SenNet recommendations for detecting senescent cells in different tissues.

Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.

Proteinarium: Multi-sample protein-protein interaction analysis and visualization tool.

We posit the likely architecture of complex diseases is that subgroups of patients share variants in genes in specific networks which are sufficient to give rise to a shared phenotype. We developed Proteinarium, a multi-sample protein-protein interaction (PPI) tool, to identify clusters of patients with shared gene networks. Proteinarium converts user defined seed genes to protein symbols and maps them onto the STRING interactome. A PPI network is built for each sample using Dijkstra's algorithm. Pairwise similarity scores are calculated to compare the networks and cluster the samples. A layered graph of PPI networks for the samples in any cluster can be visualized. To test this newly developed analysis pipeline, we reanalyzed publicly available data sets, from which modest outcomes had previously been achieved. We found significant clusters of patients with unique genes which enhanced the findings in the original study.

Exploiting sweet relief for preeclampsia by targeting autophagy-lysosomal machinery and proteinopathy.

The etiology of preeclampsia (PE), a severe complication of pregnancy with several clinical manifestations and a high incidence of maternal and fetal morbidity and mortality, remains unclear. This issue is a major hurdle for effective treatment strategies. We recently demonstrated that PE exhibits an Alzheimer-like etiology of impaired autophagy and proteinopathy in the placenta. Targeting of these pathological pathways may be a novel therapeutic strategy for PE. Stimulation of autophagy with the natural disaccharide trehalose and its lacto analog lactotrehalose in hypoxia-exposed primary human trophoblasts restored autophagy, inhibited the accumulation of toxic protein aggregates, and restored the ultrastructural features of autophagosomes and autolysosomes. Importantly, trehalose and lactotrehalose inhibited the onset of PE-like features in a humanized mouse model by normalizing autophagy and inhibiting protein aggregation in the placenta. These disaccharides restored the autophagy-lysosomal biogenesis machinery by increasing nuclear translocation of the master transcriptional regulator TFEB. RNA-seq analysis of the placentas of mice with PE indicated the normalization of the PE-associated transcriptome profile in response to trehalose and lactotrehalose. In summary, our results provide a novel molecular rationale for impaired autophagy and proteinopathy in patients with PE and identify treatment with trehalose and its lacto analog as promising therapeutic options for this severe pregnancy complication.

Dissection and Immunohistochemistry of the Drosophila Adult Leg to Detect Changes at the Neuromuscular Junction for an Identified Motor Neuron.

Drosophila melanogaster represents a genetically tractable model to study neuronal structure and function, and subsequent changes in disease states. The well characterized larval neuromuscular junction is often used for such studies. However, rapid larval development followed by muscle histolysis and nervous system remodeling during metamorphosis makes this model problematic for the study of slow age-dependent degenerative changes like those occurring in amyotrophic lateral sclerosis. Alternatively, adult flies live for 90 days and the adult leg can be used to study motor neuron changes over the course of adult lifespan using in vivo fluorescent imaging through the cuticle. Here, we describe a leg dissection technique coupled with immunocytochemistry, which allows for the study of molecular changes at the neuromuscular junction of identified adult leg motor neurons. These techniques can be coupled with a myriad of antibodies labeling both pre- and post-synaptic structures. Together these procedures allow a more complete characterization of slow age-dependent changes in adult flies and can be applied across multiple motor neuron disease models.

Protein interaction networks define the genetic architecture of preterm birth.

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein-protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia.

Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.

Age-dependent degeneration of an identified adult leg motor neuron in a Drosophila SOD1 model of ALS.

Mutations in superoxide dismutase 1 (SOD1) cause familial amyotrophic lateral sclerosis (ALS) in humans. ALS is a neurodegenerative disease characterized by progressive motor neuron loss leading to paralysis and inevitable death in affected individuals. Using a gene replacement strategy to introduce disease mutations into the orthologous Drosophila sod1 (dsod1) gene, here, we characterize changes at the neuromuscular junction using longer-lived dsod1 mutant adults. Homozygous dsod1H71Y/H71Y or dsod1null/null flies display progressive walking defects with paralysis of the third metathoracic leg. In dissected legs, we assessed age-dependent changes in a single identified motor neuron (MN-I2) innervating the tibia levitator muscle. At adult eclosion, MN-I2 of dsod1H71Y/H71Y or sod1null/null flies is patterned similar to wild-type flies indicating no readily apparent developmental defects. Over the course of 10 days post-eclosion, MN-I2 shows an overall reduction in arborization with bouton swelling and loss of the post-synaptic marker discs-large (dlg) in mutant dsod1 adults. In addition, increases in polyubiquitinated proteins correlate with the timing and extent of MN-I2 changes. Because similar phenotypes are observed between flies homozygous for either dsod1H71Y or dsod1null alleles, we conclude these NMJ changes are mainly associated with sod loss-of-function. Together these studies characterize age-related morphological and molecular changes associated with axonal retraction in a Drosophila model of ALS that recapitulate an important aspect of the human disease.This article has an associated First Person interview with the first author of the paper.

Machine learning approach to literature mining for the genetics of complex diseases.

To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated genes. Using this strategy, we previously built the following two databases for reproductive disorders: The Database for Preterm Birth (dbPTB) and The Database for Preeclampsia (dbPEC). The completeness and accuracy of these databases is essential for supporting our understanding of these complex conditions. Given the exponential increase in biomedical literature, it is becoming increasingly difficult to manually maintain these databases. Using our curated databases as reference data sets, we implemented a machine learning-based approach to optimize article selection for manual curation. We used logistic regression, random forests and neural networks as our machine learning algorithms to classify articles. We examined features derived from abstract text, annotations and metadata that we hypothesized would best classify articles with genetically relevant content associated to the disorder of interest. Combinations of these features were used build the classifiers and the performance of these feature sets were compared to a standard 'Bag-of-Words'. Several combinations of these genetic based feature sets outperformed 'Bag-of-Words' at a threshold such that 95% of the curated gene set obtained from the original manual curation of all articles were extracted from the articles classified by machine learning as 'considered'. The performance was superior in terms of the reduction of required manual curation and two measures of the harmonic mean of precision and recall. The reduction in workload ranged from 0.814 to 0.846 for the dbPTB and 0.301 to 0.371 for the dbPEC. Additionally, a database of metadata and annotations is generated which allows for rapid query of individual features. Our results demonstrate that machine learning algorithms can identify articles with relevant data for databases of genes associated with complex diseases.