis of phenotypic variation via measurements of DNA polymorphic sites, the environmental effects are still elusive. Here we propose a methodology, called CCCE ('Cell Composition Common Environment'), to identify environmental effects on the composition of immune cell functionalities. Specifically, CCCE is focused on the common experiences that are shared between siblings (the 'common environment'), designed to correct for cell subpopulation heterogeneity, and is based on a multicolor flow cytometry analysis across a large cohort of human monozygotic and dizygotic twins. We demonstrate that the CCCE methodology can provide insights on the relations between common environmental effects and the heterogenic functions of several immune cell types, such as NK cells effector functions and coagulation-related capabilities of monocytes. The software described in this article is available at http://csgi.tau.ac.il/CCCE.

Over the past decade, genetic studies have recognized hundreds of polymorphic DNA loci called response QTLs (reQTLs) as potential contributors to interindividual variation in transcriptional responses to stimulations. Such reQTLs commonly affect the transduction of signals along the regulatory network that controls gene transcription. Identifying the pathways through which reQTLs perturb the underlying network has been a major challenge. Here, we present GEVIN ("Genome-wide Embedding of Variation In Networks"), a methodology that simultaneously identifies a reQTL and the particular pathway in which the reQTL affects downstream signal transduction along the network. Using synthetic data, we show that this algorithm outperforms existing pathway identification and reQTL identification methods. We applied GEVIN to the analysis of murine and human dendritic cells in response to pathogenic components. These analyses revealed significant reQTLs together with their perturbed Toll-like receptor signaling pathways. GEVIN thus offers a powerful framework that renders a comprehensive picture of disease-related DNA loci and their molecular functions within regulatory networks., (Copyright © 2017 by the Genetics Society of America.)

: The composition of immune-cell subsets is key to the understanding of major diseases and pathologies. Computational deconvolution methods enable researchers to investigate immune cell quantities in complex tissues based on transcriptome data. Here we present ImmQuant, a software tool allowing immunologists to upload transcription profiles of multiple tissue samples, apply deconvolution methodology to predict differences in cell-type quantities between the samples, and then inspect the inferred cell-type alterations using convenient visualization tools. ImmQuant builds on the DCQ deconvolution algorithm and allows a user-friendly utilization of this method by non-bioinformatician researchers. Specifically, it enables investigation of hundreds of immune cell subsets in mouse tissues, as well as a few dozen cell types in human samples., Availability and Implementation: ImmQuant is available for download at http://csgi.tau.ac.il/ImmQuant/ CONTACT: iritgv@post.tau.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the western world, with spectrum from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis. NAFLD developments are known to be affected by host genetic background. Herein we emphasize the power of collaborative cross (CC) mouse for dissecting this complex trait and revealing quantitative trait loci (QTL) controlling hepatic fat accumulation in mice. 168 female and 338 male mice from 24 and 37 CC lines, respectively, of 18-20 weeks old, maintained on standard rodent diet, since weaning. Hepatic fat content was assessed, using dual DEXA scan in the liver. Using the available high-density genotype markers of the CC line, QTL mapping associated with percentage liver fat accumulation was performed. Our results revealed significant fatty liver accumulation QTL that were specifically, mapped in females. Two significant QTLs on chromosomes 17 and 18, with genomic intervals 3 and 2 Mb, respectively, were mapped. A third QTL, with a less significant P value, was mapped to chromosome 4, with genomic interval of 2 Mb. These QTLs were named Flal1-Flal3, referring to Fatty Liver Accumulation Locus 1-3, for the QTLs on chromosomes 17, 18, and 4, respectively. Unfortunately, no QTL was mapped with males. Searching the mouse genome database suggested several candidate genes involved in hepatic fat accumulation. Our results show that susceptibility to hepatic fat accumulations is a complex trait, controlled by multiple genetic factors in female mice, but not in male.

Mounting an effective immune response, while also protecting tissue integrity, is critical for host survival. We used a combined genomic and proteomic approach to investigate the role of extracellular matrix (ECM) proteolysis in achieving this balance in the lung during influenza virus infection. We identified the membrane-tethered matrix metalloprotease MT1-MMP as a prominent host-ECM-remodeling collagenase in influenza infection. Selective inhibition of MT1-MMP protected the tissue from infection-related structural and compositional tissue damage. MT1-MMP inhibition did not significantly alter the immune response or cytokine expression. The available flu therapeutic Oseltamivir did not prevent lung ECM damage and was less effective than anti-MT1-MMP in influenza virus Streptococcus pneumoniae coinfection paradigms. Combination therapy of Oseltamivir with anti-MT1-MMP showed a strong synergistic effect and resulted in complete recovery of infected mice. This study highlights the importance of tissue resilience in surviving infection and the potential of such host-pathogen therapy combinations for respiratory infections., (Copyright © 2016 Elsevier Inc. All rights reserved.)

A central challenge in pharmaceutical research is to investigate genetic variation in response to drugs. The Collaborative Cross (CC) mouse reference population is a promising model for pharmacogenomic studies because of its large amount of genetic variation, genetic reproducibility, and dense recombination sites. While the CC lines are phenotypically diverse, their genetic diversity in drug disposition processes, such as detoxification reactions, is still largely uncharacterized. Here we systematically measured RNA-sequencing expression profiles from livers of 29 CC lines under baseline conditions. We then leveraged a reference collection of metabolic biotransformation pathways to map potential relations between drugs and their underlying expression quantitative trait loci (eQTLs). By applying this approach on proximal eQTLs, including eQTLs acting on the overall expression of genes and on the expression of particular transcript isoforms, we were able to construct the organization of hepatic eQTL-drug connectivity across the CC population. The analysis revealed a substantial impact of genetic variation acting on drug biotransformation, allowed mapping of potential joint genetic effects in the context of individual drugs, and demonstrated crosstalk between drug metabolism and lipid metabolism. Our findings provide a resource for investigating drug disposition in the CC strains, and offer a new paradigm for integrating biotransformation reactions to corresponding variations in DNA sequences.

Motivation: Expression Quantitative Trait Locus (eQTL) mapping tackles the problem of identifying variation in DNA sequence that have an effect on the transcriptional regulatory network. Major computational efforts are aimed at characterizing the joint effects of several eQTLs acting in concert to govern the expression of the same genes. Yet, progress toward a comprehensive prediction of such joint effects is limited. For example, existing eQTL methods commonly discover interacting loci affecting the expression levels of a module of co-regulated genes. Such "modularization" approaches, however, are focused on epistatic relations and thus have limited utility for the case of additive (non-epistatic) effects., Results: Here we present POEM (Pairwise effect On Expression Modules), a methodology for identifying pairwise eQTL effects on gene modules. POEM is specifically designed to achieve high performance in the case of additive joint effects. We applied POEM to transcription profiles measured in bone marrow-derived dendritic cells across a population of genotyped mice. Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network. These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs., Availability: The software described in this article is available at csgi.tau.ac.il/POEM/.

Sequence variation can affect the physiological state of the immune system. Major experimental efforts targeted at understanding the genetic control of the abundance of immune cell subpopulations. However, these studies are typically focused on a limited number of immune cell types, mainly due to the use of relatively low throughput cell-sorting technologies. Here we present an algorithm that can reveal the genetic basis of inter-individual variation in the abundance of immune cell types using only gene expression and genotyping measurements as input. Our algorithm predicts the abundance of immune cell subpopulations based on the RNA levels of informative marker genes within a complex tissue, and then provides the genetic control on these predicted immune traits as output. A key feature of the approach is the integration of predictions from various sets of marker genes and refinement of these sets to avoid spurious signals. Our evaluation of both synthetic and real biological data shows the significant benefits of the new approach. Our method, VoCAL, is implemented in the freely available R package ComICS.

Motivation: The immune system comprises a complex network of genes, cells and tissues, coordinated through signaling pathways and cell-cell communications. However, the orchestrated role of the multiple immunological components in disease is still poorly understood. Classifications based on gene-expression data have revealed immune-related signaling pathways in various diseases, but how such pathways describe the immune cellular physiology remains largely unknown., Results: We identify alterations in cell quantities discriminating between disease states using ' Cell type of Disease' (CoD), a classification-based approach that relies on computational immune-cell decomposition in gene-expression datasets. CoD attains significantly higher accuracy than alternative state-of-the-art methods. Our approach is shown to recapitulate and extend previous knowledge acquired with experimental cell-quantification technologies., Conclusions: The results suggest that CoD can reveal disease-relevant cell types in an unbiased manner, potentially heralding improved diagnostics and treatment., Availability and Implementation: The software described in this article is available at http://www.csgi.tau.ac.il/CoD/., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Vanishing white matter (VWM) is a recessive neurodegenerative disease caused by mutations in translation initiation factor eIF2B and leading to progressive brain myelin deterioration, secondary axonal damage, and death in early adolescence. Eif2b5(R132H/R132H) mice exhibit delayed developmental myelination, mild early neurodegeneration and a robust remyelination defect in response to cuprizone-induced demyelination. In the current study we used Eif2b5(R132H/R132H) mice for mass-spectrometry analyses, to follow the changes in brain protein abundance in normal- versus cuprizone-diet fed mice during the remyelination recovery phase. Analysis of proteome profiles suggested that dysregulation of mitochondrial functions, altered proteasomal activity and impaired balance between protein synthesis and degradation play a role in VWM pathology. Consistent with these findings, we detected elevated levels of reactive oxygen species in mutant-derived primary fibroblasts and reduced 20S proteasome activity in mutant brain homogenates. These observations highlight the importance of tight translational control to precise coordination of processes involved in myelin formation and regeneration and point at cellular functions that may contribute to VWM pathology. Eif2b5(R132H/R132H) mouse model for vanishing white matter (VWM) disease was used for mass spectrometry of brain proteins at two time points under normal conditions and along recovery from cuprizone-induced demyelination. Comparisons of proteome profiles revealed the importance of mitochondrial function and tight coordination between protein synthesis and degradation to myelination formation and regeneration, pointing at cellular functions that contribute to VWM pathology., (© 2015 International Society for Neurochemistry.)

Decoding heterogeneity of pluripotent stem cell (PSC)-derived neural progeny is fundamental for revealing the origin of diverse progenitors, for defining their lineages, and for identifying fate determinants driving transition through distinct potencies. Here we have prospectively isolated consecutively appearing PSC-derived primary progenitors based on their Notch activation state. We first isolate early neuroepithelial cells and show their broad Notch-dependent developmental and proliferative potential. Neuroepithelial cells further yield successive Notch-dependent functional primary progenitors, from early and midneurogenic radial glia and their derived basal progenitors, to gliogenic radial glia and adult-like neural progenitors, together recapitulating hallmarks of neural stem cell (NSC) ontogeny. Gene expression profiling reveals dynamic stage-specific transcriptional patterns that may link development of distinct progenitor identities through Notch activation. Our observations provide a platform for characterization and manipulation of distinct progenitor cell types amenable for developing streamlined neural lineage specification paradigms for modelling development in health and disease.

There is growing recognition that co-morbidity and co-occurrence of disease traits are often determined by shared genetic and molecular mechanisms. In most cases, however, the specific mechanisms that lead to such trait-trait relationships are yet unknown. Here we present an analysis of a broad spectrum of behavioral and physiological traits together with gene-expression measurements across genetically diverse mouse strains. We develop an unbiased methodology that constructs potentially overlapping groups of traits and resolves their underlying combination of genetic loci and molecular mechanisms. For example, our method predicts that genetic variation in the Klf7 gene may influence gene transcripts in bone marrow-derived myeloid cells, which in turn affect 17 behavioral traits following morphine injection; this predicted effect of Klf7 is consistent with an in vitro perturbation of Klf7 in bone marrow cells. Our analysis demonstrates the utility of studying hidden causative mechanisms that lead to relationships between complex traits.

Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

Viruses evade detection by the host immune system through the suppression of antiviral pathways. These pathways are thus obscured when measuring the host response to viral infection and cannot be inferred by current network reconstruction methodology. Here we aim to close this gap by providing a novel computational framework for the inference of such inhibited pathways as well as the proteins targeted by the virus to achieve this inhibition. We demonstrate the power of our method by testing it on the response to influenza infection in humans, with and without the viral inhibitory protein NS1, revealing its direct targets and their inhibitory effects.

Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration, and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynamics in normal developmental processes and disease. Here, we devise a computational method, digital cell quantification (DCQ), which combines genome-wide gene expression data with an immune cell compendium to infer in vivo changes in the quantities of 213 immune cell subpopulations. DCQ was applied to study global immune cell dynamics in mice lungs at ten time points during 7 days of flu infection. We find dramatic changes in quantities of 70 immune cell types, including various innate, adaptive, and progenitor immune cells. We focus on the previously unreported dynamics of four immune dendritic cell subtypes and suggest a specific role for CD103(+) CD11b(-) DCs in early stages of disease and CD8(+) pDC in late stages of flu infection.

Background: The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question., Results: We propose a novel formulation for the problem of network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection and ER export regulation in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.

Individual genetic variation affects gene responsiveness to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness quantitative trait loci or reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant responds as an activator of the antiviral response; using RNA interference, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli.