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Feline leukemia virus C receptor 1a (FLVCR1a), initially identified as a retroviral receptor and localized on the plasma membrane, has emerged as a crucial regulator of heme homeostasis. Functioning as a positive regulator of δ-aminolevulinic acid synthase 1 (ALAS1), the rate-limiting enzyme in the heme biosynthetic pathway, FLVCR1a influences TCA cycle cataplerosis, thus impacting TCA flux and interconnected metabolic pathways. This study reveals an unexplored link between FLVCR1a, heme synthesis, and cholesterol production in endothelial cells. Using cellular models with manipulated FLVCR1a expression and inducible endothelial-specific Flvcr1a-null mice, we demonstrate that FLVCR1a-mediated control of heme synthesis regulates citrate availability for cholesterol synthesis, thereby influencing cellular cholesterol levels. Moreover, alterations in FLVCR1a expression affect membrane cholesterol content and fluidity, supporting a role for FLVCR1a in the intricate regulation of processes crucial for vascular development and endothelial function. Our results underscore FLVCR1a as a positive regulator of heme synthesis, emphasizing its integration with metabolic pathways involved in cellular energy metabolism. Furthermore, this study suggests that the dysregulation of heme metabolism may have implications for modulating lipid metabolism. We discuss these findings in the context of FLVCR1a's potential heme-independent function as a choline importer, introducing additional complexity to the interplay between heme and lipid metabolism. [ABSTRACT FROM AUTHOR]

Abstract Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

TDC are hematopoietic cells that combine dendritic cell (DC) and conventional T‐cell markers and functional properties. They were identified in secondary lymphoid organs (SLOs) of naïve mice as cells expressing CD11c, major histocompatibility molecules (MHC)‐II, and the T‐cell receptor (TCR). Despite thorough characterization, a physiological role for TDC remains to be determined. Unfortunately, using CD11c as a marker for TDC has the caveat of its upregulation on different cells, including T cells, upon activation. Here, we took advantage of Zbtb46‐GFP reporter mice to explore the frequency and localization of TDC in different tissues at steady state and upon viral infection. RNA sequencing analysis confirmed that TDC sorted from Zbtb46‐GFP mice have a gene signature that is distinct from conventional T cells and DC. In addition, this reporter model allowed for identification of TDC in situ not only in SLOs but also in the liver and lung of naïve mice. Interestingly, we found that TDC numbers in the SLOs increased upon viral infection, suggesting that TDC might play a role during viral infections. In conclusion, we propose a visualization strategy that might shed light on the physiological role of TDC in several pathological contexts, including infection and cancer. [ABSTRACT FROM AUTHOR]

Background Biological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques. Findings We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein–protein interaction networks assayed in different cell lines. Conclusions These examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques. [ABSTRACT FROM AUTHOR]

While the role of common genetic variants in multiple sclerosis (MS) has been elucidated in large genome-wide association studies, the contribution of rare variants to the disease remains unclear. Herein, a whole-genome sequencing study in four affected and four healthy relatives of a consanguineous Italian family identified a novel missense c.1801T > C (p.S601P) variant in the GRAMD1B gene that is shared within MS cases and resides under a linkage peak (LOD: 2.194). Sequencing GRAMD1B in 91 familial MS cases revealed two additional rare missense and two splice-site variants, two of which (rs755488531 and rs769527838) were not found in 1000 Italian healthy controls. Functional studies demonstrated that GRAMD1B, a gene with unknown function in the central nervous system (CNS), is expressed by several cell types, including astrocytes, microglia and neurons as well as by peripheral monocytes and macrophages. Notably, GRAMD1B was downregulated in vessel-associated astrocytes of active MS lesions in autopsied brains and by inflammatory stimuli in peripheral monocytes, suggesting a possible role in the modulation of inflammatory response and disease pathophysiology. [ABSTRACT FROM AUTHOR]

Evolution is typically thought to proceed through divergence of genes, proteins and ultimately phenotypes (1-3). However, similar traits might also evolve convergently in unrelated taxa owing to similar selection pressures (4,5). Adaptive phenotypic convergence is widespread in nature, and recent results from several genes have suggested that this phenomenon is powerful enough to also drive recurrent evolution at the sequence level (6-9). Where homoplasious substitutions do occur these have long been considered the result of neutral processes. However, recent studies have demonstrated that adaptive convergent sequence evolution can be detected in vertebrates using statistical methods that model parallel evolution (9,10), although the extent to which sequence convergence between genera occurs across genomes is unknown. Here we analyse genomic sequence data in mammals that have independently evolved echolocation and show that convergence is not a rare process restricted to several loci but is instead widespread, continuously distributed and commonly driven by natural selection acting on a small number of sites per locus. Systematic analyses of convergent sequence evolution in 805,053 amino acids within 2,326 orthologous coding gene sequences compared across 22 mammals (including four newly sequenced bat genomes) revealed signatures consistent with convergence in nearly 200 loci. Strong and significant support for convergence among bats and the bottlenose dolphin was seen in numerous genes linked to hearing or deafness, consistent with an involvement in echolocation. Unexpectedly, we also found convergence in many genes linked to vision: the convergent signal of many sensory genes was robustly correlated with the strength of natural selection. This first attempt to detect genome-wide convergent sequence evolution across divergent taxa reveals the phenomenon to be much more pervasive than previously recognized., Echolocation is a complex phenotypic trait that has evolved independently in bats and whales, and which involves the production, reception and auditory processing of ultrasonic pulses for obstacle avoidance, orientation [...]

Metastasis depends on the ability of tumor cells to establish a relationship with the newly seeded tissue that is conducive to their survival and proliferation. However, the factors that render [...]

Simple Summary: The metabolic alterations characteristic of cancer cells play a significant role in tumors' natural history and response to therapy. Recent technological advances have allowed the production of unprecedented amounts of data on many types of cancers. We exploited the most comprehensive collection of such data, The Cancer Genome Atlas (TCGA), to systematically investigate the associations between metabolic alterations and other tumor features. We used sets of genes known to be associated with specific metabolic pathways to classify patients into "metabolic subtypes". Then, we systematically looked for associations between the metabolic subtypes and other tumor features, including histological classification, patient survival, and genome alterations. Our results, while correlative in nature, can provide a guide to the formulation of specific mechanistic hypotheses to be tested experimentally so as to improve our understanding of the biology of cancer and our ability to tailor therapeutic interventions to the specific features of each patient. The alterations of metabolic pathways in cancer have been investigated for many years, beginning long before the discovery of the role of oncogenes and tumor suppressors, and the last few years have witnessed renewed interest in this topic. Large-scale molecular and clinical data on tens of thousands of samples allow us to tackle the problem from a general point of view. Here, we show that transcriptomic profiles of tumors can be exploited to define metabolic cancer subtypes, which can be systematically investigated for associations with other molecular and clinical data. We find thousands of significant associations between metabolic subtypes and molecular features such as somatic mutations, structural variants, epigenetic modifications, protein abundance and activation, and with clinical/phenotypic data, including survival probability, tumor grade, and histological types, which we make available to the community in a dedicated web resource. Our work provides a methodological framework and a rich database of statistical associations, which will contribute to the understanding of the role of metabolic alterations in cancer and to the development of precision therapeutic strategies. [ABSTRACT FROM AUTHOR]

Introduction Prevention of frailty is paramount in older adults. We evaluated the efficacy of a tailored multidomain intervention, monitored with the My Active and Healthy Aging platform, in reducing conversion from a prefrail status to overt frailty and preventing decline in quality of life. Methods We performed a multicentre, multicultural, randomised control study. The effects of multidomain interventions on frailty parameters, quality of life, physical, cognitive, psychosocial function, nutrition and sleep were evaluated in a group of 101 prefrail older subjects and compared with 100 prefrail controls, receiving general health advice. Results At the 12-month assessment, controls showed a decline in quality of life that was absent in the active group. In addition, active participants showed an increase in mood and nutrition function. No effect on remaining parameter was observed. Discussion Our study supports the use of personalised multidomain intervention, monitored with an information and communication technology platform, in preventing quality of life decline in older adults. [ABSTRACT FROM AUTHOR]

Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

Abstract Background Different approaches have been developed to dissect the interplay between transcription factors (TFs) and their cis-acting sequences on DNA in order to identify TF target genes. Here we used a combination of computational and experimental approaches to identify novel direct targets of TFAP2A, a key TF for a variety of physiological and pathological cellular processes. Gene expression profiles of HeLa cells either silenced for TFAP2A by RNA interference or not were previously compared and a set of differentially expressed genes was revealed. Results The regulatory regions of 494 TFAP2A-modulated genes were analyzed for the presence of TFAP2A binding sites, employing the canonical TFAP2A Positional Weight Matrix (PWM) reported in Jaspar http://jaspar.genereg.net/. 264 genes containing at least 2 high score TFAP2A binding sites were identified, showing a central role in "Cellular Movement" and "Cellular Development". In an attempt to identify TFs that could cooperate with TFAP2A, a statistically significant enrichment for SP1 binding sites was found for TFAP2A-activated but not repressed genes. The direct binding of TFAP2A or SP1 to a random subset of TFAP2A-modulated genes was demonstrated by Chromatin ImmunoPrecipitation (ChIP) assay and the TFAP2A-driven regulation of DCBLD2/ESDN/CLCP1 gene studied in details. Conclusions We proved that our computational approaches applied to microarray selected genes are valid tools to identify functional TF binding sites in gene regulatory regions as confirmed by experimental validations. In addition, we demonstrated a fine-tuned regulation of DCBLD2/ESDN transcription by TFAP2A.

Introduction: Previous studies showed that quarantine for pandemic diseases is associated with several psychological and medical effects. The consequences of quarantine for COVID-19 pandemic in patients with dementia are unknown. We investigated the clinical changes in patients with Alzheimer's disease and other dementias, and evaluated caregivers' distress during COVID-19 quarantine. Methods: The study involved 87 Italian Dementia Centers. Patients with Alzheimer's Disease (AD), Dementia with Lewy Bodies (DLB), Frontotemporal Dementia (FTD), and Vascular Dementia (VD) were eligible for the study. Family caregivers of patients with dementia were interviewed by phone in April 2020, 45 days after quarantine declaration. Main outcomes were patients' changes in cognitive, behavioral, and motor symptoms. Secondary outcomes were effects on caregivers' psychological features. Results: 4913 patients (2934 females, 1979 males) fulfilled the inclusion criteria. Caregivers reported a worsening in cognitive functions in 55.1% of patients, mainly in subjects with DLB and AD. Aggravation of behavioral symptoms was observed in 51.9% of patients. In logistic regression analysis, previous physical independence was associated with both cognitive and behavioral worsening (odds ratio 1.85 [95% CI 1.42–2.39], 1.84 [95% CI 1.43–2.38], respectively). On the contrary, pandemic awareness was a protective factor for the worsening of cognitive and behavioral symptoms (odds ratio 0.74 [95% CI 0.65–0.85]; and 0.72 [95% CI 0.63–0.82], respectively). Approximately 25.9% of patients showed the onset of new behavioral symptoms. A worsening in motor function was reported by 36.7% of patients. Finally, caregivers reported a high increase in anxiety, depression, and distress. Conclusion: Our study shows that quarantine for COVID-19 is associated with an acute worsening of clinical symptoms in patients with dementia as well as increase of caregivers' burden. Our findings emphasize the importance to implement new strategies to mitigate the effects of quarantine in patients with dementia. [ABSTRACT FROM AUTHOR]

Abstract Background 3' untranslated regions (3' UTRs) contain binding sites for many regulatory elements, and in particular for microRNAs (miRNAs). The importance of miRNA-mediated post-transcriptional regulation has become increasingly clear in the last few years. Results We propose two complementary approaches to the statistical analysis of oligonucleotide frequencies in mammalian 3' UTRs aimed at the identification of candidate binding sites for regulatory elements. The first method is based on the identification of sets of genes characterized by evolutionarily conserved overrepresentation of an oligonucleotide. The second method is based on the identification of oligonucleotides showing statistically significant strand asymmetry in their distribution in 3' UTRs. Conclusion Both methods are able to identify many previously known binding sites located in 3'UTRs, and in particular seed regions of known miRNAs. Many new candidates are proposed for experimental verification.

Abstract Background Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation. Results We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest. Conclusions CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.

Abstract Background Transcriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance. Results To exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set. Conclusions The method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcritpion factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network.

Abstract Background When generating transformed plants, a first step in their characterization is to obtain, for each new line, an estimate of how many copies of the transgene have been integrated in the plant genome because this can deeply influence the level of transgene expression and the ease of stabilizing expression in following generations. This task is normally achieved by Southern analysis, a procedure that requires relatively large amounts of plant material and is both costly and labour-intensive. Moreover, in the presence of rearranged copies the estimates are not correct. New approaches to the problem could be of great help for plant biotechnologists. Results By using a quantitative real-time PCR method that requires limited preliminary optimisation steps, we achieved statistically significant estimates of 1, 2 and 3 copies of a transgene in the primary transformants. Furthermore, by estimating the copy number of both the gene of interest and the selectable marker gene, we show that rearrangements of the T-DNA are not the exception, and probably happen more often than usually recognised. Conclusions We have developed a rapid and reliable method to estimate the number of integrated copies following genetic transformation. Unlike other similar procedures, this method is not dependent on identical amplification efficiency between the PCR systems used and does not need preliminary information on a calibrator. Its flexibility makes it appropriate in those situations where an accurate optimisation of all reaction components is impossible or impractical. Finally, the quality of the information produced is higher than what can be obtained by Southern blot analysis.

Abstract Background Gene regulation in eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: If this level is significantly higher or lower than the average taken over the whole genome, then the overerpresented motif shared by the genes in the set is likely to play a role in their regulation. Results The method was tested by applying it to the genome of Saccharomyces cerevisiae, using the publicly available results of a DNA microarray experiment, in which expression levels for virtually all the genes were measured during the diauxic shift from fermentation to respiration. Several known motifs were correctly identified, and a new candidate regulatory sequence was determined. Conclusions We have described and successfully tested a simple computational method to identify upstream motifs relevant to gene regulation in eukaryotes by studying the statistical correlation between overepresented upstream motifs and gene expression levels.

Background: In March 2020, the World Health Organization declared a global pandemic due to the novel coronavirus SARS-CoV-2 and several governments planned a national quarantine in order to control the virus spread. Acute psychological effects of quarantine in frail elderly subjects with special needs, such as patients with dementia, have been poorly investigated. The aim of this study was to assess modifications of neuropsychiatric symptoms during quarantine in patients with dementia and their caregivers. Methods: This is a sub-study of a multicenter nation-wide survey. A structured telephone interview was delivered to family caregivers of patients with diagnosis of Alzheimer disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and vascular dementia (VD), followed regularly at 87 Italian memory clinics. Variations in behavioral and psychological symptoms (BPSD) were collected after 1 month since quarantine declaration and associations with disease type, severity, gender, and caregiver's stress burden were analyzed. Results: A total of 4,913 caregivers participated in the survey. Increased BPSD was reported in 59.6% of patients as worsening of preexisting symptoms (51.9%) or as new onset (26%), and requested drug modifications in 27.6% of these cases. Irritability, apathy, agitation, and anxiety were the most frequently reported worsening symptoms and sleep disorder and irritability the most frequent new symptoms. Profile of BPSD varied according to dementia type, disease severity, and patients' gender. Anxiety and depression were associated with a diagnosis of AD (OR 1.35, CI: 1.12–1.62), mild to moderate disease severity and female gender. DLB was significantly associated with a higher risk of worsening hallucinations (OR 5.29, CI 3.66–7.64) and sleep disorder (OR 1.69, CI 1.25–2.29), FTD with wandering (OR 1.62, CI 1.12–2.35), and change of appetite (OR 1.52, CI 1.03–2.25). Stress-related symptoms were experienced by two-thirds of caregivers and were associated with increased patients' neuropsychiatric burden (p<0.0001). Conclusion: Quarantine induces a rapid increase of BPSD in approximately 60% of patients and stress-related symptoms in two-thirds of caregivers. Health services need to plan a post-pandemic strategy in order to address these emerging needs. [ABSTRACT FROM AUTHOR]

The RNA-binding protein, Epithelial Splicing Regulatory Protein 1 (ESRP1) can promote or suppress tumorigenesis depending on the cell type and disease context. In colorectal cancer, we have previously shown that aberrantly high ESRP1 expression can drive tumor progression. In order to unveil the mechanisms by which ESRP1 can modulate cancer traits, we searched for proteins affected by modulation of Esrp1 in two human colorectal cancer cell lines, HCA24 and COLO320DM, by proteomics analysis. Proteins hosted by endogenous ESRP1 ribonucleoprotein complex in HCA24 cells were also analyzed following RNA-immunoprecipitation. Proteomics data were complemented with bioinformatics approach to exploit publicly available data on protein-protein interaction (PPI). Gene Ontology was analysed to identify a common molecular signature possibly explaining the pro-tumorigenic role of ESRP1. Interestingly, proteins identified herein support a role for ESRP1 in response to external stimulus, regulation of cell cycle and hypoxia. Our data provide further insights into factors affected by and entwined with ESRP1 in colorectal cancer. [ABSTRACT FROM AUTHOR]