Archive for the ‘Artificial Intelligence’ Category

Reactions to Original Tweets by @Pharma_BI and by @AVIVA1950 from #BIO2018

Curator: Aviva Lev-Ari, PhD, RN


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  • Re-Tweets and Likes by @Pharma_BI and by @AVIVA1950 from #BIO2018 @IAmBiotech @BIOConvention – BIO 2018, Boston, June 4-7, 2018, BCEC


  • Original Tweets by @Pharma_BI and by @AVIVA1950 from #BIO2018 @IAmBiotech @BIOConvention – BIO 2018, Boston, June 4-7, 2018, BCEC


  • Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL


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    From Philip K Dick’s obtuse robots to Mark O’Connell’s guide to transhumanism, novelist Julian Gough picks essential reading for a helter skelter world. Can’t wait to read some of the top 10 books. to survive the digital age!

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Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place


Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME




All TWEETS from LPBI’s handles at




Wednesday, April 25, 2018

7:00 am – 7:30 am
Nuance Foyer
7:30 am – 8:20 am
NVIDIA Ballroom
Reconceiving Medical Devices in an AI Dominated Environment

Medical device companies are focused on developing smaller, faster and smarter devices. New technologies will enhance the function of medical devices throughout patient care. Leveraging AI technology to more effectively interact with patients and inform / facilitate outcomes enables smart devices that can learn and improve performance over time. The nature of AI panel based devices, the challenges inherent in developing them and how such devices can evolve over the next 5 years and beyond will be examined.

Moderator: Pat Fortune, PhD
  • VP, Market Sectors, Innovation, PHS
  • Medical devices cover equipment: BP Cuff, MRI, implantable devices are not yet amenable for #AI, #ML
  • i.e., coronary stents, artificial joints
  • #AI, #ML are used in the design of medical devices
  • class of devices that will both collect the data and learn for self modification of the design itself
  • Prostetics for amputation: will be capable of learning from the movement they are designed by #AI, #ML
  • Watch – will collect data on clinical condition in a casual mode but will be used for medical decisions in therapy/treatment
  • Opportunities for #AI in #MedicalDevices
  • What is becoming to be available
  • How Patients will be cared for by using #AI
  • CEO, Bay Labs
  • Help Cardiologists in Cardiovascular using deep
  • @MIT developed human visual system doing visual recognition for decision expert knowledge digitized for brain neurons tested by #deeplearning algorithms
  • CVD – misditection, interventions tied to monitoring results
  • Sonography combined with CVD expertise in Echo-cardiography – high quality imagery
  • Devices and #AI: where the needs are?
  • Democratization that AI allows – took 50 years
  • Founder and CEO, Butterfly Network
  • DNA Sequencing ($1Billion) moving to Devices, chips – Gemone sequence for $1000
  • Change imaging by putting Ultrasound imaging machine onto ONE chip (>$200) – FDA [investment required was $100 Million in 5 years, MDs, CS, ENG.] approved for 40 application process of approval short
  • #AI applications: compute Ejection Fraction with an application on iPhone device – detect aneurysm in aorta
  • Google took rules to train chessmaster – same approach used in Medical devices
  • In ten years – EMR and Devices – machines can watch a cancer metastasize, learn HOW it does that
  • SVP, Chief Medical and Scientific Officer, Medtronic
  • SENSING capability to modulate therapy
  • deep stimulation for Parkinson
  • Sensing ICD detect pre-occaring arhythymias
  • detect hyper glicemia
  • PRD algorithms
  • EKG device – looks for Heart FAilure, water in the lungs – this device used to detect early ketoacidosis
  • Neuromodulation, cardiac monitoring, Diabetes
  • Artificial Pancreas – three arms clinical trial – Baysian statistic informative Priors – FDA works hard to understand and approve
  • Associate Chief, Cardiology Division, MGH; Professor of Medicine, HMS – Electrophysiology
  • Disterbances detected by sensors for prediction of Heart Failure – NOT YET integrated to EMR for better care – implentable devices are not yet using #AI
  • Non-implantable are using #AI vs implantable
  • arrhythmia – needs collaborations
  • #AI application alert the MD and the Patient vd Device is smart enough that the DEVICE WILL INTERVENE on their own – Validation of #AI algorithms will define future usage of #AI
8:20 am – 8:50 am
NVIDIA Ballroom
Fireside Chat: Seema Verma, Administrator, Centers for Medicare and Medicaid Services
Moderator: Gregg Meyer, MD
  • Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2018 Forum Co-Chair
  • Administrator, Centers for Medicare and Medicaid Services
  • 20% of GDP spent on Health Care taking away from Education and Defense
  • Mission to deliver INNOVATIONS, Robotics and Precision Medicine – CMS wants patient to get access to innovation
  • MODERNIZING: Drug Pricing, Value-Based Pricing – Patient in the Center
  • Price Transparency by Hospital to place online – WHAT cost what
  • Efficiency: 30% is spent on ADMINISTRATION – Technology needed to drive efficiencies
  • Patient-over-Paperwork – Advocated by @GreggMeyer, MD

    @PHS @HMS

  • Patients – Own their data –
  • BLUE Button – claim data from CMS available in Format organizing data
  • More data @CMS – Dat of MedicCare and MedicAID made available
  • Overhaul – Patient have their data to give it to researchers and to NIH
  • EMR – ease of use
  • #AI @CMS – all is DATA – meaningful format collecting all information in EMR – #AI needed for data analysis Biomarkers that requires actions
  • Value-based is for addressing efficacies
  • ACA – and Innovations: Subsidized – Rates went up over 100% happened before 2017 Carriers are opting out – Monopolies in some geographies – concerned over that
  • MedicAID aged, blind, disabled – more people placed into it – quality of care affected
  • INNOVATION center @CMS – great leadership, Providers coordinate care, how to include the Patient as beneficiary into the discussion Providers – and values, incentives by different models
  • Competition – @CMS was price setting – competition in Price setting nor @CMS as sole Price setter
8:50 am – 9:20 am
NVIDIA Ballroom
Fireside Chat: Paying for AI: Thinking Strategically About Reimbursements and Acceptance

Understanding how AI will be absorbed into a highly defined payment system is crucial to determining the rate and breadth that the technology will play in health care in the next decade. Two senior leaders will share their perspectives on how the technology will be paid for and what mechanisms will be used to arbitrate the scope and timing of those payments.

Moderator: Peter Markell
  • EVP Administration and Finance, CFO and Treasurer, PHS
  • How #AI will effect use of labor for efficiencies and cost
  • How the market will operate by use of Data to get provider get a share in the Saving of efficiency
  • Often the cost is not know
  • Pace of #AI
  • Providers and Insurers  need to work together of what is paid, disclosure information must be a better way
  • CEO, BCBS of North Carolina – $10Millions
  • Deep Providers relations – share responsibility with providers
  • GNS Partnership
  • Dat analytics, Patiennt Segmentations
  • Customer experience for HealthCare plans – #AI to be used to engage Patients avatar for Millennials
  • Manage Providers with Technology for Customer
  • Zip code and life expectance both go down
  • Partners a pioneer in monitoring for efficiencies: Coding needed for risk adjustment
  • Radiology, Pathology, Anestesiology — #AI is more effective and assist MDs – feedback of @AI is instantaneous
  • DAt Analytics increases very fast
  • #AI is growing it can BRING COST DOWN – it is a must and existential
  • Partnership is replacing negotiation
  • CEO, @OptumInsight & Enterprise Growth Officer, @Optum – Data analytics – 5,000 Data Scientists
  • OptumIQ – subject matter expertise, #AI and analytics – Services offered to clients
  • Payer-Provider Convergence – no friction is the interest of both sides, Claim administration
  • right price, right claim
9:20 am – 9:50 am
NVIDIA Ballroom
1:1 Fireside Chat: Vasant Narasimhan, MD, CEO, Novartis
Moderator: Gregg Meyer, MD
  • Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2018 Forum Co-Chair
  • CEO, Novartis
  • R&D for transformational efficacies
  • Novartis is a Data Science 70Biliion Doses per year – efficiencies needed and a Drug developer company
  • use #AI for 500 Clincial Trials around the World, Monitoring all trials
  • Data from research, biomarker,
  • Data Mining phyno type find new signature for drug discovery
  • Navidia – quantum computing for drug discovery and blockchain used for rolling the clinical trials from discovery
  • Insurance and Banking – data discovery and mining is extraordinary
  • Science 27 nurses in Clinical trial, Para Therapeutic, Google Partnership for clinical trials
  • Real Time monitoring: Study Operation Centers for all the Clinical Trials around the World if a Monitor arrived and what was the quality score
  • 25% of drug discovery affect Drug Prices
  • App NORA application with all the Clinical Trial workflow, identify Patients by Social Media, patient meets NP, protocol starts
  • Finding Drug Targets –
  • Push with regulators: deep minting of medical history to find super responders
  • Para therapeutics FDA label, Algorithm for ADDICTION, updating algorithm, need to inform regulator, Privacy Data vs mining permission RETHINK cultural transformation Organizational
  • Gene therapy and Cell therapy
  • Biosimilar Monoclonal antibody is been deployed in Africa
  • Finding super responders – Novartis can be a Leader due to data available
  • Regain Trust of Society in Big Pharma
9:50 am – 10:40 am
NVIDIA Ballroom
Machine Learning in Image Analysis: A Diagnostician’s Best Friend…or Replacement?

Diagnostic imaging is among the clinical fields receiving the greatest attention in the early stages of AI in healthcare. Even in this initial phase it appears that the technology may have profound effects on one of the most resource intensive fields in medicine. Panelists will consider the broad implications as well as topics such as how will role of radiologists evolve? Will AI tools ever become advanced enough to make decisions autonomously within the clinical workflow?

Moderator: Giles Boland, MD
  • Chair, Department of Radiology, BWH; Philip H. Cook Professor of Radiology, HMS
  • Diagnostic Imaging and Medicine is challenged by #AI
  • #AI is different than other transformational technological REVOLUTIONS
  • How to avoid unneeded scanns
  • CEO, PathAI
  • Digital Pathology, information processing from Tissue to treatment
  • #AI is a friend not a Foe
  • #AI will bring value to Medicine by utilizing tools for Patients
  • Information Processing across specialties is for the future – Pathology and Radiology cooperation
  • VP, Medical Imaging Technology, Siemens Healthineers
  • #AI more data , better performance, optimism to apply #AI for diagnosis, intervention
  • Precision, predicting
  • Population health, outcomes
  • Deliver systems performing per specification
  • incidentals: #AI efficiency, pushing forward Precision Medicine
  • #AI is the only technology to improve efficiency in Radiology #DeepReasoning does predictions intelligent management of Data
  • Data integration is a very difficult: Validation, for trust and it is not all rule based
  • Co-Founder and Chairman, Zebra Medical Vision
  • 30 #AI Products for Memograms, ultrasounds,
  • #AI will help Radiology
  • Triage by #AI brain bleed, lung colapse, aneurysm
  • CEO, GE Healthcare Imaging
  • #AI is a Transformational Technology
  • Google DeepMind beat Chest player: Computer teach itself to play anf played against itself in few hours and computer played against best Computer, better than human
  • GE build Scanners: build Intelligent Scanners want to do it like NVIDIA
  • Simulation of CAT Labs – can avoid sending a Patient to the Lab, significantly lower the cost
  • GE is building platforms that integrate data inputs from multiple sources
  • Chief, Breast Imaging Division, MGH; Professor of Radiology, HMS
  • Improving the Patient experience
  • #AI as a friend, #AI are tools that Radiologist are excited to have
  • Is surgery needed @MGH is based on #ML #DeepLearning applications to #BreatCancer
  • #AI to be used to find the Patients that need surgery and will benefit from it – help to find the 2% for surgery
10:40 am – 10:50 am
Nuance Foyer
10:50 am – 11:20 am
NVIDIA Ballroom
1:1 Fireside Chat: John Kelly, PhD, SVP, Cognitive Solutions and Research, IBM
Moderator: James Noga
  • VP and CIO, PHS
  • Bias in #ML #AI
  • lessons from WATSON #ML #AI
  • SVP, Cognitive Solutions and Research, IBM
  • #ML #AI – @IBM in the 50s
  • @IBM in 90s Built first Computer to play Chess and win
  • 2005 – WATSON – different paradigm leverage #ML #AI – intrigued by the data
  • 2010 – #ML #AI is very big again @IBM
  • Killer App #ML #AI in Healthcare is
  1. DRUG DISCOVERY: molecule, drug target, pathways
  2. with Mayo Clinic: MATCHING Patients to Clinical Trials using #ML #AI
  3. with Memorial Sloan Kettering: WATSON in Oncology for MDs consultation
  • Avoidance BIAS by #ML #AI usage in supervised knowledge WHAT DATA is used in a source for bias avoidance
  • Financial Services is @IBM bedrock where scaling was learned Call Center Assistance using #ML #AI
  • Oil & Gas – WATSON trained deployment to all platform in Northshore
  • Health Care workflow: #ML #AI for false positive and negative
  • Blood sugar control – prediction of Hyperglicemia
  • #AI #ML Implementation @IBM best sources of knowledge as Experts has been used: WHAT DATA YOU CHOOSE, Who choose it is annotated
  • WATSON – Oncology MDs worked on User Interface for sharing WATSON results for Patient decision making – Experience of Doctor-Patient relations has been transformed
  • Opportunities in HC for #AI #ML – we are just at the beginning, compute power in 5 years or 10 years there will not be a decision making, not been made using #AI #ML


  • 11:20 am – 12:10 pm
    NVIDIA Ballroom
Illuminating the Path to Clinician Empowerment

The sacred exchange between patient and clinician at the heart of medicine is increasingly under duress driven by a range of factors. Increasing clinician burnout is recognized as among the many negative consequences of this trend. Panelists will discuss how AI may improve the quality of the patient encounter, clinician workflow and ultimately clinician quality of life. Panelist will discuss how the new technology can meet these objectives when earlier information based technologies may have exacerbated the challenge.

Moderator: Sree Chaguturu, MD
  • VP, Population Health Management, PHS
  • #AI #ML as a tool in workflow to bring back Trust, Humanity due to clinicians burnout correlated with malpractice, turnover, suicide and alcohol, recognition at Partners
  • Actionable solutions: social psychological need
  • eliminate sources of burnout
  • SVP, New Business Development, Healthcare Division, Nuance
  • #AI #ML Product Dragon: move from dictation to data analytics, first time right – the documentation of the encounter with the Patient will be done by #AI #ML
  • Medication reconciliation progress done Alaxia
  • Talk to a device on the wall – Send a document to so and so
  • inject intelligence into the workflow: Image classification, deploy technology into the workflow: prioritization a stack of films for Radiologist to look first at acuity based ranking of films
  • Chief Health Strategy Officer, US Health & Life Sciences, Microsoft
  • Building blocks for academics and clinitiVIsion Search knowledgespeech
  • #ML #AI everywhere for the office
  • using high definition cameras for triaging condition of the Patient used for screening
  • reduce ratinopathy treatment cost – image go to Retina specialist and go directly to the chart, MD is paid for the test
  • CEO, Robin AI – ex-Google
  • #ML Doctor-Patient interaction Models
  • Patterns of medical documentation – ontologies of content
  • Chief Medical Information Officer, MGPO
  • Administration Burden causes burn-out of Clinicians
  • Data capture for retrieval
  • Project @MGH: multiple departments: capturing the sources of MDs burn-out: Journey of data
  • computer voice recognition – speak to computer have computer do work for you
  • 120 offices implemented LIVE scribes, 150 virtual scribes
  • Chart capture by #ML #AI – robotics solutions
  • ROI – increased productivity: see More patients, Acuity is up? intangible ROI – Job satisfaction of physicians
  • central scheduling and ordering
  • clinicians are trained in apprantainship model hard to change the workflow
12:10 pm – 1:10 pm
NVIDIA Ballroom
Disruptive Dozen: 12 AI Technologies That Will Reinvent Care

The culture of innovation throughout Partners HealthCare naturally fosters robust discussions about new “disruptive” technologies and which ones will have the biggest impact on health care. The Disruptive Dozen was created to identify and rank the technologies that Partners faculty feel will break through over the next decade to significantly improve health care. This year, the Disruptive Dozen focuses on relevant advances and opportunities in artificial intelligence (AI).

  • Director of Research Strategy and Operations, MGH & BWH CCDS; Associate Professor, Radiology, HMS
  • Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS


#3 #DistruptiveDozen @OmarArnaout @HMS Can Personal Devices Improve Your Health?

#2 #DistruptiveDozen @HadiShafiee @BWH @HMS A Picture is Worth a Thousand Words

#1 #DistruptiveDozen @BrandonWestover @MGH @HMS #AI at the #Bedside

1:10 pm – 1:15 pm
NVIDIA Ballroom
Last Look
  • Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
  • Interdisciplinary
  • Adoption is challenging
  • Journey will be long, continue to be positions with successes along the way
  • Chief Academic Officer, PHS; Laurie Carrol Guthart Professor of Medicine, Academic Dean for Partners, HMS; 2018 Forum Co-Chair
  • THANK YOU – SW writing SW – NAVIDIA, CEO
  • Contest Biobank Disease Challenge
  • Next year – Neuroscience & AI – major advances since the first Neuroscience
  • Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2018 Forum Co-Chair
  • Many insights – SW writing SW – follow thorough innovations

*Panels and speakers are subject to change.

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Synopsis Day 2: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place


Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME




All TWEETS from LPBI’s handles at




Tuesday, April 24, 2018

7:15 am – 7:50 am
Nuance Foyer
7:50 am – 8:00 am
NVIDIA Ballroom
Opening Remarks
  • Chief Innovation Officer, PHS; President, Partners HealthCare International, PHS
8:00 am – 8:50 am
NVIDIA Ballroom
Will AI Bend the Cost and Access Curve

Historical barriers have driven increased medical costs and decreasing access since the 1960s. The “Iron Triangle of Healthcare” continues to represent a tenuous balance of quality, cost and accessibility – economists have lamented attempts to optimize one characteristic at the expense of the others. The accumulation of innovations in care delivery (e.g. shift to lower cost providers and settings), population management, value based reimbursement and hospital administration are having a measurable effect. Can AI based technologies accelerate the pace of innovation and finally bend the cost and access curves in the US?

Moderator: Timothy Ferris, MD
  • SVP and CMO, Humana, Oncologist.
  • Payment methodology changes behavior.
  • AI will be helpful. Health where AI is very important – identify people who will become sick
  • 85% of Humana is value-based care, improve health of Population as a whole less Cost of care delivery. Diabetes as single diagnosis vs Diabetes and Behavioral Health, 2nd group care is 4 times more expensive [Food insecurity may be related to Transportation and medical in-adherence
  • Focus on Healthcare not on SickCare
  • Role of Insurer, was regulatory and conflict in Fee for Service. in a Value-based Value and Outcomes are aligned between Insurer and Provider
  • Providers ask the Payer for information to serve vey well the PATIENTS
  • MD believed the increased quality must increase quality with AI is powerful to move the payment system to increase quality and to LOWER the cost of care delivery
  • CMS data is been looked by many Agents for Analysis results will be interesting
  • MDs must be successful in two systems: Fee for Service AND Value-based – transition to 100% Value-based pricing
  • CEO, Cyft, Inc. focus on Government sponsored Health Programs, only, using data to improve decision, True value-based pricing. Volume and Complexity vs Value and Outcome
  • Solutions are a service bought by Clients, they do not buy AI as a stand alone tool
  • AI is 10% 90% is understanding and design the analysis is a small part free Databases AI is a Hammer it can’t build the House on its own
  • Wrong incentive when the transition from Paper to digital records
  • Vice Chairman, Investment Banking and Managing Director, Lazard Freres
  • Role of AI and Cost of HealthCare: AI is necessary but is not sufficient, it needs systmes to improve results and reduce costs, 1/2028 – 14/2018, MediCare cost decline 1.8% related to Risk of Re-admission to Hospitals better estimated, INCENTIVES to Hospital to avoid re-admissions, lower re-admission is associated with higher mortality
  • Fee for Service vs Value-based cost – Substitution vs Complimentarity productivity enhancement
  • Massive variation of unwanted scans – move the entire Nation of the system side
  • AI will thrive in a Value-based Payment World, Provider system need to demand than new innovations using AI will be developed
  • In the near future the payment will be derived directly from the EMR
  • Quality of HC will be better than today because of DATA functions in systems operations and their control
8:50 am – 9:40 am
NVIDIA Ballroom
Drug Therapy Redefined Through Machine Learning

The drug development process is highly complex and has many drivers. The panel will discuss the strategic impact of AI on the entire process and the implications for healthcare overall. How will the combination of factors – research strategy, drug development, regulatory approvals, reimbursement and clinical effectiveness – be influenced by the implementation of AI. Panelists will discuss short and mid-term prospects and whether AI will ultimately lead to a restructuring of the pharma model to develop new therapies.

  • Partner, Atlas Venture
  • who quality control of the Data set statistical powercan be assisted by AI
  • President, Novartis Institutes for Biomedical Research
  • Advances by computer applications with downstream applications in Biotech
  • Molecule recognition, chemical classes – lead optimization in drug discovery
  • AI as catalyst to expeditiously in labs, length of experiment to shorten due to #AI
  • leverage data for drug discovery at the molecular level
  • Model autism gen in ZebraFish
  • Humbled by the challenge of the diseases: Challenges – data science and biomedical research, solution, qualtification elemental level
  • CSO, Relay Therapeutics
  • AI and Insights are connected, no magic without curation and new data and its curation
  • Structural biology using biophysical technique and simulation to understand motion, experimental data – interplay with the computational aspects
  • Toxicology transported and metabolized – Potasium Channel
Lee Lehman-Becker
  • Senior Director of Digital and Personalized Health Care Partnering, Roche
  • Retrospective studies Partnership with True experts and Technologies in #ML
  • Structuring data, Patients becoming aware of their data ownership
  • SVP, Global Head of Data Sciences, Johnson & Johnson
  • Insight driven company at J&J skills of the Data Scientists assessing Clinical Operations Data and  650 Million Patient clinical data used to assess biomarkets for onset of disease and progression of disease
  • AI tools gangers and opportunities in Big Pharma. Insight coming out
  • governance and structure and data accountability – WHO is qualified to do the analysis
  • Data Scientists: MD or Biologist with CS degrees with experience in Healthcare Tech and Science – governance of the data
  • Change management to come from Scientists who sees the hypothesis and Data scientist as partner of the Scientist
  • Seeking opportunities for collaboration: Clinical Trials area, communications with CRO, Share information wihtout compromising IP
  • CSO, Datavant – Using Data to solve Medical Problems
  • FDA regulate 24 cents of the dollar in the US
  • DNA and Microbiome – data linking by AI is needed
  • Large problem – TB – studied by NGO
  • Rare diseases
  • CRO works on Parkinson or AD on clinical Trials that never works


9:40 am – 10:10 am
NVIDIA Ballroom
1:1 Fireside Chat: Atul Gawande, MD
Introduction by: John Fish
  • CEO, Suffolk; Chairman of Board Trustees, Brigham Health
  • Dr. Gawande made most important contributor to Medicine: Teacher, Researcher, Author, Surgeon, impact on Healthcare Reform
  • Visionary: making surgery safer, healthcare cover for all, advocator for lower cost of care delivery “Be Mortal” Caring about our future
  • President, Brigham Health; Professor of Medicine, HMS
  • MGH and BWH opened TWO BRANDED hospitals in China – Academic medical centers as consultants
  • Executive Director, Ariadne Labs; Samuel O. Thier Professor of Surgery, HMS; Surgeon, BWH
  • Innovations are not self executing, #AI as well
  • #AI applied in prediction of life risks Using #AI post surgery as monitor of recovery based on smartphone
  • #checklist on the wall in rural clinics vs #AI at Teaching Hospital:
  • FDA approved #AI: monitoring Anesthesia for endoscopy, retinopathy scan by PCP to be referred directly to Retina specialist not to Ophthalmologist
  • 1947 @MGH – Aneseptic solution – adoption took 20 years  and anesthesia adopted immediately
  • Innovation stream is voluminous – #AI needs a “CheckList for diffusion as a disruptive innovation in Medicine
  • Ready Environment: Capable and motivated – 60% of Hospital were not READY for rollup
  • Predictors of Readiness before an innovation can be taken ON
  • Upstream investment: Bill and Melinda Gates Foundation – identify challenges
  • Downstream investment: structuring systems
  • MGH – treats 2.3 % of Down syndrome in the US
  • Boston is the Silicon Valley for Medicine – Consorsium of Startups and Hospital Research enabling
  • Bullish on #AI implementations in Medicine, more money is needed
10:10 am – 10:25 am
Nuance Foyer
10:25 am – 11:15 am
NVIDIA Ballroom
Data Engineering in Healthcare: Liberating Value

The promise of machine learning and big data in in healthcare seems boundless – but healthcare data is massive and complex, and organizing and managing this data is the first step to an AI-empowered healthcare system.  Technology giants are investing in solutions to overcome these data engineering challenges, but with many visions of the future of healthcare data jockeying for dominance, what will the future of healthcare data really look like?  Can we finally liberate the value of data for patient care? And how will it happen?

Moderator: Mark Michalski, MD
  • Executive Director, MGH & BWH Center Clinical Data Science
  • Engineering the data is 90% and 10% Algorithm design
  • ‎Director of Healthcare Research, Microsoft Research
  • Value circle on Patients
  • Value circle on Health system, the Hospital
  • Value circle on Health Care Workers
  • digital informed consent – is powerful
  • Problem Framing for utility of information
  • VP and Global CTO, Sales, Dell EMC
  • Making Federated Analytics, intermediate results are shared, conserve bandwidth, storage and processing capacity follows Moore’s Law
  • Data governance issues
  • SVP and CMO, Qualcomm Life, Medical grade in IOT in real time from Hospital to Home
  • Connected care
  • #AI in OR monitoring – reduction of One day stay in each Hospital in the US is saving of $2Billion
  • Chart review: Medical Record, Billing since 2009 – EMR
  • Histeria on Health Privacy, convenience in storing credit card information
  • VP, Healthcare, Google Cloud
  • Improve HC Globally
  • make HC data interoperable and accessible using #AI, silos data in Genomics, EHR
  • Privacy & Security: In Europe $400 per patient vs $200 per bank customer – liability
  • Search for Medical information is not accessible yet
  • Chief Research Information Officer, PHS; Associate Professor of Neurology, HMS
  • Gather data at Partners and make it querriable, Massive data extraction, Open Source #ML and #AI to figure out what disease the Patient has – 200 hospitals are using the Open Source
  • datachallenge.,, ,
  • Data privacy
  • Sociology: MDs – #AI can help MDs
  • Mindfulness
  • CTO, Cognitive Collaboration Group, Cisco
  • Virtual version of SIRI for implementation in Hospitals
  • #AI – secure trusted data technology is available
  • application of neural nets to HC – reduction of error, language understanding heavy lifting needed
11:15 am – 11:45 am
NVIDIA Ballroom
1:1 Fireside Chat: Jensen Huang, CEO, NVIDIA
Introduction by: Scott Sperling
  • Co-President, Thomas H Lee Partners; Chairman of the Board of Directors, PHS
  • Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
  • AI software WRITING software – teaching computers to perform task of writing software
  • Intelligent autonomous machine, augments – ROBOTICS
  • perception of the environment – like a car
  • reason about what it perceive – localization
  • develop a plan for action – change lanes, stay in lane
  • workflow – changed, skills of engineers has changed,  training data to write softward – how big your infrastructure is = your ability to generate code NOT THE NUMBER OF Programmers on the Team
  • build software that if the system fails, the backup and the backup of the back up will continue to run, backup system is the Pilot
  • 3,000 people working on the Car project
  • Source code is DATA
  • Super Human term – In Medicine data is abundant, disassociated and noisy
  • Data Science is at the intersection of domain: Be CS, Be MDwith specialty, Supercomputer
  • #AI the force of our time able to achieve super human results, extrapulate to the Future , automation of automation, Future be mindful
  • access to our computers to everyone
  • Democracy to prevail when technology is adding life and protect Democracy
  • Enhance and be augmented by #AI to make MDs SUPER MDs
12:00 pm – 1:00 pm
GE Ballroom
12:30 pm – 1:00 pm
GE Ballroom
1:1 Fireside Chat: Paul Ricci, Former Chairman and CEO of Nuance Communications
Introduction by: Cathy Minehan
  • Managing Director, Arlington Advisory Partners; Chairman, Board of Trustees, MGH
Moderator: James Brink, MD
  • Chief, Department of Radiology, MGH; Juan M. Taveras Professor of Radiology, HMS
  • Former Chairman and CEO, Nuance Communications
1:00 pm – 1:10 pm
1:10 pm – 2:00 pm
NVIDIA Ballroom
AI and Gene Sequencing

Gene sequencing technology has evolved considerably over the last 10 years, dramatically decreasing the cost to sequence a human genome. As the costs associated with the technical assay continue to decrease, data interpretation and reporting has become the new bottleneck. Can AI and ML based approaches be applied to better understand how genetic mutations play a role in diseases like cancer – where the high rate of mutation makes treatment challenging? And will continued democratization of genetic information help to accelerate the pace of innovation in the field?

Moderator: Heidi Rehm, PhD
  • Chief Laboratory Director, Laboratory for Molecular Medicine, PHS Personalized Medicine; Associate Professor of Pathology, BWH and HMS
  • Genomic Variation is still not understood
  • Commitment to sharing data
  • Three areas of Genomic Testing
  1. detection
  2. functional impact of variation to develop disease
  3. disease will be developed
  • Executive Director, Worldwide R&D, Pfizer
  • Data, technology, innovations,
  • Transcriptome data – related to disease drugability translation
  • Data collection at Clinical Trial need be collected – different data need be collected
  • Automated image analysis – detect
  • how do we represent data and provide context that allow experiment with hypothesis
  • heterogeneity of dat ain samples – scientist need to find
  • Director, Bioinformatics Program, Cancer Center and Department of Pathology, MGH; Director, Institute Member, Broad Institute; Associate Professor of Pathology, HMS
  • Whole genome was the beginning
  • Somatic mutation: Tumor vs normal Tissue
  • benchmark data sets, improve over time
  • Director, Computational Pathology and Director, Technology Development, Center for Integrated Diagnostics, MGH; Assistant Professor, Pathology, HMS
  • 2-3,000 variants on a patient – error are: Sequencing, PCR,
  • Interpreting the Variants
  • 6 Pathologists signed up – Predictability using filters 99%error validated matching sequencing
  • 50 patient data will make a run economical
  • Genomic-wide sequencing – molecular Pathology
  • Patient View need to pool several data sources
  • CEO, Freenome – genomics in the blood
  • Early detection of Cancer #ML and #AI
  • #ML (pattern recognition in data): heuristic algorithms to train local genomics to identify cut off events point
  • #AI: what are the cancer signatures (KRAS) of relevance, generate novel hypothesis, signatores in the blood elevated PD1 rather than in the Tumor
  • Non canonical variation vs PCR then look at novel events
  • SVP, Product Development, Illumina, Inc.
  • #ML #AI – sequencing platform, improvement in data quality
  • Interpretation: Not all the promise of Genomics seen yet, improving “unknown significance”
  • data on Human to add Primate data into the model “Largely begnign” Largely Pathogenic”
  • 45% of the Non-Coding DNA – model to predict mutation and gene sequences- 10,000 bases – comparison of algorithms
  • Rich data sets in a federated data model, autism patients – genes not associated with the disease
  • Data need to have true sets
2:00 pm – 2:50 pm
NVIDIA Ballroom
Tangible Returns on the AI Value Proposition

Fueled by billions in venture investments, hundreds of new companies have emerged worldwide to develop and apply AI in health care. Beyond the US, China’s high AI priority has resulted in a vast array of technology driven start-ups. Global investors will discuss which area of machine learning will have the earliest meaningful impact? How do investors critically assess differentiation in such a crowded field? How are investment priorities set among the many divergent categories where AI will take hold?

Moderator: Meg Tirrell
  • Reporter, CNBC
  • Managing Director, Santé Ventures
  • Change the way MDs are trained – Decision Support development and used by MDs is a MUST
  • VP, Venture, Innovation, PHS
  • Can a department in the PHS system adopt and only thereafter it would sell to other Health Care Systems
  • Partner, Polaris Partners
  • #EMR may be killed by #AI which bring voice recognition
  • New data architecture, Blockchain is a data architecture storage solution, Blockchain is a buzzy word, communal dat generated – who will own the data and it can be used to harm
  • Partner, Andreessen Horowitz
  • #AI machine can scale if they will capture the expertise of Medicine Specialties
  • Testing: Colon Cancer, ovarian Cancer, Breast cancer development of the process HOW to develop the Test
  • Managing Director, Northern Light Venture Capital
  • #LinearOptimization and #NonLinearOptimization this combination is relevant to #EMR
2:50 pm – 3:40 pm
NVIDIA Ballroom
CEO Roundtable: The AI Opportunity as Foundational Change

Chief executives share perspectives on the impact of AI on their respective companies and industry segments. How prominently does AI figure into current investment strategies? And how are they measuring return on existing investments in AI? Panelist will be asked to take a position on whether AI is a truly transformational technology.

Moderator: Peter Slavin, MD
  • President, MGH
  • Three revolutions: Biological, Digital, Care Delivered and Paid for
  • What is the obstacle to diffuse #AI
  • Chief Innovation Officer, GE Healthcare
  • #AI will enable quality care by ACCESS to Care
  • #AI will effect Cost of Care 0 by improvements: diagnostics, interventions, monitoring patients PRECISION and INTERCONNECTEDNESS  – what is the right way to disrupt operational business models, it is in additions with Genomics and Genetics
  • Changes of effect on Patient care Fast transition to Recovery from surgery – pay lower cost of care
  • Clinical level: Patients population using #AI on failure to respond to treatment in Clinical Trials
  • Partnership with Navidia – #AI became very impactful
  • #AI Technology shift will impact the largest number of people Consumer will become incharge on their health
  • Empowering not Overpower
  • CEO, Philips
  • Continuum of Care via silos of systems #AI will improve unlocking data and making data mode actionable
  • 500 data scientists work on Data Science toward Genomics, DIagnostics and Radiology, predict sepsis predict heart attacks by Analytics #AI embedded Intelligence at POC
  • #AI application:Sleep and Respiratory – CPAP machines – DATA and algorithms feed back to Patient and to MDs about the patient
  • #AI – utilization of assets – 40% savings if patient is positioned by using #AI
  • Good quality curated Data needed for #AI Algorithms – Europe – data privacy stringent vs China little data privacy therefore #AI advanced faster in Healthcare
  • US is ahead of Europe at the forefront


  • CEO, Vertex
  • #AI used on genomic sequences
  • #AI assists Pathologist in reading slides – Better care will be delivered
  • #AI to find modified genes – Cystic Fibrosis
  • #AI will be a LEADER Technology
  • CEO, Siemens Healthineers
  • 70% of decision made in Hospital are using Siemens delivered equipment
  • #AI preventive maintenance
  • #AI – is new way of doing business new DATA is generated on every aspect of an established business
  • Curated data: Lab, Imaging, patient History – #AI will be applied on multiple sources placed in one storage area
  • US is IT savvy
  • IOT Europe is gaining ground – Novel IT SW with #AI
  • 28 markets in Europe — create ONE market for HealthCare purposes
  • #AI will become a winner, it will take time, stars will emerge
3:40 pm – 3:50 pm
NVIDIA Ballroom
Announcement of IDG Awardees
  • Chair, Department of Radiology, BWH; Philip H. Cook Professor of Radiology, HMS
  • Chief, Department of Radiology, MGH; Juan M. Taveras Professor of Radiology, HMS
3:50 pm – 4:40 pm
NVIDIA Ballroom
Regulating AI in Healthcare, Requirements and Challenges

The increasing application of AI in health products puts pressure on the historical model of regulation – among them the agile development cycles and continuous learning environment that support AI / machine learning based algorithms. Panelists will discuss the regulatory approaches including the FDA’s recently announced Software Precertification pilot program.

Moderator: Michael Jaff, DO
  • President, NWH, PHS, Professor of Medicine, HMS
  • Regulatory channels for #AI
  • Five years prospective
  • CEO, Arterys
  • #DeepLearning asa clinical tool in Hospitals
  • Clinical Solutions with #AI is no different than a clinical Device
  • Digital Software: Equalizing democratizing Healthcare
  • Chief Regulatory Officer, Sanofi
  • 1960 drugs, devices
  • #AI Benefits: Medical Utility – Regulatory designed for drugd in 1960s for drugs of 1960s
  • Thinking #AI for regulatory
  • #AI opportunities for trasforming the marketplace, we are in baby steps, Chinese FDA transformed itself to become a US FDA to speed decisions
  • MIT works on progressive innovations, continuous Clinical Trials – adaptation to broader populations, not all regulatory innovatios are in the West
  • New thinking on Drugs on Devices
  • Moving forward – Design space with Regulators
  • VP and GM, Healthcare Digital Solutions, GE Healthcare
  • #AI – The industry is in early application of #AI
  • Well curated data sets, Ability of ALgorithm for the Application
  • High value use cases: Validation of #AI algorithms, clinician involved in validation
  • post market surveys
  • Big Shift – in few years market surveillance – large number of #AI based application giving results and automation with continue
  • Associate Director for Digital Health, FDA – Group started at FDA in 2010, MobilX – need for continuous clarity in Digital Health, Two guidance per year. in 2018 -1500 questions posed to FDA by public developing applications in Digital Health, Vocabulary, UD , Globally: Brazil, EC, Japan, China, creating a Paradigm manufacture, R&D,
  • FDA System to scale to include #AI from existing expertise in Digital Health
  • FDA thinking how Safety, Efficacy will apply to #AI tools for drugs and medical devices
  • Connectivity of patient is built in the software – Version 1
  • Countries without regulation are growing Digital Health FASTER than countries with entranched Regulatory
  • World of #AI: Data is everything, trained by algorithms, clinical data used for training the algorithms
  • Communities of Common Data used and shared
  • Post market surveillance of #AI machinery: User interface: User performance, support evidence – start low level of Claim.
4:40 pm – 5:30 pm
NVIDIA Ballroom
AI in Hospital Environments: The Learning Provider

Health systems are actively evaluating strategies to drive efficiency throughout hospital operations. The deployment of AI based technologies to automate organizational tasks (e.g. medical coding / billing, prior authorizations) and optimize resource utilization (e.g. smart scheduling, no-show prediction) promises to help hospital systems adapt to changing macro-economic factors. This panel will discuss the role of AI in hospital operations and assess various approaches to reduce healthcare administration costs and increase efficiency.

Moderator: Adam Landman, MD
  • VP and CIO, Brigham Health
  • What is the Future Hospital with #AI looking like?
  • Executive Director, IT, Personalized Medicine, PHS
  • Building application Open source, cheaper, share network across institutions
  • Algorithms driving IT applications
  • Algorithms make strands in monitoring and in detection – NOT ALL ALGORITHMS are #AI algorithms
  • Hospitals are moving to face the challenge
  • More open business models, innovate by using open data advocacy for open systems
  • Partner, Optum Ventures
  • find patients early and monitor them at home #AI is very applicable for these goals
  • Get medical information to consumer BETTER search results than Dr. Google
  • Centralization of knowledgebase, virtualizable, convenience of patients is changing and is not identical to convenience of Practinioners’
  • Patient shows up with systems at Hospital can be backed up
  • CEO, Change Healthcare
  • Connected ecosystem
  • DSS, Financial
  • $2Trillion data processed
  • Clinical DSS: Utilization Management 100,000 – $8Billion spending
  • Build platforms
  • pockets of implementations at different stages
  • CEO, Qventus
  • #ML #AI
  • US and Canada, Hospital: Flow management of Patient Pharmacy operations
  • Anticipation of patient discharge
  • Balance needed inside and outside
  • CHanges to operation data : ALexa
  • Co-Founder, Director, PokitDok
  • #AI is the third revolution
  • ID of Patient as Front end to integrated billing system integrated with scheduling
  • Saving time to MDs
5:30 pm – 6:30 pm
GE Foyer

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Synopsis Day 1: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place


Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME




All TWEETS from LPBI’s handles at




Monday, April 23, 2018

7:00 am – 8:00 am
GE Foyer
7:00 am – 8:00 am
Nuance Foyer
8:00 am – 11:30 am
NVIDIA Ballroom
First Look: The Next Wave of AI Breakthroughs in Health Care

Early career Harvard Medical School investigators kick-off the 2018 World Medical Innovation Forum with rapid fire presentations of their high potential new technologies. Nineteen rising stars from Brigham Health and Massachusetts General Hospital will give ten-minute presentations highlighting their discoveries and insights that will disrupt the field of artificial intelligence. This session is designed for investors, leaders, donors, entrepreneurs and investigators and others who share a passion for identifying emerging high-impact technologies. To view speakers and topics, click here.

Moderator: Trung Do
  • VP, Business Development, Innovation, PHS
Moderator: Clare Tempany, MD
  • Vice Chair of Research at the Department of Radiology, BWH; Ferenc Jolesz MD Professor of Radiology, HMS

6. Nathalie Agar, PhD

  • Cancer Surgery: first line for most patients with solid tumors, potentially curative
  • OR in 2018: Cancer Surgery: AMIGO – MRI, PET/CT, mass spectrometry (seconds response) molecular imaging in Oncology – tissue analysis in realtime during surgery: surgical margins – tumor vs non-tumor
  • Intraoperative paradigm: Biopsy, frozen (30 minutes), surgical resection, MRI (60minutes) – done once
  • Spectrum: targeted analysis: First Real Time intraoperative mass spectrometry
  • Multivariate data analysis – #ML: Reference Data for Classifier: Comprehensive spectra
  • Applications in Clinical Trials: Pathology combined with mass spectrometry
  • In Glioma: Marker validation Clinical Implimentation

7. Omar Arnaout, MD

  • CNOC – Computational Neuroscience Outcome Center at Harvard
  • IDH – Mutation in Brain tumor, Biomarkers status, clinical outcomes
  • Neurosurgery, ICU phynotyping data, biometrical data, pathology, MRI report classification

8. Jason Baron, MD

  • DSS for Lab Pathology – Lab Diagnosis – Result Reporting
  • Interrelationships, Multidimensionality data – Human does not extract all the interpretable insidhts in the data fromm labs
  • Computer Path AI Required: Irin deficiency – other lab data implies colon cancer causing iron defficiency
  • Ferritin must be interpreted in context: Predict Ferriting values by Algorithms  – MD missed the diagnosis
  • Detect anomalies
  • avaiding overlooked diagnoses
  • Eliminating redundant testing:
  • Computational Pathology: MIT – colleagues Time series analysis
  • immunologic dta
  • Platforms to implement algorithms and validate impact on cost of care
  • AI based and Rule-based experience , Clinical DSS


9. David Craft, PhD

  • The radiation planning problem Localization Optimization analysis for Tumor analysis and Radiation Plan development
  • What drug to give to what patient
  • Drug sensitivity prediction is HARD because of diversity of cancers
  • Value of prior knowledge in #ML of complex systems: Simulation assisted machine learning
  • System biology, Data Scientists, clinical scientists ML Specialists
  • Open source: data sharing – patient reports outcommes

10. Raul San Jose Estepar, PhD

  • Image HC Analytics: EHR, PACS, Billing – undiagnosed comorbidities and unidentified risk
  • COPD  – smoking is primary  – $32Billion COST of treatment
  • Chest Imaging – Lungs, Heart – Chest CT Scan – leverage information – exploit information in CT and CXR to define patient risk in HC systems (Hospitals)  Diasgnose COPD and predict hospitalization from CT and CXR

11. Andrey Fedorov, PhD – BWH

  • Apply AI to train data sets – Impression written by MDs are hard to be translated for AI algorim
  • Standardization of data – STANDARDS to store data
  • Contributions: Interoperability of data open source development: Generic data types: annotations, measurement, Reference implementation of the standard, Sample datasets
  • Clean data needed for AI Applications based on Standards
  • Scaling from small data sets to multiple clinical trials
  • Imaging Data needs Standards Open source

12. Greorg Gerber, MD, PhD

  • Microbiome therapeutics and diagnostics – $4Billion in 2024 – Market size
  • Pharmacology meets ecology
  • Meta genome: ML methods used for ecology interactions: Predict
  • MDSINE – Baysian (predict uncertainty) ML – Microbial Dynamical Systems INference Engine to make prediction for design of Therapeutics
  • C-diff infection – Developed a Diagnostics and a Therapeutics – Patented, License IP for Animal and Human studies – reich microbes and use the best therapeutics optimization
  • Food Allergy
  • Prediction from Time Series Analysis
  • Partnering: SW Platform to be used by Pharma, co-developed for Cloud

13. Daniel Hashimoto, MD

  • Inraoperative Adverse Events (IAE): inadvertent injury during an operation eatimated 440,000 IAEs annually, cost of admission 41% higher in patient with IAE
  • How to access quantitative data fro OR
  • Solution: Computer vision and probabilistic modeling utilizing neural networks and HMMs
  • Detects deviations from statistically expected operative course
  • 92% accuracy in detecting steps of laparascopic sleeve Cancer resection
  • Benefits: Patients, Surgeons, Hospitals
  • MGH-MIT Advantage: realtime analysis of massively streamed datasets
  • Recognition phases: Context-base vs Instrument-based
  • Deviation Detection to predict and PREVENT the complication before it occurs Predict Post OPs: Liver or colon resection
  • Maximize Surgeon success


14. Kasper Lage, PhD

  • Collaborative innovations
  • Sequence Genome at a staggering pace – AI application: Patterns in genetic data: Identify Pathways from Vast Literature keyword analysis, networks identified – AI algorithms development for Pathway Signature: Cancer, AD
  • Cancer pathways – Genomic Atlas using Harvard-MIT, Dana Farber Massive parallel assays analysis for mutations in genes. UnKnown cancer pathways guide prediction: protectable insights:
  • Outlook: Cell-type-specific social networks neurological, metabolic, CVD & Cancer

15. William Lane, MD, PhD

  • Blood Typing and Transfusion Complications –
  • Extended Serologic Typing: Patient cell Antibody Reagent
  • ABO A2, cis-AB, A9B)
  • RhD – Partial D, weak D
  • SNP Typing less expensive
  • K+
  • E+
  • @MGH Whole Genome Sequencing (WGS) Blood Typing [BloodTyper] 300 WGS 99.9% concordant
  • Types: decrease Cost: WG ($$$) –>> Whole Exome ($$)–>>Antigen Genes ($)–>> Specific Antigen (<$)


16. Quanzheng Li, PhD

  • Information Overload in high Queuing Settings
  • PACS to the Cloud – AI-enabled Screening
  • AI Sytem for pre-screening and prioritization
  • Detection of Pneumothorax: intervention in one hour: symptoms atypical or masked
  • Algorithmic PET based neural network: 16 false emphysema due to image artifacts vs Pneumothorax diagnosis – to reduce False positive Chest Xray is used in China and India. at MGH CT is used
  • same methods can be used for Stroc/Aneurysm, bone fractures, tube resuction of metal artificats

17. Maulik Majmudar, MD

  • We do not leverage past experiences to inform future, quality gap, 17 years gap fro discovery to practivce
  • Aggregate patient data
  • SmartRx System architecture – Customizable, realtime semi-automated querying using NLP
  • Custom queries, Easyto-use Filters, query results – annotated and save query for future use
  • Auto-immune hemolytic anemia: Steroid, glucosteroids (prednisone)
  • Heart Failure
  • Quality of Care: Improve outcome by therapeutic appropriatenessAutomate prior authorization requirements, automate registries
  • Commercialization Plans

18. Thomas McCoy, MD

  • Computational Pharmacology in Psychiatry: One trial, agent, indication
  • Off-target burden: Drug Burden Score RISK: Bleeding, Medical Clinical Fall, Delirium [comfusion]
  • Drug burdens – Many drugs: Bleeding
  • First Partnership: Testing: Genetic Code used to per Patients Specific Score RISK: Bleeding, Medical Clinical Fall, Delirium
  • Population management, Decision Support, Learning Health Systems

19. Ziad Obermeyer, MD

  • ER – Test for Heart attack, Chest Pain, we test too much 100,000 of tests in ER
  • $765Billion is waste
  • Analysis of Medicare Data
  • ML prediction who will benefit from which test in ER – test that will pay off Average cost in ER $120K per life-year
  • Screening smokers for lung cancer vs Cancer immunotherapy $160K
  • When no tests – high risk patients NOT tested
  • Algorithm see ST elevation vs Doctors who did not see

20. Michael Rosehthal, MD, PhD

  • BMI: Visceral fat & CVD, Higher Muscle mass Better cancer outcome and ICU Survival
  • Neural network applied to select the SLICE of Pancreatic pf cancer patient
  • Select the segmentation
  • Body composition of FAT – automated analysis from CT scanner to the Report: Low cost, high volume broad impact,
  • Potential Applications: CVS – Method to analize risk vs stress test
  • Cancer risk screening
  • Improve drug dosing for high-cost treatment
  • Guidance for dietary and fitness interventions
  • All deliverable using CT scans already performed
  • Product development and deployment
  • Develoe PACS interface tools
  • Accelerate research to prove clinical applications

21. Hadi Shafiee, PhD

  • In-Vitro fertilization – effective is very low less 30%
  • Solution: Automated, AI-empowered, low-cost optical system
  • Free access
  • charge Pay per cycle
  • Results: Implantation rate, pregnancy outcome blastocyst prediction

22. Erica Shenoy, MD, PhD

  • Infectious disease: Diagnosis vs Prevention of Infections by isolation
  • C-diff hospital acquired – $30K treat one case, $5Billion /year
  • C-diff Exposure – Increase risk: Traditional RIsk: Prediction vs ML Approcah
  • Prediction for Prevention Hospital specific models: Daily predictions: five days before diagnosis patient was identified, patient to be prevented to transmit the disease

23. Brandon Westover, MD, PhD

  • Sleep Staging: Algorithm concensus on apnea detection – Algorithm is better than human
  • Brain Age Algorithm
  • Overnight EEG ->> Brain Age = Brain Health: Patients without Disease vs Diabetes & CVD Patient – with disease 4 years “older” looking
  • AI algorithms:

24. Sabine Wilhelm, PhD

  • Mental Health Treatment: Barriers and Solutions
  • MGH developed a smartphone cognitive behavior Y=treatment (CBT) application for body with Telefonica Alpha for Disformic Disorder
  • Core  component of CBT – to develop adaptive behavior with Clinician access for suicide prevention during depression
  • Applications Average body image disturbance scores at pre, mid and post treatment: BDD – severe body image disterbence 35 is the highest in 12 weeks the score is 20
11:45 am – 1:00 pm
3rd Floor and 7th Floor
Discovery Café Workshops

Lunch with Top Leadership from across Partners HealthCare and Industry.

Seven intensive workshops addressing cutting-edge artificial intelligence topics. To view topics and speakers, click here

1:00 pm – 1:20 pm
1:20 pm – 1:45 pm
NVIDIA Ballroom
Opening Remarks
  • Governor of the Commonwealth of Massachusetts
  • Rockstars organized the Conference
  • Great Schools, increasing interest inn STEM by high school students,
  • Turns and changes, complexities in Health Care and new therapies get bigger these changes where stacks are that high, #AI can help in research, discovery, How physicians use the tools, #AI is hard to predict HOW? it will be different, yet #AI is another tool helping serve purposes of Patients care and comfort
  • Chief Academic Officer, PHS; Laurie Carrol Guthart Professor of Medicine, Academic Dean for Partners, HMS; 2018 Forum Co-Chair
  • CEO, PHS
1:45 pm – 2:25 pm
NVIDIA Ballroom
Reflecting on the Impact of AI at the Bed and the Bench: Chairs Roundtable

Senior clinical leaders, current and past Forum Chairs, will share perspectives on the range of impact of AI on clinical practice. Discussion will highlight the rapid evolution of AI as a practical clinical tool and short and mid-term prospects for adoption in cancer, cardiovascular and neurological care.

Moderator: Sue Siegel
  • CIO and CEO, Business Innovations, GE
  • Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
  • Data is not organized enough
  • AI needs be embedded to be adopted
  • FDA may not be ready for AI changing the data
  • Chief Academic Officer, PHS; Laurie Carrol Guthart Professor of Medicine, Academic Dean for Partners, HMS; 2018 Forum Co-Chair
  • ML is used interchangeably with AI – they are not the same!! Population genotypes require integration with other segments and disciplines
  • Country is technologically advanced, people are not getting healthier, great tech for monitoring, patient do not adhere, technology help yet not take you to Finish Line
  • Training new physicians: Different Cohort will emerge – Medical Education will change – redesign the structure and the content
  • Physicians will need to learn Statistics to accept or deny data results
  • Vice Chair for Scientific Innovation, Department of Medicine, BWH; Chief Executive, One Brave Idea, BWH; Associate Professor of Medicine, HMS; 2017 Forum Co-Chair
  • Early solution are here, infrastructure needed for mature solutions
  • Vested interests may slow #AI, re-engineering the HC systems will be first applications
  • Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2018 Forum Co-Chair
  • Chief, Cardiology Division, MGH; Professor of Medicine, HMS; 2017 Forum Co-Chair
  • Potential of Neuro Networks may insert disparities by limitations of its training by humans
2:25 pm – 3:15 pm
NVIDIA Ballroom
Can AI Based Drug Development Feed A Hungry Pipeline?

Given the scarcity of late-stage assets, prolonged timelines and enormous costs of bringing drugs to market, AI-based approaches to target discovery, drug design and drug repurposing hold significant promise to positively disrupt the existing R&D paradigm.

  • Chief Data Officer, Broad Institute; Cardiologist, BWH; Venture Partner, Google Ventures
  • Tech and BioTech vs Big Pharma — WHO will lead the way
  • CEO, Exscientia
  • Clinical Hypothesis developed on increase HDL as a result of combination of #ML,
  • Repurpose drug is using #AI
  • #AI is getting better – FEW molecules with one year turn around Challenge of #AI – Reengineer Processes of Drug Discovery
  • China and #AI as a National Strategy
  • 50% investment in developing new algorithms
  • Director, Center for Genomic Medicine, MGH; Ofer and Shelly Nemirovsky MGH Research Scholar; Associate Professor of Medicine, HMS
  • In CVD – New targets are under discovery using #AI
  • Genotype and Phyno type correlations – give insights in research, same role will #AI play
  • Germ Line – 5 pathways in CVD a BLEND
  • Data available  90% on analysis
  • EVP and CSO, R&D, Bristol-Myers Squibb
  • #AI will help initiate Biomarker discovery, like Genomics idintified new pathways, AI will held in discovery
  • #AI tools enabling to use BigData
  • Lack of productivity in development of drug, #AI can accelerate drug development from discovery, Pharma is ready to accelerate this process
  • We need a common language in #AI not to confuse all players using AI in Medicine
  • How long a treatment lat may make the MOSY difference in disease stage
  • Drug need come faster, lower toxicity, Patients must be engaged
  • 70% investment to be in dat acquisition 30% in data analysis
  • SVP, Strategy, Commercialization & Innovation, Amgen
  • Human help #DeepLearning #NLP – applications to Medicine is coming not yet there
  • Fusion of Pharma experience is needed for the Novice #AI companies re-inventing drug discovery targeting
3:15 pm – 4:05 pm
NVIDIA Ballroom
Smart EHRs: AI for All

The first wave of EHR adoption has focused primarily on digitizing the patient record – with a more recent focus on building interactive clinical decision support capabilities. Development and implementation of CDS applications currently  requires  clinical staff to observe trends in data, develop protocols to act on these trends and work with technical staff to codify the logic into executable form. As NLP and computer vision capabilities become more advanced, algorithms will identify and propose actions reflecting patterns in the data. The panel will discuss existing challenges and whether AI technology will ultimately support an unsupervised learning approach in the EHR to identify trends and possible responses at both the patient and population level?

  • SVP and CMO, MGH
  • INCREMENTALISM – Medicine will love that


  • CEO, Health Catalyst
  • Dta platform, integrate EMR, Financial, aggreagate data
  • Build Analytics applications
  • Expertise as Analytics Services


  • Director, Analytics & Machine Learning, Epic
  • Synthesis of Data
  • Voice detection for Clinicians
  • Google Analytics is using AI
  • President, Digital, Persistent Systems
  • Building a platform using #AI as a unifying factor for the platform
  • The purpose of #AI is to make life of MDs, NPs easier
  • EHR is system of record not Engagement System – life of user is not easy
  • Augmented Intelligence rather than Artificial Intelligence
  • CEO, Picnic Health
  • Data collection will increasing AI will play major role – will AI help will become an anachronistic term
  • “Patients will be horrified if they would to know all the data about them recorded”
  • CEO, Wolters Kluwer Health
  • Data with Long term vision
  • Interoperability Standards are needed
4:05 pm – 4:55 pm
NVIDIA Ballroom
AI and the Cost of Trials: The Impact of Real World and Real Time Evidence

AI based approaches to conduct faster and more efficient clinical trials are beginning to emerge. Current approaches include applying predictive tools to perform more targeted patient recruitment and more accurate eligibility assessment. Panelists will discuss timelines for AI technology to have a measurable effect on trial cost and time to conduct the trial. Bottlenecks to applying this technology at scale and whether there will be a measurable effect on the cost of bringing drugs to market over the next decade will also be examined.

  • Partner, Google Ventures; Instructor in Medicine, BWH
  • CMO, CSO & SVP Oncology, Flatiron Health
  • Linking Patient Data with Genomics data is in Oncology available not in other indications
  • Recruit a  Treatment arm for a clinical trial, the control arm is selected from already collected data for other studies
  • Regulatory agency
  • VP, Research IT, Eli Lilly and Company
  • State Health Systems have multiple Data sets
  • Repurpose Data is very important – Not done
  • To train data you need data – for training dat there is not enough data
  • AXIOM is GSK leading a common model building dat from multiple Pharmas
  • Model more like in Experiments
  • CEO, GNS Healthcare
  • 645 patients at Dana Farber, new Biomarker was discovered
  • CEO, BenevolentAI
  • #ML is used in cleaning Data
  • Payors have leverage over reimbursement
  • Senior Advisor, R&D, Bayer
  • Data is available for studies
  • FDA has 77 Milion patient data for Safety relted studies
  • PMDA is Japan’s FDA – have 100% data standardized for clinical Trials.
  • Validation of Data from wearable sensors and Fitbit
  • Regulators do not understand how data is transferred over most advanced computer chips
  • Executive Director, Clinical Trials Office, PHS; Associate Professor of Medicine, HMS
  • Randomization of the Clinical CVD Trial 17,000 World cost $1.2 Billion take 7 years to complete
  • How AI can help? Site selection and training the staff it may be able to find patients. How do we know that
  • Virtual Trials: will that happened what will be the cost
  • EMR can be used for Clinical Trials, randomization is the gold standard and done in silicon will not replace Randomization
5:00 pm – 6:00 pm
Nuance Foyer

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Tweets for AI and Machine Learning in Clinical Trials April 12th, 2018 hosted at Pfizer’s Innovation Research Lab in Cambridge, MA @AVIVA1950 @pharma_BI




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Disease related changes in proteomics, protein folding, protein-protein interaction

Curator: Larry H. Bernstein, MD, FCAP



Frankenstein Proteins Stitched Together by Scientists

The Frankenstein monster, stitched together from disparate body parts, proved to be an abomination, but stitched together proteins may fare better. They may, for example, serve specific purposes in medicine, research, and industry. At least, that’s the ambition of scientists based at the University of North Carolina. They have developed a computational protocol called SEWING that builds new proteins from connected or disconnected pieces of existing structures. [Wikipedia]

Unlike Victor Frankenstein, who betrayed Promethean ambition when he sewed together his infamous creature, today’s biochemists are relatively modest. Rather than defy nature, they emulate it. For example, at the University of North Carolina (UNC), researchers have taken inspiration from natural evolutionary mechanisms to develop a technique called SEWING—Structure Extension With Native-substructure Graphs. SEWING is a computational protocol that describes how to stitch together new proteins from connected or disconnected pieces of existing structures.

“We can now begin to think about engineering proteins to do things that nothing else is capable of doing,” said UNC’s Brian Kuhlman, Ph.D. “The structure of a protein determines its function, so if we are going to learn how to design new functions, we have to learn how to design new structures. Our study is a critical step in that direction and provides tools for creating proteins that haven’t been seen before in nature.”

Traditionally, researchers have used computational protein design to recreate in the laboratory what already exists in the natural world. In recent years, their focus has shifted toward inventing novel proteins with new functionality. These design projects all start with a specific structural “blueprint” in mind, and as a result are limited. Dr. Kuhlman and his colleagues, however, believe that by removing the limitations of a predetermined blueprint and taking cues from evolution they can more easily create functional proteins.

Dr. Kuhlman’s UNC team developed a protein design approach that emulates natural mechanisms for shuffling tertiary structures such as pleats, coils, and furrows. Putting the approach into action, the UNC team mapped 50,000 stitched together proteins on the computer, and then it produced 21 promising structures in the laboratory. Details of this work appeared May 6 in the journal Science, in an article entitled, “Design of Structurally Distinct Proteins Using Strategies Inspired by Evolution.”

“Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and, in some cases, remain folded at more than 100°C,” wrote the authors. “High-resolution structures of the designed proteins CA01 and DA05R1 were solved by x-ray crystallography (2.2 angstrom resolution) and nuclear magnetic resonance, respectively, and there was excellent agreement with the design models.”

Essentially, the UNC scientists confirmed that the proteins they had synthesized contained the unique structural varieties that had been designed on the computer. The UNC scientists also determined that the structures they had created had new surface and pocket features. Such features, they noted, provide potential binding sites for ligands or macromolecules.

“We were excited that some had clefts or grooves on the surface, regions that naturally occurring proteins use for binding other proteins,” said the Science article’s first author, Tim M. Jacobs, Ph.D., a former graduate student in Dr. Kuhlman’s laboratory. “That’s important because if we wanted to create a protein that can act as a biosensor to detect a certain metabolite in the body, either for diagnostic or research purposes, it would need to have these grooves. Likewise, if we wanted to develop novel therapeutics, they would also need to attach to specific proteins.”

Currently, the UNC researchers are using SEWING to create proteins that can bind to several other proteins at a time. Many of the most important proteins are such multitaskers, including the blood protein hemoglobin.


Histone Mutation Deranges DNA Methylation to Cause Cancer

In some cancers, including chondroblastoma and a rare form of childhood sarcoma, a mutation in histone H3 reduces global levels of methylation (dark areas) in tumor cells but not in normal cells (arrowhead). The mutation locks the cells in a proliferative state to promote tumor development. [Laboratory of Chromatin Biology and Epigenetics at The Rockefeller University]

They have been called oncohistones, the mutated histones that are known to accompany certain pediatric cancers. Despite their suggestive moniker, oncohistones have kept their oncogenic secrets. For example, it has been unclear whether oncohistones are able to cause cancer on their own, or whether they need to act in concert with additional DNA mutations, that is, mutations other than those affecting histone structures.

While oncohistone mechanisms remain poorly understood, this particular question—the oncogenicity of lone oncohistones—has been resolved, at least in part. According to researchers based at The Rockefeller University, a change to the structure of a histone can trigger a tumor on its own.

This finding appeared May 13 in the journal Science, in an article entitled, “Histone H3K36 Mutations Promote Sarcomagenesis Through Altered Histone Methylation Landscape.” The article describes the Rockefeller team’s study of a histone protein called H3, which has been found in about 95% of samples of chondoblastoma, a benign tumor that arises in cartilage, typically during adolescence.

The Rockefeller scientists found that the H3 lysine 36–to–methionine (H3K36M) mutation impairs the differentiation of mesenchymal progenitor cells and generates undifferentiated sarcoma in vivo.

After the scientists inserted the H3 histone mutation into mouse mesenchymal progenitor cells (MPCs)—which generate cartilage, bone, and fat—they watched these cells lose the ability to differentiate in the lab. Next, the scientists injected the mutant cells into living mice, and the animals developed the tumors rich in MPCs, known as an undifferentiated sarcoma. Finally, the researchers tried to understand how the mutation causes the tumors to develop.

The scientists determined that H3K36M mutant nucleosomes inhibit the enzymatic activities of several H3K36 methyltransferases.

“Depleting H3K36 methyltransferases, or expressing an H3K36I mutant that similarly inhibits H3K36 methylation, is sufficient to phenocopy the H3K36M mutation,” the authors of the Science study wrote. “After the loss of H3K36 methylation, a genome-wide gain in H3K27 methylation leads to a redistribution of polycomb repressive complex 1 and de-repression of its target genes known to block mesenchymal differentiation.”

Essentially, when the H3K36M mutation occurs, the cell becomes locked in a proliferative state—meaning it divides constantly, leading to tumors. Specifically, the mutation inhibits enzymes that normally tag the histone with chemical groups known as methyls, allowing genes to be expressed normally.

In response to this lack of modification, another part of the histone becomes overmodified, or tagged with too many methyl groups. “This leads to an overall resetting of the landscape of chromatin, the complex of DNA and its associated factors, including histones,” explained co-author Peter Lewis, Ph.D., a professor at the University of Wisconsin-Madison and a former postdoctoral fellow in laboratory of C. David Allis, Ph.D., a professor at Rockefeller.

The finding—that a “resetting” of the chromatin landscape can lock the cell into a proliferative state—suggests that researchers should be on the hunt for more mutations in histones that might be driving tumors. For their part, the Rockefeller researchers are trying to learn more about how this specific mutation in histone H3 causes tumors to develop.

“We want to know which pathways cause the mesenchymal progenitor cells that carry the mutation to continue to divide, and not differentiate into the bone, fat, and cartilage cells they are destined to become,” said co-author Chao Lu, Ph.D., a postdoctoral fellow in the Allis lab.

Once researchers understand more about these pathways, added Dr. Lewis, they can consider ways of blocking them with drugs, particularly in tumors such as MPC-rich sarcomas—which, unlike chondroblastoma, can be deadly. In fact, drugs that block these pathways may already exist and may even be in use for other types of cancers.

“One long-term goal of our collaborative team is to better understand fundamental mechanisms that drive these processes, with the hope of providing new therapeutic approaches,” concluded Dr. Allis.


Histone H3K36 mutations promote sarcomagenesis through altered histone methylation landscape

Chao Lu, Siddhant U. Jain, Dominik Hoelper, …, C. David Allis1,, Nada Jabado,, Peter W. Lewis,
Science  13 May 2016; 352(6287):844-849

An oncohistone deranges inhibitory chromatin

Missense mutations (that change one amino acid for another) in histone H3 can produce a so-called oncohistone and are found in a number of pediatric cancers. For example, the lysine-36–to-methionine (K36M) mutation is seen in almost all chondroblastomas. Lu et al. show that K36M mutant histones are oncogenic, and they inhibit the normal methylation of this same residue in wild-type H3 histones. The mutant histones also interfere with the normal development of bone-related cells and the deposition of inhibitory chromatin marks.

Science, this issue p. 844

Several types of pediatric cancers reportedly contain high-frequency missense mutations in histone H3, yet the underlying oncogenic mechanism remains poorly characterized. Here we report that the H3 lysine 36–to–methionine (H3K36M) mutation impairs the differentiation of mesenchymal progenitor cells and generates undifferentiated sarcoma in vivo. H3K36M mutant nucleosomes inhibit the enzymatic activities of several H3K36 methyltransferases. Depleting H3K36 methyltransferases, or expressing an H3K36I mutant that similarly inhibits H3K36 methylation, is sufficient to phenocopy the H3K36M mutation. After the loss of H3K36 methylation, a genome-wide gain in H3K27 methylation leads to a redistribution of polycomb repressive complex 1 and de-repression of its target genes known to block mesenchymal differentiation. Our findings are mirrored in human undifferentiated sarcomas in which novel K36M/I mutations in H3.1 are identified.


Mitochondria? We Don’t Need No Stinking Mitochondria!
Diagram comparing typical eukaryotic cell to the newly discovered mitochondria-free organism. [Karnkowska et al., 2016, Current Biology 26, 1–11]
  • The organelle that produces a significant portion of energy for eukaryotic cells would seemingly be indispensable, yet over the years, a number of organisms have been discovered that challenge that biological pretense. However, these so-called amitochondrial species may lack a defined organelle, but they still retain some residual functions of their mitochondria-containing brethren. Even the intestinal eukaryotic parasite Giardia intestinalis, which was for many years considered to be mitochondria-free, was proven recently to contain a considerably shriveled version of the organelle.
  • Now, an international group of scientists has released results from a new study that challenges the notion that mitochondria are essential for eukaryotes—discovering an organism that resides in the gut of chinchillas that contains absolutely no trace of mitochondria at all.
  • “In low-oxygen environments, eukaryotes often possess a reduced form of the mitochondrion, but it was believed that some of the mitochondrial functions are so essential that these organelles are indispensable for their life,” explained lead study author Anna Karnkowska, Ph.D., visiting scientist at the University of British Columbia in Vancouver. “We have characterized a eukaryotic microbe which indeed possesses no mitochondrion at all.”


Mysterious Eukaryote Missing Mitochondria

Researchers uncover the first example of a eukaryotic organism that lacks the organelles.

By Anna Azvolinsky | May 12, 2016


Scientists have long thought that mitochondria—organelles responsible for energy generation—are an essential and defining feature of a eukaryotic cell. Now, researchers from Charles University in Prague and their colleagues are challenging this notion with their discovery of a eukaryotic organism,Monocercomonoides species PA203, which lacks mitochondria. The team’s phylogenetic analysis, published today (May 12) in Current Biology,suggests that Monocercomonoides—which belong to the Oxymonadida group of protozoa and live in low-oxygen environmentsdid have mitochondria at one point, but eventually lost the organelles.

“This is quite a groundbreaking discovery,” said Thijs Ettema, who studies microbial genome evolution at Uppsala University in Sweden and was not involved in the work.

“This study shows that mitochondria are not so central for all lineages of living eukaryotes,” Toni Gabaldonof the Center for Genomic Regulation in Barcelona, Spain, who also was not involved in the work, wrote in an email to The Scientist. “Yet, this mitochondrial-devoid, single-cell eukaryote is as complex as other eukaryotic cells in almost any other aspect of cellular complexity.”

Charles University’s Vladimir Hampl studies the evolution of protists. Along with Anna Karnkowska and colleagues, Hampl decided to sequence the genome of Monocercomonoides, a little-studied protist that lives in the digestive tracts of vertebrates. The 75-megabase genome—the first of an oxymonad—did not contain any conserved genes found on mitochondrial genomes of other eukaryotes, the researchers found. It also did not contain any nuclear genes associated with mitochondrial functions.

“It was surprising and for a long time, we didn’t believe that the [mitochondria-associated genes were really not there]. We thought we were missing something,” Hampl told The Scientist. “But when the data kept accumulating, we switched to the hypothesis that this organism really didn’t have mitochondria.”

Because researchers have previously not found examples of eukaryotes without some form of mitochondria, the current theory of the origin of eukaryotes poses that the appearance of mitochondria was crucial to the identity of these organisms.

“We now view these mitochondria-like organelles as a continuum from full mitochondria to very small . Some anaerobic protists, for example, have only pared down versions of mitochondria, such as hydrogenosomes and mitosomes, which lack a mitochondrial genome. But these mitochondrion-like organelles perform essential functions of the iron-sulfur cluster assembly pathway, which is known to be conserved in virtually all eukaryotic organisms studied to date.

Yet, in their analysis, the researchers found no evidence of the presence of any components of this mitochondrial pathway.

Like the scaling down of mitochondria into mitosomes in some organisms, the ancestors of modernMonocercomonoides once had mitochondria. “Because this organism is phylogenetically nested among relatives that had conventional mitochondria, this is most likely a secondary adaptation,” said Michael Gray, a biochemist who studies mitochondria at Dalhousie University in Nova Scotia and was not involved in the study. According to Gray, the finding of a mitochondria-deficient eukaryote does not mean that the organelles did not play a major role in the evolution of eukaryotic cells.

To be sure they were not missing mitochondrial proteins, Hampl’s team also searched for potential mitochondrial protein homologs of other anaerobic species, and for signature sequences of a range of known mitochondrial proteins. While similar searches with other species uncovered a few mitochondrial proteins, the team’s analysis of Monocercomonoides came up empty.

“The data is very complete,” said Ettema. “It is difficult to prove the absence of something but [these authors] do a convincing job.”

To form the essential iron-sulfur clusters, the team discovered that Monocercomonoides use a sulfur mobilization system found in the cytosol, and that an ancestor of the organism acquired this system by lateral gene transfer from bacteria. This cytosolic, compensating system allowed Monocercomonoides to lose the otherwise essential iron-sulfur cluster-forming pathway in the mitochondrion, the team proposed.

“This work shows the great evolutionary plasticity of the eukaryotic cell,” said Karnkowska, who participated in the study while she was a postdoc at Charles University. Karnkowska, who is now a visiting researcher at the University of British Columbia in Canada, added: “This is a striking example of how far the evolution of a eukaryotic cell can go that was beyond our expectations.”

“The results highlight how many surprises may await us in the poorly studied eukaryotic phyla that live in under-explored environments,” Gabaldon said.

Ettema agreed. “Now that we’ve found one, we need to look at the bigger picture and see if there are other examples of eukaryotes that have lost their mitochondria, to understand how adaptable eukaryotes are.”

  1. Karnkowska et al., “A eukaryote without a mitochondrial organelle,” Current Biology,doi:10.1016/j.cub.2016.03.053, 2016.

organellesmitochondriagenetics & genomics and evolution


A Eukaryote without a Mitochondrial Organelle

Anna Karnkowska,  Vojtěch Vacek,  Zuzana Zubáčová,…,  Čestmír Vlček,  Vladimír HamplDOI:  Article Info

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  • Monocercomonoides sp. is a eukaryotic microorganism with no mitochondria
  • •The complete absence of mitochondria is a secondary loss, not an ancestral feature
  • •The essential mitochondrial ISC pathway was replaced by a bacterial SUF system

The presence of mitochondria and related organelles in every studied eukaryote supports the view that mitochondria are essential cellular components. Here, we report the genome sequence of a microbial eukaryote, the oxymonad Monocercomonoides sp., which revealed that this organism lacks all hallmark mitochondrial proteins. Crucially, the mitochondrial iron-sulfur cluster assembly pathway, thought to be conserved in virtually all eukaryotic cells, has been replaced by a cytosolic sulfur mobilization system (SUF) acquired by lateral gene transfer from bacteria. In the context of eukaryotic phylogeny, our data suggest that Monocercomonoides is not primitively amitochondrial but has lost the mitochondrion secondarily. This is the first example of a eukaryote lacking any form of a mitochondrion, demonstrating that this organelle is not absolutely essential for the viability of a eukaryotic cell.


HIV Particles Used to Trap Intact Mammalian Protein Complexes

Belgian scientists from VIB and UGent developed Virotrap, a viral particle sorting approach for purifying protein complexes under native conditions.

This method catches a bait protein together with its associated protein partners in virus-like particles that are budded from human cells. Like this, cell lysis is not needed and protein complexes are preserved during purification.

With his feet in both a proteomics lab and an interactomics lab, VIB/UGent professor Sven Eyckerman is well aware of the shortcomings of conventional approaches to analyze protein complexes. The lysis conditions required in mass spectrometry–based strategies to break open cell membranes often affect protein-protein interactions. “The first step in a classical study on protein complexes essentially turns the highly organized cellular structure into a big messy soup”, Eyckerman explains.

Inspired by virus biology, Eyckerman came up with a creative solution. “We used the natural process of HIV particle formation to our benefit by hacking a completely safe form of the virus to abduct intact protein machines from the cell.” It is well known that the HIV virus captures a number of host proteins during its particle formation. By fusing a bait protein to the HIV-1 GAG protein, interaction partners become trapped within virus-like particles that bud from mammalian cells. Standard proteomic approaches are used next to reveal the content of these particles. Fittingly, the team named the method ‘Virotrap’.

The Virotrap approach is exceptional as protein networks can be characterized under natural conditions. By trapping protein complexes in the protective environment of a virus-like shell, the intact complexes are preserved during the purification process. The researchers showed the method was suitable for detection of known binary interactions as well as mass spectrometry-based identification of novel protein partners.

Virotrap is a textbook example of bringing research teams with complementary expertise together. Cross-pollination with the labs of Jan Tavernier (VIB/UGent) and Kris Gevaert (VIB/UGent) enabled the development of this platform.

Jan Tavernier: “Virotrap represents a new concept in co-complex analysis wherein complex stability is physically guaranteed by a protective, physical structure. It is complementary to the arsenal of existing interactomics methods, but also holds potential for other fields, like drug target characterization. We also developed a small molecule-variant of Virotrap that could successfully trap protein partners for small molecule baits.”

Kris Gevaert: “Virotrap can also impact our understanding of disease pathways. We were actually surprised to see that this virus-based system could be used to study antiviral pathways, like Toll-like receptor signaling. Understanding these protein machines in their natural environment is essential if we want to modulate their activity in pathology.“


Trapping mammalian protein complexes in viral particles

Sven Eyckerman, Kevin Titeca, …Kris GevaertJan Tavernier
Nature Communications Apr 2016; 7(11416)

Cell lysis is an inevitable step in classical mass spectrometry–based strategies to analyse protein complexes. Complementary lysis conditions, in situ cross-linking strategies and proximal labelling techniques are currently used to reduce lysis effects on the protein complex. We have developed Virotrap, a viral particle sorting approach that obviates the need for cell homogenization and preserves the protein complexes during purification. By fusing a bait protein to the HIV-1 GAG protein, we show that interaction partners become trapped within virus-like particles (VLPs) that bud from mammalian cells. Using an efficient VLP enrichment protocol, Virotrap allows the detection of known binary interactions and MS-based identification of novel protein partners as well. In addition, we show the identification of stimulus-dependent interactions and demonstrate trapping of protein partners for small molecules. Virotrap constitutes an elegant complementary approach to the arsenal of methods to study protein complexes.

Proteins mostly exert their function within supramolecular complexes. Strategies for detecting protein–protein interactions (PPIs) can be roughly divided into genetic systems1 and co-purification strategies combined with mass spectrometry (MS) analysis (for example, AP–MS)2. The latter approaches typically require cell or tissue homogenization using detergents, followed by capture of the protein complex using affinity tags3 or specific antibodies4. The protein complexes extracted from this ‘soup’ of constituents are then subjected to several washing steps before actual analysis by trypsin digestion and liquid chromatography–MS/MS analysis. Such lysis and purification protocols are typically empirical and have mostly been optimized using model interactions in single labs. In fact, lysis conditions can profoundly affect the number of both specific and nonspecific proteins that are identified in a typical AP–MS set-up. Indeed, recent studies using the nuclear pore complex as a model protein complex describe optimization of purifications for the different proteins in the complex by examining 96 different conditions5. Nevertheless, for new purifications, it remains hard to correctly estimate the loss of factors in a standard AP–MS experiment due to washing and dilution effects during treatments (that is, false negatives). These considerations have pushed the concept of stabilizing PPIs before the actual homogenization step. A classical approach involves cross-linking with simple reagents (for example, formaldehyde) or with more advanced isotope-labelled cross-linkers (reviewed in ref. 2). However, experimental challenges such as cell permeability and reactivity still preclude the widespread use of cross-linking agents. Moreover, MS-generated spectra of cross-linked peptides are notoriously difficult to identify correctly. A recent lysis-independent solution involves the expression of a bait protein fused to a promiscuous biotin ligase, which results in labelling of proteins proximal to the activity of the enzyme-tagged bait protein6. When compared with AP–MS, this BioID approach delivers a complementary set of candidate proteins, including novel interaction partners78. Such particular studies clearly underscore the need for complementary approaches in the co-complex strategies.

The evolutionary stress on viruses promoted highly condensed coding of information and maximal functionality for small genomes. Accordingly, for HIV-1 it is sufficient to express a single protein, the p55 GAG protein, for efficient production of virus-like particles (VLPs) from cells910. This protein is highly mobile before its accumulation in cholesterol-rich regions of the membrane, where multimerization initiates the budding process11. A total of 4,000–5,000 GAG molecules is required to form a single particle of about 145 nm (ref. 12). Both VLPs and mature viruses contain a number of host proteins that are recruited by binding to viral proteins. These proteins can either contribute to the infectivity (for example, Cyclophilin/FKBPA13) or act as antiviral proteins preventing the spreading of the virus (for example, APOBEC proteins14).

We here describe the development and application of Virotrap, an elegant co-purification strategy based on the trapping of a bait protein together with its associated protein partners in VLPs that are budded from the cell. After enrichment, these particles can be analysed by targeted (for example, western blotting) or unbiased approaches (MS-based proteomics). Virotrap allows detection of known binary PPIs, analysis of protein complexes and their dynamics, and readily detects protein binders for small molecules.

Concept of the Virotrap system

Classical AP–MS approaches rely on cell homogenization to access protein complexes, a step that can vary significantly with the lysis conditions (detergents, salt concentrations, pH conditions and so on)5. To eliminate the homogenization step in AP–MS, we reasoned that incorporation of a protein complex inside a secreted VLP traps the interaction partners under native conditions and protects them during further purification. We thus explored the possibility of protein complex packaging by the expression of GAG-bait protein chimeras (Fig. 1) as expression of GAG results in the release of VLPs from the cells910. As a first PPI pair to evaluate this concept, we selected the HRAS protein as a bait combined with the RAF1 prey protein. We were able to specifically detect the HRAS–RAF1 interaction following enrichment of VLPs via ultracentrifugation (Supplementary Fig. 1a). To prevent tedious ultracentrifugation steps, we designed a novel single-step protocol wherein we co-express the vesicular stomatitis virus glycoprotein (VSV-G) together with a tagged version of this glycoprotein in addition to the GAG bait and prey. Both tagged and untagged VSV-G proteins are probably presented as trimers on the surface of the VLPs, allowing efficient antibody-based recovery from large volumes. The HRAS–RAF1 interaction was confirmed using this single-step protocol (Supplementary Fig. 1b). No associations with unrelated bait or prey proteins were observed for both protocols.

Figure 1: Schematic representation of the Virotrap strategy.


Expression of a GAG-bait fusion protein (1) results in submembrane multimerization (2) and subsequent budding of VLPs from cells (3). Interaction partners of the bait protein are also trapped within these VLPs and can be identified after purification by western blotting or MS analysis (4).

Virotrap for the detection of binary interactions

We next explored the reciprocal detection of a set of PPI pairs, which were selected based on published evidence and cytosolic localization15. After single-step purification and western blot analysis, we could readily detect reciprocal interactions between CDK2 and CKS1B, LCP2 and GRAP2, and S100A1 and S100B (Fig. 2a). Only for the LCP2 prey we observed nonspecific association with an irrelevant bait construct. However, the particle levels of the GRAP2 bait were substantially lower as compared with those of the GAG control construct (GAG protein levels in VLPs; Fig. 2a, second panel of the LCP2 prey). After quantification of the intensities of bait and prey proteins and normalization of prey levels using bait levels, we observed a strong enrichment for the GAG-GRAP2 bait (Supplementary Fig. 2).


Virotrap for unbiased discovery of novel interactions

For the detection of novel interaction partners, we scaled up VLP production and purification protocols (Supplementary Fig. 5 and Supplementary Note 1 for an overview of the protocol) and investigated protein partners trapped using the following bait proteins: Fas-associated via death domain (FADD), A20 (TNFAIP3), nuclear factor-κB (NF-κB) essential modifier (IKBKG), TRAF family member-associated NF-κB activator (TANK), MYD88 and ring finger protein 41 (RNF41). To obtain specific interactors from the lists of identified proteins, we challenged the data with a combined protein list of 19 unrelated Virotrap experiments (Supplementary Table 1 for an overview). Figure 3 shows the design and the list of candidate interactors obtained after removal of all proteins that were found in the 19 control samples (including removal of proteins from the control list identified with a single peptide). The remaining list of confident protein identifications (identified with at least two peptides in at least two biological repeats) reveals both known and novel candidate interaction partners. All candidate interactors including single peptide protein identifications are given in Supplementary Data 2 and also include recurrent protein identifications of known interactors based on a single peptide; for example, CASP8 for FADD and TANK for NEMO. Using alternative methods, we confirmed the interaction between A20 and FADD, and the associations with transmembrane proteins (insulin receptor and insulin-like growth factor receptor 1) that were captured using RNF41 as a bait (Supplementary Fig. 6). To address the use of Virotrap for the detection of dynamic interactions, we activated the NF-κB pathway via the tumour necrosis factor (TNF) receptor (TNFRSF1A) using TNFα (TNF) and performed Virotrap analysis using A20 as bait (Fig. 3). This resulted in the additional enrichment of receptor-interacting kinase (RIPK1), TNFR1-associated via death domain (TRADD), TNFRSF1A and TNF itself, confirming the expected activated complex20.

Figure 3: Use of Virotrap for unbiased interactome analysis

Figure 4: Use of Virotrap for detection of protein partners of small molecules.


Lysis conditions used in AP–MS strategies are critical for the preservation of protein complexes. A multitude of lysis conditions have been described, culminating in a recent report where protein complex stability was assessed under 96 lysis/purification protocols5. Moreover, the authors suggest to optimize the conditions for every complex, implying an important workload for researchers embarking on protein complex analysis using classical AP–MS. As lysis results in a profound change of the subcellular context and significantly alters the concentration of proteins, loss of complex integrity during a classical AP–MS protocol can be expected. A clear evolution towards ‘lysis-independent’ approaches in the co-complex analysis field is evident with the introduction of BioID6 and APEX25 where proximal proteins, including proteins residing in the complex, are labelled with biotin by an enzymatic activity fused to a bait protein. A side-by-side comparison between classical AP–MS and BioID showed overlapping and unique candidate binding proteins for both approaches78, supporting the notion that complementary methods are needed to provide a comprehensive view on protein complexes. This has also been clearly demonstrated for binary approaches15 and is a logical consequence of the heterogenic nature underlying PPIs (binding mechanism, requirement for posttranslational modifications, location, affinity and so on).

In this report, we explore an alternative, yet complementary method to isolate protein complexes without interfering with cellular integrity. By trapping protein complexes in the protective environment of a virus-like shell, the intact complexes are preserved during the purification process. This constitutes a new concept in co-complex analysis wherein complex stability is physically guaranteed by a protective, physical structure. A comparison of our Virotrap approach with AP–MS shows complementary data, with specific false positives and false negatives for both methods (Supplementary Fig. 7).

The current implementation of the Virotrap platform implies the use of a GAG-bait construct resulting in considerable expression of the bait protein. Different strategies are currently pursued to reduce bait expression including co-expression of a native GAG protein together with the GAG-bait protein, not only reducing bait expression but also creating more ‘space’ in the particles potentially accommodating larger bait protein complexes. Nevertheless, the presence of the bait on the forming GAG scaffold creates an intracellular affinity matrix (comparable to the early in vitro affinity columns for purification of interaction partners from lysates26) that has the potential to compete with endogenous complexes by avidity effects. This avidity effect is a powerful mechanism that aids in the recruitment of cyclophilin to GAG27, a well-known weak interaction (Kd=16 μM (ref. 28)) detectable as a background association in the Virotrap system. Although background binding may be increased by elevated bait expression, weaker associations are readily detectable (for example, MAL—MYD88-binding study; Fig. 2c).

The size of Virotrap particles (around 145 nm) suggests limitations in the size of the protein complex that can be accommodated in the particles. Further experimentation is required to define the maximum size of proteins or the number of protein complexes that can be trapped inside the particles.


In conclusion, Virotrap captures significant parts of known interactomes and reveals new interactions. This cell lysis-free approach purifies protein complexes under native conditions and thus provides a powerful method to complement AP–MS or other PPI data. Future improvements of the system include strategies to reduce bait expression to more physiological levels and application of advanced data analysis options to filter out background. These developments can further aid in the deployment of Virotrap as a powerful extension of the current co-complex technology arsenal.


New Autism Blood Biomarker Identified

Researchers at UT Southwestern Medical Center have identified a blood biomarker that may aid in earlier diagnosis of children with autism spectrum disorder, or ASD


In a recent edition of Scientific Reports, UT Southwestern researchers reported on the identification of a blood biomarker that could distinguish the majority of ASD study participants versus a control group of similar age range. In addition, the biomarker was significantly correlated with the level of communication impairment, suggesting that the blood test may give insight into ASD severity.

“Numerous investigators have long sought a biomarker for ASD,” said Dr. Dwight German, study senior author and Professor of Psychiatry at UT Southwestern. “The blood biomarker reported here along with others we are testing can represent a useful test with over 80 percent accuracy in identifying ASD.”

ASD1 –  was 66 percent accurate in diagnosing ASD. When combined with thyroid stimulating hormone level measurements, the ASD1-binding biomarker was 73 percent accurate at diagnosis


A Search for Blood Biomarkers for Autism: Peptoids

Sayed ZamanUmar Yazdani,…, Laura Hewitson & Dwight C. German
Scientific Reports 2016; 6(19164)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, and restricted, repetitive patterns of behavior. In order to identify individuals with ASD and initiate interventions at the earliest possible age, biomarkers for the disorder are desirable. Research findings have identified widespread changes in the immune system in children with autism, at both systemic and cellular levels. In an attempt to find candidate antibody biomarkers for ASD, highly complex libraries of peptoids (oligo-N-substituted glycines) were screened for compounds that preferentially bind IgG from boys with ASD over typically developing (TD) boys. Unexpectedly, many peptoids were identified that preferentially bound IgG from TD boys. One of these peptoids was studied further and found to bind significantly higher levels (>2-fold) of the IgG1 subtype in serum from TD boys (n = 60) compared to ASD boys (n = 74), as well as compared to older adult males (n = 53). Together these data suggest that ASD boys have reduced levels (>50%) of an IgG1 antibody, which resembles the level found normally with advanced age. In this discovery study, the ASD1 peptoid was 66% accurate in predicting ASD.


Peptoid libraries have been used previously to search for autoantibodies for neurodegenerative diseases19 and for systemic lupus erythematosus (SLE)21. In the case of SLE, peptoids were identified that could identify subjects with the disease and related syndromes with moderate sensitivity (70%) and excellent specificity (97.5%). Peptoids were used to measure IgG levels from both healthy subjects and SLE patients. Binding to the SLE-peptoid was significantly higher in SLE patients vs. healthy controls. The IgG bound to the SLE-peptoid was found to react with several autoantigens, suggesting that the peptoids are capable of interacting with multiple, structurally similar molecules. These data indicate that IgG binding to peptoids can identify subjects with high levels of pathogenic autoantibodies vs. a single antibody.

In the present study, the ASD1 peptoid binds significantly lower levels of IgG1 in ASD males vs. TD males. This finding suggests that the ASD1 peptoid recognizes antibody(-ies) of an IgG1 subtype that is (are) significantly lower in abundance in the ASD males vs. TD males. Although a previous study14 has demonstrated lower levels of plasma IgG in ASD vs. TD children, here, we additionally quantified serum IgG levels in our individuals and found no difference in IgG between the two groups (data not shown). Furthermore, our IgG levels did not correlate with ASD1 binding levels, indicating that ASD1 does not bind IgG generically, and that the peptoid’s ability to differentiate between ASD and TD males is related to a specific antibody(-ies).

ASD subjects underwent a diagnostic evaluation using the ADOS and ADI-R, and application of the DSM-IV criteria prior to study inclusion. Only those subjects with a diagnosis of Autistic Disorder were included in the study. The ADOS is a semi-structured observation of a child’s behavior that allows examiners to observe the three core domains of ASD symptoms: reciprocal social interaction, communication, and restricted and repetitive behaviors1. When ADOS subdomain scores were compared with peptoid binding, the only significant relationship was with Social Interaction. However, the positive correlation would suggest that lower peptoid binding is associated with better social interaction, not poorer social interaction as anticipated.

The ADI-R is a structured parental interview that measures the core features of ASD symptoms in the areas of reciprocal social interaction, communication and language, and patterns of behavior. Of the three ADI-R subdomains, only the Communication domain was related to ASD1 peptoid binding, and this correlation was negative suggesting that low peptoid binding is associated with greater communication problems. These latter data are similar to the findings of Heuer et al.14 who found that children with autism with low levels of plasma IgG have high scores on the Aberrant Behavior Checklist (p < 0.0001). Thus, peptoid binding to IgG1 may be useful as a severity marker for ASD allowing for further characterization of individuals, but further research is needed.

It is interesting that in serum samples from older men, the ASD1 binding is similar to that in the ASD boys. This is consistent with the observation that with aging there is a reduction in the strength of the immune system, and the changes are gender-specific25. Recent studies using parabiosis26, in which blood from young mice reverse age-related impairments in cognitive function and synaptic plasticity in old mice, reveal that blood constituents from young subjects may contain important substances for maintaining neuronal functions. Work is in progress to identify the antibody/antibodies that are differentially binding to the ASD1 peptoid, which appear as a single band on the electrophoresis gel (Fig. 4).


The ADI-R is a structured parental interview that measures the core features of ASD symptoms in the areas of reciprocal social interaction, communication and language, and patterns of behavior. Of the three ADI-R subdomains, only the Communication domain was related to ASD1 peptoid binding, and this correlation was negative suggesting that low peptoid binding is associated with greater communication problems. These latter data are similar to the findings of Heuer et al.14 who found that children with autism with low levels of plasma IgG have high scores on the Aberrant Behavior Checklist (p < 0.0001). Thus, peptoid binding to IgG1 may be useful as a severity marker for ASD allowing for further characterization of individuals, but further research is needed.


  • Titration of IgG binding to ASD1 using serum pooled from 10 TD males and 10 ASD males demonstrates ASD1’s ability to differentiate between the two groups. (B)Detecting IgG1 subclass instead of total IgG amplifies this differentiation. (C) IgG1 binding of individual ASD (n=74) and TD (n=60) male serum samples (1:100 dilution) to ASD1 significantly differs with TD>ASD. In addition, IgG1 binding of older adult male (AM) serum samples (n=53) to ASD1 is significantly lower than TD males, and not different from ASD males. The three groups were compared with a Kruskal-Wallis ANOVA, H = 10.1781, p<0.006. **p<0.005. Error bars show SEM. (D) Receiver-operating characteristic curve for ASD1’s ability to discriminate between ASD and TD males.


Association between peptoid binding and ADOS and ADI-R subdomains

Higher scores in any domain on the ADOS and ADI-R are indicative of more abnormal behaviors and/or symptoms. Among ADOS subdomains, there was no significant relationship between Communication and peptoid binding (z = 0.04, p = 0.966), Communication + Social interaction (z = 1.53, p = 0.127), or Stereotyped Behaviors and Restrictive Interests (SBRI) (z = 0.46, p = 0.647). Higher scores on the Social Interaction domain were significantly associated with higher peptoid binding (z = 2.04, p = 0.041).

Among ADI-R subdomains, higher scores on the Communication domain were associated with lower levels of peptoid binding (z = −2.28, p = 0.023). There was not a significant relationship between Social Interaction (z = 0.07, p = 0.941) or Restrictive/Repetitive Stereotyped Behaviors (z = −1.40, p = 0.162) and peptoid binding.



Computational Model Finds New Protein-Protein Interactions

Researchers at University of Pittsburgh have discovered 500 new protein-protein interactions (PPIs) associated with genes linked to schizophrenia.

Using a computational model they developed, researchers at the University of Pittsburgh School of Medicine have discovered more than 500 new protein-protein interactions (PPIs) associated with genes linked to schizophrenia. The findings, published online in npj Schizophrenia, a Nature Publishing Group journal, could lead to greater understanding of the biological underpinnings of this mental illness, as well as point the way to treatments.

There have been many genome-wide association studies (GWAS) that have identified gene variants associated with an increased risk for schizophrenia, but in most cases there is little known about the proteins that these genes make, what they do and how they interact, said senior investigator Madhavi Ganapathiraju, Ph.D., assistant professor of biomedical informatics, Pitt School of Medicine.

“GWAS studies and other research efforts have shown us what genes might be relevant in schizophrenia,” she said. “What we have done is the next step. We are trying to understand how these genes relate to each other, which could show us the biological pathways that are important in the disease.”

Each gene makes proteins and proteins typically interact with each other in a biological process. Information about interacting partners can shed light on the role of a gene that has not been studied, revealing pathways and biological processes associated with the disease and also its relation to other complex diseases.

Dr. Ganapathiraju’s team developed a computational model called High-Precision Protein Interaction Prediction (HiPPIP) and applied it to discover PPIs of schizophrenia-linked genes identified through GWAS, as well as historically known risk genes. They found 504 never-before known PPIs, and noted also that while schizophrenia-linked genes identified historically and through GWAS had little overlap, the model showed they shared more than 100 common interactors.

“We can infer what the protein might do by checking out the company it keeps,” Dr. Ganapathiraju explained. “For example, if I know you have many friends who play hockey, it could mean that you are involved in hockey, too. Similarly, if we see that an unknown protein interacts with multiple proteins involved in neural signaling, for example, there is a high likelihood that the unknown entity also is involved in the same.”

Dr. Ganapathiraju and colleagues have drawn such inferences on protein function based on the PPIs of proteins, and made their findings available on a website Schizo-Pi. This information can be used by biologists to explore the schizophrenia interactome with the aim of understanding more about the disease or developing new treatment drugs.

Schizophrenia interactome with 504 novel protein–protein interactions

MK GanapathirajuM Thahir,…,  CE LoscherEM Bauer & S Chaparala
npj Schizophrenia 2016;  2(16012)

(GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein–protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at with advanced search capabilities.

Schizophrenia (SZ) is a common, potentially severe psychiatric disorder that afflicts all populations.1 Gene mapping studies suggest that SZ is a complex disorder, with a cumulative impact of variable genetic effects coupled with environmental factors.2 As many as 38 genome-wide association studies (GWAS) have been reported on SZ out of a total of 1,750 GWAS publications on 1,087 traits or diseases reported in the GWAS catalog maintained by the National Human Genome Research Institute of USA3 (as of April 2015), revealing the common variants associated with SZ.4 The SZ Working Group of the Psychiatric Genomics Consortium (PGC) identified 108 genetic loci that likely confer risk for SZ.5 While the role of genetics has been clearly validated by this study, the functional impact of the risk variants is not well-understood.6,7 Several of the genes implicated by the GWAS have unknown functions and could participate in possibly hitherto unknown pathways.8 Further, there is little or no overlap between the genes identified through GWAS and ‘candidate genes’ proposed in the pre-GWAS era.9

Interactome-based studies can be useful in discovering the functional associations of genes. For example,disrupted in schizophrenia 1 (DISC1), an SZ related candidate gene originally had no known homolog in humans. Although it had well-characterized protein domains such as coiled-coil domains and leucine-zipper domains, its function was unknown.10,11 Once its protein–protein interactions (PPIs) were determined using yeast 2-hybrid technology,12 investigators successfully linked DISC1 to cAMP signaling, axon elongation, and neuronal migration, and accelerated the research pertaining to SZ in general, and DISC1 in particular.13 Typically such studies are carried out on known protein–protein interaction (PPI) networks, or as in the case of DISC1, when there is a specific gene of interest, its PPIs are determined by methods such as yeast 2-hybrid technology.

Knowledge of human PPI networks is thus valuable for accelerating discovery of protein function, and indeed, biomedical research in general. However, of the hundreds of thousands of biophysical PPIs thought to exist in the human interactome,14,15 <100,000 are known today (Human Protein Reference Database, HPRD16 and BioGRID17 databases). Gold standard experimental methods for the determination of all the PPIs in human interactome are time-consuming, expensive and may not even be feasible, as about 250 million pairs of proteins would need to be tested overall; high-throughput methods such as yeast 2-hybrid have important limitations for whole interactome determination as they have a low recall of 23% (i.e., remaining 77% of true interactions need to be determined by other means), and a low precision (i.e., the screens have to be repeated multiple times to achieve high selectivity).18,19Computational methods are therefore necessary to complete the interactome expeditiously. Algorithms have begun emerging to predict PPIs using statistical machine learning on the characteristics of the proteins, but these algorithms are employed predominantly to study yeast. Two significant computational predictions have been reported for human interactome; although they have had high false positive rates, these methods have laid the foundation for computational prediction of human PPIs.20,21

We have created a new PPI prediction model called High-Confidence Protein–Protein Interaction Prediction (HiPPIP) model. Novel interactions predicted with this model are making translational impact. For example, we discovered a PPI between OASL and DDX58, which on validation showed that an increased expression of OASL could boost innate immunity to combat influenza by activating the RIG-I pathway.22 Also, the interactome of the genes associated with congenital heart disease showed that the disease morphogenesis has a close connection with the structure and function of cilia.23Here, we describe the HiPPIP model and its application to SZ genes to construct the SZ interactome. After computational evaluations and experimental validations of selected novel PPIs, we present here 504 highly confident novel PPIs in the SZ interactome, shedding new light onto several uncharacterized genes that are associated with SZ.

We developed a computational model called HiPPIP to predict PPIs (see Methods and Supplementary File 1). The model has been evaluated by computational methods and experimental validations and is found to be highly accurate. Evaluations on a held-out test data showed a precision of 97.5% and a recall of 5%. 5% recall out of 150,000 to 600,000 estimated number of interactions in the human interactome corresponds to 7,500–30,000 novel PPIs in the whole interactome. Note that, it is likely that the real precision would be higher than 97.5% because in this test data, randomly paired proteins are treated as non-interacting protein pairs, whereas some of them may actually be interacting pairs with a small probability; thus, some of the pairs that are treated as false positives in test set are likely to be true but hitherto unknown interactions. In Figure 1a, we show the precision versus recall of our method on ‘hub proteins’ where we considered all pairs that received a score >0.5 by HiPPIP to be novel interactions. In Figure 1b, we show the number of true positives versus false positives observed in hub proteins. Both these figures also show our method to be superior in comparison to the prediction of membrane-receptor interactome by Qi et al’s.24 True positives versus false positives are also shown for individual hub proteins by our method in Figure 1cand by Qi et al’s.23 in Figure 1d. These evaluations showed that our predictions contain mostly true positives. Unlike in other domains where ranked lists are commonly used such as information retrieval, in PPI prediction the ‘false positives’ may actually be unlabeled instances that are indeed true interactions that are not yet discovered. In fact, such unlabeled pairs predicted as interactors of the hub gene HMGB1 (namely, the pairs HMGB1-KL and HMGB1-FLT1) were validated by experimental methods and found to be true PPIs (See the Figures e–g inSupplementary File 3). Thus, we concluded that the protein pairs that received a score of ⩾0.5 are highly confident to be true interactions. The pairs that receive a score less than but close to 0.5 (i.e., in the range of 0.4–0.5) may also contain several true PPIs; however, we cannot confidently say that all in this range are true PPIs. Only the PPIs predicted with a score >0.5 are included in the interactome.

Figure 1

Computational evaluation of predicted protein–protein interactions on hub proteins: (a) precision recall curve. (b) True positive versus false positives in ranked lists of hub type membrane receptors for our method and that by Qi et al. True positives versus false positives are shown for individual membrane receptors by our method in (c) and by Qi et al. in (d). Thick line is the average, which is also the same as shown in (b). Note:x-axis is recall in (a), whereas it is number of false positives in (bd). The range of y-axis is observed by varying the threshold from 1.0–0 in (a), and to 0.5 in (bd).

SZ interactome

By applying HiPPIP to the GWAS genes and Historic (pre-GWAS) genes, we predicted over 500 high confidence new PPIs adding to about 1400 previously known PPIs.

Schizophrenia interactome: network view of the schizophrenia interactome is shown as a graph, where genes are shown as nodes and PPIs as edges connecting the nodes. Schizophrenia-associated genes are shown as dark blue nodes, novel interactors as red color nodes and known interactors as blue color nodes. The source of the schizophrenia genes is indicated by its label font, where Historic genes are shown italicized, GWAS genes are shown in bold, and the one gene that is common to both is shown in italicized and bold. For clarity, the source is also indicated by the shape of the node (triangular for GWAS and square for Historic and hexagonal for both). Symbols are shown only for the schizophrenia-associated genes; actual interactions may be accessed on the web. Red edges are the novel interactions, whereas blue edges are known interactions. GWAS, genome-wide association studies of schizophrenia; PPI, protein–protein interaction.


Webserver of SZ interactome

We have made the known and novel interactions of all SZ-associated genes available on a webserver called Schizo-Pi, at the address This webserver is similar to Wiki-Pi33 which presents comprehensive annotations of both participating proteins of a PPI side-by-side. The difference between Wiki-Pi which we developed earlier, and Schizo-Pi, is the inclusion of novel predicted interactions of the SZ genes into the latter.

Despite the many advances in biomedical research, identifying the molecular mechanisms underlying the disease is still challenging. Studies based on protein interactions were proven to be valuable in identifying novel gene associations that could shed new light on disease pathology.35 The interactome including more than 500 novel PPIs will help to identify pathways and biological processes associated with the disease and also its relation to other complex diseases. It also helps identify potential drugs that could be repurposed to use for SZ treatment.

Functional and pathway enrichment in SZ interactome

When a gene of interest has little known information, functions of its interacting partners serve as a starting point to hypothesize its own function. We computed statistically significant enrichment of GO biological process terms among the interacting partners of each of the genes using BinGO36 (see online at


Protein aggregation and aggregate toxicity: new insights into protein folding, misfolding diseases and biological evolution

Massimo Stefani · Christopher M. Dobson

Abstract The deposition of proteins in the form of amyloid fibrils and plaques is the characteristic feature of more than 20 degenerative conditions affecting either the central nervous system or a variety of peripheral tissues. As these conditions include Alzheimer’s, Parkinson’s and the prion diseases, several forms of fatal systemic amyloidosis, and at least one condition associated with medical intervention (haemodialysis), they are of enormous importance in the context of present-day human health and welfare. Much remains to be learned about the mechanism by which the proteins associated with these diseases aggregate and form amyloid structures, and how the latter affect the functions of the organs with which they are associated. A great deal of information concerning these diseases has emerged, however, during the past 5 years, much of it causing a number of fundamental assumptions about the amyloid diseases to be reexamined. For example, it is now apparent that the ability to form amyloid structures is not an unusual feature of the small number of proteins associated with these diseases but is instead a general property of polypeptide chains. It has also been found recently that aggregates of proteins not associated with amyloid diseases can impair the ability of cells to function to a similar extent as aggregates of proteins linked with specific neurodegenerative conditions. Moreover, the mature amyloid fibrils or plaques appear to be substantially less toxic than the prefibrillar aggregates that are their precursors. The toxicity of these early aggregates appears to result from an intrinsic ability to impair fundamental cellular processes by interacting with cellular membranes, causing oxidative stress and increases in free Ca2+ that eventually lead to apoptotic or necrotic cell death. The ‘new view’ of these diseases also suggests that other degenerative conditions could have similar underlying origins to those of the amyloidoses. In addition, cellular protection mechanisms, such as molecular chaperones and the protein degradation machinery, appear to be crucial in the prevention of disease in normally functioning living organisms. It also suggests some intriguing new factors that could be of great significance in the evolution of biological molecules and the mechanisms that regulate their behaviour.

The genetic information within a cell encodes not only the specific structures and functions of proteins but also the way these structures are attained through the process known as protein folding. In recent years many of the underlying features of the fundamental mechanism of this complex process and the manner in which it is regulated in living systems have emerged from a combination of experimental and theoretical studies [1]. The knowledge gained from these studies has also raised a host of interesting issues. It has become apparent, for example, that the folding and unfolding of proteins is associated with a whole range of cellular processes from the trafficking of molecules to specific organelles to the regulation of the cell cycle and the immune response. Such observations led to the inevitable conclusion that the failure to fold correctly, or to remain correctly folded, gives rise to many different types of biological malfunctions and hence to many different forms of disease [2]. In addition, it has been recognised recently that a large number of eukaryotic genes code for proteins that appear to be ‘natively unfolded’, and that proteins can adopt, under certain circumstances, highly organised multi-molecular assemblies whose structures are not specifically encoded in the amino acid sequence. Both these observations have raised challenging questions about one of the most fundamental principles of biology: the close relationship between the sequence, structure and function of proteins, as we discuss below [3].

It is well established that proteins that are ‘misfolded’, i.e. that are not in their functionally relevant conformation, are devoid of normal biological activity. In addition, they often aggregate and/or interact inappropriately with other cellular components leading to impairment of cell viability and eventually to cell death. Many diseases, often known as misfolding or conformational diseases, ultimately result from the presence in a living system of protein molecules with structures that are ‘incorrect’, i.e. that differ from those in normally functioning organisms [4]. Such diseases include conditions in which a specific protein, or protein complex, fails to fold correctly (e.g. cystic fibrosis, Marfan syndrome, amyotonic lateral sclerosis) or is not sufficiently stable to perform its normal function (e.g. many forms of cancer). They also include conditions in which aberrant folding behaviour results in the failure of a protein to be correctly trafficked (e.g. familial hypercholesterolaemia, α1-antitrypsin deficiency, and some forms of retinitis pigmentosa) [4]. The tendency of proteins to aggregate, often to give species extremely intractable to dissolution and refolding, is of course also well known in other circumstances. Examples include the formation of inclusion bodies during overexpression of heterologous proteins in bacteria and the precipitation of proteins during laboratory purification procedures. Indeed, protein aggregation is well established as one of the major difficulties associated with the production and handling of proteins in the biotechnology and pharmaceutical industries [5].

Considerable attention is presently focused on a group of protein folding diseases known as amyloidoses. In these diseases specific peptides or proteins fail to fold or to remain correctly folded and then aggregate (often with other components) so as to give rise to ‘amyloid’ deposits in tissue. Amyloid structures can be recognised because they possess a series of specific tinctorial and biophysical characteristics that reflect a common core structure based on the presence of highly organised βsheets [6]. The deposits in strictly defined amyloidoses are extracellular and can often be observed as thread-like fibrillar structures, sometimes assembled further into larger aggregates or plaques. These diseases include a range of sporadic, familial or transmissible degenerative diseases, some of which affect the brain and the central nervous system (e.g. Alzheimer’s and Creutzfeldt-Jakob diseases), while others involve peripheral tissues and organs such as the liver, heart and spleen (e.g. systemic amyloidoses and type II diabetes) [7, 8]. In other forms of amyloidosis, such as primary or secondary systemic amyloidoses, proteinaceous deposits are found in skeletal tissue and joints (e.g. haemodialysis-related amyloidosis) as well as in several organs (e.g. heart and kidney). Yet other components such as collagen, glycosaminoglycans and proteins (e.g. serum amyloid protein) are often present in the deposits protecting them against degradation [9, 10, 11]. Similar deposits to those in the amyloidoses are, however, found intracellularly in other diseases; these can be localised either in the cytoplasm, in the form of specialised aggregates known as aggresomes or as Lewy or Russell bodies or in the nucleus (see below).

The presence in tissue of proteinaceous deposits is a hallmark of all these diseases, suggesting a causative link between aggregate formation and pathological symptoms (often known as the amyloid hypothesis) [7, 8, 12]. At the present time the link between amyloid formation and disease is widely accepted on the basis of a large number of biochemical and genetic studies. The specific nature of the pathogenic species, and the molecular basis of their ability to damage cells, are however, the subject of intense debate [13, 14, 15, 16, 17, 18, 19, 20]. In neurodegenerative disorders it is very likely that the impairment of cellular function follows directly from the interactions of the aggregated proteins with cellular components [21, 22]. In the systemic non-neurological diseases, however, it is widely believed that the accumulation in vital organs of large amounts of amyloid deposits can by itself cause at least some of the clinical symptoms [23]. It is quite possible, however, that there are other more specific effects of aggregates on biochemical processes even in these diseases. The presence of extracellular or intracellular aggregates of a specific polypeptide molecule is a characteristic of all the 20 or so recognised amyloid diseases. The polypeptides involved include full length proteins (e.g. lysozyme or immunoglobulin light chains), biological peptides (amylin, atrial natriuretic factor) and fragments of larger proteins produced as a result of specific processing (e.g. the Alzheimer βpeptide) or of more general degradation [e.g. poly(Q) stretches cleaved from proteins with poly(Q) extensions such as huntingtin, ataxins and the androgen receptor]. The peptides and proteins associated with known amyloid diseases are listed in Table 1. In some cases the proteins involved have wild type sequences, as in sporadic forms of the diseases, but in other cases these are variants resulting from genetic mutations associated with familial forms of the diseases. In some cases both sporadic and familial diseases are associated with a given protein; in this case the mutational variants are usually associated with early-onset forms of the disease. In the case of the neurodegenerative diseases associated with the prion protein some forms of the diseases are transmissible. The existence of familial forms of a number of amyloid diseases has provided significant clues to the origins of the pathologies. For example, there are increasingly strong links between the age at onset of familial forms of disease and the effects of the mutations involved on the propensity of the affected proteins to aggregate in vitro. Such findings also support the link between the process of aggregation and the clinical manifestations of disease [24, 25].

The presence in cells of misfolded or aggregated proteins triggers a complex biological response. In the cytosol, this is referred to as the ‘heat shock response’ and in the endoplasmic reticulum (ER) it is known as the ‘unfolded protein response’. These responses lead to the expression, among others, of the genes for heat shock proteins (Hsp, or molecular chaperone proteins) and proteins involved in the ubiquitin-proteasome pathway [26]. The evolution of such complex biochemical machinery testifies to the fact that it is necessary for cells to isolate and clear rapidly and efficiently any unfolded or incorrectly folded protein as soon as it appears. In itself this fact suggests that these species could have a generally adverse effect on cellular components and cell viability. Indeed, it was a major step forward in understanding many aspects of cell biology when it was recognised that proteins previously associated only with stress, such as heat shock, are in fact crucial in the normal functioning of living systems. This advance, for example, led to the discovery of the role of molecular chaperones in protein folding and in the normal ‘housekeeping’ processes that are inherent in healthy cells [27, 28]. More recently a number of degenerative diseases, both neurological and systemic, have been linked to, or shown to be affected by, impairment of the ubiquitin-proteasome pathway (Table 2). The diseases are primarily associated with a reduction in either the expression or the biological activity of Hsps, ubiquitin, ubiquitinating or deubiquitinating enzymes and the proteasome itself, as we show below [29, 30, 31, 32], or even to the failure of the quality control mechanisms that ensure proper maturation of proteins in the ER. The latter normally leads to degradation of a significant proportion of polypeptide chains before they have attained their native conformations through retrograde translocation to the cytosol [33, 34].


It is now well established that the molecular basis of protein aggregation into amyloid structures involves the existence of ‘misfolded’ forms of proteins, i.e. proteins that are not in the structures in which they normally function in vivo or of fragments of proteins resulting from degradation processes that are inherently unable to fold [4, 7, 8, 36]. Aggregation is one of the common consequences of a polypeptide chain failing to reach or maintain its functional three-dimensional structure. Such events can be associated with specific mutations, misprocessing phenomena, aberrant interactions with metal ions, changes in environmental conditions, such as pH or temperature, or chemical modification (oxidation, proteolysis). Perturbations in the conformational properties of the polypeptide chain resulting from such phenomena may affect equilibrium 1 in Fig. 1 increasing the population of partially unfolded, or misfolded, species that are much more aggregation-prone than the native state.

Fig. 1 Overview of the possible fates of a newly synthesised polypeptide chain. The equilibrium ① between the partially folded molecules and the natively folded ones is usually strongly in favour of the latter except as a result of specific mutations, chemical modifications or partially destabilising solution conditions. The increased equilibrium populations of molecules in the partially or completely unfolded ensemble of structures are usually degraded by the proteasome; when this clearance mechanism is impaired, such species often form disordered aggregates or shift equilibrium ② towards the nucleation of pre-fibrillar assemblies that eventually grow into mature fibrils (equilibrium ③). DANGER! indicates that pre-fibrillar aggregates in most cases display much higher toxicity than mature fibrils. Heat shock proteins (Hsp) can suppress the appearance of pre-fibrillar assemblies by minimising the population of the partially folded molecules by assisting in the correct folding of the nascent chain and the unfolded protein response target incorrectly folded proteins for degradation.


Little is known at present about the detailed arrangement of the polypeptide chains themselves within amyloid fibrils, either those parts involved in the core βstrands or in regions that connect the various β-strands. Recent data suggest that the sheets are relatively untwisted and may in some cases at least exist in quite specific supersecondary structure motifs such as β-helices [6, 40] or the recently proposed µ-helix [41]. It seems possible that there may be significant differences in the way the strands are assembled depending on characteristics of the polypeptide chain involved [6, 42]. Factors including length, sequence (and in some cases the presence of disulphide bonds or post-translational modifications such as glycosylation) may be important in determining details of the structures. Several recent papers report structural models for amyloid fibrils containing different polypeptide chains, including the Aβ40 peptide, insulin and fragments of the prion protein, based on data from such techniques as cryo-electron microscopy and solid-state magnetic resonance spectroscopy [43, 44]. These models have much in common and do indeed appear to reflect the fact that the structures of different fibrils are likely to be variations on a common theme [40]. It is also emerging that there may be some common and highly organised assemblies of amyloid protofilaments that are not simply extended threads or ribbons. It is clear, for example, that in some cases large closed loops can be formed [45, 46, 47], and there may be specific types of relatively small spherical or ‘doughnut’ shaped structures that can result in at least some circumstances (see below).


The similarity of some early amyloid aggregates with the pores resulting from oligomerisation of bacterial toxins and pore-forming eukaryotic proteins (see below) also suggest that the basic mechanism of protein aggregation into amyloid structures may not only be associated with diseases but in some cases could result in species with functional significance. Recent evidence indicates that a variety of micro-organisms may exploit the controlled aggregation of specific proteins (or their precursors) to generate functional structures. Examples include bacterial curli [52] and proteins of the interior fibre cells of mammalian ocular lenses, whose β-sheet arrays seem to be organised in an amyloid-like supramolecular order [53]. In this case the inherent stability of amyloid-like protein structure may contribute to the long-term structural integrity and transparency of the lens. Recently it has been hypothesised that amyloid-like aggregates of serum amyloid A found in secondary amyloidoses following chronic inflammatory diseases protect the host against bacterial infections by inducing lysis of bacterial cells [54]. One particularly interesting example is a ‘misfolded’ form of the milk protein α-lactalbumin that is formed at low pH and trapped by the presence of specific lipid molecules [55]. This form of the protein has been reported to trigger apoptosis selectively in tumour cells providing evidence for its importance in protecting infants from certain types of cancer [55]. ….

Amyloid formation is a generic property of polypeptide chains ….

It is clear that the presence of different side chains can influence the details of amyloid structures, particularly the assembly of protofibrils, and that they give rise to the variations on the common structural theme discussed above. More fundamentally, the composition and sequence of a peptide or protein affects profoundly its propensity to form amyloid structures under given conditions (see below).

Because the formation of stable protein aggregates of amyloid type does not normally occur in vivo under physiological conditions, it is likely that the proteins encoded in the genomes of living organisms are endowed with structural adaptations that mitigate against aggregation under these conditions. A recent survey involving a large number of structures of β-proteins highlights several strategies through which natural proteins avoid intermolecular association of β-strands in their native states [65].  Other surveys of protein databases indicate that nature disfavours sequences of alternating polar and nonpolar residues, as well as clusters of several consecutive hydrophobic residues, both of which enhance the tendency of a protein to aggregate prior to becoming completely folded [66, 67].


Precursors of amyloid fibrils can be toxic to cells

It was generally assumed until recently that the proteinaceous aggregates most toxic to cells are likely to be mature amyloid fibrils, the form of aggregates that have been commonly detected in pathological deposits. It therefore appeared probable that the pathogenic features underlying amyloid diseases are a consequence of the interaction with cells of extracellular deposits of aggregated material. As well as forming the basis for understanding the fundamental causes of these diseases, this scenario stimulated the exploration of therapeutic approaches to amyloidoses that focused mainly on the search for molecules able to impair the growth and deposition of fibrillar forms of aggregated proteins. ….

Structural basis and molecular features of amyloid toxicity

The presence of toxic aggregates inside or outside cells can impair a number of cell functions that ultimately lead to cell death by an apoptotic mechanism [95, 96]. Recent research suggests, however, that in most cases initial perturbations to fundamental cellular processes underlie the impairment of cell function induced by aggregates of disease-associated polypeptides. Many pieces of data point to a central role of modifications to the intracellular redox status and free Ca2+ levels in cells exposed to toxic aggregates [45, 89, 97, 98, 99, 100, 101]. A modification of the intracellular redox status in such cells is associated with a sharp increase in the quantity of reactive oxygen species (ROS) that is reminiscent of the oxidative burst by which leukocytes destroy invading foreign cells after phagocytosis. In addition, changes have been observed in reactive nitrogen species, lipid peroxidation, deregulation of NO metabolism [97], protein nitrosylation [102] and upregulation of heme oxygenase-1, a specific marker of oxidative stress [103]. ….

Results have recently been reported concerning the toxicity towards cultured cells of aggregates of poly(Q) peptides which argues against a disease mechanism based on specific toxic features of the aggregates. These results indicate that there is a close relationship between the toxicity of proteins with poly(Q) extensions and their nuclear localisation. In addition they support the hypotheses that the toxicity of poly(Q) aggregates can be a consequence of altered interactions with nuclear coactivator or corepressor molecules including p53, CBP, Sp1 and TAF130 or of the interaction with transcription factors and nuclear coactivators, such as CBP, endowed with short poly(Q) stretches ([95] and references therein)…..

Concluding remarks
The data reported in the past few years strongly suggest that the conversion of normally soluble proteins into amyloid fibrils and the toxicity of small aggregates appearing during the early stages of the formation of the latter are common or generic features of polypeptide chains. Moreover, the molecular basis of this toxicity also appears to display common features between the different systems that have so far been studied. The ability of many, perhaps all, natural polypeptides to ‘misfold’ and convert into toxic aggregates under suitable conditions suggests that one of the most important driving forces in the evolution of proteins must have been the negative selection against sequence changes that increase the tendency of a polypeptide chain to aggregate. Nevertheless, as protein folding is a stochastic process, and no such process can be completely infallible, misfolded proteins or protein folding intermediates in equilibrium with the natively folded molecules must continuously form within cells. Thus mechanisms to deal with such species must have co-evolved with proteins. Indeed, it is clear that misfolding, and the associated tendency to aggregate, is kept under control by molecular chaperones, which render the resulting species harmless assisting in their refolding, or triggering their degradation by the cellular clearance machinery [166, 167, 168, 169, 170, 171, 172, 173, 175, 177, 178].

Misfolded and aggregated species are likely to owe their toxicity to the exposure on their surfaces of regions of proteins that are buried in the interior of the structures of the correctly folded native states. The exposure of large patches of hydrophobic groups is likely to be particularly significant as such patches favour the interaction of the misfolded species with cell membranes [44, 83, 89, 90, 91, 93]. Interactions of this type are likely to lead to the impairment of the function and integrity of the membranes involved, giving rise to a loss of regulation of the intracellular ion balance and redox status and eventually to cell death. In addition, misfolded proteins undoubtedly interact inappropriately with other cellular components, potentially giving rise to the impairment of a range of other biological processes. Under some conditions the intracellular content of aggregated species may increase directly, due to an enhanced propensity of incompletely folded or misfolded species to aggregate within the cell itself. This could occur as the result of the expression of mutational variants of proteins with decreased stability or cooperativity or with an intrinsically higher propensity to aggregate. It could also occur as a result of the overproduction of some types of protein, for example, because of other genetic factors or other disease conditions, or because of perturbations to the cellular environment that generate conditions favouring aggregation, such as heat shock or oxidative stress. Finally, the accumulation of misfolded or aggregated proteins could arise from the chaperone and clearance mechanisms becoming overwhelmed as a result of specific mutant phenotypes or of the general effects of ageing [173, 174].

The topics discussed in this review not only provide a great deal of evidence for the ‘new view’ that proteins have an intrinsic capability of misfolding and forming structures such as amyloid fibrils but also suggest that the role of molecular chaperones is even more important than was thought in the past. The role of these ubiquitous proteins in enhancing the efficiency of protein folding is well established [185]. It could well be that they are at least as important in controlling the harmful effects of misfolded or aggregated proteins as in enhancing the yield of functional molecules.


Nutritional Status is Associated with Faster Cognitive Decline and Worse Functional Impairment in the Progression of Dementia: The Cache County Dementia Progression Study1

Sanders, Chelseaa | Behrens, Stephaniea | Schwartz, Sarahb | Wengreen, Heidic | Corcoran, Chris D.b; d | Lyketsos, Constantine G.e | Tschanz, JoAnn T.a; d;
Journal of Alzheimer’s Disease 2016; 52(1):33-42,

Nutritional status may be a modifiable factor in the progression of dementia. We examined the association of nutritional status and rate of cognitive and functional decline in a U.S. population-based sample. Study design was an observational longitudinal study with annual follow-ups up to 6 years of 292 persons with dementia (72% Alzheimer’s disease, 56% female) in Cache County, UT using the Mini-Mental State Exam (MMSE), Clinical Dementia Rating Sum of Boxes (CDR-sb), and modified Mini Nutritional Assessment (mMNA). mMNA scores declined by approximately 0.50 points/year, suggesting increasing risk for malnutrition. Lower mMNA score predicted faster rate of decline on the MMSE at earlier follow-up times, but slower decline at later follow-up times, whereas higher mMNA scores had the opposite pattern (mMNA by time β= 0.22, p = 0.017; mMNA by time2 β= –0.04, p = 0.04). Lower mMNA score was associated with greater impairment on the CDR-sb over the course of dementia (β= 0.35, p <  0.001). Assessment of malnutrition may be useful in predicting rates of progression in dementia and may provide a target for clinical intervention.


Shared Genetic Risk Factors for Late-Life Depression and Alzheimer’s Disease

Ye, Qing | Bai, Feng* | Zhang, Zhijun
Journal of Alzheimer’s Disease 2016; 52(1): 1-15.                            

Background: Considerable evidence has been reported for the comorbidity between late-life depression (LLD) and Alzheimer’s disease (AD), both of which are very common in the general elderly population and represent a large burden on the health of the elderly. The pathophysiological mechanisms underlying the link between LLD and AD are poorly understood. Because both LLD and AD can be heritable and are influenced by multiple risk genes, shared genetic risk factors between LLD and AD may exist. Objective: The objective is to review the existing evidence for genetic risk factors that are common to LLD and AD and to outline the biological substrates proposed to mediate this association. Methods: A literature review was performed. Results: Genetic polymorphisms of brain-derived neurotrophic factor, apolipoprotein E, interleukin 1-beta, and methylenetetrahydrofolate reductase have been demonstrated to confer increased risk to both LLD and AD by studies examining either LLD or AD patients. These results contribute to the understanding of pathophysiological mechanisms that are common to both of these disorders, including deficits in nerve growth factors, inflammatory changes, and dysregulation mechanisms involving lipoprotein and folate. Other conflicting results have also been reviewed, and few studies have investigated the effects of the described polymorphisms on both LLD and AD. Conclusion: The findings suggest that common genetic pathways may underlie LLD and AD comorbidity. Studies to evaluate the genetic relationship between LLD and AD may provide insights into the molecular mechanisms that trigger disease progression as the population ages.


Association of Vitamin B12, Folate, and Sulfur Amino Acids With Brain Magnetic Resonance Imaging Measures in Older Adults: A Longitudinal Population-Based Study

B Hooshmand, F Mangialasche, G Kalpouzos…, et al.
AMA Psychiatry. Published online April 27, 2016.

Importance  Vitamin B12, folate, and sulfur amino acids may be modifiable risk factors for structural brain changes that precede clinical dementia.

Objective  To investigate the association of circulating levels of vitamin B12, red blood cell folate, and sulfur amino acids with the rate of total brain volume loss and the change in white matter hyperintensity volume as measured by fluid-attenuated inversion recovery in older adults.

Design, Setting, and Participants  The magnetic resonance imaging subsample of the Swedish National Study on Aging and Care in Kungsholmen, a population-based longitudinal study in Stockholm, Sweden, was conducted in 501 participants aged 60 years or older who were free of dementia at baseline. A total of 299 participants underwent repeated structural brain magnetic resonance imaging scans from September 17, 2001, to December 17, 2009.

Main Outcomes and Measures  The rate of brain tissue volume loss and the progression of total white matter hyperintensity volume.

Results  In the multi-adjusted linear mixed models, among 501 participants (300 women [59.9%]; mean [SD] age, 70.9 [9.1] years), higher baseline vitamin B12 and holotranscobalamin levels were associated with a decreased rate of total brain volume loss during the study period: for each increase of 1 SD, β (SE) was 0.048 (0.013) for vitamin B12 (P < .001) and 0.040 (0.013) for holotranscobalamin (P = .002). Increased total homocysteine levels were associated with faster rates of total brain volume loss in the whole sample (β [SE] per 1-SD increase, –0.035 [0.015]; P = .02) and with the progression of white matter hyperintensity among participants with systolic blood pressure greater than 140 mm Hg (β [SE] per 1-SD increase, 0.000019 [0.00001]; P = .047). No longitudinal associations were found for red blood cell folate and other sulfur amino acids.

Conclusions and Relevance  This study suggests that both vitamin B12 and total homocysteine concentrations may be related to accelerated aging of the brain. Randomized clinical trials are needed to determine the importance of vitamin B12supplementation on slowing brain aging in older adults.



Notes from Kurzweill

This vitamin stops the aging process in organs, say Swiss researchers

A potential breakthrough for regenerative medicine, pending further studies

Improved muscle stem cell numbers and muscle function in NR-treated aged mice: Newly regenerated muscle fibers 7 days after muscle damage in aged mice (left: control group; right: fed NR). (Scale bar = 50 μm). (credit: Hongbo Zhang et al./Science)

EPFL researchers have restored the ability of mice organs to regenerate and extend life by simply administering nicotinamide riboside (NR) to them.

NR has been shown in previous studies to be effective in boosting metabolism and treating a number of degenerative diseases. Now, an article by PhD student Hongbo Zhang published in Science also describes the restorative effects of NR on the functioning of stem cells for regenerating organs.

As in all mammals, as mice age, the regenerative capacity of certain organs (such as the liver and kidneys) and muscles (including the heart) diminishes. Their ability to repair them following an injury is also affected. This leads to many of the disorders typical of aging.

Mitochondria —> stem cells —> organs

To understand how the regeneration process deteriorates with age, Zhang teamed up with colleagues from ETH Zurich, the University of Zurich, and universities in Canada and Brazil. By using several biomarkers, they were able to identify the molecular chain that regulates how mitochondria — the “powerhouse” of the cell — function and how they change with age. “We were able to show for the first time that their ability to function properly was important for stem cells,” said Auwerx.

Under normal conditions, these stem cells, reacting to signals sent by the body, regenerate damaged organs by producing new specific cells. At least in young bodies. “We demonstrated that fatigue in stem cells was one of the main causes of poor regeneration or even degeneration in certain tissues or organs,” said Zhang.

How to revitalize stem cells

Which is why the researchers wanted to “revitalize” stem cells in the muscles of elderly mice. And they did so by precisely targeting the molecules that help the mitochondria to function properly. “We gave nicotinamide riboside to 2-year-old mice, which is an advanced age for them,” said Zhang.

“This substance, which is close to vitamin B3, is a precursor of NAD+, a molecule that plays a key role in mitochondrial activity. And our results are extremely promising: muscular regeneration is much better in mice that received NR, and they lived longer than the mice that didn’t get it.”

Parallel studies have revealed a comparable effect on stem cells of the brain and skin. “This work could have very important implications in the field of regenerative medicine,” said Auwerx. This work on the aging process also has potential for treating diseases that can affect — and be fatal — in young people, like muscular dystrophy (myopathy).

So far, no negative side effects have been observed following the use of NR, even at high doses. But while it appears to boost the functioning of all cells, it could include pathological ones, so further in-depth studies are required.

Abstract of NAD+ repletion improves mitochondrial and stem cell function and enhances life span in mice

Adult stem cells (SCs) are essential for tissue maintenance and regeneration yet are susceptible to senescence during aging. We demonstrate the importance of the amount of the oxidized form of cellular nicotinamide adenine dinucleotide (NAD+) and its impact on mitochondrial activity as a pivotal switch to modulate muscle SC (MuSC) senescence. Treatment with the NAD+ precursor nicotinamide riboside (NR) induced the mitochondrial unfolded protein response (UPRmt) and synthesis of prohibitin proteins, and this rejuvenated MuSCs in aged mice. NR also prevented MuSC senescence in the Mdx mouse model of muscular dystrophy. We furthermore demonstrate that NR delays senescence of neural SCs (NSCs) and melanocyte SCs (McSCs), and increased mouse lifespan. Strategies that conserve cellular NAD+ may reprogram dysfunctional SCs and improve lifespan in mammals.


Hongbo Zhang, Dongryeol Ryu, Yibo Wu, Karim Gariani, Xu Wang, Peiling Luan, Davide D’amico, Eduardo R. Ropelle, Matthias P. Lutolf, Ruedi Aebersold, Kristina Schoonjans, Keir J. Menzies, Johan Auwerx. NAD repletion improves mitochondrial and stem cell function and enhances lifespan in mice. Science, 2016 DOI: 10.1126/science.aaf2693


Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin

Sean WhalenRebecca M Truty & Katherine S Pollard
Nature Genetics 2016; 48:488–496

Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer–promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer–promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.

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Gene Editing with CRISPR gets Crisper

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN



CRISPR Moves from Butchery to Surgery   

More Genomes Are Going Under the CRISPR Knife, So Surgical Standards Are Rising

  • The Dharmacon subsidary of GE Healthcare provides the Edit-R Lentiviral Gene Engineering platform. It is based on the natural S. pyrogenes system, but unlike that system, which uses a single guide RNA (sgRNA), the platform uses two component RNAs, a gene-specific CRISPR RNA (crRNA) and a universal trans-activating crRNA (tracrRNA). Once hybridized to the universal tracrRNA (blue), the crRNA (green) directs the Cas9 nuclease to a specific genomic region to induce a double- strand break.

    Scientists recently convened at the CRISPR Precision Gene Editing Congress, held in Boston, to discuss the new technology. As with any new technique, scientists have discovered that CRISPR comes with its own set of challenges, and the Congress focused its discussion around improving specificity, efficiency, and delivery.

    In the naturally occurring system, CRISPR-Cas9 works like a self-vaccination in the bacterial immune system by targeting and cleaving viral DNA sequences stored from previous encounters with invading phages. The endogenous system uses two RNA elements, CRISPR RNA (crRNA) and trans-activating RNA (tracrRNA), which come together and guide the Cas9 nuclease to the target DNA.

    Early publications that demonstrated CRISPR gene editing in mammalian cells combined the crRNA and tracrRNA sequences to form one long transcript called asingle-guide RNA (sgRNA). However, an alternative approach is being explored by scientists at the Dharmacon subsidiary of GE Healthcare. These scientists have a system that mimics the endogenous system through a synthetic two-component approach thatpreserves individual crRNA and tracrRNA. The tracrRNA is universal to any gene target or species; the crRNA contains the information needed to target the gene of interest.

    Predesigned Guide RNAs

    In contrast to sgRNAs, which are generated through either in vitro transcription of a DNA template or a plasmid-based expression system, synthetic crRNA and tracrRNA eliminate the need for additional cloning and purification steps. The efficacy of guide RNA (gRNA), whether delivered as a sgRNA or individual crRNA and tracrRNA, depends not only on DNA binding, but also on the generation of an indel that will deliver the coup de grâce to gene function.

    “Almost all of the gRNAs were able to create a break in genomic DNA,” said Louise Baskin, senior product manager at Dharmacon. “But there was a very wide range in efficiency and in creating functional protein knock-outs.”

    To remove the guesswork from gRNA design, Dharmacon developed an algorithm to predict gene knockout efficiency using wet-lab data. They also incorporated specificity as a component of their algorithm, using a much more comprehensive alignment tool to predict potential off-target effects caused by mismatches and bulges often missed by other alignment tools. Customers can enter their target gene to access predesigned gRNAs as either two-component RNAs or lentiviral sgRNA vectors for multiple applications.

    “We put time and effort into our algorithm to ensure that our guide RNAs are not only functional but also highly specific,” asserts Baskin. “As a result, customers don’t have to do any design work.”

    Donor DNA Formats
    MilliporeSigma’s CRISPR Epigenetic Activator is based on fusion of a nuclease-deficient Cas9 (dCas9) to the catalytic histone acetyltransferase (HAT) core domain of the human E1A-associated protein p300. This technology allows researchers to target specific DNA regions or gene sequences. Researchers can localize epigenetic changes to their target of interest and see the effects of those changes in gene expression.

    Knockout experiments are a powerful tool for analyzing gene function. However, for researchers who want to introduce DNA into the genome, guide design, donor DNA selection, and Cas9 activity are paramount to successful DNA integration.MilliporeSigma offers two formats for donor DNA: double-stranded DNA (dsDNA) plasmids and single-stranded DNA (ssDNA) oligonucleotides. The most appropriate format depends on cell type and length of the donor DNA. “There are some cell types that have immune responses to dsDNA,” said Gregory Davis, Ph.D., R&D manager, MilliporeSigma.

  • The ssDNA format can save researchers time and money, but it has a limited carrying capacity of approximately 120 base pairs.In addition to selecting an appropriate donor DNA format, controlling where, how, and when the Cas9 enzyme cuts can affect gene-editing efficiency. Scientists are playing tug-of-war, trying to pull cells toward the preferred homology-directed repair (HDR) and away from the less favored nonhomologous end joining (NHEJ) repair mechanism.One method to achieve this modifies the Cas9 enzyme to generate a nickase that cuts only one DNA strand instead of creating a double-strand break. Accordingly, MilliporeSigma has created a Cas9 paired-nickase system that promotes HDR, while also limiting off-target effects and increasing the number of sequences available for site-dependent gene modifications, such as disease-associated single nucleotide polymorphisms (SNPs).“The best thing you can do is to cut as close to the SNP as possible,” advised Dr. Davis. “As you move the double-stranded break away from the site of mutation you get an exponential drop in the frequency of recombination.”


  • Ribonucleo-protein Complexes

    Another strategy to improve gene-editing efficiency, developed by Thermo Fisher, involves combining purified Cas9 protein with gRNA to generate a stable ribonucleoprotein (RNP) complex. In contrast to plasmid- or mRNA-based formats, which require transcription and/or translation, the Cas9 RNP complex cuts DNA immediately after entering the cell. Rapid clearance of the complex from the cell helps to minimize off-target effects, and, unlike a viral vector, the transient complex does not introduce foreign DNA sequences into the genome.

    To deliver their Cas9 RNP complex to cells, Thermo Fisher has developed a lipofectamine transfection reagent called CRISPRMAX. “We went back to the drawing board with our delivery, screened a bunch of components, and got a brand-new, fully  optimized lipid nanoparticle formulation,” explained Jon Chesnut, Ph.D., the company’s senior director of synthetic biology R&D. “The formulation is specifically designed for delivering the RNP to cells more efficiently.”

    Besides the reagent and the formulation, Thermo Fisher has also developed a range of gene-editing tools. For example, it has introduced the Neon® transfection system for delivering DNA, RNA, or protein into cells via electroporation. Dr. Chesnut emphasized the company’s focus on simplifying complex workflows by optimizing protocols and pairing everything with the appropriate up- and downstream reagents.

From Mammalian Cells to Microbes

One of the first sources of CRISPR technology was the Feng Zhang laboratory at the Broad Institute, which counted among its first licensees a company called GenScript. This company offers a gene-editing service called GenCRISPR™ to establish mammalian cell lines with CRISPR-derived gene knockouts.

“There are a lot of challenges with mammalian cells, and each cell line has its own set of issues,” said Laura Geuss, a marketing specialist at GenScript. “We try to offer a variety of packages that can help customers who have difficult-to-work-with cells.” These packages include both viral-based and transient transfection techniques.

However, the most distinctive service offered by GenScript is its microbial genome-editing service for bacteria (Escherichia coli) and yeast (Saccharomyces cerevisiae). The company’s strategy for gene editing in bacteria can enable seamless knockins, knockouts, or gene replacements by combining CRISPR with lambda red recombineering. Traditionally one of the most effective methods for gene editing in microbes, recombineering allows editing without restriction enzymes through in vivo homologous recombination mediated by a phage-based recombination system such as lambda red.

On its own, lambda red technology cannot target multiple genes, but when paired with CRISPR, it allows the editing of multiple genes with greater efficiency than is possible with CRISPR alone, as the lambda red proteins help repair double-strand breaks in E. coli. The ability to knockout different gene combinations makes Genscript’s microbial editing service particularly well suited for the optimization of metabolic pathways.

Pooled and Arrayed Library Strategies

Scientists are using CRISPR technology for applications such as metabolic engineering and drug development. Yet another application area benefitting from CRISPR technology is cancer research. Here, the use of pooled CRISPR libraries is becoming commonplace. Pooled CRISPR libraries can help detect mutations that affect drug resistance, and they can aid in patient stratification and clinical trial design.

Pooled screening uses proliferation or viability as a phenotype to assess how genetic alterations, resulting from the application of a pooled CRISPR library, affect cell growth and death in the presence of a therapeutic compound. The enrichment or depletion of different gRNA populations is quantified using deep sequencing to identify the genomic edits that result in changes to cell viability.

MilliporeSigma provides pooled CRISPR libraries ranging from the whole human genome to smaller custom pools for these gene-function experiments. For pharmaceutical and biotech companies, Horizon Discovery offers a pooled screening service, ResponderSCREEN, which provides a whole-genome pooled screen to identify genes that confer sensitivity or resistance to a compound. This service is comprehensive, taking clients from experimental design all the way through to suggestions for follow-up studies.

Horizon Discovery maintains a Research Biotech business unit that is focused on target discovery and enabling translational medicine in oncology. “Our internal backbone gives us the ability to provide expert advice demonstrated by results,” said Jon Moore, Ph.D., the company’s CSO.

In contrast to a pooled screen, where thousands of gRNA are combined in one tube, an arrayed screen applies one gRNA per well, removing the need for deep sequencing and broadening the options for different endpoint assays. To establish and distribute a whole-genome arrayed lentiviral CRISPR library, MilliporeSigma partnered with the Wellcome Trust Sanger Institute. “This is the first and only arrayed CRISPR library in the world,” declared Shawn Shafer, Ph.D., functional genomics market segment manager, MilliporeSigma. “We were really proud to partner with Sanger on this.”

Pooled and arrayed screens are powerful tools for studying gene function. The appropriate platform for an experiment, however, will be determined by the desired endpoint assay.

Detection and Quantification of Edits


The QX200 Droplet Digital PCR System from Bio-Rad Laboratories can provide researchers with an absolute measure of target DNA molecules for EvaGreen or probe-based digital PCR applications. The system, which can provide rapid, low-cost, ultra-sensitive quantification of both NHEJ- and HDR-editing events, consists of two instruments, the QX200 Droplet Generator and the QX200 Droplet Reader, and their associated consumables.

Finally, one last challenge for CRISPR lies in the detection and quantification of changes made to the genome post-editing. Conventional methods for detecting these alterations include gel methods and next-generation sequencing. While gel methods lack sensitivity and scalability, next-generation sequencing is costly and requires intensive bioinformatics.

To address this gap, Bio-Rad Laboratories developed a set of assay strategies to enable sensitive and precise edit detection with its Droplet Digital PCR (ddPCR) technology. The platform is designed to enable absolute quantification of nucleic acids with high sensitivity, high precision, and short turnaround time through massive droplet partitioning of samples.

Using a validated assay, a typical ddPCR experiment takes about five to six hours to complete. The ddPCR platform enables detection of rare mutations, and publications have reported detection of precise edits at a frequency of <0.05%, and of NHEJ-derived indels at a frequency as low as 0.1%. In addition to quantifying precise edits, indels, and computationally predicted off-target mutations, ddPCR can also be used to characterize the consequences of edits at the RNA level.

According to a recently published Science paper, the laboratory of Charles A. Gersbach, Ph.D., at Duke University used ddPCR in a study of muscle function in a mouse model of Duchenne muscular dystrophy. Specifically, ddPCR was used to assess the efficiency of CRISPR-Cas9 in removing the mutated exon 23 from the dystrophin gene. (Exon 23 deletion by CRISPR-Cas9 resulted in expression of the modified dystrophin gene and significant enhancement of muscle force.)

Quantitative ddPCR showed that exon 23 was deleted in ~2% of all alleles from the whole-muscle lysate. Further ddPCR studies found that 59% of mRNA transcripts reflected the deletion.

“There’s an overarching idea that the genome-editing field is moving extremely quickly, and for good reason,” asserted Jennifer Berman, Ph.D., staff scientist, Bio-Rad Laboratories. “There’s a lot of exciting work to be done, but detection and quantification of edits can be a bottleneck for researchers.”

The gene-editing field is moving quickly, and new innovations are finding their way into the laboratory as researchers lay the foundation for precise, well-controlled gene editing with CRISPR.


Are Current Cancer Drug Discovery Methods Flawed?

GEN May 3, 2016


Researchers utilized a systems biology approach to develop new methods to assess drug sensitivity in cells. [The Institute for Systems Biology]

Understanding how cells respond and proliferate in the presence of anticancer compounds has been the foundation of drug discovery ideology for decades. Now, a new study from scientists at Vanderbilt University casts significant suspicion on the primary method used to test compounds for anticancer activity in cells—instilling doubt on methods employed by the entire scientific enterprise and pharmaceutical industry to discover new cancer drugs.

“More than 90% of candidate cancer drugs fail in late-stage clinical trials, costing hundreds of millions of dollars,” explained co-senior author Vito Quaranta, M.D., director of the Quantitative Systems Biology Center at Vanderbilt. “The flawed in vitro drug discovery metric may not be the only responsible factor, but it may be worth pursuing an estimate of its impact.”

The Vanderbilt investigators have developed what they believe to be a new metric for evaluating a compound’s effect on cell proliferation—called the DIP (drug-induced proliferation) rate—that overcomes the flawed bias in the traditional method.

The findings from this study were published recently in Nature Methods in an article entitled “An Unbiased Metric of Antiproliferative Drug Effect In Vitro.”

For more than three decades, researchers have evaluated the ability of a compound to kill cells by adding the compound in vitro and counting how many cells are alive after 72 hours. Yet, proliferation assays that measure cell number at a single time point don’t take into account the bias introduced by exponential cell proliferation, even in the presence of the drug.

“Cells are not uniform, they all proliferate exponentially, but at different rates,” Dr. Quaranta noted. “At 72 hours, some cells will have doubled three times and others will not have doubled at all.”

Dr. Quaranta added that drugs don’t all behave the same way on every cell line—for example, a drug might have an immediate effect on one cell line and a delayed effect on another.

The research team decided to take a systems biology approach, a mixture of experimentation and mathematical modeling, to demonstrate the time-dependent bias in static proliferation assays and to develop the time-independent DIP rate metric.

“Systems biology is what really makes the difference here,” Dr. Quaranta remarked. “It’s about understanding cells—and life—as dynamic systems.”This new study is of particular importance in light of recent international efforts to generate data sets that include the responses of thousands of cell lines to hundreds of compounds. Using the

  • Cancer Cell Line Encyclopedia (CCLE) and
  • Genomics of Drug Sensitivity in Cancer (GDSC) databases

will allow drug discovery scientists to include drug response data along with genomic and proteomic data that detail each cell line’s molecular makeup.

“The idea is to look for statistical correlations—these particular cell lines with this particular makeup are sensitive to these types of compounds—to use these large databases as discovery tools for new therapeutic targets in cancer,” Dr. Quaranta stated. “If the metric by which you’ve evaluated the drug sensitivity of the cells is wrong, your statistical correlations are basically no good.”

The Vanderbilt team evaluated the responses from four different melanoma cell lines to the drug vemurafenib, currently used to treat melanoma, with the standard metric—used for the CCLE and GDSC databases—and with the DIP rate. In one cell line, they found a glaring disagreement between the two metrics.

“The static metric says that the cell line is very sensitive to vemurafenib. However, our analysis shows this is not the case,” said co-lead study author Leonard Harris, Ph.D., a systems biology postdoctoral fellow at Vanderbilt. “A brief period of drug sensitivity, quickly followed by rebound, fools the static metric, but not the DIP rate.”

Dr. Quaranta added that the findings “suggest we should expect melanoma tumors treated with this drug to come back, and that’s what has happened, puzzling investigators. DIP rate analyses may help solve this conundrum, leading to better treatment strategies.”

The researchers noted that using the DIP rate is possible because of advances in automation, robotics, microscopy, and image processing. Moreover, the DIP rate metric offers another advantage—it can reveal which drugs are truly cytotoxic (cell killing), rather than merely cytostatic (cell growth inhibiting). Although cytostatic drugs may initially have promising therapeutic effects, they may leave tumor cells alive that then have the potential to cause the cancer to recur.

The Vanderbilt team is currently in the process of identifying commercial entities that can further refine the software and make it widely available to the research community to inform drug discovery.


An unbiased metric of antiproliferative drug effect in vitro

Leonard A HarrisPeter L FrickShawn P GarbettKeisha N HardemanB Bishal PaudelCarlos F LopezVito Quaranta & Darren R Tyson
Nature Methods 2 May (2016)

In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.

  1. Zuber, J. et al. Nat. Biotechnol. 29, 7983 (2011).
  2. Berns, K. et al. Nature 428, 431437 (2004).
  3. Bonnans, C., Chou, J. & Werb, Z. Nat. Rev. Mol. Cell Biol. 15, 786801 (2014).
  4. Garnett, M.J. et al. Nature 483, 570575 (2012)


Mapping Traits to Genes with CRISPR

Researchers develop a technique to direct chromosome recombination with CRISPR/Cas9, allowing high-resolution genetic mapping of phenotypic traits in yeast.

By Catherine Offord | May 5, 2016


Researchers used CRISPR/Cas9 to make a targeted double-strand break (DSB) in one arm of a yeast chromosome labeled with a green fluorescent protein (GFP) gene. A within-cell mechanism called homologous repair (HR) mends the broken arm using its homolog, resulting in a recombined region from the site of the break to the chromosome tip. When this cell divides by mitosis, each daughter cell will contain a homozygous section in an outcome known as “loss of heterozygosity” (LOH). One of the daughter cells is detectable because, due to complete loss of the GFP gene, it will no longer be fluorescent.REPRINTED WITH PERMISSION FROM M.J. SADHU ET AL., SCIENCE

When mapping phenotypic traits to specific loci, scientists typically rely on the natural recombination of chromosomes during meiotic cell division in order to infer the positions of responsible genes. But recombination events vary with species and chromosome region, giving researchers little control over which areas of the genome are shuffled. Now, a team at the University of California, Los Angeles (UCLA), has found a way around these problems by using CRISPR/Cas9 to direct targeted recombination events during mitotic cell division in yeast. The team described its technique today (May 5) in Science.

“Current methods rely on events that happen naturally during meiosis,” explained study coauthor Leonid Kruglyak of UCLA. “Whatever rate those events occur at, you’re kind of stuck with. Our idea was that using CRISPR, we can generate those events at will, exactly where we want them, in large numbers, and in a way that’s easy for us to pull out the cells in which they happened.”

Generally, researchers use coinheritance of a trait of interest with specific genetic markers—whose positions are known—to figure out what part of the genome is responsible for a given phenotype. But the procedure often requires impractically large numbers of progeny or generations to observe the few cases in which coinheritance happens to be disrupted informatively. What’s more, the resolution of mapping is limited by the length of the smallest sequence shuffled by recombination—and that sequence could include several genes or gene variants.

“Once you get down to that minimal region, you’re done,” said Kruglyak. “You need to switch to other methods to test every gene and every variant in that region, and that can be anywhere from challenging to impossible.”

But programmable, DNA-cutting champion CRISPR/Cas9 offered an alternative. During mitotic—rather than meiotic—cell division, rare, double-strand breaks in one arm of a chromosome preparing to split are sometimes repaired by a mechanism called homologous recombination. This mechanism uses the other chromosome in the homologous pair to replace the sequence from the break down to the end of the broken arm. Normally, such mitotic recombination happens so rarely as to be impractical for mapping purposes. With CRISPR/Cas9, however, the researchers found that they could direct double-strand breaks to any locus along a chromosome of interest (provided it was heterozygous—to ensure that only one of the chromosomes would be cut), thus controlling the sites of recombination.

Combining this technique with a signal of recombination success, such as a green fluorescent protein (GFP) gene at the tip of one chromosome in the pair, allowed the researchers to pick out cells in which recombination had occurred: if the technique failed, both daughter cells produced by mitotic division would be heterozygous, with one copy of the signal gene each. But if it succeeded, one cell would end up with two copies, and the other cell with none—an outcome called loss of heterozygosity.

“If we get loss of heterozygosity . . . half the cells derived after that loss of heterozygosity event won’t have GFP anymore,” study coauthor Meru Sadhu of UCLA explained. “We search for these cells that don’t have GFP out of the general population of cells.” If these non-fluorescent cells with loss of heterozygosity have the same phenotype as the parent for a trait of interest, then CRISPR/Cas9-targeted recombination missed the responsible gene. If the phenotype is affected, however, then the trait must be linked to a locus in the recombined, now-homozygous region, somewhere between the cut site and the GFP gene.

By systematically making cuts using CRISPR/Cas9 along chromosomes in a hybrid, diploid strain ofSaccharomyces cerevisiae yeast, picking out non-fluorescent cells, and then observing the phenotype, the UCLA team demonstrated that it could rapidly identify the phenotypic contribution of specific gene variants. “We can simply walk along the chromosome and at every [variant] position we can ask, does it matter for the trait we’re studying?” explained Kruglyak.

For example, the team showed that manganese sensitivity—a well-defined phenotypic trait in lab yeast—could be pinpointed using this method to a single nucleotide polymorphism (SNP) in a gene encoding the Pmr1 protein (a manganese transporter).

Jason Moffat, a molecular geneticist at the University of Toronto who was not involved in the work, toldThe Scientist that researchers had “dreamed about” exploiting these sorts of mechanisms for mapping purposes, but without CRISPR, such techniques were previously out of reach. Until now, “it hasn’t been so easy to actually make double-stranded breaks on one copy of a pair of chromosomes, and then follow loss of heterozygosity in mitosis,” he said, adding that he hopes to see the approach translated into human cell lines.

Applying the technique beyond yeast will be important, agreed cell and developmental biologist Ethan Bier of the University of California, San Diego, because chromosomal repair varies among organisms. “In yeast, they absolutely demonstrate the power of [this method],” he said. “We’ll just have to see how the technology develops in other systems that are going to be far less suited to the technology than yeast. . . . I would like to see it implemented in another system to show that they can get the same oomph out of it in, say, mammalian somatic cells.”

Kruglyak told The Scientist that work in higher organisms, though planned, is still in early stages; currently, his team is working to apply the technique to map loci responsible for trait differences between—rather than within—yeast species.

“We have a much poorer understanding of the differences across species,” Sadhu explained. “Except for a few specific examples, we’re pretty much in the dark there.”

M.J. Sadhu, “CRISPR-directed mitotic recombination enables genetic mapping without crosses,” Science, doi:10.1126/science.aaf5124, 2016.


CRISPR-directed mitotic recombination enables genetic mapping without crosses

Meru J Sadhu, Joshua S Bloom, Laura Day, Leonid Kruglyak

Thank you, David, for the kind words and comments. We agree that the most immediate applications of the CRISPR-based recombination mapping will be in unicellular organisms and cell culture. We also think the method holds a lot of promise for research in multicellular organisms, although we did not mean to imply that it “will be an efficient mapping method for all multicellular organisms”. Every organism will have its own set of constraints as well as experimental tools that will be relevant when adapting a new technique. To best help experts working on these organisms, here are our thoughts on your questions.

You asked about mutagenesis during recombination. We Sanger sequenced 72 of our LOH lines at the recombination site and did not observe any mutations, as described in the supplementary materials. We expect the absence of mutagenesis is because we targeted heterozygous sites where the untargeted allele did not have a usable PAM site; thus, following LOH, the targeted site is no longer present and cutting stops. In your experiments you targeted sites that were homozygous; thus, following recombination, the CRISPR target site persisted, and continued cutting ultimately led to repair by NHEJ and mutagenesis.

As to the more general question of the optimal mapping strategies in different organisms, they will depend on the ease of generating and screening for editing events, the cost and logistics of maintaining and typing many lines, and generation time, among other factors. It sounds like in Drosophila today, your related approach of generating markers with CRISPR, and then enriching for natural recombination events that separate them, is preferable. In yeast, we’ve found the opposite to be the case. As you note, even in Drosophila, our approach may be preferable for regions with low or highly non-uniform recombination rates.

Finally, mapping in sterile interspecies hybrids should be straightforward for unicellular hybrids (of which there are many examples) and for cells cultured from hybrid animals or plants. For studies in hybrid multicellular organisms, we agree that driving mitotic recombination in the early embryo may be the most promising approach. Chimeric individuals with mitotic clones will be sufficient for many traits. Depending on the system, it may in fact be possible to generate diploid individuals with uniform LOH genotype, but this is certainly beyond the scope of our paper. The calculation of the number of lines assumes that the mapping is done in a single step; as you note in your earlier comment, mapping sequentially can reduce this number dramatically.

This is a lovely method and should find wide applicability in many settings, especially for microorganisms and cell lines. However, it is not clear that this approach will be, as implied by the discussion, an efficient mapping method for all multicellular organisms. I have performed similar experiments in Drosophila, focused on meiotic recombination, on a much smaller scale, and found that CRISPR-Cas9 can indeed generate targeted recombination at gRNA target sites. In every case I tested, I found that the recombination event was associated with a deletion at the gRNA site, which is probably unimportant for most mapping efforts, but may be a concern in some specific cases, for example for clinical applications. It would be interesting to know how often mutations occurred at the targeted gRNA site in this study.

The wider issue, however, is whether CRISPR-mediated recombination will be more efficient than other methods of mapping. After careful consideration of all the costs and the time involved in each of the steps for Drosophila, we have decided that targeted meiotic recombination using flanking visible markers will be, in most cases, considerably more efficient than CRISPR-mediated recombination. This is mainly due to the large expense of injecting embryos and the extensive effort and time required to screen injected animals for appropriate events. It is both cheaper and faster to generate markers (with CRISPR) and then perform a large meiotic recombination mapping experiment than it would be to generate the lines required for CRISPR-mediated recombination mapping. It is possible to dramatically reduce costs by, for example, mapping sequentially at finer resolution. But this approach would require much more time than marker-assisted mapping. If someone develops a rapid and cheap method of reliably introducing DNA into Drosophila embryos, then this calculus might change.

However, it is possible to imagine situations where CRISPR-mediated mapping would be preferable, even for Drosophila. For example, some genomic regions display extremely low or highly non-uniform recombination rates. It is possible that CRISPR-mediated mapping could provide a reasonable approach to fine mapping genes in these regions.

The authors also propose the exciting possibility that CRISPR-mediated loss of heterozygosity could be used to map traits in sterile species hybrids. It is not entirely obvious to me how this experiment would proceed and I hope the authors can illuminate me. If we imagine driving a recombination event in the early embryo (with maternal Cas9 from one parent and gRNA from a second parent), then at best we would end up with chimeric individuals carrying mitotic clones. I don’t think one could generate diploid animals where all cells carried the same loss of heterozygosity event. Even if we could, this experiment would require construction of a substantial number of stable transgenic lines expressing gRNAs. Mapping an ~20Mbp chromosome arm to ~10kb would require on the order of two-thousand transgenic lines. Not an undertaking to be taken lightly. It is already possible to perform similar tests (hemizygosity tests) using D. melanogaster deficiency lines in crosses with D. simulans, so perhaps CRISPR-mediated LOH could complement these deficiency screens for fine mapping efforts. But, at the moment, it is not clear to me how to do the experiment.

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