Biosystems

Circadian rhythms are self-sustained biological oscillations that coordinate diverse physiological processes in plants, including growth, metabolism, and environmental responses. These rhythms arise from an interconnected transcriptional translational feedback network that integrates multiple entrainment cues such as light and temperature. The plant circadian clock is organized around key regulatory loops involving CCA1, LHY, PRRs, TOC1, ELF4, LUX, and other transcriptional regulators, whose coordinated interactions ensure precise and robust oscillations. In this study, we developed an ordinary differential equation based mathematical model, building upon a previous framework to incorporate additional regulatory modules and transcriptional controls that better reflect experimentally observed behaviour. To elucidate the regulatory organization of this model, we performed a multi-layered computational analysis combining four complementary approaches: (i) period sensitivity analysis to quantify how parameter perturbations influence the systems timing, (ii) phase portrait analysis to visualize dynamic interactions among key components, (iii) knockout analysis to identify parameters essential for sustained rhythmicity, and (iv) network impact analysis using composite weighted network indices to evaluate hierarchical control across the network. Together, these analyses reveal that transcriptional repression, protein degradation, and light-regulated synthesis form the dominant control mechanisms within the circadian system. The results highlight a hierarchical and robust network structure centred on the CCA1/LHY and PRRs feedback loop, with redundant modules ensuring stability under perturbations. Thus, this model provides an improved, biologically consistent framework for dissecting the dynamic architecture of the plant circadian clock and guiding future experimental validation.

Humans order numbers in space from left to right, with smaller quantities represented preferentially in the left hemispace and larger ones in the right hemispace. The direction of this mental number line (MNL), or more generally of number-space associations (NSA), is influenced by cultural habits such as reading and writing direction. However, a growing body of evidence from pre-verbal infants and non-human animals suggests that number-space mappings may also have biological foundations. In non-human primates, evidence for a directional MNL remains mixed, partly due to small sample sizes and methodological heterogeneity. Here, we tested samples of rhesus (Macaca mulatta) and crab-eating macaques (Macaca fascicularis) across two experiments using spontaneous food-related tasks. In Experiment 1, monkeys chose between identical food quantities (1x1 to 24x24) presented on the left and right. No systematic spatial choice bias emerged as a function of numerical magnitude, and hand use did not differ across exact numerical pairs, although exploratory analyses revealed magnitude-related modulations of manual responses. In Experiment 2, monkeys were habituated to small (4x4) or large (16x16) quantities and subsequently tested with the alternative quantity. Result showed significantly more leftward choices following numerical decreases (16[->]4) and more rightward choices following numerical increases (4[->]16), indicating that relative numerical context, rather than absolute magnitude, elicited directional spatial biases. These findings suggest that in macaques, number-space associations emerge most robustly in comparative contexts involving expectancy violations of magnitude.

MicroRNAs (miRNAs) play essential roles in the posttranscriptional regulation of gene expression in organisms. In the process of synthesizing mature miRNAs from miRNA precursors, the miRNA precursors are cleaved via Dicer at their loop structure, after which the miRNA precursors become mature and regulate transcription. However, the consequences of altering the loop sequence are not fully understood. The silkworm Bombyx mori is a lepidopteran insect with many genetic strains. We identified a mutant of the miRNA miR-3260 whose the part of the loop structure was lacking in a silkworm strain with translucent larval skin. Here, we aimed to analyze the role of wild-type miR-3260 and the influence of the mutation of the loop structure in B. mori. First, we identified the genomic region responsible for the translucent larval skin phenotype and determined that the mutated miR-3260 nucleotide sequences. Then, we predicted the binding partners of wild-type miR-3260 using the RNA hybrid tool and found two juvenile hormone (JH)-related genes as targets of wild-type miR-3260. Next, we assessed the relationships between miR-3260 and JH and found that miR-3260 was highly expressed in the Corpora allata and its expression responded to JH treatment. Meanwhile, miR-3260 mimic and inhibitor did not induce the typical phenotypes associated with JH in B. mori. Then, we compared the dicing products from wild-type and mutant miR-3260 precursors and observed that neither form underwent Dicer-mediated cleavage when the loop structure was altered. These results suggest that loop mutations in the miR-3260 precursor may not influence dicing activity, consistent with the lack of observable phenotypic effects.

A morphospace is an abstract space of theoretically possible biological traits, shapes, or property values. It is interesting to explore which parts of a morphospace life occupies, as compared to those parts which could be occupied, but are not. Comparing random and natural non-coding (nc) RNA secondary structures is an established approach to studying morphospace occupation for RNA structures. Most earlier studies have focused on the minimum free energy (MFE) structure, while relatively few have looked at the Boltzmann distribution, describing the ensemble of energetically suboptimal RNA folds. These suboptimal structures may have important roles and functions, and hence should be examined carefully. Here we compare random and natural ncRNA in terms of their Boltzmann distributions, finding that natural RNA tend to have very similar profiles to random RNA, with the main difference being that natural RNA are slightly more energetically stable, except for very short sequences (20 to 30 nucleotides) which tend to be slightly less stable. We infer that natural ncRNA occupy similar parts of the morphospace that random RNA do, indicating that the biophysics of the genotype-phenotype map largely determines the ensemble properties of ncRNA.

At the fundamental conceptual level, two alternatives have traditionally been considered for how mutations arise and how evolution happens: 1) random mutation and natural selection, and 2) Lamarckism. Recently, the theory of Interaction-based Evolution (IBE) has been proposed, according to which mutations are neither random nor Lamarckian, but are influenced by information accumulating internally in the genome over generations. Based on the estimation-of-distribution algorithms framework, we present a simulation model that demonstrates nonrandom, non-Lamarckian mutation concretely while capturing indirectly several aspects of IBE: selection, recombination, and nonrandom, non-Lamarckian mutation interact in a complementary fashion; evolution is driven by the interaction of parsimony and fit; and random bits do not directly encode improvement but enable generalization by the manner in which they connect with the rest of the evolutionary process. Connections are drawn to Darwins observations that changed conditions increase the rate of production of heritable variation; to the causes of bell-shaped distributions of traits and how these distributions respond to selection; and to computational learning theory, where analogizing evolution to learning in accord with IBE casts individuals as examples and places the learned hypothesis at the population level. The model highlights the importance of incorporating internal integration of information through heritable change in both evolutionary theory and evolutionary computation.

BackgroundConventional models of human ribosomal DNA (rDNA) array organization have historically depended on transcription-centric boundaries, partitioning the unit into a [~]13 kb rDNA transcription region and a monolithic [~]31 kb intergenic spacer (IGS). While our previous identification of Duplication Segment Units (DSUs) mapped these arrays based on an intuitive analysis of the microsatellite density landscape of the complete reference human genome, our present deep mining of this landscape has revealed a more accurate rDNA Gene Unit Pattern. Methods & ResultsIn this study, we conducted a deep mining analysis of our previously established microsatellite density landscape of the T2T-CHM13 assembly, focusing specifically on nucleolar organizing regions (NORs). We suggest a more accurate rDNA Gene Unit Pattern containing a (CTTT)n microsatellite aggregation ahead of the rDNA gene and a (CT)n microsatellite aggregation behind the gene, rather than a pattern featuring an IGS region inserted between two rDNA genes. ConclusionsA correct rDNA gene pattern of the human genome probably includes a (CTTT)n microsatellite aggregation ahead of the gene and a (CT)n microsatellite aggregation behind it, which possibly constitute cis- and trans-regulating regions; the (CTTT)n and (CT)n microsatellite aggregations may provide two different local stable DNA structures for regulatory protein binding.

Resource sharing is a fundamental form of social exchange underlying the formation and maintenance of social bonds in humans and other species. While reciprocity has long been proposed as a key mechanism in group interactions, the dynamic processes underlying resource allocation remain poorly understood. In this study, we employed computational modeling to investigate the temporal dynamics of resource sharing in a novel group decision-making task across three experiments. We found that, beyond the well-documented reciprocity, participants exhibited consistent alternating behavior, characterized by the switching between potential recipients. This alternation was not driven by fairness concerns but reflected a strategic balance between maintaining stable partnerships and exploring alternatives. Crucially, a reinforcement learning model incorporating Theory of Mind (ToM) consistently outperformed all alternative models. These findings highlight the critical role of ToM in social decision-making and suggest that mentalizing others intentions may be essential for effective resource sharing and social bond formation.

Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are driven by a mass conservation law and by variations in the amount of substance contained in each pattern, which we refer to as "pattern flux". However, it is challenging to mathematically investigate these pattern dynamics. In this study, we introduce a reaction-diffusion compartment model to investigate the pattern dynamics in view of the conservation law and the pattern flux. This model is defined on multiple intervals (compartments), and diffusive couplings are imposed on each boundary of the compartments. Corresponding to the transient dynamics in the original system, we consider the dynamics around stripe patterns in the compartment model. We derive ordinary differential equations describing the pattern dynamics of the compartment model and analyze the existence and stability of equilibria for the reduced ODE with respect to the boundary parameters. For a specific parameter setting, we obtained results consistent with previous studies. Moreover, we present that the stripe patterns in the compartment model are potentially stabilized by changing the parameter, which is not observed in the original system. We expect that the methodology developed in this paper is extendable to various directions, such as membrane-induced pattern control.

Developing approaches to link structure and function is an ongoing challenge in computational and structural biology. Using a systems-level framework, we present here an analysis pipeline in a Python package, mdsa-tools, that constructs network representations of structures in a time series of trajectory frames from molecular dynamics (MD) simulations. Here, we demonstrate its use on a ribosomal subsystem. The subsystem is centered on the CAR interaction surface, a "brake pad" adjacent to the aminoacyl (A-site) decoding center that tunes protein translation rates. We leverage unsupervised learning algorithms to explore the conformational landscape of behaviors visited by two versions of the subsystem (brake-on and brake-off) that differ at the codon 3 adjacent to the A-site codon. Our network representations of MD frames embody H-bond interactions between all pairwise combinations of residues in the system. By utilizing per-frame vector representations of network edges, we can apply standard clustering and dimensionality reduction methods to explore behavioral differences between the brake-on and brake-off versions of the system. K-means clustering of frame vectors revealed a striking separation of the two system versions, consistent with principal components analysis (PCA) embeddings and Uniform Manifold Approximation and Projection (UMAP) embeddings. Dissection of K-means centroids and PCA loadings highlighted H-bond interactions between residue pairs in the ribosomes peptidyl site (P site), suggesting potential allosteric signaling across the subsystem. Author summaryWith the impressive development of computational algorithms to successfully simulate the dynamics of biological molecules over time, the exploration and incorporation of systems modes of analysis is a natural next step to begin to understand the molecular dynamics behaviors that emerge from these experiments. Following the approaches of classical molecular genetics, we used a "computational genetics" paradigm where we introduced changes (mutations) in potentially important residues, changing their identities or modifying their chemical properties, and asked how the dynamic system responded to these changes, viewing the simulations as a series of movie frames of the dynamic structure over time. Starting with network representations of each frames structure, where the nodes are residues, and the edges denote H-bond interactions between the residues, we used several unsupervised machine learning algorithms to uncover behavioral changes in the different mutated versions of the system. Applied to our ribosome neighborhood, this revealed unexpected changes in behavior at the ribosome peptidyl site (P site) in response to mutating mRNA residues on the other side of the aminoacyl site (A site) codon, suggesting long-range allosteric interactions across the neighborhood.

In the search for universals shaping acoustic communication across species, we increasingly look for patterns known from human languages and music in non-human animals. These parallels are often explored separately and with limited ecological context. Here, we take a deep dive into the temporal structure of a complex call used by the little auk (Alle alle), a pelagic seabird with elaborate vocal behaviour and socially complex colonial life. Based on syllable durations, intervals and silences, we examine its conformance to linguistic laws, rhythmic structure and information content. This reveals intricate problems of temporal organisation: while the calls conform not only to linguistic laws of brevity but also to the initial and final lengthening known from human prosody, these effects interact with the internal structure of the call and information carried within it. To our knowledge, this is the first time that conformance to multiple linguistic laws, exceeding simple vocal efficiency, has been described for a non-human, non-vocal learning animal. The calls rhythmic structure shows a progressive rallentando -- a systematic slowing driven by changes in syllable and silence durations and the intervals between syllable onsets. The exact patterns of this rallentando are indicative of the callers sex and individually specific. These results reveal how seabird communication is shaped not only by efficiency universals, but also the specific pressures of colonial life. Our work highlights the temporal structure as an important axis of communication evolution, but also serves as a reminder to consider the species ecological reality and the function, not only presence, of temporal organisation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/713940v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@13de3a8org.highwire.dtl.DTLVardef@2d64adorg.highwire.dtl.DTLVardef@2ca53aorg.highwire.dtl.DTLVardef@113c38d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cells must maintain stable protein levels despite the inherently stochastic nature of gene expression, as excessive fluctuations can disrupt cellular function and impair the reliability of decision-making. Regulatory mechanisms, such as negative feedback, buffer protein fluctuations. Yet, it remains unclear how fluctuations are affected by delays that depend on a molecules specific state. Here, we develop a stochastic model in which proteins are produced in bursts as inactive molecules and pass through a series of intermediate steps before becoming active. The duration of such activation delays depends on the current level of active protein, creating a state-dependent feedback loop. Our model provides explicit analytical expressions relating the delay structure and feedback strength to the variability of active protein levels, quantified using the Fano factor, and shows that state-dependent delays can reduce fluctuations below the baseline expected from simple bursty production. Stochastic simulations confirm these predictions, and incorporating negative feedback in burst production further decreases variability while keeping system behavior predictable. These results reveal how temporal and state-dependent regulation stabilizes protein expression, offering guidance for understanding natural cellular control and designing robust synthetic gene circuits.

Net radiation (Rn) can be estimated using models that apply the Brunt equation for the incoming longwave radiation and air temperature (Tair) for the outgoing longwave radiation under reference conditions. This study aimed to estimate Rn using two previously regionally calibrated Brunt model, thereby eliminating the need site-specific calibration, and to assess whether Tair can be used as a substitute for canopy temperature (Tc) under well-watered crop conditions. Measurements were conducted in sesame and cotton fields during the first year and in a cotton field during the second year. Canopy temperature was measured during the second year, and the calculations were performed at hourly and daily time scales. Regardless of the method used to estimate sky emissivity or whether Tc or Tair was used, errors were greater at hourly time scale. The overall RMSE, MAE, Bias and KGE values at the daily time scales were 11.88, 9.13, 2.53, and 0.91, in the first year, and 13.45, 10.56, 0.10 and 0.74, in the second year, respectively. When using both regionally calibrated Brunt model, Rn simulation performance was superior to that of the Allen/FAO method. The comparison between Rn estimated using Tair and Tc, indicated statistical differences. Nevertheless, linear regression and error metrics showed that these differences were modest, especially at daily time scale. Thus, for practical purposes both regionally calibrated Brunt equations can be used to calculate clear-sky emissivity and improve Rn estimations, and Tair can be used as a substitute for Tc at the daily time scale under well-watered conditions.

Human walking is intrinsically variable. For example, there is considerable stride to stride variability even when walking speed is constant. This variability is due to uncertainty in the sensorimotor system and the environment, and is shaped by both musculoskeletal dynamics (e.g. joint stiffness and damping originating from muscles) and the control strategy used to mitigate the effects of uncertainty. Yet, insight into how sensorimotor noise shapes walking variability is limited due to a lack of experimental methods to assess sensorimotor noise and control strategies during walking. Simulations that account for uncertainty can elucidate how sensorimotor noise affects movement variability but due to numerical challenges, accounting for sensorimotor noise is not common in simulations of walking. Existing simulations have hugely simplified musculoskeletal dynamics (e.g. no muscles), the control policy (e.g. pre-defined feedback loops), or sensorimotor noise sources (e.g. only motor noise). Here, we performed stochastic optimal control simulations of walking based on a model with 9 degrees of freedom and 18 muscles to study how the level of sensory and motor noise influences walking. We solved for feedforward muscle excitations and full-state time-varying feedback gains that minimised expected effort while generating periodic, and hence stable, gait patterns. To enable these simulations, we approximated the state distribution with a Gaussian and used an unscented transform to propagate the state covariance. Resulting optimisation problems were solved with direct collocation. Sensorimotor noise level had a small effect on the mean kinematics but shaped kinematic and muscle activity variability as well as expected effort. Although simulations underestimated the magnitude of experimental positional variability, they captured its structure. In agreement with experimental results, the control policy prioritised limiting variability of centre of mass kinematics and minimal swing foot clearance over limiting joint angle variability. Hence, our simulations suggest that effort minimisation underlies these observations. Author summaryWhen performing a movement multiple times, each repetition will be slightly different due to random disturbances in the neural signals used to control movement, i.e. sensorimotor noise. Because it is difficult to measure inside the nervous system of a moving person, computer simulations are used to study movement control. They found that both sensorimotor noise and musculoskeletal mechanics determine how people control arm movements and standing. However, there are no simulations of walking that systematically evaluated how sensorimotor noise level influences walking kinematics because they pose computational challenges. Here, we proposed and used an approach for minimal effort simulations of walking in the presence of uncertainty. We imposed forward speed and stability but not kinematics. We found that the level of sensorimotor noise had little effect on the mean movement but a strong effect on the variability and the expected effort. The control strategy prioritised reducing the variability of the centre of mass position and swing foot clearance over reducing the variability of individual joint angles, which is also observed in experiments. Interestingly, strict control of centre of mass position and foot clearance in our simulations emerged from minimising effort.

Mediterranean Basin, one of the most important hot spots for reptiles, is also expected to experience significant impacts with climate change, posing a severe risk for the herpetofauna of the region. This study uses the snake-eyed lizard Ophisops elegans as a model organism to investigate the potential impacts of past and future climate change on reptile distributions in the region. An ecological niche model (ENM) was developed with the Maxent algorithm, with location points from GBIF and bioclimatic variables from the CHELSA dataset, then projected onto past LGM ([~]21 kya) and future (2071-2100 SSP3-7.0 and SSP5-8.5) scenarios. Results show that the present-day distribution of O. elegans is primarily driven by temperature seasonality and precipitation, indicating a preference for coastal Mediterranean climates with dry summers. The LGM projection suggests a fragmented and contracted range, confined to coastal refugia around the Mediterranean and Caspian Seas. Future projections for 2071-2100 show consistent and alarming contraction of suitable habitats under both SSP scenarios. In conclusion these findings indicate that O. elegans is vulnerable to significant habitat loss under projected climate change. This severe impact on a wide-spread species implies that the herpetofauna of the Mediterranean Basin may face a significant threat in future.

Cancer therapeutics are increasingly incorporating engineered receptors due to their ability to detect extracellular ligands and initiate intracellular responses that regulate gene expression. By redesigning these natural signaling systems, synthetic receptors hold great potential for use in novel cell-based therapies. One particularly promising direction is modifying the Notch receptor, a transmembrane protein that naturally mediates ligand-dependent signaling at the cell surface to regulate cell proliferation and differentiation in neurogenesis. Both the intracellular and extracellular domains of Notch can be replaced with alternative domains, creating the family of modified Notch receptors known as synthetic Notch (synNotch). In existing synNotch-activated chimeric antigen receptor (CAR) T-cells, the extracellular domain can be engineered to adjust binding affinity for a specific cancer antigen, enabling precise tuning of therapeutic activity while minimizing off-target effects. To quantify and inform such tuning, we develop differential equations models of synNotch receptor signaling and subsequent gene expression. The mathematical models couple activation dynamics on fast timescales (characteristic of receptor-ligand interactions) and on slow timescales (characteristic of downstream gene expression dynamics). Global Sobol sensitivity analysis of the proposed models highlights parameters that yield the greatest variability in synNotch signal transduction and gene expression, indicating their potential to be engineered for different functions in future cancer therapeutics. For the receptor-ligand interactions in the synNotch model, we find that ligand association and ligand-independent activation are the most sensitive parameters. In the downstream gene expression model, promoter strength and degradation rates of mRNA and gene product are found to be most amenable to engineering.

Background/ObjectivesRNA polymerase II is a multifunctional complex that is critical for gene regulation and environmental responses. Its POLR2I subunit in human is associated with various pathologies, including cancer chemoresistance. However, much of our understanding of how POLR2I could function indirectly derives from studies of its homologs in yeasts called Rpb9. Here, we endogenously humanized the rpb9 gene of the fission yeast Schizosaccharomyces pombe to examine the functional capabilities of POLR2I. MethodsWe edited the genomic rpb9 locus in S. pombe so that it encodes the human POLR2I protein, and investigated functional and structural conservation. ResultsWith our humanized yeast system, we find widespread functional complementation by human POLR2I of S. pombe rpb9 roles in yeast growth, chronological aging, and stress responses. We also find that POLR2I complements novel roles for yeast rpb9 in facultative heterochromatin assembly, resistance against the chemotherapy 5-fluorouracil, and resistance against the fungicide thiabendazole. In contrast, we find that POLR2I cannot complement the role of rpb9 in resistance against the transcription elongation inhibitor 6-azauracil (6-AU) in our system. Interestingly, POLR2I could complement 6-AU resistance if ectopically expressed. Lastly, we observe extensive structural homology between Rpb9 and POLR2I proteins. ConclusionsOur study establishes an endogenous cross-species gene complementation strategy that uncovers both conserved and rewired functions of fission yeast rpb9 and its human homolog, POLR2I. In addition to validating conserved roles, we also identified conservation of previously unrecognized roles of rpb9 in heterochromatin formation and chemoresistance.

The environments inhabited by flying insects demand a balance between flight efficiency and flight manoeuvrability. In structural oscillators such as the insect indirect flight motor, efficiency (arising from resonance) and manoeuvrability (arising from kinematic modulation) are typically quid pro quo, with modulation incurring penalties to efficiency. Band-type resonance is a phenomenon that offers, in theory, a strategy to lessen these penalties via careful navigation through a band of efficient kinematic states. However, identifying this band is challenging: no methods exist to identify the complete band in realistic motor models, involving elasticity distributed across thorax and wing. Nor are the effects of elasticity distribution on the band known. In this work, we address both open topics. We present a suite of numerical methods for identifying the complete resonance band in general systems. Applying them to models of the insect flight motor with distributed elasticity--thoracic and wing flexion--reveals that distributed elasticity is moderate-risk but high-reward morphological feature. Well-tuned distributions expand the resonance band over fourfold whereas poorly-tuned distributions completely extinguish the resonance band. These results indicate that distributing elasticity across the insect flight motor can have adaptive value, and motivate broader work identifying distributions across species.

The midsession reversal (MSR) task is frequently used to study behavioral flexibility and decision strategies in animals. In a typical version of the task, subjects complete 80 trials in which they choose between two simultaneously presented stimuli, S1 and S2. During the first 40 trials, responses to S1 are reinforced, whereas responses to S2 are not. The contingencies then reverse without warning: From trial 41 to 80, only responses to S2 are reinforced. In birds, performance in this task is often characterized by anticipatory and perseverative errors around the reversal point, suggesting a reliance on elapsed time since the session began. In contrast, rats tested in operant conditioning chambers typically show near-optimal performance with few errors, a pattern often interpreted as evidence that rats rely primarily on local reinforcement cues rather than temporal information. The present study investigated whether rats exclusively rely on local cues in the MSR task or whether temporal information also contributes to the decision process. Two groups of rats were trained with different intertrial intervals (ITIs; 5 s or 10 s) while the reversal point remained fixed at Trial 41. During acquisition, both groups diplayed similar learning rates and near-optimal steady-state performance with minimal anticipatory or perseverative errors. However, when the ITI was manipulated in probe sessions, systematic shifts in switching behavior emerged. Rats adjusted their choices according to the temporal midpoint experienced during training rather than the nominal trial number of the reversal. These results suggest that rats rely on a mixed strategy that integrates local reinforcement cues with global timing information. Temporal control may therefore be present even when it is not expressed during standard training conditions.

Short Interrupted Repeats Cassettes (SIRC) are recently discovered eukaryotic DNA elements possessing many traits of satellite DNA and mobile genetic elements, and consisted of short direct repeats interspersed with diverse spacer sequences. The SIRC ensemble of individual species is highly heterogenous and cannot be studied using alignment methods. It was found that number of similar SIRC sequences in a given pair of species is in general correlated with their taxonomic distance, and, at the same time, closely related species can possess very diverged SIRC ensembles, which makes SIRC evolutionary pattern closer to mobile genetic element type. The SIRC sequences make up clusters with comparable sequence patterns, that are likely to demonstrate doublet evolutionary model which strongly supports that the SIRC structure is supported by the evolutionary selection. Several SIRC sequences of Arabidopsis were found to be of ancient origin with traceable evolution history as far as to the moss clade. We carried out unbiased detection of SIRC ensembles in 10 plant genomes and found that, despite very high intraspecies heterogeneity, SIRC sets possess strong interspecies phylogenetic signal. Key messageShort Interrupted Repeats Cassettes are elements of ancient origin, and could potentially be used to trace organism history, and to facilitate syntheny and Hi-C analysis.

Cyanobacteria utilize type IV pili for many behavioural responses, such as phototaxis, aggregation, floating, and DNA uptake. Type IV pilus-dependent functions are regulated by the nucleotide second messengers, c-di-GMP and cAMP. In this study, we investigated the role of a recently identified c-di-GMP receptor (CdgR) in cyanobacteria that harbours a ComFB domain. ComFB-domain proteins are widespread in cyanobacteria and are also present in heterotrophic bacteria. We demonstrated that the CdgR homolog from the cyanobacterium Synechocystis sp. PCC 6803, a model organism for studying type IV pilus-dependent functions, specifically binds to c-di-GMP. Genetic and phenotypic analyses revealed that Synechocystis CdgR is involved in phototactic motility and natural competence. Inactivation of cdgR resulted in altered expression of specific sets of minor pilins, which are essential for motility or natural competence. We identified interactions between CdgR and the CRP-family transcription factors, SyCRP1 and SyCRP2. Disruption of these CdgR-SyCRP1 and CdgR/SyCRP2 complexes is initiated by elevated c-di-GMP levels. Moreover, the assembly and stability of these complexes are influenced by other cyclic nucleotides, such as cAMP and c-di-AMP. These observed interactions imply a complex regulatory mechanism by which CdgR influences gene expression in response to cyclic nucleotide messenger signalling, particularly c-di-GMP. The present findings highlight the importance of CdgR in c-di-GMP signalling and its role in regulating type IV pilus-dependent functions in Synechocystis. The modulation of the expression of specific minor pilin genes by CdgR, through interactions with the transcription factors SyCRP1 and SyCRP2, contributes to the establishment of multiple type IV pilus functions and adaptive behaviours of cyanobacteria.