Long-term culture of patient-derived mammary organoids in non-biogenic electrospun scaffolds for identifying metalloprotein and motor protein activities in aging and senescence.

We recently identified TMEM230 as a master regulator of the endomembrane system of cells. TMEM230 expression is necessary for promoting motor protein dependent intracellular trafficking of metalloproteins for cellular energy production in mitochondria. TMEM230 is also required for transport and secretion of metalloproteinases for autophagy and phagosome dependent clearance of misfolded proteins, defective RNAs and damaged cells, activities that decline with aging. This suggests that aberrant levels of TMEM230 may contribute to aging and regain of proper levels may have therapeutic applications. The components of the endomembrane system include the Golgi complex, other membrane bound organelles, and secreted vesicles and factors. Secreted cellular components modulate immune response and tissue regeneration in aging. Upregulation of intracellular packaging, trafficking and secretion of endosome components while necessary for tissue homeostasis and normal wound healing, also promote secretion of pro-inflammatory and pro-senescence factors. We recently determined that TMEM230 is co-regulated with trafficked cargo of the endomembrane system, including lysosome factors such as RNASET2. Normal tissue regeneration (in aging), repair (following injury) and aberrant destructive tissue remodeling (in cancer or autoimmunity) likely are regulated by TMEM230 activities of the endomembrane system, mitochondria and autophagosomes. The role of TMEM230 in aging is supported by its ability to regulate the pro-inflammatory secretome and senescence-associated secretory phenotype in tissue cells of patients with advanced age and chronic disease. Identifying secreted factors regulated by TMEM230 in young patients and patients of advanced age will facilitate identification of aging associated targets that aberrantly promote, inhibit or reverse aging. Ex situ culture of patient derived cells for identifying secreted factors in tissue regeneration and aging provides opportunities in developing therapeutic and personalized medicine strategies. Identification and validation of human secreted factors in tissue regeneration requires long-term stabile scaffold culture conditions that are different from those previously reported for cell lines used as cell models for aging. We describe a 3 dimensional (3D) platform utilizing non-biogenic and non-labile poly ε-caprolactone scaffolds that supports maintenance of long-term continuous cultures of human stem cells, in vitro generated 3D organoids and patient derived tissue. Combined with animal component free culture media, non-biogenic scaffolds are suitable for proteomic and glycobiological analyses to identify human factors in aging. Applications of electrospun nanofiber technologies in 3D cell culture allow for ex situ screening and the development of patient personalized therapeutic strategies and predicting their effectiveness in mitigating or promoting aging.

Transmembrane protein TMEM230, regulator of metalloproteins and motor proteins in gliomas and gliosis.

Glial cells provide physical and chemical support and protection for neurons and for the extracellular compartments of neural tissue through secretion of soluble factors, insoluble scaffolds, and vesicles. Additionally, glial cells have regenerative capacity by remodeling their physical microenvironment and changing physiological properties of diverse cell types in their proximity. Various types of aberrant glial and macrophage cells are associated with human diseases, disorders, and malignancy. We previously demonstrated that transmembrane protein, TMEM230 has tissue revascularization and regenerating capacity by its ability to secrete pro-angiogenic factors and metalloproteinases, inducing endothelial cell sprouting and channel formation. In healthy normal neural tissue, TMEM230 is predominantly expressed in glial and marcophate cells, suggesting a prominent role in neural tissue homeostasis. TMEM230 regulation of the endomembrane system was supported by co-expression with RNASET2 (lysosome, mitochondria, and vesicles) and STEAP family members (Golgi complex). Intracellular trafficking and extracellular secretion of glial cellular components are associated with endocytosis, exocytosis and phagocytosis mediated by motor proteins. Trafficked components include metalloproteins, metalloproteinases, glycans, and glycoconjugate processing and digesting enzymes that function in phagosomes and vesicles to regulate normal neural tissue microenvironment, homeostasis, stress response, and repair following neural tissue injury or degeneration. Aberrantly high sustained levels TMEM230 promotes metalloprotein expression, trafficking and secretion which contribute to tumor associated infiltration and hypervascularization of high tumor grade gliomas. Following injury of the central nervous or peripheral systems, transcient regulated upregulation of TMEM230 promotes tissue wound healing, remodeling and revascularization by activating glial and macrophage generated microchannels/microtubules (referred to as vascular mimicry) and blood vessel sprouting and branching. Our results support that TMEM230 may act as a master regulator of motor protein mediated trafficking and compartmentalization of a large class of metalloproteins in gliomas and gliosis.

Genetic Contribution to Medium-Term Disease Activity in Multiple Sclerosis.

Multiple sclerosis (MS) is a complex disorder characterized by high heterogeneity in terms of phenotypic expression, prognosis and treatment response. In the present study, we aimed to explore the genetic contribution to MS disease activity at different levels: genes, pathways and tissue-specific networks. Two cohorts of relapsing-remitting MS patients who started a first-line treatment (n = 1294) were enrolled to evaluate the genetic association with disease activity after 4 years of follow-up. The analyses were performed at whole-genome SNP and gene level, followed by the construction of gene-gene interaction networks specific for brain and lymphocytes. The resulting gene modules were evaluated to highlight key players from a topological and functional perspective. We identified 23 variants and 223 genes with suggestive association to 4-years disease activity, highlighting genes like PON2 involved in oxidative stress and in mitochondria functions and other genes, like ILRUN, involved in the modulation of the immune system. Network analyses led to the identification of a brain module composed of 228 genes and a lymphocytes module composed of 287 genes. The network analysis allowed us to prioritize genes relevant for their topological properties; among them, there are MPHOSPH9 (connector hub in both brain and lymphocyte module) and OPA1 (in brain module), two genes already implicated in MS. Modules showed the enrichment of both shared and tissue-specific pathways, mainly implicated in inflammation. In conclusion, our results suggest that the processes underlying disease activity act on shared mechanisms across brain and lymphocyte tissues.

scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.

INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

Cyclic AMP induces reversible EPAC1 condensates that regulate histone transcription.

The second messenger cyclic AMP regulates many nuclear processes including transcription, pre-mRNA splicing and mitosis. While most functions are attributed to protein kinase A, accumulating evidence suggests that not all nuclear cyclic AMP-dependent effects are mediated by this kinase, implying that other effectors may be involved. Here we explore the nuclear roles of Exchange Protein Activated by cyclic AMP 1. We find that it enters the nucleus where forms reversible biomolecular condensates in response to cyclic AMP. This phenomenon depends on intrinsically disordered regions present at its amino-terminus and is independent of protein kinase A. Finally, we demonstrate that nuclear Exchange Protein Activated by cyclic AMP 1 condensates assemble at genomic loci on chromosome 6 in the proximity of Histone Locus Bodies and promote the transcription of a histone gene cluster. Collectively, our data reveal an unexpected mechanism through which cyclic AMP contributes to nuclear spatial compartmentalization and promotes the transcription of specific genes.

Framing Apache Spark in life sciences.

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

A Boron Delivery Antibody (BDA) with Boronated Specific Residues: New Perspectives in Boron Neutron Capture Therapy from an In Silico Investigation.

Boron Neutron Capture Therapy (BNCT) is a tumor cell-selective radiotherapy based on a nuclear reaction that occurs when the isotope boron-10 (10B) is radiated by low-energy thermal neutrons or epithermal neutrons, triggering a nuclear fission response and enabling a selective administration of irradiation to cells. Hence, we need to create novel delivery agents containing 10B with high tumor selectivity, but also exhibiting low intrinsic toxicity, fast clearance from normal tissue and blood, and no pharmaceutical effects. In the past, boronated monoclonal antibodies have been proposed using large boron-containing molecules or dendrimers, but with no investigations in relation to maintaining antibody specificity and structural and functional features. This work aims at improving the potential of monoclonal antibodies applied to BNCT therapy, identifying in silico the best native residues suitable to be substituted with a boronated one, carefully evaluating the effect of boronation on the 3D structure of the monoclonal antibody and on its binding affinity. A boronated monoclonal antibody was thus generated for specific 10B delivery. In this context, we have developed a case study of Boron Delivery Antibody Identification Pipeline, which has been tested on cetuximab. Cetuximab is an epidermal growth factor receptor (EGFR) inhibitor used in the treatment of metastatic colorectal cancer, metastatic non-small cell lung cancer, and head and neck cancer.

Characterization and comparison of gene-centered human interactomes.

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.

Genetic Determinants in a Critical Domain of NS5A Correlate with Hepatocellular Carcinoma in Cirrhotic Patients Infected with HCV Genotype 1b.

HCV is an important cause of hepatocellular carcinoma (HCC). HCV NS5A domain-1 interacts with cellular proteins inducing pro-oncogenic pathways. Thus, we explore genetic variations in NS5A domain-1 and their association with HCC, by analyzing 188 NS5A sequences from HCV genotype-1b infected DAA-naïve cirrhotic patients: 34 with HCC and 154 without HCC. Specific NS5A mutations significantly correlate with HCC: S3T (8.8% vs. 1.3%, p = 0.01), T122M (8.8% vs. 0.0%, p < 0.001), M133I (20.6% vs. 3.9%, p < 0.001), and Q181E (11.8% vs. 0.6%, p < 0.001). By multivariable analysis, the presence of >1 of them independently correlates with HCC (OR (95%CI): 21.8 (5.7-82.3); p < 0.001). Focusing on HCC-group, the presence of these mutations correlates with higher viremia (median (IQR): 5.7 (5.4-6.2) log IU/mL vs. 5.3 (4.4-5.6) log IU/mL, p = 0.02) and lower ALT (35 (30-71) vs. 83 (48-108) U/L, p = 0.004), suggesting a role in enhancing viral fitness without affecting necroinflammation. Notably, these mutations reside in NS5A regions known to interact with cellular proteins crucial for cell-cycle regulation (p53, p85-PIK3, and ß-catenin), and introduce additional phosphorylation sites, a phenomenon known to ameliorate NS5A interaction with cellular proteins. Overall, these results provide a focus for further investigations on molecular bases of HCV-mediated oncogenesis. The role of theseNS5A domain-1 mutations in triggering pro-oncogenic stimuli that can persist also despite achievement of sustained virological response deserves further investigation.

Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes.

PURPOSE: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS: We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations (SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia-like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.

Genome, Environment, Microbiome and Metabolome in Autism (GEMMA) Study Design: Biomarkers Identification for Precision Treatment and Primary Prevention of Autism Spectrum Disorders by an Integrated Multi-Omics Systems Biology Approach.

Autism Spectrum Disorder (ASD) affects approximately 1 child in 54, with a 35-fold increase since 1960. Selected studies suggest that part of the recent increase in prevalence is likely attributable to an improved awareness and recognition, and changes in clinical practice or service availability. However, this is not sufficient to explain this epidemiological phenomenon. Research points to a possible link between ASD and intestinal microbiota because many children with ASD display gastro-intestinal problems. Current large-scale datasets of ASD are limited in their ability to provide mechanistic insight into ASD because they are predominantly cross-sectional studies that do not allow evaluation of perspective associations between early life microbiota composition/function and later ASD diagnoses. Here we describe GEMMA (Genome, Environment, Microbiome and Metabolome in Autism), a prospective study supported by the European Commission, that follows at-risk infants from birth to identify potential biomarker predictors of ASD development followed by validation on large multi-omics datasets. The project includes clinical (observational and interventional trials) and pre-clinical studies in humanized murine models (fecal transfer from ASD probands) and in vitro colon models. This will support the progress of a microbiome-wide association study (of human participants) to identify prognostic microbiome signatures and metabolic pathways underlying mechanisms for ASD progression and severity and potential treatment response.

Transcriptomic Analysis of Rhodococcus opacus R7 Grown on o-Xylene by RNA-Seq.

Xylenes are considered one of the most common hazardous sources of environmental contamination. The biodegradation of these compounds has been often reported, rarer the ability to oxidize the ortho-isomer. Among few o-xylene-degrading bacteria, Rhodococcus opacus R7 is well known for its capability to degrade diverse aromatic hydrocarbons and toxic compounds, including o-xylene as only carbon and energy source. This work shows for the first time the RNA-seq approach to elucidate the genetic determinants involved in the o-xylene degradation pathway in R. opacus R7. Transcriptomic data showed 542 differentially expressed genes that are associated with the oxidation of aromatic hydrocarbons and stress response, osmotic regulation and central metabolism. Gene ontology (GO) enrichment and KEGG pathway analysis confirmed significant changes in aromatic compound catabolic processes, fatty acid metabolism, beta-oxidation, TCA cycle enzymes, and biosynthesis of metabolites when cells are cultured in the presence of o-xylene. Interestingly, the most up-regulated genes belong to the akb gene cluster encoding for the ethylbenzene (Akb) dioxygenase system. Moreover, the transcriptomic approach allowed identifying candidate enzymes involved in R7 o-xylene degradation for their likely participation in the formation of the metabolites that have been previously identified. Overall, this approach supports the identification of several oxidative systems likely involved in o-xylene metabolism confirming that R. opacus R7 possesses a redundancy of sequences that converge in o-xylene degradation through R7 peculiar degradation pathway. This work advances our understanding of o-xylene metabolism in bacteria belonging to Rhodococcus genus and provides a framework of useful enzymes (molecular tools) that can be fruitfully targeted for optimized o-xylene consumption.

Profiling the Course of Resolving vs. Persistent Inflammation in Human Monocytes: The Role of IL-1 Family Molecules.

Monocytes and macrophages have a central role in all phases of an inflammatory reaction. To understanding the regulation of monocyte activation during a physiological or pathological inflammation, we propose two in vitro models that recapitulate the different phases of the reaction (recruitment, initiation, development, and resolution vs. persistence of inflammation), based on human primary blood monocytes exposed to sequential modifications of microenvironmental conditions. These models exclusively describe the functional development of blood-derived monocytes that first enter an inflammatory site. All reaction phases were profiled by RNA-Seq, and the two models were validated by studying the modulation of IL-1 family members. Genes were differentially modulated, and distinct clusters were identified during the various phases of inflammation. Pathway analysis revealed that both models were enriched in pathways involved in innate immune activation. We observe that monocytes acquire an M1-like profile during early inflammation, and switch to a deactivated M2-like profile during both the resolving and persistent phases. However, during persistent inflammation they partially maintain an M1 profile, although they lose the ability to produce inflammatory cytokines compared to M1 cells. The production of IL-1 family molecules by ELISA reflected the transcriptomic profiles in the distinct phases of the two inflammatory reactions. Based on the results, we hypothesize that persistence of inflammatory stimuli cannot maintain the M1 activated phenotype of incoming monocytes for long, suggesting that the persistent presence of M1 cells and effects in a chronically inflamed tissue is mainly due to activation of newly incoming cells. Moreover, being IL-1 family molecules mainly expressed and secreted by monocytes during the early stages of the inflammatory response (within 4-14 h), and the rate of their production decreasing during the late phase of both resolving and persistent inflammation, we suppose that IL-1 factors are key regulators of the acute defensive innate inflammatory reaction that precedes establishment of longer-term adaptive immunity, and are mainly related to the presence of recently recruited blood monocytes. The well-described role of IL-1 family cytokines and receptors in chronic inflammation is therefore most likely dependent on the continuous influx of blood monocytes into a chronically inflamed site.

Assessment of haptoglobin alleles in autism spectrum disorders.

Gene-environment interactions, by means of abnormal macromolecular intestinal adsorption, is one of the possible causes of autism spectrum disorders (ASD) predominantly in patients with gastrointestinal disorders. Pre-haptoglobin-2 (zonulin), encoded by the Haptoglobin (HP) allele-2 gene, enhances the intestinal permeability by modulation of intercellular tight junctions. The two alleles of HP, HP1 and HP2, differ for 2 extra exons in HP2 that result in exon duplication undetectable by classic genome-wide association studies. To evaluate the role of HP2 in ASD pathogenesis and to set up a method to discriminate HP alleles, Italian subjects with ASD (n = 398) and healthy controls (n = 379) were genotyped by PCR analysis; subsequently, the PCR results were integrated with microarray genotypes (Illumina Human Omni 1S-8), obtained using a subset from the same subjects, and then we developed a computational method to predict HP alleles. On the contrary to our expectations, there was no association between HP2 and ASD (P > 0.05), and there was no significant allele association in subjects with ASD with or without gastrointestinal disorders (P > 0.05). With the aid of bioinformatics analysis, from a window frame of ~2 Mb containing 314 SNPs, we obtain imputation accuracy (r2) between 0.4 and 0.9 (median 0.7) and correct predictions were between 70% and 100% (median 90%). The conclusions endorse that enhanced intestinal permeability in subjects with ASD should not be imputed to HP2 but to other members of the zonulin family and/or to environmental factors.

Network Diffusion Promotes the Integrative Analysis of Multiple Omics.

The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion-also referred to as network propagation-has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.

Age-Related Variation of Bacterial and Fungal Communities in Different Body Habitats across the Young, Elderly, and Centenarians in Sardinia.

Human body microbes interact with the host, forming microbial communities that are in continual flux during the aging process. Previous studies have mostly focused on surveying a single body habitat to determine the age-related variation in the bacterial and fungal communities. A more comprehensive understanding of the variation in the human microbiota and mycobiota across multiple body habitats related to aging is still unclear. To obtain an integrated view of the spatial distribution of microbes in a specific Mediterranean population across a wide age range, we surveyed the bacterial and fungal communities in the skin, oral cavity, and gut in the young, elderly, and centenarians in Sardinia using 16S rRNA gene and internal transcribed spacer 1 (ITS1) sequencing. We found that the distribution and correlation of bacterial and fungal communities in Sardinians were largely determined by body site. In each age group, the bacterial and fungal communities found in the skin were significantly different in structure. In the oral cavity, age had a marginal impact on the structures of the bacterial and fungal communities. Furthermore, the gut bacterial communities in centenarians clustered separately from those of the young and elderly, while the fungal communities in the gut habitat could not be separated by host age.IMPORTANCE Site-specific microbial communities are recognized as important factors in host health and disease. To better understand how the human microbiota potentially affects and is affected by its host during the aging process, the fundamental issue to address is the distribution of microbiota related to age. Here, we show an integrated view of the spatial distribution of microbes in a specific Mediterranean population (Sardinians) across a wide age range. Our study indicates that age plays a critical role in shaping the human microbiota in a habitat-dependent manner. The dynamic age-related microbiota changes we observed across multiple body sites may provide possibilities for modulating microbe communities to maintain or improve health during aging.

Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach.

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.