Multi-netclust: an efficient tool for finding connected clusters in multi-parametric networks

Summary: Multi-netclust is a simple tool that allows users to extract connected clusters of data represented by different networks given in the form of matrices. The tool uses user-defined threshold values to combine the matrices, and uses a straightforward, memory-efficient graph algorithm to find clusters that are connected in all or in either of the networks. The tool is written in C/C++ and is available either as a form-based or as a command-line-based program running on Linux platforms. The algorithm is fast, processing a network of > 106 nodes and 108 edges takes only a few minutes on an ordinary computer

Survival and longevity of European rulers: geographical influences and exploring potential factors, including the Mediterranean diet - a historical analysis from 1354 to the twentieth century

Significant regional variability in lifespan in Europe is influenced by environmental factors and lifestyle behaviors, including diet. This study investigates the impact of geographical region on the lifespan of European rulers spanning from the fourteenth century to the present day. By analyzing historical records and literature, we aim to identify region-specific dietary patterns and lifestyle factors that may have contributed to longer lifespans among rulers. The hypothesis to be tested is that rulers from Southern European countries, where the traditional Mediterranean diet is consumed by the local people, may exhibit longer lifespans compared to rulers from other regions, due to the well-documented health benefits associated with this dietary pattern. We extracted comprehensive information for each ruler, encompassing their sex, birth and death dates, age, age of enthronement, duration of rulership, country, and cause of death (natural vs. non-natural). To determine their nationality, we coded rulers based on their hypothetical present-day residence (2023). Utilizing the EuroVoc Geographical classification, we categorized the countries into four regions: Northern, Western, Southern, Central and Eastern Europe. While Cox regression models did not find significant differences in survival rates among regions, further analysis stratified by time periods revealed intriguing trends. Contrary to our initial predictions, the Northern region displayed better survival rates compared to the Southern region between 1354 and 1499, whereas survival rates were similar across regions from 1500 to 1749. However, after 1750, all regions, except the Southern region, exhibited significantly improved survival rates, suggesting advancements in healthcare and lifestyle factors. These findings underscore the dynamic influence of both region and time period on health and longevity. Interestingly, despite the prevalence of the Mediterranean diet in the Southern region of Europe, rulers from this region did not demonstrate longer lifespans compared to their counterparts in other regions. This suggests that additional lifestyle factors may have played a more prominent role in their longevity. In conclusion, our study sheds light on the intricate relationship between region, time period, and lifespan among European rulers. Although the Mediterranean diet is often associated with health benefits, our findings indicate that it alone may not account for differences in ruler longevity across regions. Further research is warranted to explore the impact of other lifestyle factors on the health and lifespan of European rulers throughout history

Exposome and unhealthy aging: environmental drivers from air pollution to occupational exposures

The aging population worldwide is facing a significant increase in age-related non-communicable diseases, including cardiovascular and brain pathologies. This comprehensive review paper delves into the impact of the exposome, which encompasses the totality of environmental exposures, on unhealthy aging. It explores how environmental factors contribute to the acceleration of aging processes, increase biological age, and facilitate the development and progression of a wide range of age-associated diseases. The impact of environmental factors on cognitive health and the development of chronic age-related diseases affecting the cardiovascular system and central nervous system is discussed, with a specific focus on Alzheimer’s disease, Parkinson’s disease, stroke, small vessel disease, and vascular cognitive impairment (VCI). Aging is a major risk factor for these diseases. Their pathogenesis involves cellular and molecular mechanisms of aging such as increased oxidative stress, impaired mitochondrial function, DNA damage, and inflammation and is influenced by environmental factors. Environmental toxicants, including ambient particulate matter, pesticides, heavy metals, and organic solvents, have been identified as significant contributors to cardiovascular and brain aging disorders. These toxicants can inflict both macro- and microvascular damage and many of them can also cross the blood–brain barrier, inducing neurotoxic effects, neuroinflammation, and neuronal dysfunction. In conclusion, environmental factors play a critical role in modulating cardiovascular and brain aging. A deeper understanding of how environmental toxicants exacerbate aging processes and contribute to the pathogenesis of neurodegenerative diseases, VCI, and dementia is crucial for the development of preventive strategies and interventions to promote cardiovascular, cerebrovascular, and brain health. By mitigating exposure to harmful environmental factors and promoting healthy aging, we can strive to reduce the burden of age-related cardiovascular and brain pathologies in the aging population

Applied models and molecular characteristics of small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care

Co-Swarming and Local Collapse: Quorum Sensing Conveys Resilience to Bacterial Communities by Localizing Cheater Mutants in Pseudomonas aeruginosa

Background: Members of swarming bacterial consortia compete for nutrients but also use a co-operation mechanism called quorum sensing (QS) that relies on chemical signals as well as other secreted products (‘‘public goods’’) necessary for swarming. Deleting various genes of this machinery leads to cheater mutants impaired in various aspects of swarming cooperation. Methodology/Principal Findings: Pairwise consortia made of Pseudomonas aeruginosa, its QS mutants as well as B. cepacia cells show that a interspecies consortium can ‘‘combine the skills’ ’ of its participants so that the strains can cross together barriers that they could not cross alone. In contrast, deleterious mutants are excluded from consortia either by competition or by local population collapse. According to modeling, both scenarios are the consequence of the QS signalling mechanism itself. Conclusion/Significance: The results indirectly explain why it is an advantage for bacteria to maintain QS systems that can cross-talk among different species, and conversely, why certain QS mutants which can be abundant in isolated niches

Emergence of Collective Territorial Defense in Bacterial Communities: Horizontal Gene Transfer Can Stabilize Microbiomes

Multispecies bacterial communities such as the microbiota of the gastrointestinal tract can be remarkably stable and resilient even though they consist of cells and species that compete for resources and also produce a large number of antimicrobial agents. Computational modeling suggests that horizontal transfer of resistance genes may greatly contribute to the formation of stable and diverse communities capable of protecting themselves with a battery of antimicrobial agents while preserving a varied metabolic repertoire of the constituent species. In other words horizontal transfer of resistance genes makes a community compatible in terms of exoproducts and capable to maintain a varied and mature metagenome. The same property may allow microbiota to protect a host organism, or if used as a microbial therapy, to purge pathogens and restore a protective environment

A network-based target overlap score for characterizing drug combinations: High correlation with cancer clinical trial results

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane-based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer. © 2015 Ligeti et al

Predictors of Chemosensitivity in Triple Negative Breast Cancer: An Integrated Genomic Analysis

Background: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive disease, and although no effective targeted therapies are available to date, about one-third of patients with TNBC achieve pathologic complete response (pCR) from standard-of-care anthracycline/taxane (ACT) chemotherapy. The heterogeneity of these tumors, however, has hindered the discovery of effective biomarkers to identify such patients. Methods and Findings: We performed whole exome sequencing on 29 TNBC cases from the MD Anderson Cancer Center (MDACC) selected because they had either pCR (n = 18) or extensive residual disease (n = 11) after neoadjuvant chemotherapy, with cases from The Cancer Genome Atlas (TCGA; n = 144) and METABRIC (n = 278) cohorts serving as validation cohorts. Our analysis revealed that mutations in the AR- and FOXA1-regulated networks, in which BRCA1 plays a key role, are associated with significantly higher sensitivity to ACT chemotherapy in the MDACC cohort (pCR rate of 94.1% compared to 16.6% in tumors without mutations in AR/FOXA1 pathway, adjusted p = 0.02) and significantly better survival outcome in the TCGA TNBC cohort (log-rank test, p = 0.05). Combined analysis of DNA sequencing, DNA methylation, and RNA sequencing identified tumors of a distinct BRCA-deficient (BRCA-D) TNBC subtype characterized by low levels of wild-type BRCA1/2 expression. Patients with functionally BRCA-D tumors had significantly better survival with standard-of-care chemotherapy than patients whose tumors were not BRCA-D (log-rank test, p = 0.021), and they had significantly higher mutation burden (p < 0.001) and presented clonal neoantigens that were associated with increased immune cell activity. A transcriptional signature of BRCA-D TNBC tumors was independently validated to be significantly associated with improved survival in the METABRIC dataset (log-rank test, p = 0.009). As a retrospective study, limitations include the small size and potential selection bias in the discovery cohort. Conclusions: The comprehensive molecular analysis presented in this study directly links BRCA deficiency with increased clonal mutation burden and significantly enhanced chemosensitivity in TNBC and suggests that functional RNA-based BRCA deficiency needs to be further examined in TNBC. © 2016 Jiang et al

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Genome-Wide Analysis Unveils DNA Helicase RECQ1 as a Regulator of Estrogen Response Pathway in Breast Cancer Cells

Susceptibility to breast cancer is significantly increased in individuals with germ line mutations in RECQ1 (also known as RECQL or RECQL1), a gene encoding a DNA helicase essential for genome maintenance. We previously reported that RECQ1 expression predicts clinical outcomes for sporadic breast cancer patients stratified by estrogen receptor (ER) status. Here, we utilized an unbiased integrative genomics approach to delineate a cross talk between RECQ1 and ERα, a known master regulatory transcription factor in breast cancer. We found that expression of ESR1, the gene encoding ERα, is directly activated by RECQ1. More than 35% of RECQ1 binding sites were cobound by ERα genome-wide. Mechanistically, RECQ1 cooperates with FOXA1, the pioneer transcription factor for ERα, to enhance chromatin accessibility at the ESR1 regulatory regions in a helicase activity-dependent manner. In clinical ERα-positive breast cancers treated with endocrine therapy, high RECQ1 and high FOXA1 coexpressing tumors were associated with better survival. Collectively, these results identify RECQ1 as a novel cofactor for ERα and uncover a previously unknown mechanism by which RECQ1 regulates disease-driving gene expression in ER-positive breast cancer cells