Biology subjects

Galli, G.

Publications and source records attributed to Galli, G..

13 recordsLinked to original sources

bioRxiv2026

Microtubule-associated protein 1S (MAP1S): a cardioprotective factor against post-myocardial infarction remodeling via apoptosis inhibition

Adverse cardiac remodeling following myocardial infarction (MI) are driven by processes including autophagy and apoptosis. While microtubule-associated protein 1S (MAP1S) is known to regulate autophagy, its role in the cardiac pathological conditions remains unclear. This study aimed to elucidate the role of MAP1S and its underlying mechanisms in pathological cardiac remodeling. Following MI, MAP1S knockout mice exhibited increased mortality, impaired cardiac function, and elevated apoptosis. Similarly, MAP1S silencing in cultured cardiomyocytes augmented apoptosis under oxidative stress. Mechanistic investigation revealed potential link to the Hippo pathway. MAP1S knockdown in cardiomyocytes increased Mammalian Ste-20 like 1/2 (MST1/2) activation and reduced Yes-associated protein (YAP) activity, potentially explaining apoptosis regulation. Conversely, MAP1S overexpression reduced apoptosis and positively modulated autophagy. Importantly, in vivo modRNA-mediated MAP1S overexpression protected against apoptosis and adverse remodeling. This study reveals that MAP1S protects the heart from excessive apoptosis and adverse remodeling following MI, likely by modulating the Hippo signaling pathway.

cell biology↗

bioRxiv2025

The 18S rRNA methyltransferase, BUD23, is required for appropriate lipid and mitochondrial metabolism

Efficient energy metabolism is essential for health and its dysregulation drives cardiometabolic disease. Delivery of regulatory control through translation and ribosome function is emerging as important. Here, we identify the rRNA methyltransferase BUD23 as a potent regulator of cellular and systemic energy homeostasis. Adipocyte-specific deletion of BUD23 in mice regulates lipid and mitochondrial metabolism resulting in a pronounced lean phenotype and resistance to diet-induced obesity. Mechanistically, BUD23 modulates translation initiation and efficiency of mRNAs with specific features - including short 5 UTR length and GC-rich post-initiation codon usage - characteristic of mitochondrial and lipogenic proteins. Genetic analyses and Mendelian randomisation support a role for BUD23 in human cardiometabolic traits and disease burden. Together, our findings uncover a conserved translational control mechanism that regulates energy state, from cellular metabolism through to human cardiometabolic health.

cell biology↗

bioRxiv2025

Sex, Stress and the Heart: Long-term Cardiovascular Effects of Embryonic Metabolic Disruption

Adverse conditions within the embryonic environment can alter embryogenesis, programming systemic physiological changes that may manifest as disease states in adult life. The process of developmental programming represents an important factor underlying cardiometabolic diseases, many of which are leading causes of death globally. Importantly, there is evidence that males are less tolerant to certain environmental perturbations during embryogenesis, mirrored by sex differences in the incidence of certain cardiometabolic diseases. Understanding sex differences in programmed responses in mammalian models is complicated by maternal compensation and placental factors. Avian models offer a valuable comparable system in which such effects are not present. Here, we investigate the influence of developmental hypoxia and hypothermia in programming cardiovascular structure and function in the domestic chicken (Gallus gallus domesticus). In agreement to mammalian studies, adult males but not females show pathological mitochondrial morphology and respiratory capacity, ventricular hypertrophy and reduced body weight programmed by embryonic hypothermia and hypoxia. These data not only represent novel findings in birds but demonstrate the utility of the avian model for understanding sex differences in prenatal stress responses, revealing common responses among endothermic amniotes.

physiology↗

bioRxiv2022

Genome-wide association analysis of hyperspectral reflectance data to dissect growth-related traits genetic architecture in maize under inoculation with plant growth-promoting bacteria

Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1,021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growthrelated traits under PGPB inoculation. Eight SNPs were detected, which were associated with manually measured and hyperspectral phenotypes. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multi-phenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.

genetics↗

bioRxiv2022

Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data

Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyper-spectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyper-spectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies. Core ideasO_LIHyperspectral reflectance data can classify plant growth-promoting bacteria inoculation status C_LIO_LIPhenomic prediction performs better than genomic prediction depending on the target phenotype C_LIO_LIAutoML is a promising approach for automating hyperparameter tuning for classification and prediction C_LI

genetics↗

bioRxiv2022

Genetic and epigenetic driven variation in regulatory regions activitycontribute to adaptation and evolution under endocrine treatment

Comprehensive profiling of hormone-dependent breast cancer (HDBC) has identified hundreds of protein-coding alterations contributing to cancer initiation1, 2, but only a handful have been linked to endocrine therapy resistance, potentially contributing to 40% of relapses1, 3-9. If other mechanisms underlie the evolution of HDBC under adjuvant therapy is currently unknown. In this work, we employ integrative functional genomics to dissect the contribution of cis-regulatory elements (CREs) to cancer evolution by focusing on 12 megabases of non-coding DNA, including clonal enhancers10, gene promoters, and boundaries of topologically associating domains11. Massive parallel perturbation in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance12, 13 and endocrine therapy resistance9. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies non-coding changes involved in therapy resistance. Overall, our data uncover actionable transient transcriptional programs critical for dormant persister cells and unveil new regulatory nodes driving evolutionary trajectories towards disease progression.

cancer biology↗

bioRxiv2021

A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria

Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24-0.47) and between HTP-based plant height and manually measured plant height (0.55-0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth. Core ideasO_LIA low-cost greenhouse-based HTP platform was developed. C_LIO_LIImage-derived phenotypes presented moderate to high genomic heritabilities and correlations. C_LIO_LIPlant growth-promoting bacteria can improve plant resilience under nitrogen-limited conditions. C_LI

genetics↗

bioRxiv2020

CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction

Usually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-). The CV- was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV- results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV- method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV- can be considered a more precise validation methodology for model selection.

genomics↗

bioRxiv2020

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

Envirotyping is an essential technique used to unfold the non-genetic drivers associated with the phenotypic adaptation of living organisms. Here we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically-smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

genetics↗

bioRxiv2020

Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning

We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex [1] and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral PFC. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models. Author summaryNon-Invasive Brain Stimulation techniques (in our case, transcranial direct current stimulation) are widely used among neuroscientists to map cognitive processes, for example, memory, decision making, emotional processing. In many cases, the sample size is limited, or methods applied for the data analysis are questionable. Here we propose a replication study aiming at confirmation of memory enhancement as a result of the application of anodal transcranial direct stimulation over the ventrolateral prefrontal cortex. Additionally, the involvement of the dorsolateral prefrontal cortex into episodic memory was analyzed. We used a larger sample size and applied different data analysis methods, including machine learning techniques. Surprisingly, we did not replicate the results of the original study, and we found weak effects of memory impairment after stimulating the DLPFC. However, what is most remarkable is that we have observed no significant effect of tDCS involvement on memory performance. Machine learning methods revealed no effect of linguistic factors on tDCS effect for both the original study and the replication at the level of individual trials and participants. Our findings highlight the importance of considering individual differences and data on the level of a single trial; in our data, participants memory responses resembled guessing behavior when recognition performance was measured by AUROC and highlight the need for modifications in the memory test and the use of other performance measures.

neuroscience↗

bioRxiv2019

Offset-related brain activity in the ventrolateral prefrontal cortex promotes long-term memory formation of verbal events

Recent evidence suggests that brain activity following the offset of a stimulus during encoding contributes to long-term memory formation, however the exact mechanisms underlying offset-related encoding are still unclear. Here we used repetitive transcranial magnetic stimulation (rTMS) to investigate offset-related activity in the left ventrolateral prefrontal cortex (VLPFC). rTMS was administered at different points in time around stimulus offset while male and female participants encoded visually-presented words (first rTMS experiment) or pairs of words (second rTMS experiment) and the analyses focused on the effects of the stimulation on subsequent memory performance. The results show that rTMS administered at the offset of the stimuli, but not during online encoding, disrupted subsequent memory performance. In the first experiment we show that rTMS specifically disrupted encoding mechanisms initiated by the offset of the stimuli rather than general, post-stimulus processes. In the second experiment, we show a robust decline in associative memory performance when rTMS was delivered at the offset of the word pairs, suggesting that offset-related encoding may contribute to the binding of information into an episodic memory trace. A meta-analysis conducted on the two studies and on a previously published dataset confirmed that the involvement of the left VLPFC in memory formation is initiated by the offset of the stimulus. The offset of the stimulus may represent an event boundary that promotes the reinstatement of the previously experienced event and episodic binding.\n\nSIGNIFICANCE STATEMENTHow well an event is encoded predicts how well it is remembered, and verbal encoding is an important part of everyday memory that, if disrupted, can lead to difficulties and disorders. The timing of encoding processes relative to the presentation of an event is important for successful retrieval, and little is known about the interval immediately after an events presentation (post-stimulus offset) which is thought to involve critical encoding processes in the VLPFC and hippocampus. The current studies demonstrate that indeed, verbal encoding processes in the VLPFC that are necessary for memory formation are triggered by the offset of the word, and these processes may involve VLPFC-hippocampal interactions that promote binding of event features into a single, coherent memory trace.

neuroscience↗

bioRxiv2019

Evaluation of the possible association of Body Mass Index and four metabolic gene polymorphisms with longevity in an Italian cohort: a role for APOE, eNOS, and FTO gene polymorphisms.

Backgroundlongevity is considered the result of interactions between environmental and genetic factors.\n\nAimwe investigated the possible association of body mass index and the frequencies of APOE, ACE, eNOS, and FTO gene polymorphisms with longevity.\n\nSubjects and Method1,100 healthy volunteers aged 10-100 were recruited. We genotyped subjects for APOE, ACE, eNOS, and FTO gene polymorphisms. Data about height and weight were also collected. The sample was split in four age groups: 1-24, 25-49, 50-85 and 86-100.\n\nResultssignificant differences were found in BMI values between age groups, with exception of 1-24 with respect to 86-100. A significant decrease of the APO E4, eNOS 393 and FTO A and allele frequencies was observed in the 86-100 age group with respect to younger groups. For ACE gene, no significant differences were found in the allele frequencies between groups. A similar trend was also observed subdividing the sample in two main age groups: 1-85 and 86-100.\n\nConclusionthis study provides evidences for a role of APOE, eNOS, and FTO gene polymorphisms in longevity. It has been estimated that the number of centenarians worldwide will double each decade until 2100, making population data about gene polymorphisms relevant for further studies about longevity.

genetics↗

bioRxiv2018

Yap regulates glucose utilization and sustains nucleotide synthesis to enable organ growth

The Hippo pathway and its nuclear effector Yap regulate organ size and cancer formation. While many modulators of Hippo activity have been identified, little is known about the Yap target genes that mediate these growth effects. Here, we show that yap-/- mutant zebrafish exhibit defects in hepatic progenitor potential and liver growth due to impaired glucose transport and nucleotide biosynthesis: transcriptomic and metabolomic analyses reveal that Yap regulates expression of glucose transporter glut1, causing decreased glucose uptake and use for nucleotide biosynthesis in yap-/- mutants, and impaired glucose tolerance in adults. Nucleotide supplementation improved Yap-deficiency phenotypes, indicating functional importance of glucose-fuelled nucleotide biosynthesis. Yap-regulated Glut1 expression and glucose uptake are conserved in mammals, suggesting that stimulation of anabolic glucose metabolism is an evolutionarily conserved mechanism by which the Hippo pathway controls organ growth. Together, our results reveal a central role for Hippo signalling in metabolic homeostasis.

developmental biology↗