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Disease Ecologist Awarded $3.5 Million to Build Revolutionary Epidemiological Platform

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Mathematical modeling—which combines math, statistics, computing and data—is a critical tool for public health professionals, who use it to study how diseases spread, predict the future course of outbreaks and evaluate strategies for controlling epidemics.

Mathematical modeling—which combines math, statistics, computing and data—is a critical tool for public health professionals, who use it to study how diseases spread, predict the future course of outbreaks and evaluate strategies for controlling epidemics.

As the COVID-19 pandemic drove public health decision-making nationwide, a wide range of disease models proliferated. Across the country, city, county and state officials worked with academic modeling teams to develop custom models to predict what would happen in their jurisdictions. Municipalities that did not have the resources to develop models specific to their locations were forced to extrapolate data from other models and make decisions based on less-than-ideal information. Since there was no cyber infrastructure for executing these models in a standardized way, the confusion caused by the cacophony of inconsistent models very likely eroded public trust in modeling as a powerful tool.

Assistant professor Joe Mihaljevic of Northern Arizona University’s School of Informatics, Computing, and Cyber Systems (SICCS) has been working with public health partners across the state and the country to share computer models mapping the spread of the coronavirus. Mihaljevic, a disease ecologist who applies epidemiological modeling techniques to wildlife and, more recently, to human diseases, was awarded more than $3.5 million by the National Institutes for Health to take modeling to the next level with EpiMoRPH (Epidemiological Modeling Resources for Public Health), which will substantially automate and expedite the development of epidemiological models.

“Throughout the pandemic, we realized we needed models that were at spatial scales relevant to the needs of specific public health partners,” Mihaljevic said. “Across the country smaller municipalities, like cities, were often forced to inform their decisions based on models that were developed at larger spatial scales, like county scales or even statewide scales, when what they really needed was a customized model for their location. As we thought about the complex challenges we faced and the things we learned modeling the coronavirus, we posed this question: if a new epidemic or pandemic were to emerge, could we envision a system that would make things much easier for modelers to get up and running and to collaborate across groups? And could we use this to develop locally customized models that are better for decision-making?”

“As we developed the proposal for EpiMoRPH, we tried to define a manageable piece of that answer that we could accomplish in a five-year timeframe, to develop a good proof of concept modeling system for what we envision as the ‘next generation’ of epidemiological modeling that increases automation, promotes sharing and collaboration, accelerates discovery and rapidly advances our understanding of epidemics,” he said.

The project will use two different virus-based diseases as case studies: COVID-19 and SLEV (St. Louis Encephalitis Virus), but EpiMoRPH will work with any transmissible pathogen affecting humans, animals, or even plants.

“EpiMoRPH will provide a framework for characterizing meta-population disease models,” Mihaljevic said, “supporting rapid model development and uniform evaluation of models against data benchmarks. Beyond that, however, EpiMoRPH will provide an accessible interface for public health professionals to identify models relevant to their locale and to then use these models to generate municipality-specific forecasts.”

Multi-institutional collaboration to include Public Health Advisory Council

Mihaljevic’s co-investigators on the project are SICCS professor Eck Doerry, who will lead software development and cloud-based computing; SICCS associate professor Crystal Hepp, also with the Translational Genomics Research Institute (TGen), who will lead the procurement and management of surveillance data on viral cases; and Samantha Sabo, associate professor from NAU’s Center for Health Equity Research, who will assist with mobilizing and liaising with public health partners and lead the efforts in formal assessment.

NAU investigators will work with researchers from several other institutions, including Esma Gel from University of Nebraska, who will assist with optimization theory and algorithm developments; Sanjay Mehrotra from Northwestern University, who will lead the overall work on optimization theory development; and Timothy Lant from Arizona State University, who will assist with mobilizing and coordinating a Public Health Advisory Council.

The team will form a Public Health Advisory Council (PHAC) consisting of 15 local, regional and national stakeholders in public health and epidemiological modeling who will provide critical input and evaluation on the system as it is being developed. Collaborators from the Arizona Department of Health Services, with whom Mihaljevic and his team have worked extensively during the COVID-19 pandemic, will be part of this effort.

“The PHAC will help us better understand the logistical constraints and drive the development of the user interface so that it reflects the level of detail required by the intended users,” Mihaljevic said. “We will work closely with the advisory council to evaluate and refine our technologies, ensuring that our innovations meet the evolving needs of public health partners, while also appealing to the community of epidemiological modelers.”

In addition, many graduate and undergraduate students in informatics and computer science will assist with efforts to develop the web-based cyberinfrastructures, coding automation scripts and writing technical documentation. Two undergraduate researchers in public health will assist the team’s efforts to conduct formal evaluations of the technology and develop outreach methods with the PHAC.

Could EpiMoRPH help make forecasting epidemics as reliable as forecasting the weather?

“Once EpiMoRPH is built, a typical user could be someone who represents public health in Flagstaff, for instance. During the pandemic, this user might have wanted to understand what they should expect with COVID-19 in terms of hospitalizations in the next 30 days. Because our model at that time was at the scale of Coconino County, we could tell them what was happening at the county level, but not specifically for Flagstaff,” Mihaljevic said.

“And so, once EpiMoRPH is in place, if a model hasn’t been built for Flagstaff, a public health official could enter some characteristics of this particular location, such as population density, geography, etc., and immediately see which models are currently most accurate. And then the EpiMoRPH system would use those models to develop a customized forecast for Flagstaff.

“In the ideal scenario, the modelers in the community could contribute models and public health professionals could contribute data, too. Our system would pair the models and the data and run them against each other and try to figure out which models are best for specific locations.

“Eventually, as models become more and more accurate, forecasting outbreaks could become as routine, and as reliable, as forecasting the weather,” Mihaljevic said.

Revolutionizing how modeling is done

“This is a whole new way of thinking about developing models on a mass scale,” co-investigator Doerry said, “so that next time we have a pandemic, we are ready and can produce coherent, intelligible and consistent models from the very start.

“Our ultimate aim is to revolutionize how modeling is done by defining a uniform conceptual standard that all current and existing models can be characterized with. This will allow for massive automation of model validation and parameter refinement and will support automatically testing them across thousands of different locales to discover what model is best given any set of local conditions. Finally, we will add an infinitely scalable cloud computing infrastructure that can bring to bear massive computing power to do all this heavy lifting. EpiMoRPH is so powerful precisely because it explores what you could achieve if you took cutting-edge infectious pathogen modeling and combined it with the cutting edge in cloud-based big data computation.”

EpiMoRPH to contribute to national modeling community

With an increased emphasis on disease modeling, the EpiMoRPH platform could potentially be adopted as a national hub. Academic labs and national organizations across the country are racing to make epidemic modeling more accessible, more useful and more accurate. For instance, the Centers for Disease Control and Prevention (CDC) recently launched its Center for Forecasting and Outbreak Analytics (CFA), which will enhance the nation’s ability to use data, models and analytics to enable timely, effective decision-making in response to public health threats for CDC and its public health partners. Mihaljevic hopes that EpiMoRPH could make a strong contribution to national efforts towards standardizing and automating epidemic modeling, with the goal of creating reliable forecasts for local decision-makers.

About Northern Arizona University

Founded in 1899, Northern Arizona University is a higher-research institution providing exceptional educational opportunities and outcomes in Arizona and beyond. NAU delivers a student-centered experience to its nearly 30,000 students in Flagstaff, statewide and online through rigorous academic programs in a supportive, inclusive and diverse environment. As a community-engaged engine of opportunity, NAU powers social impact and economic mobility for the students and communities it serves. The university’s longstanding history of educating and partnering with diverse students and communities throughout Arizona is enhanced by its recent designation as a Hispanic-Serving Institution (HSI). Dedicated, world-renowned faculty and staff help ensure students achieve academic excellence, experience personal growth, have meaningful research and experiential learning opportunities and are positioned for personal and professional success. Located on the Colorado Plateau, in one of the highest-ranked college towns in the country, the NAU Flagstaff Mountain Campus is truly a jewel of the Southwest.

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Ovarian Cancer in the Fatty Omentum: Metabolic Enzyme’s Key Role in Tumor Metastasis

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(LOS ANGELES) – July 1, 2022 – In their recent publication in Cell Reports, a team of scientists, led by Xiling Shen, Ph.D., Chief Scientific Officer at the Terasaki Institute for Biomedical Innovation (TIBI), has demonstrated the pivotal role of an enzyme, glucose-6-phosphate dehydrogenase (G6PD), in facilitating ovarian cancer (OC) growth and metastasis in the omentum, a curtain of fatty tissue found in the abdominal cavity.

(LOS ANGELES) – July 1, 2022 – In their recent publication in Cell Reports, a team of scientists, led by Xiling Shen, Ph.D., Chief Scientific Officer at the Terasaki Institute for Biomedical Innovation (TIBI), has demonstrated the pivotal role of an enzyme, glucose-6-phosphate dehydrogenase (G6PD), in facilitating ovarian cancer (OC) growth and metastasis in the omentum, a curtain of fatty tissue found in the abdominal cavity.

OC is a particularly deadly metastatic disease, with stage III or higher diagnoses occurring in 80% of patients, along with approximately 30% five-year survival rates. OC often shows a particular preference for migrating to and aggressively proliferating in the omentum, which provides fatty acids as a fuel source for OC cells.

As a part of this increase in fatty acid metabolism by the OC cells, certain oxidative compounds are produced, which impose a degree of oxidative stress in the omental microenvironment. As a result, a metabolic pathway called the pentose phosphate pathway (PPP) is activated, which not only serves as a counteractive response to this stress but is also an essential part of certain metabolisms in cancer cells.

Although it is known that G6PD is the rate-controlling enzyme in the PPP, its effects on OC metastasis in the omentum had not been previously examined. Dr. Shen’s team has shed light on this question by conducting a series of revealing experiments.

Genetic and metabolic analyses revealed elevated levels of PPP oxidative compounds and metabolites, including G6PD, in the omental metastases compared to primary tumors in OC patients. Similar observations were made in mice injected with different OC cell lines and in OC cells or organoids cultured in media conditioned with omental tissue.  

These initial experiments confirmed the OM OC cells’ PPP response to oxidative stress generated by omental fatty acid metabolism. The elevated levels of G6PD observed in these samples provided a link. Ensuing inhibition experiments definitively demonstrated G6PD’s influence on OM OC cells. Genetic silencing or pharmacological inhibition of G6PD induced significant cell death and increased levels of key oxidative compounds in the cells grown in omental conditioned media compared with the others. The results from these experiments illustrated the need for the presence of G6PD to activate the PPP in order to counteract the omental production of oxidative compounds.

This observation was further confirmed by in vivo studies in mice injected with genetically altered G6PD-inhibited OMC cells or treated with the G6PD-inhibiting drug, which resulted in much smaller metastatic tumors in the omentum.

Taken together, the results signify that G6PD is an essential component used to offset the oxidative stresses created from fatty acid metabolism by OC cells in the omentum. Without this enzyme, the PPP cannot function, and the metastatic cells succumb to the resultant buildup oxidative compounds.

“Elucidation of the metabolic interplay which influences tumor survival and metastasis increases the potential for targeted therapeutic development,” said Ali Khademhosseini, Ph.D., TIBI’s Director and CEO. “This work is a step in that direction and has significant clinical relevance for aggressively metastatic disease like ovarian cancer.”

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Stewart Han, [email protected], +1 818-836-4393

Terasaki Institute for Biomedical Innovation

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The Terasaki Institute for Biomedical Innovation (terasaki.org) is a non-profit research organizationthat invents and fosters practical solutions that restore or enhance the health of individuals.  Research at the Terasaki Institute leverages scientific advancements that enable an understanding of what makes each person unique, from the macroscale of human tissues down to the microscale of genes, to create technological solutions for some of the most pressing medical problems of our time.  We use innovative technology platforms to study human disease on the level of individual patients by incorporating advanced computational and tissue-engineering methods.  Findings yielded by these studies are translated by our research teams into tailored diagnostic and therapeutic approaches encompassing personalized materials, cells and implants with unique potential and broad applicability to a variety of diseases, disorders, and injuries. 

The Institute is made possible through an endowment from the late Dr. Paul I Terasaki, a pioneer in the field of organ transplant technology.

Authors are Shree Bose, Qiang Huang, Yunhan Ma, Lihua Wang, Grecia O. Rivera, Yunxin Ouyang, Regina Whitaker, Rebecca A. Gibson, Christopher D. Kontos, Andrew Berchuck, Rebecca A. Previs, Xiling Shen

This work was supported by National Cancer Institute grants NIH-U01CA217514 and 289 U01CA214300, as well as National Institutes of Health F30 fellowship 1F30CA257365- 290 01.

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New Screening Technique Could Accelerate and Improve MRNA Therapies

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Therapeutics based on messenger RNA, or mRNA, can potentially treat a wide range of maladies, including cancer, genetic diseases, and as the world has learned in recent years, deadly viruses.

Therapeutics based on messenger RNA, or mRNA, can potentially treat a wide range of maladies, including cancer, genetic diseases, and as the world has learned in recent years, deadly viruses.

To work, these drugs must be delivered directly to target cells in nanoscale bubbles of fat called lipid nanoparticles, or LNPs — mRNA isn’t much good if doesn’t reach the right cell type. 

A team of researchers at the Georgia Institute of Technology and Emory University’s School of Medicine has taken another step toward improving development of these custom-made delivery vehicles, reporting their work June 30 in Nature Nanotechnology. Curtis Dobrowolski and Kalina Paunovska, trainees in the lab of James Dahlman, have developed a system to make pre-clinical nanoparticle studies more predictive. Their discoveries already are influencing the direction of research in this growing, competitive field.

“I’m very excited about this study and anticipate shifting most of our future projects to this methodology,” said Dahlman, associate professor and McCamish Foundation Early Career Professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory. 

Sequencing of Events

For the past few years, Dahlman, has partnered with Coulter BME Professor Philip Santangelo in a busy research enterprise. Santangelo’s lab develops mRNA therapies, and Dahlman’s lab delivers it using LNPs.

To speed up the process of testing the effectiveness of their LNPs, Dahlman’s team has developed a technique called DNA barcoding. In this process, researchers insert a snippet of DNA that corresponds to a given LNP. The LNPs are then injected and cells are subsequently examined for the presence of the “barcodes” using genetic sequencing. The system identifies which barcodes have reached which specific targets, highlighting the most promising nanoparticles. Since many DNA sequences can be read at once, the barcoding process allows many experiments to be performed simultaneously, thereby accelerating the discovery of effective lipid nanoparticle carriers.

DNA barcoding has significantly improved the nanoparticle pre-clinical screening process. But there is still a significant barrier impacting drug delivery. Because of their diversity, cells are kind of like moving targets. Dahlman noted that cells previously thought to be homogeneous are composed of distinct and varied cell subsets. His team surmised that this chemical and genetic heterogeneity has a powerful influence on how well LNPs can deliver mRNA therapies into the cells.

“Cells don’t have just one protein that defines them — they’re complicated,” Dahlman said. “They can be defined by a combination of things, and if we’re being honest, they are best defined using by all the genes they do, or do not, express.”

To test their hypothesis, the researchers developed a new tool to measure all of these things at once. Their multiomic nanoparticle delivery system is called single-cell nanoparticle targeting-sequencing, or SENT-seq. 

Multiomics Approach

Using SENT-seq, the researchers were able to quantify how LNPs deliver DNA barcodes and mRNA into cells, the subsequent protein production facilitated by the mRNA drug, as well as the identity of the cell, in thousands of individual cells. 

This multiomics approach could represent an important leap forward for high-throughput LNP discovery. The SENT-seq technique allowed the team to identify cell subtypes that demonstrate particularly high or low nanoparticle uptake, and the genes associated with those subtypes. 

So, in addition to testing the efficacy of a drug and how certain cell subtypes react to nanoparticles, they’re identifying which genes are involved in the successful uptake of LNPs. And they’re doing it all at once.

“The data suggests that these different cell subsets have distinct responses to nanoparticles that influence how well an mRNA therapy works,” Dahlman said. “There’s still a lot of work to be done, but we think the ability to simultaneously read out high-throughput nanoparticle delivery and the cellular response to nanoparticles will lead to better mRNA therapies.”

Co-lead author Paunovska said that she and Dobrowolski came up with the idea for the SENT-seq system, “organically, after two months of working together.”

Dahlman added: “I’m proud of the work that Curtis, Kalina, and the team did in the lab. I think this is the beginning of an extremely interesting phase in our work.”

This research was supported by the National Institutes of Health, grant Nos. UG3-TR002855 and R01DE026941. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agency.

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Novel Sewage Treatment System Removes up to 70% of Nitrogen That Would Otherwise Be Discarded Into Nature

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 A new type of biofilm reactor adapted to Brazilian conditions and using polyurethane foam to lower costs can reduce the amount of nitrogen compounds in wastewater by as much as 70%, according to an article in Environmental Technology. The researchers who conducted the study developed a mathematical model to analyze and predict the nitrogen removal mechanism. The biofilm comprised bacteria that converted nitrogen compounds into nitrogen gas, which is environmentally harmless. 

 A new type of biofilm reactor adapted to Brazilian conditions and using polyurethane foam to lower costs can reduce the amount of nitrogen compounds in wastewater by as much as 70%, according to an article in Environmental Technology. The researchers who conducted the study developed a mathematical model to analyze and predict the nitrogen removal mechanism. The biofilm comprised bacteria that converted nitrogen compounds into nitrogen gas, which is environmentally harmless. 

The study was led by Bruno Garcia Silva during his doctoral research in hydraulic engineering and sanitation at the University São Paulo (USP) in Brazil, with Eugenio Foresti as thesis advisor. Foresti is a professor at the São Carlos School of Engineering (EESC-USP). The study was supported by FAPESP

The article was one of the results of the Thematic Project “Biorefinery concept applied to biological wastewater treatment plants: environmental pollution control coupled with material and energy recovery”, for which Marcelo Zaiat, also a professor at EESC-USP, was principal investigator. Researchers at the Federal University of São Carlos (UFSCar) and Mauá Institute of Technology (IMT) collaborated. 

“Nitrogen removal is still achieved by only a few wastewater treatment plants in Brazil, whereas it’s regularly performed in Europe and the United States,” Garcia told Agência FAPESP. “The idea is to adapt [the necessary infrastructure] to our reality. The usual method here is based on anaerobic reactors, which produce effluent with low levels of organic matter, making nitrogen removal difficult.”

Removal of nitrogen compounds (nitrite, nitrate and ammonia, among others) from both domestic sewage and industrial wastewater is essential because they contaminate surface water (lakes, reservoirs and streams) as well as aquifers and other ground water, letting the growth of bacteria, algae and plants spiral out of control in a process known as eutrophication.

Furthermore, consumption of water contaminated by nitrate can lead to diseases such as infant methemoglobinemia (blue baby syndrome), which causes headache, dizziness, fatigue, lethargy, breathlessness, and neurological alterations such as seizures and coma in severe cases. 

“When algal blooms proliferate, as seen in reservoirs like Billings [one of the main water sources for São Paulo], for example, lack of oxygen in the water leads to the death of fish and loss of water supply as well as leisure areas. It’s very hard to remove algae from reservoirs,” said Foresti, who leads the group.

Differentiators

One of the key differentiators of this new reactor model is the biofilm formed by a biological process in which bacteria create a film on the polyurethane foam. Another is the configuration of the equipment to permit what the researchers call counterdiffusion, where oxygen is introduced on the opposite side to the contaminants.

“Oxygen is transported into the foam because this ensures that it remains only where it’s needed for the reaction to occur,” Garcia explained. “We don’t want oxygen to come into contact with organic matter all the time. If it did, the bacteria would use up all the oxygen to break it down and nothing would be left over to consume the nitrite and nitrate. So we insert the oxygen on the other side of the biofilm. The goal is for the organic matter that reaches the biofilm on the opposite side to be oxidized not just by oxygen but also by nitrite and nitrate.” 

When oxygen does not enter the reactor, the ammonia remains unchanged. When ammonia enters the site of the reactor with oxygen input, however, it is converted into nitrite and nitrate. “The only way out is via the biofilm, and the compounds cross this barrier by diffusion in the opposite direction to the organic matter. Their collision with organic matter in contraflow creates optimal conditions for nitrite and nitrate removal because there’s no longer any oxygen and there’s enough organic matter for denitrification,” Garcia said.

Foresti explained that in Brazil, anaerobic reactors (which break down organic matter using bacteria that do not require oxygen to survive) are increasingly being used by municipal wastewater treatment companies because of the predominant climate, which is warmer than that of the northern hemisphere. Bacteria decompose organic matter faster in warm weather. In Europe and the US, where mean temperatures are lower, the process is different. The organic matter present in the liquid phase after sludge removal is oxidized aerobically (by oxygen). 

In Brazil, however, nitrogen compounds are not completely removed for cost reasons and are directly released into nature. The new type of reactor developed by the researchers is designed to add a second, easier and cheaper, stage to wastewater treatment, for development with future technologies and partnerships.

Scholarship for research in the US 

Researchers who work at the laboratory of Robert Nerenberg, a professor at the University of Notre Dame in the US, collaborated with Garcia, who was there as a visiting researcher in 2019-20 with FAPESP’s support.  

“The difference between my project and theirs is that instead of polyurethane foam they use a semipermeable membrane, which resembles a drinking straw full of air. When this capillary comes into contact with water, it lets through oxygen but not water, so that the biofilm sticks to the surface and grows on it. In other words, oxygen is supplied to the bacteria through the walls of this thin tube. The oxygen comes out, and the water provides ammonia and organic matter. It’s the same system as counterdiffusion, except that the material we use is simpler and cheaper,” Garcia said.

“The bacteria grow on the surface to form a biofilm, but it’s not a filter properly speaking because it doesn’t offer mechanical resistance to the passage of particles. What the reactor does in fact is serve as a support for the bacteria to grow and consume soluble organic matter and nitrogen compounds.”

Next steps

According to Foresti, the new configuration of the reactor is inspiring further research by the group. In a program of cooperation between the São Paulo State Basic Sanitation Corporation (SABESP) and FAPESP, the researchers plan to test the new model with real sewage that has been through an aerobic reactor in the treatment plant operated by SAAE, the municipal sanitation service in São Carlos. Researchers at UFSCar and IMT are also part of the program and will develop other systems to be tested.

“Bruno’s research is the first to use counterdiffusion in this way here in Brazil,” Foresti said. “It’s proof of concept for synthetic wastewater. The efficiency found in this reactor configuration was greatly superior to that observed in previous research, but we still need to evaluate several factors.” 

The new configuration has been tested in the laboratory. Efficiency will be measured in further projects, as it is not possible to predict how the equipment will behave when processing large volumes of effluent, and the system needs to be tested with actual domestic sewage and industrial wastewater. Hitherto it has been tested only on samples of synthetic waste prepared by the researchers themselves.

“We may have to improve the design and geometry,” Garcia said. “How can the design be optimized to obtain the largest optimal surface area per reactor volume so as to lower the cost? The study provides a basis, a foundation on which we can go on thinking about the process and the mathematical tool.”         

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About São Paulo Research Foundation (FAPESP)

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.

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