Single-cell-led drug repurposing for Alzheimer's disease

: Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy

Integration of transcriptomic and genomic data suggests candidate mechanisms for APOE4-mediated pathogenic action in Alzheimer’s disease

Among the genetic factors known to increase the risk of late onset Alzheimer's diseases (AD), the presence of the apolipoproteine e4 (APOE4) allele has been recognized as the one with the strongest effect. However, despite decades of research, the pathogenic role of APOE4 in Alzheimer's disease has not been clearly elucidated yet. In order to investigate the pathogenic action of APOE4, we applied a systems biology approach to the analysis of transcriptomic and genomic data of APOE44 vs. APOE33 allele carriers affected by Alzheimer's disease. Network analysis combined with a novel technique for biomarker computation allowed the identification of an alteration in aging-associated processes such as inflammation, oxidative stress and metabolic pathways, indicating that APOE4 possibly accelerates pathological processes physiologically induced by aging. Subsequent integration with genomic data indicates that the Notch pathway could be the nodal molecular mechanism altered in APOE44 allele carriers with Alzheimer's disease. Interestingly, PSEN1 and APP, genes whose mutation are known to be linked to early onset Alzheimer's disease, are closely linked to this pathway. In conclusion, APOE4 role on inflammation and oxidation through the Notch signaling pathway could be crucial in elucidating the risk factors of Alzheimer's disease

A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space

Weaning is a period of marked physiological change. The introduction of solid foods and the changes in milk consumption are accompanied by significant gastrointestinal, immune, developmental, and microbial adaptations. Defining a reduced number of infections as the desired health benefit for infants around weaning, we identified in silico (i.e., by advanced public domain mining) infant gut microbes as potential deliverers of this benefit. We then investigated the requirements of these bacteria for exogenous metabolites as potential prebiotic feeds that were subsequently searched for in the natural product space

Lysosomal cystine export regulates mTORC1 signaling to guide kidney epithelial cell fate specialization

Differentiation is critical for cell fate decisions, but the signals involved remain unclear. The kidney proximal tubule (PT) cells reabsorb disulphide-rich proteins through endocytosis, generating cystine via lysosomal proteolysis. Here we report that defective cystine mobilization from lysosomes through cystinosin (CTNS), which is mutated in cystinosis, diverts PT cells towards growth and proliferation, disrupting their functions. Mechanistically, cystine storage stimulates Ragulator-Rag GTPase-dependent recruitment of mechanistic target of rapamycin complex 1 (mTORC1) and its constitutive activation. Re-introduction of CTNS restores nutrient-dependent regulation of mTORC1 in knockout cells, whereas cell-permeant analogues of L-cystine, accumulating within lysosomes, render wild-type cells resistant to nutrient withdrawal. Therapeutic mTORC1 inhibition corrects lysosome and differentiation downstream of cystine storage, and phenotypes in preclinical models of cystinosis. Thus, cystine serves as a lysosomal signal that tailors mTORC1 and metabolism to direct epithelial cell fate decisions. These results identify mechanisms and therapeutic targets for dysregulated homeostasis in cystinosis

Ancient Migratory Events in the Middle East: New Clues from the Y-Chromosome Variation of Modern Iranians

Knowledge of high resolution Y-chromosome haplogroup diversification within Iran provides important geographic context regarding the spread and compartmentalization of male lineages in the Middle East and southwestern Asia. At present, the Iranian population is characterized by an extraordinary mix of different ethnic groups speaking a variety of Indo-Iranian, Semitic and Turkic languages. Despite these features, only few studies have investigated the multiethnic components of the Iranian gene pool. In this survey 938 Iranian male DNAs belonging to 15 ethnic groups from 14 Iranian provinces were analyzed for 84 Y-chromosome biallelic markers and 10 STRs. The results show an autochthonous but non-homogeneous ancient background mainly composed by J2a sub-clades with different external contributions. The phylogeography of the main haplogroups allowed identifying post-glacial and Neolithic expansions toward western Eurasia but also recent movements towards the Iranian region from western Eurasia (R1b-L23), Central Asia (Q-M25), Asia Minor (J2a-M92) and southern Mesopotamia (J1-Page08). In spite of the presence of important geographic barriers (Zagros and Alborz mountain ranges, and the Dasht-e Kavir and Dash-e Lut deserts) which may have limited gene flow, AMOVA analysis revealed that language, in addition to geography, has played an important role in shaping the nowadays Iranian gene pool. Overall, this study provides a portrait of the Y-chromosomal variation in Iran, useful for depicting a more comprehensive history of the peoples of this area as well as for reconstructing ancient migration routes. In addition, our results evidence the important role of the Iranian plateau as source and recipient of gene flow between culturally and genetically distinct population

A QSP model of prostate cancer immunotherapy to identify effective combination therapies

Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models

Ancestors’ dietary patterns and environments could drive positive selection in genes involved in micronutrient metabolism—the case of cofactor transporters

BACKGROUND: During evolution, humans colonized different ecological niches and adopted a variety of subsistence strategies that gave rise to diverse selective pressures acting across the genome. Environmentally induced selection of vitamin, mineral, or other cofactor transporters could influence micronutrient-requiring molecular reactions and contribute to inter-individual variability in response to foods and nutritional interventions. METHODS: A comprehensive list of genes coding for transporters of cofactors or their precursors was built using data mining procedures from the HGDP dataset and then explored to detect evidence of positive genetic selection. This dataset was chosen since it comprises several genetically diverse worldwide populations whom ancestries have evolved in different environments and thus lived following various nutritional habits and lifestyles. RESULTS: We identified 312 cofactor transporter (CT) genes involved in between-cell or sub-cellular compartment distribution of 28 cofactors derived from dietary intake. Twenty-four SNPs distributed across 14 CT genes separated populations into continental and intra-continental groups such as African hunter-gatherers and farmers, and between Native American sub-populations. Notably, four SNPs were located in SLC24A3 with one being a known eQTL of the NCKX3 protein. CONCLUSIONS: These findings could support the importance of considering individual’s genetic makeup along with their metabolic profile when tailoring personalized dietary interventions for optimizing health

An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets

In the past few years, the role of long noncoding RNAs (lncRNAs) in tumor development and progression has been disclosed although their mechanisms of action remain to be elucidated. An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets. However, the growing availability of public datasets requires new data mining techniques to integrate and describe relationship among data. In this perspective, we explored the powerness of the Association Rule Mining (ARM) approach in gene expression data analysis. By the ARM method, we performed a meta-analysis of cancer-related microarray data which allowed us to identify and characterize a set of ten lncRNAs simultaneously altered in different brain tumor datasets. The expression profiles of the ten lncRNAs appeared to be sufficient to distinguish between cancer and normal tissues. A further characterization of this lncRNAs signature through a comodulation expression analysis suggested that biological processes specific of the nervous system could be compromised

Additional file 11: Table S6. of Ancestors’ dietary patterns and environments could drive positive selection in genes involved in micronutrient metabolism—the case of cofactor transporters

Regulome DB annotation. (XLSX 10 kb