Mapping Functional dysregulation.

What pathways are active in your data? How do they group together to drive functions? What miRNAs regulate them?

Pathways work together – just how is the problem. One neat way to figure out what functions are made up is to see how pathways co-activate. When groups of them show similar activation, they form a module of function. A neat aspect of miRNAs is that they tend to modulate “functions” – at least at the level of preferring to modulate gene expression of genes that have products that interact . So it’s natural that interacting pathways, with formal roles in function, attract common miRNAs. We have built a tool to expose these events – and so open the door to a new view on functional interaction. Using Panomir you can find

  • What pathways are activated in your data
  • How they interact together
  • Find modules of interacting pathways and their prioritized targeting miRNAs

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.

Pourya Naderi Yeganeh,  Yue Y Teo,  Dimitra Karagkouni,  Yered Pita-Juárez, Sarah L Morgan,  Frank J Slack,  Ioannis S Vlachos,  Winston A Hide

This work has taken us quite a while – Driven by Pourya Naderi Yagneh our team and Ioannis Vlachos team.

Addressing the Burden of Alzheimer’s

Broad effects of disease reflected in immune, metabolic, depression-related pathways

Nearly six million older adults have Alzheimer’s disease in the United States, a number expected to double by 2050. 

Already the sixth leading cause of death, Alzheimer’s disease is a complex neurodegenerative disease that causes memory loss, confusion, poor judgment, depression, delusions, and agitation that robs people of their ability to live independently. 

Currently, the biological mechanisms underlying Alzheimer’s disease are poorly understood; as a result, there are few effective treatments and no cure for the disease.

Get more HMS news here

In a recent study, a research team led by scientists at Harvard Medical School and Beth Israel Deaconess Medical Center conducted a systematic assessment of more than 200,000 scientific publications from the last 30 years to understand the breadth and diversity of biological pathways—key molecular chain reactions that drive changes in cells—that contribute to Alzheimer’s disease. 

The team found that, although nearly all known pathways have been linked to the disease, the most frequently associated biological mechanisms, including those related to the immune system, metabolism, and long-term depression, have not significantly changed in 30 years, despite major technological advances. 

The scientists’ work, published June 24 in Frontiers in Aging Neuroscience, will advance research into the mechanisms of neurodegeneration.

“The burden of Alzheimer’s disease is steadily increasing, driving us towards a neurological epidemic,” said Winston Hide, HMS associate professor of pathology and director of the Precision RNA Medicine Core at Beth Israel Deaconess. 

“Our findings suggest that not only is this disorder incredibly complex but that its pathology includes most known biological pathways. This means that the disease’s effects are far broader in the body than we realized.”

The team performed an exhaustive text search of 206,324 pathway-specific dementia publication abstracts published since 1990. Next, they looked at 341 known biological pathways and determined how many publications linked a given pathway to the disease. 

The researchers found that 91 percent of pathways—all but seven—were linked to Alzheimer’s disease. Nearly half of the pathways were linked to Alzheimer’s disease in more than 100 scientific papers. 

They also found that the top-ranked 30 pathways most frequently referred to in literature remained relatively consistent over the last 30 years, suggesting that most studies of the disease have focused on a small subset of all the known disease-associated pathways.

“Clinical trials aiming to either delay the onset or slow the progression of Alzheimer’s disease have largely failed,” said study first author Sarah Morgan, a postdoctoral researcher at Beth Israel Deaconess and HMS during the extent of this research and now a lecturer at Queen Mary University of London. 

“Given that an unexpected diversity of pathways is associated with Alzheimer’s disease, a wide range of disease processes are not being successfully targeted in clinical trials. We hypothesize that comprehensively targeting more of the associated underlying mechanisms in Alzheimer’s disease will increase the chances of success in future drug trials.” 

Co-authors included Pourya Naderi, Yered Pita-Juarez, and Ioannis Vlachos of Beth Israel Deaconess; Katjusa Koler of University of Sheffield; Lars Bertram of University of Lubeck; and Dmitri Prokopenko and Rudolph Tanzi of Massachusetts General Hospital and HMS.

This work was supported in part by the Cure Alzheimer’s Fund. 

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest

Bioinformatics Thriving at BILH!

BILH Integrative Bioinformatics Initiative

Center for Life Sciences room 421 (Contact @winhide for details)

1-2pm Wednesday 13 September

Data integration, visualization and support actions in a BioCore facility

Reinhard Schneider

Luxembourg Centre for Systems Biomedicine

Prof. Reinhard Schneider is a full professor for bioinformatics at the University of Luxembourg. He established and is heading the Bioinformatics Core facility at the Luxembourg Centre for Systems Biomedicine (LCSB) and since 2017 heading the ELIXIR node in Luxembourg. Between 2004-2010 he was a Team Leader at the European Molecular Biology Laboratory (EMBL) in Heidelberg. Before he was co-founder and CIO in the LION bioscience AG and CEO of LION bioscience Research Inc., Cambridge, Massachusetts, establishing an IT based knowledge management system for Bayer. Till 1997 he was a postdoc in the biocomputing department at the EMBL-Heidelberg, where he studied various aspects of protein structures and became an expert in HPC. He received his Ph.D. in biology at the University of Heidelberg.

Celebrating a new PhD in the field of drug discovery: from drug name relationships to network drug repositioning

Dr. Katjuša Koler, has passed her PhD viva!

Dr. Katjuša Koler

Driving drug discovery

Kat developed PDxN, a drug discovery system that we hope to use to discover drugs that promote resilience against the pathologies of Alzheimer’s while maintaining cognition.

Kat’s thesis; A systematic pathway-based network approach for in silico drug repositioning; focused on the worlds of drug discovery, benchmarking and the taxonomy of drug names. During her PhD training, Kat helped us write a successful National Institutes of Health R01.

Mapping drug name relationships


The same drugs have different names, depending on where they are stored, named, created and used. Kat created a semantic translation resource: KATdb, a graph-theoretic structure which addresses the need to identify a drug synonym.

Trichostatin A in KATdb. Three connected components represent trichostatin A.

KATdb unifies and connects drug synonym information, connecting drug names and identifiers from 17 drug databases. 45 different types of drug synonyms are linked, including standardised chemical descriptors, names and database identifiers.

An innovator and teacher, Kat has been a valued member of the Computer Sciences department at the University of Sheffield, as well as the Sheffield Institute for Translational Neurosciences. Kat led the development of students and the community. She is a true leader and we celebrate her success. Dr. Koler is currently a data scientist supporting the NIHR Sheffield Biomedical Research Center.

For access and further information contact us!

Colliding galaxies: Alzheimer’s and big data


Big Data: Could It Ever Cure Alzheimer’s Disease? I was struck by this article by Masud Husain (closed access in BRAIN so you cannot read the original) which is reprinted on medcsape.

The article nudged me to write a post because it reflects the challenge and opportunities created by two behemoths which like galaxies, are slowly colliding.  Its addressing an area of growing interest because organisations are waking up to the value of having information that comes from existing datasets, generating targeted data, and looking within it to drive insight, rather than establishing an hypothesis and finding data to support or refute it.

“From business to government, many have been seduced by the possibilities that Big Data seems to offer for a whole range of problems.”

Alzhiemer’s (AD) has been accreting datasets and large scale studies. Projects such as Alzheimer’s Disease Neuroimaging Initiative (ADNI) ($200M invested so far) is sequencing several hundred full human genome sequences of patients with AD. At the same time, supported by the cure Alzheimer’s foundation, our group at Harvard, at Massachusetts General Hospital and here in UK at the Sheffield Institute for Translational Nuerosciences (SITraN) is analysing a set of 1500 whole genome sequences of AD sufferers. Our group is amongst the first laboratories world wide to have undertaken a study of this magnitude, and that we have done so outside of the domains of the current academic sequencing centres is difficult for funding agencies and patients alike to comprehend. Given this relatively pioneering approach, it has take a lot of time to develop the infrastructure and analytical capacity to address the magnitude of the study we have undertaken. We are learning the hard way that datasets of this size are at best unwieldy. The compute resources alone have taken a year to master and apply, yielding the variations in each genome that seem to be more frequent in patients that have the disease.

In parallel, Schizophrenia studies, like the  Psychiatric Genomics Consortium (PGC) boast 123,000 samples from people with a diagnosis of schizophrenia, bipolar disorder, ADHD, or autism and 80,000 controls collected by over 300 scientists from 80 institutions in 20 countries. Given the magnitude and complexity of these projects, it fast becomes clear that collaboration, data sharing and internal communication are powerful components i.e.: drivers of success in contrast to traditional innovation, insight and raw scientific discovery.

Other diseases are by no means on the sidelines. Although relatively rare, the debilitating and lethal neurodegenerative disorder Amytrophic Lateral Sclerosis is also on the “galactic plane”. Combining resources at a global scale, Project Mine has generated over 5000 full genome sequences with a goal of completing another 10 000 within a year. Whole countries are sequencing their populations. Here in UK the plan is to complete 100 000 genomes by 2017. In Qatar they will sequence 300 000, in USA the plan is to complete 1 million peoples whole genomes. The tiniest nations are also on the plane – even the Faero Islands plan to complete 50 000 subjects and Iceland has just published the first 2000 whole genome sequences in their population. Although this sounds like a great deal, the means to adequately process and analyse these, and other large scale datasets is in its infancy. How can we analyse all this data? One way is the obvious route of training and education. We are part of a new National programme to establish graduate training in genome medicine. Offered here at Sheffield the MSc makes a solid step towards beginning to understand the use of genome data for health.

“The critical intersection of Genomics Big Data Medicine, delving into ‘bleeding edge’ technology & approaches that will deeply shape the future.”

Also here in UK we have been building groups across institutions so that we can collaborate to analyse and handle big health data. Later today I meet with representatives across Sheffield that will become part of a “Health North’ initiative that looks to combine de-identified, consented, health and environmental data across cities so that we can ultimately engage in actioning new forms of health data analysis.

In my view, Eric Shcadt currently leads the new field at the intersection between big data and genomics and medicine – at least in terms of vision. He has driven the development of multi-scale biological research projects that have captured thousands of genomes, clinical records, related datasets and drug profiles to launch a new form of highly networked big data medicine. The first really broadly accessible application of this is will be the launch of a new app together with Apple’s health ecosystem  Apple ResearchKit  that will help doctors interpret medical data on an iPhone. What data is that? Simply put its your lifestyle – how many steps you take, how many stairs you climb, your blood pressure, blood oxygen, when and where. Ultimately combining that with genomics and other health data means that apps in the future could have the potential to truly and effectively predict when you and you alone are most likely to die. Schadt calls his adventure the ‘death app’ – not a name that is likely to live long.