MultiRNAflow: integrated analysis of temporal RNA-seq data with multiple biological conditions.

MOTIVATION: The dynamic transcriptional mechanisms that govern eukaryotic cell function can now be analyzed by RNA sequencing. However, the packages currently available for the analysis of raw sequencing data do not provide automatic analysis of complex experimental designs with multiple biological conditions and multiple analysis time-points. RESULTS: The MultiRNAflow suite combines several packages in a unified framework allowing exploratory and supervised statistical analyses of temporal data for multiple biological conditions. AVAILABILITY AND IMPLEMENTATION: The R package MultiRNAflow is freely available on Bioconductor (https://bioconductor.org/packages/MultiRNAflow/), and the latest version of the source code is available on a GitHub repository (https://github.com/loubator/MultiRNAflow).

Short-range interactions between fibrocytes and CD8+ T cells in COPD bronchial inflammatory response.

Bronchi of chronic obstructive pulmonary disease (COPD) are the site of extensive cell infiltration, allowing persistent contact between resident cells and immune cells. Tissue fibrocytes interaction with CD8+ T cells and its consequences were investigated using a combination of in situ, in vitro experiments and mathematical modeling. We show that fibrocytes and CD8+ T cells are found in the vicinity of distal airways and that potential interactions are more frequent in tissues from COPD patients compared to those of control subjects. Increased proximity and clusterization between CD8+ T cells and fibrocytes are associated with altered lung function. Tissular CD8+ T cells from COPD patients promote fibrocyte chemotaxis via the CXCL8-CXCR1/2 axis. Live imaging shows that CD8+ T cells establish short-term interactions with fibrocytes, that trigger CD8+ T cell proliferation in a CD54- and CD86-dependent manner, pro-inflammatory cytokines production, CD8+ T cell cytotoxic activity against bronchial epithelial cells and fibrocyte immunomodulatory properties. We defined a computational model describing these intercellular interactions and calibrated the parameters based on our experimental measurements. We show the model's ability to reproduce histological ex vivo characteristics, and observe an important contribution of fibrocyte-mediated CD8+ T cell proliferation in COPD development. Using the model to test therapeutic scenarios, we predict a recovery time of several years, and the failure of targeting chemotaxis or interacting processes. Altogether, our study reveals that local interactions between fibrocytes and CD8+ T cells could jeopardize the balance between protective immunity and chronic inflammation in the bronchi of COPD patients.

Probabilistic cellular automata modelling of intercellular interactions in airways: complex pattern formation in patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a highly prevalent lung disease characterized by chronic inflammation and tissue remodeling possibly induced by unusual interactions between fibrocytes and CD8+ T lymphocytes in the peribronchial area. To investigate this phenomenon, we developed a probabilistic cellular automata type model where the two types of cells follow simple local interaction rules taking into account cell death, proliferation, migration and infiltration. We conducted a rigorous mathematical analysis using multiscale experimental data obtained in control and disease conditions to estimate the model's parameters accurately. The simulation of the model is straightforward to implement, and two distinct patterns emerged that we can analyse quantitatively. In particular, we show that the change in fibrocyte density in the COPD condition is mainly the consequence of their infiltration into the lung during exacerbations, suggesting possible explanations for experimental observations in normal and COPD tissue. Our integrated approach that combines a probabilistic cellular automata model and experimental findings will provide further insights into COPD in future studies.

Seroprevalence of SARS-CoV-2, Symptom Profiles and Sero-Neutralization in a Suburban Area, France.

The World Health Organisation recommends monitoring the circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated anti-SARS-CoV-2 total immunoglobulin (IgT) antibody seroprevalence and in vitro sero-neutralization in Nancy, France, in spring 2020. Individuals were randomly sampled from electoral lists and invited with household members over 5 years old to be tested for anti-SARS-CoV-2 (IgT, i.e., IgA/IgG/IgM) antibodies by ELISA (Bio-rad); the sero-neutralization activity was evaluated on Vero CCL-81 cells. Among 2006 individuals, the raw seroprevalence was 2.1% (95% confidence interval 1.5 to 2.9), was highest for 20- to 34-year-old participants (4.7% (2.3 to 8.4)), within than out of socially deprived area (2.5% vs. 1%, p = 0.02) and with than without intra-family infection (p < 10-6). Moreover, 25% of participants presented at least one COVID-19 symptom associated with SARS-CoV-2 positivity (p < 10-13), with highly discriminant anosmia or ageusia (odds ratio 27.8 [13.9 to 54.5]); 16.3% (6.8 to 30.7) of seropositive individuals were asymptomatic. Positive sero-neutralization was demonstrated in vitro for 31/43 seropositive subjects. Regarding the very low seroprevalence, a preventive effect of the lockdown in March 2020 can be assumed for the summer, but a second COVID-19 wave, as expected, could be subsequently observed in this poorly immunized population.

Statistical significance based on length and position of the local score in a model of i.i.d. sequences.

Motivation: The local score of a biological sequence analysis is a mathematical tool largely used to analyse biological sequences. Consequently, determining an accurate estimation of its distribution is crucial. Results: First, we study the accuracy of classical results on the local score distribution in independent and identically distributed model using a Kolmogorov-Smirnov goodness of fit test. Second, we highlight how the length of the segment that realizes the local score improves the classical setting based on local score only. Finally, we study which part of the sequence contributes to the local score. Contact: mercier@univ-tlse2.fr.

Nonrandom variations in human cancer ESTs indicate that mRNA heterogeneity increases during carcinogenesis.

Virtually all cancer biological attributes are heterogeneous. Because of this, it is currently difficult to reconcile results of cancer transcriptome and proteome experiments. It is also established that cancer somatic mutations arise at rates higher than suspected, but yet are insufficient to explain all cancer cell heterogeneity. We have analyzed sequence variations of 17 abundantly expressed genes in a large set of human ESTs originating from either normal or cancer samples. We show that cancer ESTs have greater variations than normal ESTs for >70% of the tested genes. These variations cannot be explained by known and putative SNPs. Furthermore, cancer EST variations were not random, but were determined by the composition of the substituted base (b0) as well as that of the bases located upstream (up to b - 4) and downstream (up to b + 3) of the substitution event. The replacement base was also not randomly selected but corresponded in most cases (73%) to a repetition of b - 1 or of b + 1. Base substitutions follow a specific pattern of affected bases: A and T substitutions were preferentially observed in cancer ESTs. In contrast, cancer somatic mutations [Sjoblom T, et al. (2006) Science 314:268-274] and SNPs identified in the genes of the current study occurred preferentially with C and G. On the basis of these observations, we developed a working hypothesis that cancer EST heterogeneity results primarily from increased transcription infidelity.