Joubert syndrome-derived induced pluripotent stem cells show altered neuronal differentiation in vitro

Joubert syndrome (JS) is a recessively inherited congenital ataxia characterized by hypotonia, psychomotor delay, abnormal ocular movements, intellectual disability, and a peculiar cerebellar and brainstem malformation, the "molar tooth sign." Over 40 causative genes have been reported, all encoding for proteins implicated in the structure or functioning of the primary cilium, a subcellular organelle widely present in embryonic and adult tissues. In this paper, we developed an in vitro neuronal differentiation model using patient-derived induced pluripotent stem cells (iPSCs), to evaluate possible neurodevelopmental defects in JS. To this end, iPSCs from four JS patients harboring mutations in distinct JS genes (AHI1, CPLANE1, TMEM67, and CC2D2A) were differentiated alongside healthy control cells to obtain mid-hindbrain precursors and cerebellar granule cells. Differentiation was monitored over 31 days through the detection of lineage-specific marker expression by qRT-PCR, immunofluorescence, and transcriptomics analysis. All JS patient-derived iPSCs, regardless of the mutant gene, showed a similar impairment to differentiate into mid-hindbrain and cerebellar granule cells when compared to healthy controls. In addition, analysis of primary cilium count and morphology showed notable ciliary defects in all differentiating JS patient-derived iPSCs compared to controls. These results confirm that patient-derived iPSCs are an accessible and relevant in vitro model to analyze cellular phenotypes connected to the presence of JS gene mutations in a neuronal context.

Fat-to-blood recirculation of partially dysfunctional PD-1+CD4 Tconv cells is associated with dysglycemia in human obesity

Obesity is characterized by the accumulation of T cells in insulin-sensitive tissues, including the visceral adipose tissue (VAT), that can interfere with the insulin signaling pathway eventually leading to insulin resistance (IR) and type 2 diabetes. Here, we found that PD-1+CD4 conventional T (Tconv) cells, endowed with a transcriptomic and functional profile of partially dysfunctional cells, are diminished in VAT of obese patients with dysglycemia (OB-Dys), without a concomitant increase in apoptosis. These cells showed enhanced capacity to recirculate into the bloodstream and had a non-restricted TCRβ repertoire divergent from that of normoglycemic obese and lean individuals. PD-1+CD4 Tconv were reduced in the circulation of OB-Dys, exhibited an altered migration potential, and were detected in the liver of patients with non-alcoholic steatohepatitis. The findings suggest a potential role for partially dysfunctional PD-1+CD4 Tconv cells as inter-organ mediators of IR in obese patients with dysglycemic.

Alterations in glutamate, arginine, and energy metabolism characterize cerebrospinal fluid and plasma metabolome of persons with HIV-associated dementia

OBJECTIVES

HIV-associated dementia (HAD) is the most severe clinical expression of HIV-mediated neuropathology, and the processes underlying its development remain poorly understood. We aimed to exploit high-dimensional metabolic profiling to gain insights into the pathological mechanisms associated to HAD.

DESIGN

In this cross-sectional study, we utilized metabolomics to profile matched cerebrospinal fluid (CSF) and plasma samples of HAD individuals ( n  = 20) compared with neurologically asymptomatic people with HIV (ASYM, n  = 20) and healthy controls (NEG, n  = 20).

METHODS

Identification of plasma and CSF metabolites was performed by liquid-chromatography or gas-chromatography following a validated experimental pipeline. The resulting metabolic profiles were analyzed by machine-learning algorithms, and altered pathways were identified by comparison with KEGG pathway database.

RESULTS

In CSF, HAD patients displayed an imbalance in glutamine/glutamate ratio, decreased levels of isocitrate and arginine, and increased oxidative stress when compared with ASYM or NEG. These changes were confirmed in matched plasma samples, which in addition revealed an accumulation of eicosanoids and unsaturated fatty acids in HAD individuals. Pathway analysis in both biological fluids suggested that alterations in several metabolic processes, including protein biosynthesis, glutamate and arginine metabolism, and energy metabolism, in association to a perturbed eicosanoid metabolism in plasma, may represent the metabolic signature associated to HAD.

CONCLUSION

These findings show that HAD may be associated with metabolic modifications in CSF and plasma. These preliminary data may be useful to identify novel metabolic biomarkers and therapeutic targets in HIV-associated neurological impairment.

The transcription factor HIF2α partakes in the differentiation block of acute myeloid leukemia

One of the defining features of acute myeloid leukemia (AML) is an arrest of myeloid differentiation whose molecular determinants are still poorly defined. Pharmacological removal of the differentiation block contributes to the cure of acute promyelocytic leukemia (APL) in the absence of cytotoxic chemotherapy, but this approach has not yet been translated to non-APL AMLs. Here, by investigating the function of hypoxia-inducible transcription factors HIF1α and HIF2α, we found that both genes exert oncogenic functions in AML and that HIF2α is a novel regulator of the AML differentiation block. Mechanistically, we found that HIF2α promotes the expression of transcriptional repressors that have been implicated in suppressing AML myeloid differentiation programs. Importantly, we positioned HIF2α under direct transcriptional control by the prodifferentiation agent all-trans retinoic acid (ATRA) and demonstrated that HIF2α blockade cooperates with ATRA to trigger AML cell differentiation. In conclusion, we propose that HIF2α inhibition may open new therapeutic avenues for AML treatment by licensing blasts maturation and leukemia debulking.

RNA sequencing of blood from sex- and age-matched discordant siblings supports immune and transcriptional dysregulation in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition with onset in early childhood, still diagnosed only through clinical observation due to the lack of laboratory biomarkers. Early detection strategies would be especially useful in screening high-risk newborn siblings of children already diagnosed with ASD. We performed RNA sequencing on peripheral blood, comparing 27 pairs of ASD children vs their sex- and age-matched unaffected siblings. Differential gene expression profiling, performed applying an unpaired model found two immune genes, EGR1 and IGKV3D-15, significantly upregulated in ASD patients (both p adj = 0.037). Weighted gene correlation network analysis identified 18 co-expressed modules. One of these modules was downregulated among autistic individuals (p = 0.035) and a ROC curve using its eigengene values yielded an AUC of 0.62. Genes in this module are primarily involved in transcriptional control and its hub gene, RACK1, encodes for a signaling protein critical for neurodevelopment and innate immunity, whose expression is influenced by various hormones and known "endocrine disruptors". These results indicate that transcriptomic biomarkers can contribute to the sensitivity of an intra-familial multimarker panel for ASD and provide further evidence that neurodevelopment, innate immunity and transcriptional regulation are key to ASD pathogenesis.

Abstract B027: The transcription factor HIF2alpha partakes in the differentiation block of acute myeloid leukemia

Acute myeloid leukemia (AML) is characterized by epigenetic silencing of differentiation genes and accumulation of aberrant myeloid cells arrested at different stages of myeloid development. The combination of epigenetic and differentiation therapies represents an attractive opportunity for AML patients by promoting chromatin remodeling at differentiation genes and induction of terminal maturation and leukemia debulking. The aim of our work is to define the function of the hypoxia inducible transcription factor HIF2alpha in the pathogenesis of AML. By its genetic inactivation and pharmacological inhibition in AML cell lines and patient-derived xenograft models, we found that HIF2alpha acts as a novel regulator of the AML differentiation block and promotes leukemia progression. Gene expression profiling of HIF2alpha-dependent transcriptome revealed that HIF2alpha mainly promotes the expression of genes involved in transcriptional modulation and epigenetic modifications, whilst inhibiting genes of myeloid maturation and activation. Mechanistically, inhibition of HIF2alpha results in decreased global levels of the heterochromatin marker H3K27me3, which is specifically deposited by the catalytic subunit EZH2 of the Polycomb repressive complex 2. Intriguingly, we found that HIF2alpha and EZH2 cooperate at favoring EZH2-mediated deposition of H3K27me3 at reverse hypoxia responsive elements in the regulatory regions of myeloid maturation genes. Additionally, we demonstrate that HIF2alpha is positively regulated by the pro-differentiation agent all-trans retinoic acid (ATRA), and its inhibition cooperates with ATRA in triggering AML cell differentiation. In conclusion, we provide new mechanistic insights into the role of HIF2alpha in the pathogenesis of AML, by promoting an undifferentiated state via EZH2-mediated epigenetic silencing of myeloid differentiation genes. Importantly, small molecule inhibitors of HIF2alpha have been recently generated and are tested for solid cancers. Therefore, HIF2alpha inhibition may open new therapeutic avenues for AML patients and add therapeutic value to ATRA-based therapies by interrupting HIF2alpha upregulation and promoting an epigenetic state permissive to maturation of leukemic blasts and leukemia exhaustion.

Citation Format: Daniela Magliulo, Carolina Caserta, Serena Belluschi, Cristina Fracassi, Kety Giannetti, Raffaella Pini, Ettore Zapparoli, Stefano Beretta, Eleonora Draghi, Marco J. Morelli, Raffaella Di Micco, Bernhard Gentner, Luca Vago, Rosa Bernardi. The transcription factor HIF2alpha partakes in the differentiation block of acute myeloid leukemia. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr B027.

Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes

An unbiased and replicable profiling of type 1 diabetes (T1D)-specific circulating immunome at disease onset has yet to be identified due to experimental and patient selection limitations. Multicolor flow cytometry was performed on whole blood from a pediatric cohort of 107 patients with new-onset T1D, 85 relatives of T1D patients with 0-1 islet autoantibodies (pre-T1D_LR), 58 patients with celiac disease or autoimmune thyroiditis (CD_THY) and 76 healthy controls (HC). Unsupervised clustering of flow cytometry data, validated by a semi-automated gating strategy, confirmed previous findings showing selective increase of naïve CD4 T cells and plasmacytoid DCs, and revealed a decrease in CD56brightNK cells in T1D. Furthermore, a non-selective decrease of CD3+CD56+ regulatory T cells was observed in T1D. The frequency of naïve CD4 T cells at disease onset was associated with partial remission, while it was found unaltered in the pre-symptomatic stages of the disease. Thanks to a broad cohort of pediatric individuals and the implementation of unbiased approaches for the analysis of flow cytometry data, here we determined the circulating immune fingerprint of newly diagnosed pediatric T1D and provide a reference dataset to be exploited for validation or discovery purposes to unravel the pathogenesis of T1D.

High-Throughput Monoclonal Antibody Discovery from Phage Libraries: Challenging the Current Preclinical Pipeline to Keep the Pace with the Increasing mAb Demand

Simple Summary

Monoclonal antibodies are increasingly used for a broad range of diseases. Rising demand must face with time time-consuming and laborious processes to isolate novel monoclonal antibodies. Next-generation sequencing coupled to phage display provides timely and sustainable high throughput selection strategy to rapidly access novel target. Here, we describe the current NGS-guided strategies to identify potential binders from enriched sub-libraires by applying a user-friendly informatic pipeline to identify and discard false positive clones. Rescue step and strategies to boost mAb yield are also discussed to improve the limiting selection and screening steps.

Abstract

Monoclonal antibodies are among the most powerful therapeutics in modern medicine. Since the approval of the first therapeutic antibody in 1986, monoclonal antibodies keep holding great expectations for application in a range of clinical indications, highlighting the need to provide timely and sustainable access to powerful screening options. However, their application in the past has been limited by time-consuming and expensive steps of discovery and production. The screening of antibody repertoires is a laborious step; however, the implementation of next-generation sequencing-guided screening of single-chain antibody fragments has now largely overcome this issue. This review provides a detailed overview of the current strategies for the identification of monoclonal antibodies from phage display-based libraries. We also discuss the challenges and the possible solutions to improve the limiting selection and screening steps, in order to keep pace with the increasing demand for monoclonal antibodies.

Radiomic and gEnomic approaches for the enhanced DIagnosis of clear cell REnal Cancer (REDIRECt): a translational pilot methodological study

Background

The combination of radiomic and transcriptomic approaches for patients diagnosed with small clear-cell renal cell carcinoma (ccRCC) might improve decision making. In this pilot and methodological study, we investigate whether imaging features obtained from computed tomography (CT) may correlate with gene expression patterns in ccRCC patients.

Methods

Samples from 6 patients who underwent partial nephrectomy for unilateral non-metastatic ccRCC were included in this pilot cohort. Transcriptomic analysis was conducted through RNA-sequencing on tumor samples, while radiologic features were obtained from pre-operative 4-phase contrast-enhanced CT. To evaluate the heterogeneity of the transcriptome, after a 1,000 re-sampling via bootstrapping, a first Principal Component Analyses (PCA) were fitted with all transcripts and a second ones with transcripts deriving from a list of 369 genes known to be associated with ccRCC from The Cancer Genome Atlas (TCGA). Significant pathways in each Principal Components for the 50 genes with the highest loadings absolute values were assessed with pathways enrichment analysis. In addition, Pearson’s correlation coefficients among radiomic features themselves and between radiomic features and transcripts expression values were computed.

Results

The transcriptomes of the analysed samples showed a high grade of heterogeneity. However, we found four radiogenomic patterns, in which the correlation between radiomic features and transcripts were statistically significant.

Conclusions

We showed that radiogenomic approach is feasible, however its clinical meaning should be further investigated.

Integrated requirement of non-specific and sequence-specific DNA binding in Myc-driven transcription

Eukaryotic transcription factors recognize specific DNA sequence motifs, but are also endowed with generic, non-specific DNA-binding activity. How these binding modes are integrated to determine select transcriptional outputs remains unresolved. We addressed this question by site-directed mutagenesis of the Myc transcription factor. Impairment of non-specific DNA backbone contacts caused pervasive loss of genome interactions and gene regulation, associated with increased intra-nuclear mobility of the Myc protein in murine cells. In contrast, a mutant lacking base-specific contacts retained DNA-binding and mobility profiles comparable to those of the wild-type protein, but failed to recognize its consensus binding motif (E-box) and could not activate Myc-target genes. Incidentally, this mutant gained weak affinity for an alternative motif, driving aberrant activation of different genes. Altogether, our data show that non-specific DNA binding is required to engage onto genomic regulatory regions; sequence recognition in turn contributes to transcriptional activation, acting at distinct levels: stabilization and positioning of Myc onto DNA, and-unexpectedly-promotion of its transcriptional activity. Hence, seemingly pervasive genome interaction profiles, as detected by ChIP-seq, actually encompass diverse DNA-binding modalities, driving defined, sequence-dependent transcriptional responses.

Chest CT in the emergency department for suspected COVID-19 pneumonia

PURPOSE

In overwhelmed emergency departments (EDs) facing COVID-19 outbreak, a swift diagnosis is imperative. CT role was widely debated for its limited specificity. Here we report the diagnostic role of CT in two EDs in Lombardy, epicenter of Italian outbreak.

MATERIAL AND METHODS

Admitting chest CT from 142 consecutive patients with suspected COVID-19 were retrospectively analyzed. CT scans were classified in "highly likely," "likely," and "unlikely" COVID-19 pneumonia according to the presence of typical, indeterminate, and atypical findings, or "negative" in the absence of findings, or "alternative diagnosis" when a different diagnosis was found. Nasopharyngeal swab results, turnaround time, and time to positive results were collected. CT diagnostic performances were assessed considering RT-PCR as reference standard.

RESULTS

Most of cases (96/142, 68%) were classified as "highly likely" COVID-19 pneumonia. Ten (7%) and seven (5%) patients were classified as "likely" and "unlikely" COVID-19 pneumonia, respectively. In 21 (15%) patients a differential diagnosis was provided, including typical pneumonia, pulmonary edema, neoplasia, and pulmonary embolism. CT was negative in 8/142 (6%) patients. Mean turnaround time for the first COVID-19 RT-PCR was 30 ± 13 h. CT diagnostic accuracy in respect of the first test swab was 79% and increased to 91.5% after repeated swabs and/or BAL, for 18 false-negative first swab. CT performance was good with 76% specificity, 99% sensitivity, 90% positive predictive value and 97% negative predictive value.

CONCLUSION

Chest CT was useful to streamline patients' triage while waiting for RT-PCR in the ED, supporting the clinical suspicion of COVID-19 or providing alternative diagnosis.

An early Myc-dependent transcriptional program orchestrates cell growth during B-cell activation

Upon activation, lymphocytes exit quiescence and undergo substantial increases in cell size, accompanied by activation of energy-producing and anabolic pathways, widespread chromatin decompaction, and elevated transcriptional activity. These changes depend upon prior induction of the Myc transcription factor, but how Myc controls them remains unclear. We addressed this issue by profiling the response to LPS stimulation in wild-type and c-myc-deleted primary mouse B-cells. Myc is rapidly induced, becomes detectable on virtually all active promoters and enhancers, but has no direct impact on global transcriptional activity. Instead, Myc contributes to the swift up- and down-regulation of several hundred genes, including many known regulators of the aforementioned cellular processes. Myc-activated promoters are enriched for E-box consensus motifs, bind Myc at the highest levels, and show enhanced RNA Polymerase II recruitment, the opposite being true at down-regulated loci. Remarkably, the Myc-dependent signature identified in activated B-cells is also enriched in Myc-driven B-cell lymphomas: hence, besides modulation of new cancer-specific programs, the oncogenic action of Myc may largely rely on sustained deregulation of its normal physiological targets.

FunChIP: an R/Bioconductor package for functional classification of ChIP-seq shapes

Summary

Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) generates local accumulations of sequencing reads on the genome ("peaks"), which correspond to specific protein-DNA interactions or chromatin modifications. Peaks are detected by considering their total area above a background signal, usually neglecting their shapes, which instead may convey additional biological information. We present FunChIP, an R/Bioconductor package for clustering peaks according to a functional representation of their shapes: after approximating their profiles with cubic B-splines, FunChIP minimizes their functional distance and classifies the peaks applying a k-mean alignment and clustering algorithm. The whole pipeline is user-friendly and provides visualization functions for a quick inspection of the results. An application to the transcription factor Myc in 3T9 murine fibroblasts shows that clusters of peaks with different shapes are associated with different genomic locations and different transcriptional regulatory activity.

Availability and implementation

The package is implemented in R and is available under Artistic Licence 2.0 from the Bioconductor website (http://bioconductor.org/packages/FunChIP).

Contact

marco.morelli@iit.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

MuSERA: Multiple Sample Enriched Region Assessment

Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser. We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www.bioinformatics.deib.polimi.it/MuSERA/.

Pin1 is required for sustained B cell proliferation upon oncogenic activation of Myc

The c-myc proto-oncogene is activated by translocation in Burkitt's lymphoma and substitutions in codon 58 stabilize the Myc protein or augment its oncogenic potential. In wild-type Myc, phosphorylation of Ser 62 and Thr 58 provides a landing pad for the peptidyl prolyl-isomerase Pin1, which in turn promotes Ser 62 dephosphorylation and Myc degradation. However, the role of Pin1 in Myc-induced lymphomagenesis remains unknown. We show here that genetic ablation of Pin1 reduces lymphomagenesis in Eμ-myc transgenic mice. In both Pin1-deficient B-cells and MEFs, the proliferative response to oncogenic Myc was selectively impaired, with no alterations in Myc-induced apoptosis or mitogen-induced cell cycle entry. This proliferative defect wasn't attributable to alterations in either Ser 62 phosphorylation or Myc-regulated transcription, but instead relied on the activity of the ARF-p53 pathway. Pin1 silencing in lymphomas retarded disease progression in mice, making Pin1 an attractive therapeutic target in Myc-driven tumors.

The cohesin complex prevents Myc-induced replication stress

The cohesin complex is mutated in cancer and in a number of rare syndromes collectively known as Cohesinopathies. In the latter case, cohesin deficiencies have been linked to transcriptional alterations affecting Myc and its target genes. Here, we set out to understand to what extent the role of cohesins in controlling cell cycle is dependent on Myc expression and activity. Inactivation of the cohesin complex by silencing the RAD21 subunit led to cell cycle arrest due to both transcriptional impairment of Myc target genes and alterations of replication forks, which were fewer and preferentially unidirectional. Ectopic activation of Myc in RAD21 depleted cells rescued Myc-dependent transcription and promoted S-phase entry but failed to sustain S-phase progression due to a strong replicative stress response, which was associated to a robust DNA damage response, DNA damage checkpoint activation and synthetic lethality. Thus, the cohesin complex is dispensable for Myc-dependent transcription but essential to prevent Myc-induced replicative stress. This suggests the presence of a feed-forward regulatory loop where cohesins by regulating Myc level control S-phase entry and prevent replicative stress.

Mutual epithelium-macrophage dependency in liver carcinogenesis mediated by ST18

The ST18 gene has been proposed to act either as a tumor suppressor or as an oncogene in different human cancers, but direct evidence for its role in tumorigenesis has been lacking thus far. Here, we demonstrate that ST18 is critical for tumor progression and maintenance in a mouse model of liver cancer, based on oncogenic transformation and adoptive transfer of primary precursor cells (hepatoblasts). ST18 messenger RNA (mRNA) and protein were detectable neither in normal liver nor in cultured hepatoblasts, but were readily expressed after subcutaneous engraftment and tumor growth. ST18 expression in liver cells was induced by inflammatory cues, including acute or chronic inflammation in vivo, as well as coculture with macrophages in vitro. Knocking down the ST18 mRNA in transplanted hepatoblasts delayed tumor progression. Induction of ST18 knockdown in pre-established tumors caused rapid tumor involution associated with pervasive morphological changes, proliferative arrest, and apoptosis in tumor cells, as well as depletion of tumor-associated macrophages, vascular ectasia, and hemorrhage. Reciprocally, systemic depletion of macrophages in recipient animals had very similar phenotypic consequences, impairing either tumor development or maintenance, and suppressing ST18 expression in hepatoblasts. Finally, RNA sequencing of ST18-depleted tumors before involution revealed down-regulation of inflammatory response genes, pointing to the suppression of nuclear factor kappa B-dependent transcription.

CONCLUSION

ST18 expression in epithelial cells is induced by tumor-associated macrophages, contributing to the reciprocal feed-forward loop between both cell types in liver tumorigenesis. Our findings warrant the exploration of means to interfere with ST18-dependent epithelium-macrophage interactions in a therapeutic setting. (Hepatology 2017;65:1708-1719).

Tumorigenesis by Meis1 overexpression is accompanied by a change of DNA target-sequence specificity which allows binding to the AP-1 element

Meis1 overexpression induces tumorigenicity but its activity is inhibited by Prep1 tumor suppressor. Why does overexpression of Meis1 cause cancer and how does Prep1 inhibit? Tumor profiling and ChIP-sequencing data in a genetically-defined set of cell lines show that: 1) The number of Meis1 and Prep1 DNA binding sites increases linearly with their concentration resulting in a strong increase of “extra” target genes. 2) At high concentration, Meis1 DNA target specificity changes such that the most enriched consensus becomes that of the AP-1 regulatory element, whereas the specific OCTA consensus is not enriched because diluted within the many extra binding sites. 3) Prep1 inhibits Meis1 tumorigenesis preventing the binding to many of the “extra” genes containing AP-1 sites. 4) The overexpression of Prep1, but not of Meis1, changes the functional genomic distribution of the binding sites, increasing seven fold the number of its “enhancer” and decreasing its “promoter” targets. 5) A specific Meis1 “oncogenic” and Prep1 “tumor suppressing” signature has been identified selecting from the pool of genes bound by each protein those whose expression was modified uniquely by the “tumor-inducing” Meis1 or tumor-inhibiting Prep1 overexpression. In both signatures, the enriched gene categories are the same and are involved in signal transduction. However, Meis1 targets stimulatory genes while Prep1 targets genes that inhibit the tumorigenic signaling pathways.

Genome-wide analysis of p53 transcriptional programs in B cells upon exposure to genotoxic stress in vivo

The tumor suppressor p53 is a transcription factor that coordinates the cellular response to DNA damage. Here we provide an integrated analysis of p53 genomic occupancy and p53-dependent gene regulation in the splenic B and non-B cell compartments of mice exposed to whole-body ionizing radiation, providing insight into general principles of p53 activity in vivo. In unstressed conditions, p53 bound few genomic targets; induction of p53 by ionizing radiation increased the number of p53 bound sites, leading to highly overlapping profiles in the different cell types. Comparison of these profiles with chromatin features in unstressed B cells revealed that, upon activation, p53 localized at active promoters, distal enhancers, and a smaller set of unmarked distal regions. At promoters, recognition of the canonical p53 motif as well as binding strength were associated with p53-dependent transcriptional activation, but not repression, indicating that the latter was most likely indirect. p53-activated targets constituted the core of a cell type-independent response, superimposed onto a cell type-specific program. Core response genes included most of the known p53-regulated genes, as well as many new ones. Our data represent a unique characterization of the p53-regulated response to ionizing radiation in vivo.

Proteasome machinery is instrumental in a common gain-of-function program of the p53 missense mutants in cancer

In cancer, the tumour suppressor gene TP53 undergoes frequent missense mutations that endow mutant p53 proteins with oncogenic properties. Until now, a universal mutant p53 gain-of-function program has not been defined. By means of multi-omics: proteome, DNA interactome (chromatin immunoprecipitation followed by sequencing) and transcriptome (RNA sequencing/microarray) analyses, we identified the proteasome machinery as a common target of p53 missense mutants. The mutant p53-proteasome axis globally affects protein homeostasis, inhibiting multiple tumour-suppressive pathways, including the anti-oncogenic KSRP-microRNA pathway. In cancer cells, p53 missense mutants cooperate with Nrf2 (NFE2L2) to activate proteasome gene transcription, resulting in resistance to the proteasome inhibitor carfilzomib. Combining the mutant p53-inactivating agent APR-246 (PRIMA-1MET) with the proteasome inhibitor carfilzomib is effective in overcoming chemoresistance in triple-negative breast cancer cells, creating a therapeutic opportunity for treatment of solid tumours and metastasis with mutant p53.

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: (1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; (2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; (3) ensuring complete traceability of the analysis performed; (4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; (5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts.

p53 transcriptional programs in B cells upon exposure to genotoxic stress in vivo: Computational analysis of next-generation sequencing data

The transcriptional programs activated by p53 in B cells in vivo following exposure to ionizing radiation were studied through the integrated analysis of various types of next-generation sequencing data: genome-wide profiling of p53 binding sites, mapping of histone marks and open chromatin regions and quantification of gene expression. Moreover, the binding of p53 was associated to a series of specific motifs on the DNA, which were directly inferred from the data. Here, we describe in detail the computational analysis of the datasets associated with our study (Tonelli et al., Oncotarget 6 (2015), 24611-26), deposited in the GEO archive (accession code GSE71180), and we provide the R scripts needed to generated the figures of the paper.

Abstract B10: Genome-wide analysis of p53 transcriptional programs in Myc-induced lymphomas

Oncogenic activation of Myc induces cellular transformation and hyper-proliferation, while it activates intrinsic tumor surveillance pathways aimed at restraining the expansion of pre-cancerous cells. All these Myc-induced tumor suppressive mechanisms converge on p53, and loss of p53 activity is the major selected event in lymphomagenesis. Yet, the genetic programs triggered by p53 in tumor suppression have been only partially clarified. Using whole genome mapping of p53 binding and gene expression profiling, we investigated the transcriptional circuitry employed by p53 in suppressing cancer development. We studied the progression of Myc-induced lymphomas in Eµ-myc transgenic mice, as well as the regression of these lymphomas following restoration of p53 function, by either pharmacological or genetic means. We thus identified a set of p53 target genes that are strong candidates for mediating tumor suppression. Currently, we are testing the impact of these new components of the p53 transcriptional program on tumorigenesis with an RNA interference (RNAi)-based functional genetic screen. Altogether our data expand our understanding of the p53 response to Myc-induced oncogenic stress and will hopefully highlight new tumor suppressive mechanisms, paving the way for their therapeutic application.

Citation Format: Claudia Tonelli, Marco J. Morelli, Arianna Sabò, Andrea Piontini, Mattia Pelizzola, Stefano Campaner, Bruno Amati. Genome-wide analysis of p53 transcriptional programs in Myc-induced lymphomas. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr B10.

Abstract A44: Selective transcriptional regulation by Myc in growth control and tumorigenesis

Myc binds DNA with a preference for the E-box consensus CACGTG. In vivo, however, DNA recognition is primarily determined by chromatin context, preceding sequence-specific DNA binding. Myc preferentially associates with active/poised promoters and, to a lesser extent, distal enhancer elements. When expressed at high levels, Myc targets virtually all active promoters and enhancers in the genome, a phenomenon termed “invasion”, which contributed to the concept that Myc acts as a general amplifier of transcriptional activity (1, 2). We recently presented data that support a different scenario (3). While Myc has the potential to interact with all active/poised regulatory elements in the genome, this does not systematically lead to a productive regulatory interaction: instead, promoter/enhancer invasion by Myc most likely reflects the detection of low-affinity interactions (protein-protein or protein-DNA) that allow the transcription factor to target the relevant genomic regions, preceding local scanning of the DNA sequence for high-affinity binding sites (4). Most importantly, when productively engaged by Myc, target genes can be either induced or repressed, as shown in different cell types (3, 5). Our data also show that the global increase in mRNA copies per cell (or amplification), observable either during tumor progression or upon mitogenic stimulation of normal cells, can occur in the absence of overt promoter/enhancer invasion by Myc. RNA amplification is thus indirect, most likely attributable to changes in physiological features that impact on global RNA production and turnover (e.g. cell size, energy metabolism, etc..). Consistent with this view, many Myc-regulated genes have - or may have - central roles in these physiological processes. Putting together all available data, the emerging unifying model supports a central role for Myc in the control of cellular physiology and metabolism, including - albeit indirectly - global RNA production and turnover. An important challenge lying ahead is the full characterization of the Myc-regulated genes and pathways that mediate these biological effects and - by the same token - endow Myc with such pervasive oncogenic potential. 1. Lin CY, et al. (2012) Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151:56-67. 2. Nie Z, et al. (2012) c-Myc is a universal amplifier of expressed genes in lymphocytes and embryonic stem cells. Cell 151:68-79. 3. Sabo A, et al. (2014) Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature 511:488-492. 4. Sabo A & Amati B (2014) Genome Recognition by MYC. Cold Spring Harb Perspect Med 4. 5. Walz S, et al. (2014) Activation and repression by oncogenic MYC shape tumour-specific gene expression profiles. Nature 511:483-487.

Citation Format: Arianna Sabò, Theresia K. Kress, Mattia Pelizzola, Stefano de Pretis, Marcin Gorski, Alessandra Tesi, Marco J. Morelli, Pranami Bora, Mirko Doni, Alessandro Verrecchia, Claudia Tonelli, Giovanni Fagà, Valerio Bianchi, Alberto Ronchi, Diana Low, Heiko Muller, Ernesto Guccione, Stefano Campaner, Bruno Amati. Selective transcriptional regulation by Myc in growth control and tumorigenesis. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr A44.

methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data

BACKGROUND

Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets.

RESULTS

To overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks.

CONCLUSIONS

methylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians.

Selective transcriptional regulation by Myc: Experimental design and computational analysis of high-throughput sequencing data

The gene expression programs regulated by the Myc transcription factor were evaluated by integrated genome-wide profiling of Myc binding sites, chromatin marks and RNA expression in several biological models. Our results indicate that Myc directly drives selective transcriptional regulation, which in certain physiological conditions may indirectly lead to RNA amplification. Here, we illustrate in detail the experimental design concerning the high-throughput sequencing data associated with our study (Sabò et al., Nature. (2014) 511:488–492) and the R scripts used for their computational analysis.

INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments

MOTIVATION

Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking.

RESULTS

INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design.

AVAILABILITY AND IMPLEMENTATION

INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1.

CONTACT

mattia.pelizzola@iit.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

BACKGROUND

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

RESULTS

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site's coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

CONCLUSIONS

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Comparative evaluation of DNase-seq footprint identification strategies

DNase I is an enzyme preferentially cleaving DNA in highly accessible regions. Recently, Next-Generation Sequencing has been applied to DNase I assays (DNase-seq) to obtain genome-wide maps of these accessible chromatin regions. With high-depth sequencing, DNase I cleavage sites can be identified with base-pair resolution, revealing the presence of protected regions ("footprints"), corresponding to bound molecules on the DNA. Integrating footprint positions close to transcription start sites with motif analysis can reveal the presence of regulatory interactions between specific transcription factors (TFs) and genes. However, this inference heavily relies on the accuracy of the footprint call and on the sequencing depth of the DNase-seq experiment. Using ENCODE data, we comprehensively evaluate the performances of two recent footprint callers (Wellington and DNaseR) and one metric (the Footprint Occupancy Score, or FOS), and assess the consequences of different footprint calls on the reconstruction of TF-TF regulatory networks. We rate Wellington as the method of choice among those tested: not only its predictions are the best in terms of accuracy, but also the properties of the inferred networks are robust against sequencing depth.

Evolution of foot-and-mouth disease virus intra-sample sequence diversity during serial transmission in bovine hosts

RNA virus populations within samples are highly heterogeneous, containing a large number of minority sequence variants which can potentially be transmitted to other susceptible hosts. Consequently, consensus genome sequences provide an incomplete picture of the within- and between-host viral evolutionary dynamics during transmission. Foot-and-mouth disease virus (FMDV) is an RNA virus that can spread from primary sites of replication, via the systemic circulation, to found distinct sites of local infection at epithelial surfaces. Viral evolution in these different tissues occurs independently, each of them potentially providing a source of virus to seed subsequent transmission events. This study employed the Illumina Genome Analyzer platform to sequence 18 FMDV samples collected from a chain of sequentially infected cattle. These data generated snap-shots of the evolving viral population structures within different animals and tissues. Analyses of the mutation spectra revealed polymorphisms at frequencies >0.5% at between 21 and 146 sites across the genome for these samples, while 13 sites acquired mutations in excess of consensus frequency (50%). Analysis of polymorphism frequency revealed that a number of minority variants were transmitted during host-to-host infection events, while the size of the intra-host founder populations appeared to be smaller. These data indicate that viral population complexity is influenced by small intra-host bottlenecks and relatively large inter-host bottlenecks. The dynamics of minority variants are consistent with the actions of genetic drift rather than strong selection. These results provide novel insights into the evolution of FMDV that can be applied to reconstruct both intra- and inter-host transmission routes.

Observing micro-evolutionary processes of viral populations at multiple scales

Advances in sequencing technology coupled with new integrative approaches to data analysis provide a potentially transformative opportunity to use pathogen genome data to advance our understanding of transmission. However, to maximize the insights such genetic data can provide, we need to understand more about how the microevolution of pathogens is observed at different scales of biological organization. Here, we examine the evolutionary processes in foot-and-mouth disease virus observed at different scales, ranging from the tissue, animal, herd and region. At each scale, we observe analogous processes of population expansion, mutation and selection resulting in the accumulation of mutations over increasing time scales. While the current data are limited, rates of nucleotide substitution appear to be faster over individual-to-individual transmission events compared with those observed at a within-individual scale suggesting that viral population bottlenecks between individuals facilitate the fixation of polymorphisms. Longer-term rates of nucleotide substitution were found to be equivalent in individual-to-individual transmission compared with herd-to-herd transmission indicating that viral diversification at the herd level is not retained at a regional scale.

A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

In order to most effectively control the spread of an infectious disease, we need to better understand how pathogens spread within a host population, yet this is something we know remarkably little about. Cases close together in their locations and timing are often thought to be linked, but timings and locations alone are usually consistent with many different scenarios of who-infected-who. The genome of many pathogens evolves so quickly relative to the rate that they are transmitted, that even over single short epidemics we can identify which hosts contain pathogens that are most closely related to each other. This information is valuable because when combined with the spatial and timing data it should help us infer more reliably who-transmitted-to-who over the course of a disease outbreak. However, doing this so that these three different lines of evidence are appropriately weighted and interpreted remains a major statistical challenge. In our paper we present a new statistical method for combining these different types of data and estimating trees that show how infection was most likely transmitted between individuals in a host population. Because sequencing genetic material has become so affordable, we think methods like ours will become very important for future epidemiology.

Ultradeep sequencing analysis of population dynamics of virus escape mutants in RNAi-mediated resistant plants

Plant artificial micro-RNAs (amiRs) have been engineered to target viral genomes and induce their degradation. However, the exceptional evolutionary plasticity of RNA viruses threatens the durability of the resistance conferred by these amiRs. It has recently been shown that viral populations not experiencing strong selective pressure from an antiviral amiR may already contain enough genetic variability in the target sequence to escape plant resistance in an almost deterministic manner. Furthermore, it has also been shown that viral populations exposed to subinhibitory concentrations of the antiviral amiR speed up this process. In this article, we have characterized the molecular evolutionary dynamics of an amiR target sequence in a viral genome under both conditions. The use of Illumina ultradeep sequencing has allowed us to identify virus sequence variants at frequencies as low as 2 × 10(-6) and to track their variation in time before and after the viral population was able of successfully infecting plants fully resistant to the ancestral virus. We found that every site in the amiR-target sequence of the viral genome presented variation and that the variant that eventually broke resistance was sampled among the many coexisting ones. In this system, viral evolution in fully susceptible plants results from an equilibrium between mutation and genetic drift, whereas evolution in partially resistant plants originates from more complex dynamics involving mutation, selection, and drift.

Effects of macromolecular crowding on genetic networks

The intracellular environment is crowded with proteins, DNA, and other macromolecules. Under physiological conditions, macromolecular crowding can alter both molecular diffusion and the equilibria of bimolecular reactions and therefore is likely to have a significant effect on the function of biochemical networks. We propose a simple way to model the effects of macromolecular crowding on biochemical networks via an appropriate scaling of bimolecular association and dissociation rates. We use this approach, in combination with kinetic Monte Carlo simulations, to analyze the effects of crowding on a constitutively expressed gene, a repressed gene, and a model for the bacteriophage λ genetic switch, in the presence and absence of nonspecific binding of transcription factors to genomic DNA. Our results show that the effects of crowding are mainly caused by the shift of association-dissociation equilibria rather than the slowing down of protein diffusion, and that macromolecular crowding can have relevant and counterintuitive effects on biochemical network performance.

Beyond the consensus: dissecting within-host viral population diversity of foot-and-mouth disease virus by using next-generation genome sequencing

The diverse sequences of viral populations within individual hosts are the starting material for selection and subsequent evolution of RNA viruses such as foot-and-mouth disease virus (FMDV). Using next-generation sequencing (NGS) performed on a Genome Analyzer platform (Illumina), this study compared the viral populations within two bovine epithelial samples (foot lesions) from a single animal with the inoculum used to initiate experimental infection. Genomic sequences were determined in duplicate sequencing runs, and the consensus sequence of the inoculum determined by NGS was identical to that previously determined using the Sanger method. However, NGS revealed the fine polymorphic substructure of the viral population, from nucleotide variants present at just below 50% frequency to those present at fractions of 1%. Some of the higher-frequency polymorphisms identified encoded changes within codons associated with heparan sulfate binding and were present in both foot lesions, revealing intermediate stages in the evolution of a tissue culture-adapted virus replicating within a mammalian host. We identified 2,622, 1,434, and 1,703 polymorphisms in the inoculum and in the two foot lesions, respectively: most of the substitutions occurred in only a small fraction of the population and represented the progeny from recent cellular replication prior to onset of any selective pressures. We estimated the upper limit for the genome-wide mutation rate of the virus within a cell to be 7.8 × 10(-4) per nucleotide. The greater depth of detection achieved by NGS demonstrates that this method is a powerful and valuable tool for the dissection of FMDV populations within hosts.

The relationship between mutation frequency and replication strategy in positive-sense single-stranded RNA viruses

For positive-sense single-stranded RNA virus genomes, there is a trade-off between the mutually exclusive tasks of transcription, translation and encapsidation. The replication strategy that maximizes the intracellular growth rate of the virus requires iterative genome transcription from positive to negative, and back to positive sense. However, RNA viruses experience high mutation rates, and the proportion of genomes with lethal mutations increases with the number of replication cycles. Thus, intracellular mutant frequency will depend on the replication strategy. Introducing apparently realistic mutation rates into a model of viral replication demonstrates that strategies that maximize viral growth rate could result in an average of 26 mutations per genome by the time plausible numbers of positive strands have been generated, and that virus viability could be as low as 0.1 per cent. At high mutation rates or when a high proportion of mutations are deleterious, the optimal strategy shifts towards synthesizing more negative strands per positive strand, and in extremis towards a 'stamping-machine' replication mode where all the encapsidated genomes come from only two transcriptional steps. We conclude that if viral mutation rates are as high as current estimates suggest, either mutation frequency must be considerably higher than generally anticipated and the proportion of viable viruses produced extremely small, or replication strategies cannot be optimized to maximize viral growth rate. Mechanistic models linking mutation frequency to replication mechanisms coupled with data generated through new deep-sequencing technologies could play an important role in improving the estimates of viral mutation rate.

Reaction Brownian dynamics and the effect of spatial fluctuations on the gain of a push-pull network

Brownian Dynamics algorithms have been widely used for simulating systems in soft-condensed matter physics. In recent times, their application has been extended to the simulation of coarse-grained models of biochemical networks. In these models, components move by diffusion and interact with one another upon contact. However, when reactions are incorporated into a Brownian dynamics algorithm, care must be taken to avoid violations of the detailed-balance rule, which would introduce systematic errors in the simulation. We present a Brownian dynamics algorithm for simulating reaction-diffusion systems that rigorously obeys detailed balance for equilibrium reactions. By comparing the simulation results to exact analytical results for a bimolecular reaction, we show that the algorithm correctly reproduces both equilibrium and dynamical quantities. We apply our scheme to a "push-pull" network in which two antagonistic enzymes covalently modify a substrate. Our results highlight that spatial fluctuations of the network components can strongly reduce the gain of the response of a biochemical network.

Computing stationary distributions in equilibrium and nonequilibrium systems with forward flux sampling

We present a method for computing stationary distributions for activated processes in equilibrium and nonequilibrium systems using forward flux sampling. In this method, the stationary distributions are obtained directly from the rate constant calculations for the forward and backward reactions; there is no need to perform separate calculations for the stationary distribution and the rate constant. We apply the method to the nonequilibrium rare event problem proposed by Maier and Stein, to nucleation in a 2-dimensional Ising system, and to the flipping of a genetic switch.

Diffusion of transcription factors can drastically enhance the noise in gene expression

We study by Green's Function Reaction Dynamics the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels for a gene that is under the control of a repressor. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. After dissociation from the operator, a repressor can rapidly rebind to the DNA. Our results show that the rebinding trajectories are so short that, on this timescale, the RNA polymerase (RNAP) cannot effectively compete with the repressor for binding to the promoter. As a result, a dissociated repressor molecule will on average rebind many times, before it eventually diffuses away. These rebindings thus lower the effective dissociation rate, and this increases the noise in gene expression. Another consequence of the timescale separation between repressor rebinding and RNAP association is that the effect of spatial fluctuations can be described by a well-stirred, zero-dimensional, model by renormalizing the reaction rates for repressor-DNA (un) binding. Our results thus support the use of well-stirred, zero-dimensional models for describing noise in gene expression. We also show that for a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. Lastly, our results emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression.

Pulmonary aspergilloma diagnosed by fiberoptic bronchoscopy

We report a patient with pulmonary aspergilloma in whom the mycetoma was visualized and biopsied during fiberoptic bronchoscopy. To our knowledge, this is the first report of pulmonary aspergilloma diagnosed in this manner.