The crosstalk between bone metabolism, lncRNAs, microRNAs and mRNAs in coronary artery calcification.

The association between Coronary Artery Calcification (CAC) and osteoporosis has been reported but not fully understood. Therefore, using an original bioinformatic framework we analyzed transcriptomic profiles of 20 elderly women with high CAC score and 31 age- and sex-matching controls from São Paulo Ageing & Health study (SPAH). We integrated differentially expressed microRNA (miRNA) and long-noncoding RNA (lncRNA) interactions with coding genes associated with CAC, in the context of bone-metabolism genes mined from literature. Top non-coding regulators of bone metabolism in CAC included miRNA 497-5p/195 and 106a-5p, and lncRNA FAM197Y7. Top non-coding RNAs revealed significant interplay between genes regulating bone metabolism, vascularization-related processes, chromatin organization, prostaglandin and calcium co-signaling. Prostaglandin E2 receptor 3 (PTGER3), Fibroblasts Growth Factor Receptor 1 (FGFR1), and One Cut Homeobox 2 (ONECUT2) were identified as the most susceptible to regulation by the top non-coding RNAs. This study provides a flexible transcriptomic framework including non-coding regulation for biomarker-related studies.

ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. METHODS: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. We compared them with changes in ACE-2 networks following SARS-CoV-2 infection by analyzing public data of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). This analysis was performed using the Network by Relative Importance (NERI) algorithm, which integrates protein-protein interaction with co-expression networks. We also performed miRNA-target predictions to identify which miRNAs regulate ACE2-related networks and could play a role in the COVID19 outcome. Finally, we performed enrichment analysis for identifying the main COVID-19 risk groups. RESULTS: We found similar ACE2 expression confidence levels in respiratory and cardiovascular systems, supporting that heart tissue is a potential target of SARS-CoV-2. Analysis of ACE2 interaction networks in infected hiPSC-CMs identified multiple hub genes with corrupted signaling which can be responsible for cardiovascular symptoms. The most affected genes were EGFR (Epidermal Growth Factor Receptor), FN1 (Fibronectin 1), TP53, HSP90AA1, and APP (Amyloid Beta Precursor Protein), while the most affected interactions were associated with MAST2 and CALM1 (Calmodulin 1). Enrichment analysis revealed multiple diseases associated with the interaction networks of ACE2, especially cancerous diseases, obesity, hypertensive disease, Alzheimer's disease, non-insulin-dependent diabetes mellitus, and congestive heart failure. Among affected ACE2-network components connected with the SARS-Cov-2 interactome, we identified AGT (Angiotensinogen), CAT (Catalase), DPP4 (Dipeptidyl Peptidase 4), CCL2 (C-C Motif Chemokine Ligand 2), TFRC (Transferrin Receptor) and CAV1 (Caveolin-1), associated with cardiovascular risk factors. We described for the first time miRNAs which were common regulators of ACE2 networks and virus-related proteins in all analyzed datasets. The top miRNAs regulating ACE2 networks were miR-27a-3p, miR-26b-5p, miR-10b-5p, miR-302c-5p, hsa-miR-587, hsa-miR-1305, hsa-miR-200b-3p, hsa-miR-124-3p, and hsa-miR-16-5p. CONCLUSION: Our study provides a complete mechanistic framework for investigating the ACE2 network which was validated by expression data. This framework predicted risk groups, including the established ones, thus providing reliable novel information regarding the complexity of signaling pathways affected by SARS-CoV-2. It also identified miRNAs that could be used in personalized diagnosis in COVID-19.

Natural ITD statistics predict human auditory spatial perception.

A neural code adapted to the statistical structure of sensory cues may optimize perception. We investigated whether interaural time difference (ITD) statistics inherent in natural acoustic scenes are parameters determining spatial discriminability. The natural ITD rate of change across azimuth (ITDrc) and ITD variability over time (ITDv) were combined in a Fisher information statistic to assess the amount of azimuthal information conveyed by this sensory cue. We hypothesized that natural ITD statistics underlie the neural code for ITD and thus influence spatial perception. To test this hypothesis, sounds with invariant statistics were presented to measure human spatial discriminability and spatial novelty detection. Human auditory spatial perception showed correlation with natural ITD statistics, supporting our hypothesis. Further analysis showed that these results are consistent with classic models of ITD coding and can explain the ITD tuning distribution observed in the mammalian brainstem.

When a person hears a sound, how do they work out where it is coming from? A sound coming from your right will reach your right ear a few fractions of a millisecond earlier than your left. The brain uses this difference, known as the interaural time difference or ITD, to locate the sound. But humans are also much better at localizing sounds that come from sources in front of them than from sources by their sides. This may be due in part to differences in the number of neurons available to detect sounds from these different locations. It may also reflect differences in the rates at which those neurons fire in response to sounds. But these factors alone cannot explain why humans are so much better at localizing sounds in front of them. Pavão et al. showed that the brain has evolved the ability to detect natural patterns that exist in sounds as a result of their location, and to use those patterns to optimize the spatial perception of sounds. Pavão et al. showed that the way in which the head and inner ear filter incoming sounds has two consequences for how we perceive them. Firstly, the change in ITD for sounds coming from different sources in front of a person is greater than for sounds coming from their sides. And secondly, the ITD for sounds that originate in front of a person varies more over time than the ITD for sounds coming from the periphery. By playing sounds to healthy volunteers while removing these differences, Pavão et al. found that natural ITD statistics were correlated with a person's ability to tell where a sound was coming from. By revealing the features the brain uses to determine the location of sounds, the work of Pavão et al. could ultimately lead to the development of more effective hearing aids. The results also provide clues to how other senses, including vision, may have evolved to respond optimally to the environment.

On Information Metrics for Spatial Coding.

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice.

Distinct Correlation Structure Supporting a Rate-Code for Sound Localization in the Owl's Auditory Forebrain.

While a topographic map of auditory space exists in the vertebrate midbrain, it is absent in the forebrain. Yet, both brain regions are implicated in sound localization. The heterogeneous spatial tuning of adjacent sites in the forebrain compared to the midbrain reflects different underlying circuitries, which is expected to affect the correlation structure, i.e., signal (similarity of tuning) and noise (trial-by-trial variability) correlations. Recent studies have drawn attention to the impact of response correlations on the information readout from a neural population. We thus analyzed the correlation structure in midbrain and forebrain regions of the barn owl's auditory system. Tetrodes were used to record in the midbrain and two forebrain regions, Field L and the downstream auditory arcopallium (AAr), in anesthetized owls. Nearby neurons in the midbrain showed high signal and noise correlations (R NC s), consistent with shared inputs. As previously reported, Field L was arranged in random clusters of similarly tuned neurons. Interestingly, AAr neurons displayed homogeneous monotonic azimuth tuning, while response variability of nearby neurons was significantly less correlated than the midbrain. Using a decoding approach, we demonstrate that low R NC in AAr restricts the potentially detrimental effect it can have on information, assuming a rate code proposed for mammalian sound localization. This study harnesses the power of correlation structure analysis to investigate the coding of auditory space. Our findings demonstrate distinct correlation structures in the auditory midbrain and forebrain, which would be beneficial for a rate-code framework for sound localization in the nontopographic forebrain representation of auditory space.

On Sequence Learning Models: Open-loop Control Not Strictly Guided by Hick's Law.

According to the Hick's law, reaction times increase linearly with the uncertainty of target stimuli. We tested the generality of this law by measuring reaction times in a human sequence learning protocol involving serial target locations which differed in transition probability and global entropy. Our results showed that sigmoid functions better describe the relationship between reaction times and uncertainty when compared to linear functions. Sequence predictability was estimated by distinct statistical predictors: conditional probability, conditional entropy, joint probability and joint entropy measures. Conditional predictors relate to closed-loop control models describing that performance is guided by on-line access to past sequence structure to predict next location. Differently, joint predictors relate to open-loop control models assuming global access of sequence structure, requiring no constant monitoring. We tested which of these predictors better describe performance on the sequence learning protocol. Results suggest that joint predictors are more accurate than conditional predictors to track performance. In conclusion, sequence learning is better described as an open-loop process which is not precisely predicted by Hick's law.

Multifactoriality in Psychiatric Disorders: A Computational Study of Schizophrenia.

The search for biological causes of mental disorders has up to now met with limited success, leading to growing dissatisfaction with diagnostic classifications. However, it is questionable whether most clinical syndromes should be expected to correspond to specific microscale brain alterations, as multiple low-level causes could lead to similar symptoms in different individuals. In order to evaluate the potential multifactoriality of alterations related to psychiatric illness, we performed a parametric exploration of published computational models of schizophrenia. By varying multiple parameters simultaneously, such as receptor conductances, connectivity patterns, and background excitation, we generated 5625 different versions of an attractor-based network model of schizophrenia symptoms. Among networks presenting activity within valid ranges, 154 parameter combinations out of 3002 (5.1%) presented a phenotype reminiscent of schizophrenia symptoms as defined in the original publication. We repeated this analysis in a model of schizophrenia-related deficits in spatial working memory, building 3125 different networks, and found that 41 (4.9%) out of 834 networks with valid activity presented schizophrenia-like alterations. In isolation, none of the parameters in either model showed adequate sensitivity or specificity to identify schizophrenia-like networks. Thus, in computational models of schizophrenia, even simple network phenotypes related to the disorder can be produced by a myriad of causes at the molecular and circuit levels. This suggests that unified explanations for either the full syndrome or its behavioral and network endophenotypes are unlikely to be expected at the genetic and molecular levels.

Local field potentials in the gustatory cortex carry taste information.

It has been recently shown that local field potentials (LFPs) from the auditory and visual cortices carry information about sensory stimuli, but whether this is a universal property of sensory cortices remains to be determined. Moreover, little is known about the temporal dynamics of sensory information contained in LFPs following stimulus onset. Here we investigated the time course of the amount of stimulus information in LFPs and spikes from the gustatory cortex of awake rats subjected to tastants and water delivery on the tongue. We found that the phase and amplitude of multiple LFP frequencies carry information about stimuli, which have specific time courses after stimulus delivery. The information carried by LFP phase and amplitude was independent within frequency bands, since the joint information exhibited neither synergy nor redundancy. Tastant information in LFPs was also independent and had a different time course from the information carried by spikes. These findings support the hypothesis that the brain uses different frequency channels to dynamically code for multiple features of a stimulus.

Parkinson's disease progression: implicit acquisition, cognitive and motor impairments, and medication effects.

Parkinson's disease (PD) symptoms have been collectively ascribed to malfunctioning of dopamine-related nigro-striatal and cortico-striatal loops. However, some doubts about this proposition are raised by controversies about the temporal progression of the impairments, and whether they are concomitant or not. The present study consists of a systematic revision of literature data on both functional PD impairments and dopaminergic medication effects in order to draw a coherent picture about the disease progression. It was done in terms of an explanatory model for the disruption of implicit knowledge acquisition, motor and cognitive impairments, and the effects of dopaminergic medication on these functions. Cognitive impairments arise at early stages of PD and stabilizes while disruption of implicit knowledge acquisition and motor impairments, are still in progression; additionally, dopaminergic medication reduces motor impairments and increases disruption of implicit knowledge acquisition. Since this model revealed consistency and plausibility when confronted with data of others studies not included in model's formulation, it may turn out to be a useful tool for understanding the multifaceted characteristics of PD.

Identificação computadorizada de crises tipo ausência no eletrocorticograma de ratos / Automatized identification of absence-like seizures in electrocorticogram of rats

Crises tipo epilepsia de ausência são expressas no eletrocorticograma de ratos como surtos paroxísticos de descargas espícula-onda. O objetivo do presente trabalho foi desenvolver e testar um sistema automatizado de identificação de crises. O princípio do sistema é distinguir sinais de crise dos demais sinais pela análise da potência na faixa de frequência entre 7 e 11 Hz, que é alta apenas durante as crises. A rotina, elaborada no Matlab®, é informada com um intervalo de registro em que não ocorreram crises (crivo), a fim de estabelecer o critério de identificação. O sistema compara, por meio da transformada rápida de Fourier, o crivo com o registro inteiro. A saída do programa é uma tabela informando a presença de crises nesses intervalos. Quatro ratos Wistar geneticamente portadores de crises tipo ausência e implantados com eletrodos corticais tiveram eletrocorticograma por 80 minutos. As crises foram quantificadas manualmente minuto a minuto. O registro e a quantificação das crises de um animal foram usados no refinamento do sistema. Os valores da quantificação automatizada foram bastante similares aos da quantificação manual para o animal usado na elaboração do sistema. Os registros dos demais animais foram usados para testar o sistema e apresentaram padrão similar. O sistema é capaz de quantificar com grande fidelidade as descargas eletrocorticográficas espícula-onda, mostrando-se como potente instrumento na análise de registros em estudos com modelos animais dessa patologia. Adicionalmente, pequenas adaptações permitem identificar outros padrões encontrados em registros eletroencefalográficos (sono, vigília, manifestações patológicas). A rotina está disponível para download em www.ib.usp.br/~rpavao.

Absence-like seizures are presented in rat electrocorticogram as paroxistics spike-wave discharges. The aim of this study was to develop and test an automatized system for seizures identification. System fundament is to distinguish seizure signals from others by power analysis in frequency range between 7 and 11 Hz, which is high only during seizures. Routine, elaborated at Matlab®, is inputted with an interval without seizures (sample) in order to set up the criterion of identification. The system compares, through the Fourier fast transformed, the sample with the entire recording. Program output is a table that reports the presence of seizures in such intervals. Four Wistar rats genetically prone to show absence-like seizures with implanted cortical electrodes were submitted to 80 minutes of electrocorticogram. Seizures were counted manually minute by minute. Recording and seizure quantification of one animal were used to refine the system. Automatized quantification was very similar to manual quantification for the animal used as sample. Recordings of the other animals, used to test the system, shows similar pattern. The system is able to quantify with high fidelity the spike-wave discharges, so that it is a powerful instrument to analyze electroencephalogram in studies with animal models of this pathology. Additionally, minor changes allow identifying other patterns found in electrophysiological recordings (sleep, wakefulness, pathological manifestations etc). The routine is available for download at www.ib.usp.br/~rpavao.