Biology subjects

Laine, A.

Publications and source records attributed to Laine, A..

9 recordsLinked to original sources

bioRxiv2024

Cell cycle-dependent mRNA localization in P-bodies

Understanding the dynamics of RNA targeting to membraneless organelles is essential to disentangle their functions. Here, we investigate how P-bodies (PBs) evolve during cell cycle progression. PB purification across the cell cycle uncovers widespread changes in their RNA content, which are partly uncoupled from cell cycle-dependent changes in RNA expression. Single molecule FISH shows various mRNA localization patterns in PBs peaking in G1, S, or G2, with examples illustrating the timely capture of mRNAs in PBs when their encoded protein becomes dispensable. Yet, rather than directly reflecting absence of translation, cyclic mRNA localization in PBs can be controlled by RBPs, such as HuR in G2, and by RNA features. Indeed, while PB mRNAs are AU-rich at all cell cycle phases, they are specifically longer in G1, possibly related to post-mitotic PB reassembly. Altogether, our study supports a model where PBs are more than a default location for excess untranslated mRNAs.

cell biology↗

bioRxiv2022

Awakening adult neural stem cells: NOX signalling as a positive regulator of quiescence to proliferation transition in the Xenopus retina

Reactive oxygen species (ROS) are both harmful molecules sustaining the pathogenesis of several diseases and essential modulators of cell behaviours. In particular, a growing wealth of data suggest that ROS-dependent signalling pathways might be critical in conferring embryonic or adult stem cells their specific properties. However, how stem cells control ROS production and scavenging, and how ROS in turn contribute to stemness remain poorly understood. Using the Xenopus retina as a model system, we first investigated the redox status of retinal stem cells (RSCs). We discovered that they exhibit higher ROS levels compared to progenitors and retinal neurons and express a set of specific antioxidant genes. We next addressed the question of ROS functional involvement in these cells. Using pharmacological or genetic tools, we demonstrate that inhibition of NADPH oxidase (NOX)-dependent ROS production increases the proportion of quiescent RSCs. This is surprisingly accompanied by an apparent acceleration of the mean division speed within the remaining proliferating pool. Our data further unveil that such impact on RSC cell cycling is achieved by modulation of the Wnt/Hedgehog signalling balance. Altogether, we highlight that RSCs exhibit distinctive redox characteristics and exploit NOX signalling to limit quiescence and fine-tune their proliferation rate.

developmental biology↗

bioRxiv2022

Engineered bacterial swarm patterns as spatial records of environmental inputs

A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility--a highly coordinated, rapid movement of bacteria powered by flagella1-5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to "write" external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from endpoint images using a segmentation model. This work creates an approach for building a macroscale bacterial recorder and expands the framework for engineering emergent microbial behaviors.

synthetic biology↗

bioRxiv2022

A deep learning pipeline for segmentation of Proteus mirabilis colony patterns

The motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring boundary segmentation achieved test Dice scores of 93.28% and 83.24%, respectively. The predicted masks at times improved on the ground truths from our automated annotation algorithms. We demonstrate how application of our pipeline to a typical swarming assay enables ease of colony analysis and precise measurements of more complex pattern features than those which have been historically quantified.

microbiology↗

bioRxiv2021

The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma

BackgroundTranscriptome analysis of cancer tissues has been instrumental in defining tumor subtypes, diagnostic signatures and cancer regulatory networks. Cancer transcriptomes are still predominantly analyzed at the level of gene expression. Few studies have addressed transcript-level variations, and most of these only looked at splice variants. Previously we introduced a k-mer based, reference-free method, DE-kupl, that performs differential analysis of RNA-seq data at the k-mer level, which enables distinguishing RNAs differing by a single nucleotide. Here we evaluate the significance of differential events discovered by this method in two independent lung adenocarcinoma RNA-seq datasets (N=583 and N=154). ResultsFocusing on differential events in a tumor vs normal setting, we found events in endogenous repeats, alternative splicing and polyadenylation sites, long non-coding RNAs, retained introns and unmapped RNAs. Replicability was highly significant for most event classes (assessed by comparing to events shared between unrelated tumors). Overall about 160,000 differential k-mer contigs were shared between datasets, including a large set of sequences from hypervariable genes such as immunoglobulins, SFTP and mucin genes. Most interestingly, we identified a set of novel tumor-specific long non-coding RNAs in intergenic and intronic regions. We found that expressed endogenous transposons defined two major groups of patients (high/low repeat expression) with distinct clinical characteristic. A number of repeats, intronic RNAs and lincRNA achieved strong patient stratification in univariate or multivariate survival models. Finally, using antigen presentation prediction, we identified 55 contigs predicted to produce recurrent tumor-specific antigens. ConclusionsK-mer based RNA-seq analysis enables description of cancer transcriptomes at nucleotide precision, independently of prior transcript annotation. Application to lung cancer data uncovered events stemming from a wide variety of transcriptional and postranscriptional mechanisms. Among those events, a significant subset was replicable between cohorts, thus constituting novel RNA hallmarks of cancer. The code is available at: https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort.

bioinformatics↗

bioRxiv2021

ID-Seg: An Accurate and Reliable Infant Deep learning Segmentation Framework for Limbic Structures

Early postnatal period brain magnetic resonance imaging (MRI) is becoming an important approach to measure the impact of prenatal exposures on neurodevelopment and to investigate early biomarkers for risk. Among brain structures, Limbic structures are particular of interest in psychiatric disorder-related research. However, despite the promise of infant neuroimaging and the success of initial infant MRI studies, assessing limbic regions structure and function remains a significant challenge due to low inter-regional intensity contrast and high curvature (e.g., hippocampus). In addition, the agreement between existing automatic techniques and manual segmentation remains either untested or insufficient, particularly for the amygdala and hippocampus. In this work, we developed an accurate (based on three segmentation evaluation metrics), reliable and efficient infant deep learning segmentation framework (ID-Seg) to address the aforementioned challenges. Specifically, we leveraged a large dataset of 473 infant MRI scans to train ID-Seg and rigorously evaluated ID-Segs performance on internal and external datasets with manual segmentations. Compared with a state-of-the-art segmentation pipeline, we demonstrated that ID-Seg significantly improved the segmentation accuracy of limbic structures (hippocampus and amygdala) in newborn infants. Moreover, in a medium-size dataset, we found that ID-Seg-derived morphometric measures yield strong brain-behavior associations. As such, our ID-Seg may improve our capacity and efficiency to measure MRI-based brain features relevant to neuropsychological development and ultimately advance the success of quantitative analyses on large-scale datasets.

bioengineering↗

bioRxiv2020

CIP2A interacts with TopBP1 and is selectively essential for DNA damage-induced basal-like breast cancer tumorigenesis

Despite saturated genetic profiling of breast cancers, oncogenic drivers for the clinically challenging basal-like breast cancer (BLBC) subtype are still poorly understood. Here, we demonstrate that CIP2A is selectively essential for DNA damage-induced initiation of mouse BLBC tumors, but not of other cancer types. Mechanistically, CIP2A was discovered genome-widely the closest functional homologue for DNA-damage proteins TopBP1, RHNO, POLQ, NBN and PARP1. CIP2A directly interacts with the ATR-activation domain of TopBP1, and dampens both, chromatin binding of TopBP1 and RAD51, and G2/M checkpoint in DNA-damaged cells. CIP2A also drives BLBC-associated proliferative MYC and E2F1 signaling. Consistently with high DNA-damage response activity BLBCs, and CIP2As novel role in checkpoint signaling, CIP2A was found essential for DNA-damaged, and BRCA-mutant BLBC cells. Selective role for CIP2A as BLBC driver was supported by association of high CIP2A expression with poor patient prognosis only in BLBC, but not in other breast cancer types. Therapeutically, small molecule reactivators of PP2A (SMAPs) phenocopy CIP2A-dependent DNA damage response, and inhibit in vivo growth of patient-derived BLBC xenograft. In summary, we discover sub-type selective essential role for CIP2A in BLBC initiation and maintenance that can be explained by its newly discovered association with DNA-damage response, coordinated with regulation of proliferative signaling. The results also identify therapeutic strategy for CIP2A-dependent BLBCs.

cancer biology↗

bioRxiv2020

A transfer-learning approach for first-year developmental infant brain segmentation using deep neural networks

The months between birth and age 2 are increasingly recognized as a period critical for neuro-development, with potentially life-long implications for cognitive functioning. However, little is known about the growth trajectories of brain structure and function across this time period. This is in large part because of insufficient approaches to analyze infant MRI scans at different months, especially brain segmentation. Addressing technical gaps in infant brain segmentation would significantly improve our capacity to efficiently measure and identify relevant infant brain structures and connectivity, and their role in long-term development. In this paper, we propose a transfer-learning approach based on convolutional neural network (CNN)-based image segmentation architecture, QuickNAT, to segment brain structures for newborns and 6-month infants separately. We pre-trained QuickNAT on auxiliary labels from a large-scale dataset, fine-tuned on manual labels, and then cross-validated the models performance on two separate datasets. Compared to other commonly used methods, our transfer-learning approach showed superior segmentation performance on both newborns and 6-month infants. Moreover, we demonstrated improved hippocampus segmentation performance via our approach in preterm infants.

neuroscience↗

bioRxiv2018

Rules for PP2A-controlled phosphosignalling and drug responses

Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation. These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2A-modulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.

systems biology↗