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Abstract Follicular lymphoma (FL), the most common indolent non-Hodgkin lymphoma, constitutes a paradigm of immune tumor microenvironment (TME) contribution to disease onset, progression, and heterogenous clinical outcome. Here we present the first FL-Patient Derived Lymphoma Spheroid (FL-PDLS), including fundamental immune actors and features of TME in FL lymph nodes (LNs). FL-PDLS is organized in disc-shaped 3D structures composed of proliferating B and T cells, together with macrophages with an intermediate M1/M2 phenotype. FL-PDLS recapitulates the most relevant B-cell transcriptional pathways present in FL-LN (proliferation, epigenetic regulation, mTOR, adaptive immune system, among others). The T cell compartment in the FL-PDLS preserves CD4 subsets (follicular helper, regulatory, and follicular regulatory), also encompassing the spectrum of activation/exhaustion phenotypes in CD4 and CD8 populations. Moreover, this system is suitable for chemo and immunotherapy testing, recapitulating results obtained in the clinic. FL-PDLS allowed uncovering that soluble galectin-9 limits rituximab, rituximab, plus nivolumab/TIM-3 antitumoral activities. Blocking galectin-9 improves rituximab efficacy, highlighting galectin-9 as a novel immunotherapeutic target in FL. In conclusion, FL-PDLS maintains the crosstalk between malignant B cells and the immune LN-TME and constitutes a robust and multiplexed pre-clinical tool to perform drug screening in a patient-derived system, advancing toward personalized therapeutic approaches.

Abstract MALAT1 long non-coding RNA has oncogenic roles but has been poorly studied in indolent B-cell neoplasms. Here, MALAT1 expression was analyzed using RNA-seq, microarrays or qRT-PCR in primary samples from clinico-biological subtypes of chronic lymphocytic leukemia (CLL, n = 266), paired Richter transformation (RT, n = 6) and follicular lymphoma (FL, n = 61). In peripheral blood (PB) CLL samples, high MALAT1 expression was associated with a significantly shorter time to treatment independently from other known prognostic factors. Coding genes expressed in association with MALAT1 in CLL were predominantly related to oncogenic pathways stimulated in the lymph node (LN) microenvironment. In RT paired samples, MALAT1 levels were lower, concordant with their acquired increased independency of external signals. Moreover, MALAT1 levels in paired PB/LN CLLs were similar, suggesting that the prognostic value of MALAT1 expression in PB is mirroring expression differences already present in LN. Similarly, high MALAT1 expression in FL predicted for a shorter progression-free survival, in association with expression pathways promoting FL pathogenesis. In summary, MALAT1 expression is related to pathophysiology and more aggressive clinical behavior of indolent B-cell neoplasms. Particularly in CLL, its levels could be a surrogate marker of the microenvironment stimulation and may contribute to refine the clinical management of these patients.

Models of perceptual decision making typically take into account either reactive responses to external stimuli or proactive aspects to decision making. Here the authors found that rat perceptual responses are generated by a combination of the standard evidence accumulation process with a fixed decision boundary, and a separate stochastic boundary collapse triggered by a parallel proactive process.

Microbial populations generally evolve in volatile environments, under conditions fluctuating between harsh and mild, e.g. as the result of sudden changes in toxin concentration or nutrient abundance. Environmental variability (EV) thus shapes the long-time population dynamics, notably by influencing the ability of different strains of microorganisms to coexist. Inspired by the evolution of antimicrobial resistance, we study the dynamics of a community consisting of two competing strains subject to twofold EV. The level of toxin varies in time, favouring the growth of one strain under low drug concentration and the other strain when the toxin level is high. We also model time-changing resource abundance by a randomly switching carrying capacity that drives the fluctuating size of the community. While one strain dominates in a static environment, we show that species coexistence is possible in the presence of EV. By computational and analytical means, we determine the environmental conditions under which long-lived coexistence is possible and when it is almost certain. Notably, we study the circumstances under which environmental and demographic fluctuations promote, or hinder, the strains coexistence. We also determine how the make-up of the coexistence phase and the average abundance of each strain depend on the EV.

The physics governing the formation of extreme coherent events, i.e., the systemwide emergence of an observable taking extraordinary values in a short time window, is a relevant yet elusive problem to a variety of disciplines ranging from climate science to neuroscience. Despite their inherent differences, systems exhibiting episodes of extreme coherence can be abstracted as a set of coupled nonlinear elements in a noisy and networked environment. Here, we propose a model describing the generation of extreme coherence by exploring theoretically and numerically the capacity of noise and network correlations to amplify a critical core of the system and trigger an extreme event. Although we principally center our study in modeling bursting phenomena in neuronal circuits, we extend our analysis to other systems such as algae blooms and infectious diseases. We show that extreme events originate in a relatively small core of the system and that different cores may coexist. We also show that the amplification mechanisms within a system are highly robust, so that the deletion of central nodes leads to other nodes taking leadership.

Fundamental laws governing the wiring of neuronal networks in the brain are still largely unknown. Understanding both the role and importance of the connectivity patterns that can be observed at different scales in the central nervous system are of crucial interest for advancing our knowledge on neuroscience in health and disease. Intellectual disability pathologies provide an excellent opportunity to address these questions due to their characteristic alterations in neuronal structure and connectivity that induce relative changes of the neuronal network dynamics and, eventually, in their function. Our aim is to identify what are the essential properties of brain connectivity and how they constrain cognitive functions such as information processing, learning and memory. Our approach is based on a mixed experimental and computational approach, addressing different scales of interest ranging from the local connectivity in neuronal modules to the long-range connectomes of mammal brains. Specifically we use Down syndrome mouse models (Ts65Dn and TgDyrk1A) that recapitulate both cognitive impairments and neuromorphological alterations observed in the human chromosome 21 trisomy. Homogeneous neuronal cultures provide a simple enough framework to study spontaneous electrical activity and its dependence on specific structural parameters (dendritic arborisation, axonal path length, connectivity density and excitatory-inhibitory balance). This allows to link concepts such as information processing efficiency or percept storage capacity with the observed connectivity patterns detected in well-controlled in vitro experiments where the biological constraints are minimal. In addition, the study of fixed intact brains (by means of the state of the art CLARITY technique) brings us closer to biologically and medically relevant situations, allowing not only to confirm whether the functional links in neuronal cultures are also present in vivo, but also enabling the introduction of functional information (like behavioral studies and functional imaging) and another layer of structural alterations such as brain region morphology, neuronal density, and long-range connectivity. Taking together the experimental information from these systems we want to feed self-developed computational models that allow us to understand what are the fundamental characteristics of the observed connectivity patterns and the impact of each of the alterations on neuronal network function. These models will also provide a framework able to account for the emergent properties that bridge the gap between spontaneous electrical activity arousal/transmission and higher order information processing and memory storage capacities in the brain. As an additional part of the project we are now working on the application of the clearing, labeling and imaging protocols to human biopsy samples. Our aim is to obtain neuronal architecture and connectivity information from focal cortical dysplasia microcircuits using samples from intractable temporal lobe epilepsy patients that undergo deep-brain electrode recording diagnosis and posterior surgical extraction of the tissue. Our computational models can allow us to discern the contributions of the observed abnormalities to neuronal hyperactivity and epileptic seizure generation.