A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways.

Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D

Pathway relevance ranking for tumor samples through network-based data integration

The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival

Green Pathways Out of Poverty: Workforce Development Initiatives

Workforce development practitioners face a set of common questions about services, partnerships, curriculum, certifications, links to employers, funding and measuring their results. On March 30 and 31, 2009, Green For All convened a working group of practitioners focused on providing green pathways out of poverty to start developing shared answers to these shared questions. Participants spent the two days connecting with each other, sharing expertise, and collaborating in order to produce new knowledge that will advance the field. This group began to identify the best practices and resources that make effective workforce development projects in green jobs. By the end of the two-day meeting, it had identified five keys to success for green workforce development. These keys, when combined with effective leadership and staff, help these programs serve the workers the programs train, the businesses and industry they support, and the environment they aim to protect. This document is a product of that two-day meeting and links to resources recommended by Community of Practice members. It is meant to guide and support anyone seeking to create pathways out of poverty through green job training

A comparative study on psychiatric disorders: Identification of shared pathways and common agents

Distinct but closely related diseases generally present shared symptoms, which address possible overlaps among their pathogenic mechanisms. Identification of significantly impacted shared pathways and other common agents are expected to elucidate etiology of these disorders and to help design better intervention strategies. In this research effort, we studied six psychiatric disorders including schizophrenia (SCZ), anorexia (AN), bipolar disorder (BD), depressive disorder (DD), autism (AU) and attention deficit hyperactivity disorder (ADHD). Our methodology can be classified into the following two parts: In Part I, common susceptibility genes; and in Part II, genome-wide association studies (GWAS) data were used to find enriched pathways of psychiatric disorders. 59 KEGG pathways were commonly identified in both parts. 31 of these pathways are disease pathways. Pathways related to cancer and infectious diseases were predominant compared to others. Most of the acquired pathways were in accordance with previous studies in literature. A combination of susceptibility genes and GWAS data is an effective approach to identify significantly impacted pathways in multifactorial diseases. In this respect, shared modules were determined after applying hierarchical clustering of the enriched pathways. These identified modules may tell us the association of psychiatric disorders with the enriched pathways. Taken all together, common pathways and shared modules are expected to highlight the causative factors and important mechanisms behind complex psychiatric diseases, leading to effective drug discovery. © 2022 IEEE

Knowledge, understanding and the dynamics of medical innovation

This paper investigates the processes by which scientific knowledge is created and legitimized. It focuses on scientific developments in a branch of medicine and explores the pathways through which the growth of knowledge enables advances in medical science and in clinical practice. This work draws conceptually on evolutionary approaches to technological change. The empirical part presents a longitudinal analysis of a database of scientific publications in the field of ophthalmology over a period of 50 years. Such an exercise allows us to identify pathways of shared understanding on a disease area, and to map out distinctive trajectories followed by the ophthalmology research community. The paper also contributes to general understanding of the innovation process by supporting the notion that knowledge coordination is a distributed process that cuts across and connects complementary areas of expertise.

Bi-Directional Learning: Identifying Contaminants on the Yurok Indian Reservation.

The Yurok Tribe partnered with the University of California Davis (UC Davis) Superfund Research Program to identify and address contaminants in the Klamath watershed that may be impairing human and ecosystem health. We draw on a community-based participatory research approach that begins with community concerns, includes shared duties across the research process, and collaborative interpretation of results. A primary challenge facing University and Tribal researchers on this project is the complexity of the relationship(s) between the identity and concentrations of contaminants and the diversity of illnesses plaguing community members. The framework of bi-directional learning includes Yurok-led river sampling, Yurok traditional ecological knowledge, University lab analysis, and collaborative interpretation of results. Yurok staff and community members share their unique exposure pathways, their knowledge of the landscape, their past scientific studies, and the history of landscape management, and University researchers use both specific and broad scope chemical screening techniques to attempt to identify contaminants and their sources. Both university and tribal knowledge are crucial to understanding the relationship between human and environmental health. This paper examines University and Tribal researchers' shared learning, progress, and challenges at the end of the second year of a five-year Superfund Research Program (SRP) grant to identify and remediate toxins in the lower Klamath River watershed. Our water quality research is framed within a larger question of how to best build university-Tribal collaboration to address contamination and associated human health impacts

Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'

We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

Dynamic scaffolds for neuronal signaling: in silico analysis of the TANC protein family

AbstractThe emergence of genes implicated across multiple comorbid neurologic disorders allows to identify shared underlying molecular pathways. Recently, investigation of patients with diverse neurologic disorders found TANC1 and TANC2 as possible candidate disease genes. While the TANC proteins have been reported as postsynaptic scaffolds influencing synaptic spines and excitatory synapse strength, their molecular functions remain unknown. Here, we conducted a comprehensive in silico analysis of the TANC protein family to characterize their molecular role and understand possible neurobiological consequences of their disruption. The known Ankyrin and tetratricopeptide repeat (TPR) domains have been modeled. The newly predicted N-terminal ATPase domain may function as a regulated molecular switch for downstream signaling. Several putative conserved protein binding motifs allowed to extend the TANC interaction network. Interestingly, we highlighted connections with different signaling pathways converging to modulate neuronal activity. Beyond a known role for TANC family members in the glutamate receptor pathway, they seem linked to planar cell polarity signaling, Hippo pathway, and cilium assembly. This suggests an important role in neuron projection, extension and differentiation.</jats:p

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking study

BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.The cost of this publication was funded by Vladimir Brusic. (Vladimir Brusic)Published versio