Biology subjects

Martin, O. C.

Publications and source records attributed to Martin, O. C..

16 recordsLinked to original sources

bioRxiv2026

Microbiota-gut-brain axis modulation drives glioblastoma progression and therapy resistance

Glioblastoma is a highly aggressive brain tumour with poor prognosis, whose aetiology, progression, and therapeutic resistance remain incompletely understood. While the microbiota-gut-brain axis has emerged as a key regulator of neurological disorders, its role in glioblastoma biology and treatment response is still largely unexplored. Using a clinically relevant immunocompetent murine model combining glioblastoma stem cell implantation, dextran sodium sulfate-induced gut inflammation, and a full Stupp-like therapeutic protocol, we investigated bidirectional gut-brain communication in glioblastoma. Tumour growth and recurrence were monitored by bioluminescence imaging, tumour transcriptomic profiles were analysed by RNA sequencing, and brain and colon tissues were subjected to histological and molecular analyses. Gut microbiota composition was assessed by 16S rRNA sequencing, while systemic metabolites and cytokines were quantified in plasma. Cross-compartment association bioinformatic analyses were performed to correlate multi-organ readouts. Gut inflammation enhanced glioblastoma growth and promoted tumour recurrence following therapy. Tumour progression was associated with increased infiltration of immunosuppressive macrophages, whereas recurrence correlated with elevated oxidative DNA damage. Remarkably, glioblastoma exerted systemic immunomodulatory effects, attenuating intestinal and systemic inflammatory responses, and induced profound remodelling of gut microbiota composition and predicted metabolic function, including enrichment of Akkermansia and depletion of Lactobacillus. Systemic metabolic profiling was investigated as a route of communication within the gut-brain axis and revealed adaptations in DSS-treated mice associated with tumour burden and therapeutic response. Multi-compartment correlation and multivariable association analyses identified specific bacterial genera and circulating metabolites associated with tumour volume, intestinal inflammation, and genomic instability. These findings uncover a dynamic, bidirectional microbiota-gut-brain axis in glioblastoma and identify intestinal inflammation as a critical determinant of tumour progression and therapeutic outcome. Targeting gut disturbances and microbiota-associated metabolic pathways may represent novel strategies to modulate glioblastoma aggressiveness and treatment response.

cancer biology↗

bioRxiv2026

Response to divergent selection on meiotic recombination in Saccharomyces cerevisiae

Meiotic recombination is a key driver of evolution in sexually reproducing species, reshaping genetic diversity by generating novel allelic combinations. The rate of recombination varies substantially across living organisms depending on cis- or trans-acting genetic elements, as seen in many species, including the yeast Saccharomyces cerevisiae. Here, we report on an experimental evolution-based study to better understand the factors shaping this natural variation. Starting with a genetically diverse population of S. cerevisiae, we have carried out recurrent divergent selection on recombination rate using a fluorescence-based sorting approach in four independent lineages. After ten generations, we observed an average response of recombination rate of +28% after positive selection and -24% after negative selection, within the interval used for selection. In the adjacent region, however, we observed a weaker response in the opposite direction, and no response in four other unlinked genomic regions. Whole-genome sequencing of individuals selected for high recombination revealed mixed outcomes in the four independently evolved lineages for high genome-wide recombination rates. However, all four lineages showed selection for high recombination locally, with particular haplotypes heavily favored and sequence- or structural variation-based heterozygosity selected against within the selection interval. Overall, this experimental evolution approach provides original and useful insights into the evolvability of the meiotic recombination rate and the associated genetic determinants.

genetics↗

bioRxiv2025

Simple threshold-based Boolean rules fall short in capturing biological regulatory network dynamics

Among the various frameworks for modeling gene regulatory network (GRN) dynamics, Boolean modeling remains both powerful and accessible. Nevertheless, selecting update rules so that they capture the combinatorial control of various targets remains challenging due to limited quantitative data. Threshold majority rules (TMRs), a subtype of threshold functions (ThFs), update a genes state based on the signed sum of its regulators and its own activity, offering an elegant simplification. However, does the use of TMRs come at the cost of biological realism? Here, we rigorously evaluate the two standard TMR variants regarding their suitability in GRN modeling. We find that they provide limited canalyzation, exhibit discordant bias patterns, and often eliminate self-inhibitions. They are also underrepresented in empirical datasets, the opposite of what is expected for biologically relevant rules. Compared to nested canalyzing functions (NCFs), another class of ThFs but known to be preponderant in empirical datasets, TMRs exhibit heightened complexity and sensitivity to perturbations. At the network level, TMRs frequently fail to recover biological attractors and the associated basin size distributions. Using ensembles of random Boolean networks, we also show that one TMR type drives the network dynamics toward the chaotic regime. These findings invite a thoughtful reevaluation of TMRs as an appropriate logic for modeling GRNs despite their theoretical appeal.

systems biology↗

bioRxiv2025

Boosting reliability when inferring interactions from time series data in gene regulatory networks.

In the context of the dynGENIE3 [13] approach for inferring regulatory network interactions from time-series data, we show that it is possible to modify that algorithm to significantly enhance its prediction reliability. To quantify the level of reliability, we used ground-zero truths based on simulated datasets generated by the GeneNetWeaver [22] tool. Our work introduces novel methods leveraging time-lagged correlations and estimators of mRNA decay rates, leading to significantly improved driver-target inference. Additionally, a temperature-based rescaling of priors was developed to further enhance prediction reliability. Results demonstrate substantial improvements in performance with a particularly notable increase in AUPRC scores. These advances underscore the possible gains resulting from incorporating priors into gene regulatory network inference.

bioinformatics↗

bioRxiv2024

Choices of regulatory logic class modulate the dynamical regime in random Boolean networks

Random Boolean networks (RBNs) have been widely explored as models for understanding complex systems, in particular gene regulatory networks (GRNs). Stability (order) and instability (chaos), critical characteristics of any dynamic system, have naturally been a central focus in RBN research. Several measures have previously been introduced to assess stability, e.g., via damage spreading or via statistical properties of attractors such as their number, lengths or basin sizes. Undoubtedly, network topology plays an important role in shaping the dynamics of RBN. Another key factor influencing the dynamics is the Boolean functions (BFs) assigned to each node. In this work, we conduct a systematic investigation to examine the influence of five different classes of BFs on the dynamics of RBNs. By employing various dynamical stability measures, we show that biologically meaningful BFs consistently drive the dynamics toward the stable regime compared to random BFs. This observation holds across networks with varying size, connectivity, and degree distributions. Additionally, we show that the change in the values of our stability measures with increasing connectivity or size of RBNs is different across the BF classes. For most observables, random BFs change the values significantly, pushing the dynamics toward a more chaotic regime with increasing connectivity or network size. In contrast, biologically meaningful BFs show very minimal fluctuations for most stability measures. These findings emphasize the advantage of restricting to biologically meaningful classes in the reconstruction and modeling of biological systems within Boolean framework.

systems biology↗

bioRxiv2024

Genomic divergence shaped the genetic regulation of meiotic homologous recombination in Brassica allopolyploids

The tight regulation of meiotic recombination between homologs is disrupted in Brassica AAC allotriploids, a genomic configuration that may have facilitated the formation of rapeseed (Brassica napus L.) [~]7,500 years ago. Indeed, the presence of the haploid C genome induces supernumerary crossovers between homologous A chromosomes with dramatically reshaped distribution. However, the genetic mechanisms driving this phenomenon and their divergence between nascent and established lineages remain unclear. To address these concerns, we generated hybrids carrying additional C chromosomes derived either from an established lineage of the allotetraploid B. napus or from its diploid progenitor B. oleracea. We then assessed recombination variation across twelve populations by mapping male meiotic crossovers using Single Nucleotide Polymorphism markers evenly distributed across the sequenced A genome. Our findings reveal that the C09 chromosome of B. oleracea is responsible for the formation of additional crossovers near pericentromeric regions. Interestingly, its counterpart from an established lineage of B. napus shows no significant effect on its own, despite having a similar content of meiotic genes. However, we showed that the B. napus C09 chromosome influences crossover formation through inter-chromosomal epistatic interactions with other specific C chromosomes. These results provide new insights into the genetic regulation of homologous recombination in Brassica and emphasize the role of genomic divergence since the formation of the allopolyploid B. napus.

genetics↗

bioRxiv2024

AURA: Automated Universal RNAscope Analysis for high-throughput applications

In situ hybridization visualizes RNA in cells, but image analysis is complex. We present a protocol based on open-source software for automated high-content multiplex fluorescence in situ transcriptomics analysis. Steps include nuclei segmentation with a Fiji macro and quantification of up to 14 mRNA probes per image. We describe procedures for storing raw data, quality control images and the use of a Python app to summarize all the results in one spreadsheet detailing the number of single or co-positive cells.

cell biology↗

bioRxiv2023

Preponderance of generalized chain functions in reconstructed Boolean models of biological networks

Boolean networks (BNs) have been extensively used to model the dynamics of gene regulatory networks (GRNs) that underlie cellular decisions. The dynamics of BNs depend on the network architecture and regulatory logic rules (or Boolean functions (BFs)) associated with nodes, both of which have been shown to be far from random in large-scale studies of reconstructed Boolean models. At the level of the BFs, nested canalyzing functions (NCFs) have been shown to be strongly enriched in such GRN models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. To answer this, we build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose 2 other sub-types of NCFs, namely, the class of chain-1 functions which is the dual of the class of chain-0 functions, and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 functions decays exponentially with the number of inputs, and exhibits a fractal-like behaviour as a function of the bias for a fixed number of inputs. Moreover, we explain several of these observations analytically. Then, by analyzing 5990 BFs extracted from a large dataset of reconstructed Boolean models, and 2 other datasets, we find that generalized chain functions are significantly enriched within the NCFs. Lastly, we illustrate the severe restriction imposed by generalized chain functions compared to NCFs for 3 biological models and perform model selection on them using known relative stability constraints.

systems biology↗

bioRxiv2023

Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph

Boolean network (BN) models of gene regulatory networks (GRNs) have gained widespread traction as they can easily recapitulate cellular phenotypes via their attractor states. The overall dynamics of such models are embodied in the systems state transition graph (STG) which is highly informative. Indeed, even if two BN models have the same network structure and recover the same attractors, their STGs can be drastically different depending on the type of regulatory logic rules or Boolean functions (BFs) employed. A key objective of the present work is to systematically delineate the effects of different classes of regulatory logic rules on the structural features of the STG of reconstructed Boolean GRNs, while keeping BN structure and biological attractors fixed. Furthermore, we ask how such global features might be driven by characteristics of the underlying BFs. For that, we draw from ideas and concepts proposed in cellular automata for both the structural features and their associated proxies. We use the network of 10 reconstructed Boolean GRNs to generate ensembles that differ in the type of logic used while keeping their structure fixed and recovering their biological attractors, and compute quantities associated with the structural features of the STG: bushiness and convergence, that are based on the number of garden-of-Eden (GoE) states and transient times to reach attractor states when originating at them. We find that ensembles employing biologically meaningful BFs have higher bushiness and convergence than those employing random ones. Computing these global measures gets expensive with larger network sizes, stressing the need for more feasible proxies. We thus adapt Wuensches Z-parameter to BFs in BNs and provide 4 natural variants, which along with the network sensitivity, comprise our descriptors of local dynamics. One variant of the network Z-parameter as well as the network sensitivity correlate particularly very well with the bushiness, serving as a good proxy for the same. Finally, we provide an excellent proxy for the convergence based on computing transient lengths originating at random states rather than GoE states.

systems biology↗

bioRxiv2023

Leveraging Developmental Landscapes for Model Selection in Boolean Gene Regulatory Networks

Boolean models are a well-established framework to model developmental gene regulatory networks (DGRN) for acquisition of cellular identity. During the reconstruction of Boolean DGRNs, even if the network structure is given, there is generally a very large number of combinations of Boolean functions (BFs) that will reproduce the different cell fates (biological attractors). Here we leverage the developmental landscape to enable model selection on such ensembles using the relative stability of the attractors. First we show that 5 previously proposed measures of relative stability are strongly correlated and we stress the usefulness of the one that captures best the cell state transitions via the mean first passage time (MFPT) as it also allows the construction of a cellular lineage tree. A property of great computational convenience is the relative insensitivity of the different measures to changes in noise intensities. That allows us to use stochastic approaches to estimate the MFPT and thus to scale up the computations to large networks. Given this methodology, we study the landscape of 3 Boolean models of Arabidopsis thaliana root development and find that the latest one (a 2020 model) does not respect the biologically expected hierarchy of cell states based on their relative stabilities. Therefore we developed an iterative greedy algorithm that searches for models which satisfy the expected hierarchy of cell states. By applying our algorithm to the 2020 model, we find many Boolean models that do satisfy the expected hierarchy. Our methodology thus provides new tools that can enable reconstruction of more realistic and accurate Boolean models of DGRNs.

systems biology↗

bioRxiv2022

Composition structures and biologically meaningful logics: plausibility and relevance in bipartite models of gene regulation

Boolean network models have widely been used to study the dynamics of gene regulatory networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. In contrast, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such models, multiple TFs may simultaneously contribute to the regulation of a gene by forming heteromeric complexes. The formation of heteromeric complexes gives rise to composition structures in the corresponding bipartite network. Remarkably, composition structures can severely restrict the number of Boolean functions (BFs) that can be assigned to a gene. The introduction of bipartite Boolean network models is relatively recent, and so far an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes in Homo sapiens. Moreover, we present an additional mechanism by which composition structures arise as a result of multiple TFs binding to the cis-regulatory regions of a gene and we provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are more restrictive than composition structures. Finally, using a compiled dataset of 2687 BFs from published models, we find that composition structures are highly enriched in real biological networks, but that this enrichment is most likely driven by NCFs and RoFs.

systems biology↗

bioRxiv2021

Minimum complexity drives regulatory logic in Boolean models of living systems

The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network topology nor Boolean functions (or logical update rules) should be expected to be random. In this contribution, we focus on biologically meaningful types of Boolean functions, and perform a systematic study of their preponderance in gene regulatory networks. By applying the k[P] classification based on number of inputs k and bias P of functions, we find that most Boolean functions astonishingly have odd bias in a reference biological dataset of 2687 functions compiled from published models. Subsequently, we are able to explain this observation along with the enrichment of read-once functions (RoFs) and its subset, nested canalyzing functions (NCFs), in the reference dataset in terms of two complexity measures: Boolean complexity based on string lengths in formal logic which is yet unexplored in the biological context, and the average sensitivity. Minimizing the Boolean complexity naturally sifts out a subset of odd-biased Boolean functions which happen to be the RoFs. Finally, we provide an analytical proof that NCFs minimize not only the Boolean complexity, but also the average sensitivity in their k[P] set.

systems biology↗

bioRxiv2020

Influence of the microenvironment on the modulation of the host response by the typhoid toxin

Bacterial genotoxins cause DNA damage in eukaryotic cells, resulting in activation of the DNA damage response (DDR) in vitro. These toxins are produced by Gram negative bacteria, enriched in the microbiota of Inflammatory Bowel Disease (IBD) and colorectal cancer (CRC) patients. However, their role in infection remains poorly characterized. We have addressed the role of the typhoid toxin in the modulation of the host-microbial interaction in health and disease. Infection with a genotoxigenic Salmonella protected mice from intestinal inflammation. The toxin-induced DNA damage caused senescence in vivo, which was uncoupled from the inflammatory response, and associated with the maintenance of an anti-inflammatory environment. This effect was lost when infection occurred in mice suffering from inflammatory conditions that mimic Ulcerative Colitis, a form of IBD. These data highlight a complex context-dependent crosstalk between bacterial genotoxins-induced DDR and the host immune response, underlining an unexpected role for bacterial genotoxins.

immunology↗

bioRxiv2020

Enhancing backcross programs through increased recombination

Introgression of a QTL by successive backcrosses is a strategy that can be used to improve elite lines (recurrent parent) by bringing in alleles from exotic material (donor parent). In the absence of selection, the proportion of the donor genome decreases by half at each generation. However, since one selects for the donor allele at the QTL, the elimination of the donor genome in the neighborhood of that QTL will be much slower (linkage drag). Using markers to monitor the genome around the QTL and in the background can accelerate the return to the recurrent parent genome. The success of an introgression will partly depend on the occurrence of crossovers at favorable positions. However, the number of crossovers per generation is limited and their distribution along the genome is heterogeneous. Recently, techniques have been developed to modify these two aspects of recombination. Here, we assess, by simulation, their effect on the efficiency of introgression programs by studying the reduction of the linkage drag and the recovery of the recurrent genome. The selection schemes we simulate begin by two generations of foreground selection and continue with one or more generations of background selection. Our results show that when the QTL is in a region that was initially lacking crossovers, increasing the recombination rate can decrease the linkage drag nearly ten-fold after the foreground selection and improves the return to the recurrent parent. However, if the QTL is in a region already rich in crossovers then increasing recombination proves to be detrimental. Key messageIn breeding programs, recombination is essential for introgression, but introducing more crossovers is beneficial only when the target is in a cold region, otherwise it is detrimental.

genetics↗

bioRxiv2019

CNVmap: a method and software to detect copy number variants from linkage mapping data

Single nucleotide polymorphisms (SNPs) are widely used for detecting quantitative trait loci or for searching for causal variants of diseases. Nevertheless, structural variations such as copy-number variants (CNVs) represent a large part of natural genetic diversity and contribute significantly to trait variation. Over the past decade, numerous methods and softwares have been developed to detect CNVs. Such approaches are based on exploiting sequencing data or SNP arrays, but they bypass a wealth of information such as genotyping data from segregating populations, produced e.g. for QTL mapping. Here we propose an original method to both detect and genetically map CNVs using mapping panels. Specifically, we exploit the apparent heterozygous state of duplicated loci: peaks in appropriately defined genome-wide allelic profiles provide highly specific signatures that identify the nature and position of the CNVs. Our original method and software can detect and map automatically up to 33 different predefined types of CNVs based on segregation data only. We validate this approach on simulated and experimental bi-parental mapping panels in two maize and one wheat populations. Most of the events found correspond to having just one extra copy in one of the parental lines but the corresponding allelic value can be that of either parent. We also find cases with two or more additional copies, especially in wheat where these copies locate to homeologues. More generally, our computational tool can be used to give additional value, at no cost, to many datasets produced over the past decade from genetic mapping panels.

genetics↗

bioRxiv2019

Assessing by modeling the consequences of increased recombination in genomic selection of Oryza sativa and Brassica rapa.

Recombination generates genetic diversity but the number of crossovers per meiosis is limited in most species. Previous studies showed that increasing recombination can enhance response to selection. However, such studies did not assume a specific method of modifying recombination. Our objective was to test whether two methods used to increase recombination in plants could increase the genetic gain in a population undergoing genomic selection. The first method, in Oryza sativa, used a mutant of anti-crossover genes to increase global recombination without affecting the recombination landscape. The second one uses the ploidy level of a cross between Brassica rapa and Brassica napus to increase the recombination particularly in pericentromeric regions. These recombination landscapes were used to model recombination while quantitative trait loci positions were based on the actual gene distribution. We simulated selection programs with initially a cross between two inbred lines, for two species. Increased recombination enhanced the response to selection. The amount of enhancement in the cumulative gain largely depended on the species and the number of quantitative trait loci (2, 10, 20, 50, 200 or 1000 per chromosome). Genetic gains were increased up to 30% after 20 generations. Furthermore, modifying the recombination landscape was the most effective: the gain was larger by 25% with the first method and 33% with the second one in B. rapa, and 15% compared to 11% in O. sativa. Thus, increased recombination enhances the genetic gain in genomic selection for long-term selection programs, with visible effects after four to five generations.

genetics↗