BCM's Center for Precision Medicine Models has received funding that will allow it to study more complex diseases. Photo via Getty Images

Baylor College of Medicine’s Center for Precision Medicine Models received a $10 million, five-year grant from the National Institutes of Health last month that will allow it to continue its work studying rare genetic diseases.

The Center for Precision Medicine Models creates customized cell, fly and mouse models that mimic specific genetic variations found in patients, helping scientists to better understand how genetic changes cause disease and explore potential treatments.

The center was originally funded by an NIH grant, and its models have contributed to the discovery of several new rare disease genes and new symptoms caused by known disease genes. It hosts an online portal that allows physicians, families and advocacy groups to nominate genetic variants or rare diseases that need further investigation or new treatments.

Since its founding in 2020, it has received 156 disease/variant nominations, accepted 63 for modeling and produced more than 200 precision models, according to Baylor.

The center plans to use the latest round of funding to bring together more experts in rare disease research, animal modeling and bioinformatics, and to expand its focus and model more complex diseases.

Dr. Jason Heaney, associate professor in the Department of Molecular and Human Genetics at BCM, serves as the lead principal investigator of the center.

“The Department of Molecular and Human Genetics is uniquely equipped to bring together the diverse expertise needed to connect clinical human genetics, animal research and advanced bioinformatics tools,” Heaney added in the release. “This integration allows us to drive personalized medicine forward using precision animal models and to turn those discoveries into better care for patients.”

A new AI tool from a Baylor College of Medicine Lab could help better diagnose specific types of autism spectrum disorder, epilepsy and developmental delay disorders. Photo via Getty Images.

Houston lab develops AI tool to improve neurodevelopmental diagnoses

developing news

One of the hardest parts of any medical condition is waiting for answers. Speeding up an accurate diagnosis can be a doctor’s greatest mercy to a family. A team at Baylor College of Medicine has created technology that may do exactly that.

Led by Dr. Ryan S. Dhindsa, assistant professor of pathology and immunology at Baylor and principal investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, the scientists have developed an artificial intelligence-based approach that will help doctors to identify genes tied to neurodevelopmental disorders. Their research was recently published the American Journal of Human Genetics.

According to its website, Dhindsa Lab uses “human genomics, human stem cell models, and computational biology to advance precision medicine.” The diagnoses that stem from the new computational tool could include specific types of autism spectrum disorder, epilepsy and developmental delay, disorders that often don’t come with a genetic diagnosis.

“Although researchers have made major strides identifying different genes associated with neurodevelopmental disorders, many patients with these conditions still do not receive a genetic diagnosis, indicating that there are many more genes waiting to be discovered,” Dhindsa said in a news release.

Typically, scientists must sequence the genes of many people with a diagnosis, as well as people not affected by the disorder, to find new genes associated with a particular disease or disorder. That takes time, money, and a little bit of luck. AI minimizes the need for all three, explains Dhindsa: “We used AI to find patterns among genes already linked to neurodevelopmental diseases and predict additional genes that might also be involved in these disorders.”

The models, made using patterns expressed at the single-cell level, are augmented with north of 300 additional biological features, including data on how intolerant genes are to mutations, whether they interact with other known disease-associated genes, and their functional roles in different biological pathways.

Dhindsa says that these models have exceptionally high predictive value.

“Top-ranked genes were up to two-fold or six-fold, depending on the mode of inheritance, more enriched for high-confidence neurodevelopmental disorder risk genes compared to genic intolerance metrics alone,” he said in the release. “Additionally, some top-ranking genes were 45 to 500 times more likely to be supported by the literature than lower-ranking genes.”

That means that the models may actually validate genes that haven’t yet been proven to be involved in neurodevelopmental conditions. Gene discovery done with the help of AI could possibly become the new normal for families seeking answers beyond umbrella terms like “autism spectrum disorder.”

“We hope that our models will accelerate gene discovery and patient diagnoses, and future studies will assess this possibility,” Dhindsa added.

Baylor Genetics has paired with Baylor’s department of molecular and human genetics to launch the Medical Genetics Multiomics Laboratory with a goal for the collaboration is to turn research into clinical diagnostics. Photo via Getty Images

This new Houston lab is translating genetics research into clinical diagnostics

DNA innovation

A new lab at Baylor College of Medicine is primed to do groundbreaking work in the field of genetics.

Baylor Genetics has paired with Baylor’s department of molecular and human genetics to launch the Medical Genetics Multiomics Laboratory (MGML). The goal for the collaboration is to turn research into clinical diagnostics.

MGML’s freshly launched first clinical test is Whole Transcriptomic RNA Sequencing (WT RNAseq). The new test builds upon the success of existing tests like whole exome sequencing (WES) and whole genome sequencing (WGS) currently on offer from Baylor Genetics by focusing on additional variants that could be missed by the other tests.

Baylor Genetics is offering WT RNAseq to the Undiagnosed Diseases Network (UDN) and its affiliated institutions. For more than a decade, the NIH-funded UDN has united clinical and research experts from across many fields and institutions to give answers to patients with rare genetic diseases. Since it became one of the first institutions to join the UDN in 2014, Baylor Genetics has been the UDN’s sequencing core, using WES, WGS and RNA sequencing to help diagnose patients. The additional offering of WT RNAseq could improve the diagnostic yield by as much as 17 percent.

“This agreement, and the MGML lab, bring to life our vision of innovation, allowing us to co-develop new tests, evaluate in terms of clinical utility, and offer commercially in either a research or clinical setting,” says Dr. Brendan Lee, professor, chair and Robert and Janice McNair Endowed Chair of Molecular and Human Genetics at Baylor College of Medicine, and scientific advisory and board of directors member at Baylor Genetics. “Baylor Genetics is turning around critical high-volume testing, but the challenge is also maintaining our innovative edge and our position as leaders in discovery and genomic health implementation. This agreement is a realization of the vision when Baylor Genetics was founded 10 years ago.”

The lab’s product offerings will continue to expand as it becomes commercially feasible to do so, and the new tests will be used both commercially and clinically.

Baylor Genetics combines the powers of Baylor College of Medicine, which has the NIH’s best-funded department of molecular and human genetics, and Japanese clinical diagnostic testing company H.U. Group Holdings.

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How Houston innovators played a role in the historic Artemis II splashdown

safe landing

Research from Rice University played a critical role in the safe return of U.S. astronauts aboard NASA’s Artemis II mission this month.

Rice mechanical engineer Tayfun E. Tezduyar and longtime collaborator Kenji Takizawa developed a key computational parachute fluid-structure interaction (FSI) analysis system that proved vital in NASA’s Orion capsule’s descent into the Pacific Ocean. The FSI system, originally developed in 2013 alongside NASA Johnson Space Center, was critical in Orion’s three-parachute design, which slowed the capsule as it returned to Earth, according to Rice.

The model helped ensure that the parachute design was large enough to slow the capsule for a safe landing while also being stable enough to prevent the capsule from oscillating as it descended.

“You cannot separate the aerodynamics from the structural dynamics,” Tezduyar said in a news release. “They influence each other continuously and even more so for large spacecraft parachutes, so the analysis must capture that interaction in a robustly coupled way.”

The end result was a final parachute system, refined through NASA drop tests and Rice’s computational FSI analysis, that eliminated fluctuations and produced a stable descent profile.

Apart from the dynamic challenges in design, modeling Orion’s parachutes also required solving complex equations that considered airflow and fabric deformation and accounted for features like ringsail canopy construction and aerodynamic interactions among multiple parachutes in a cluster.

“Essentially, my entire group was dedicated to that work, because I considered it a national priority,” Tezduyar added in the release. “Kenji and I were personally involved in every computer simulation. Some of the best graduate students and research associates I met in my career worked on the project, creating unique, first-of-its-kind parachute computer simulations, one after the other.”

Current Intuitive Machines engineer Mario Romero also worked on Orion during his time at NASA. From 2018 to 2021, Romero was a member of the Orion Crew Capsule Recovery Team, which focused on creating likely scenarios that crewmembers could encounter in Orion.

The team trained in NASA’s 6.2-million-gallon pool, using wave machines to replicate a range of sea conditions. They also simulated worst-case scenarios by cutting the lights, blasting high-powered fans and tipping a mock capsule to mimic distress situations. In some drills, mock crew members were treated as “injured,” requiring the team to practice safe, controlled egress procedures.

“It’s hard to find the appropriate descriptors that can fully encapsulate the feeling of getting to witness all the work we, and everyone else, did being put into action,” Romero tells InnovationMap. “I loved seeing the reactions of everyone, but especially of the Houston communities—that brought me a real sense of gratitude and joy.”

Intuitive Machines was also selected to support the Artemis II mission using its Space Data Network and ground station infrastructure. The company monitored radio signals sent from the Orion spacecraft and used Doppler measurements to help determine the spacecraft's precise position and speed.

Tim Crain, Chief Technology Officer at Intuitive Machines, wrote about the experience last week.

"I specialized in orbital mechanics and deep space navigation in graduate school,” Crain shared. “But seeing the theory behind tracking spacecraft come to life as they thread through planetary gravity fields on ultra-precise trajectories still seems like magic."

UH breakthrough moves superconductivity closer to real-world use

Energy Breakthrough

University of Houston researchers have set a new benchmark in the field of superconductivity.

Researchers from the UH physics department and the Texas Center for Superconductivity (TcSUH) have broken the transition temperature record for superconductivity at ambient pressure. The accomplishment could lead to more efficient ways to generate, transmit and store energy, which researchers believe could improve power grids, medical technologies and energy systems by enabling electricity to flow without resistance, according to a release from UH.

To break the record, UH researchers achieved a transition temperature 151 Kelvin, which is the highest ever recorded at ambient pressure since the discovery of superconductivity in 1911.

The transition temperature represents the point just before a material becomes superconducting, where electricity can flow through it without resistance. Scientists have been working for decades to push transition temperature closer to room temperature, which would make superconducting technologies more practical and affordable.

Currently, most superconductors must be cooled to extremely low temperatures, making them more expensive and difficult to operate.

UH physicists Ching-Wu Chu and Liangzi Deng published the research in the Proceedings of the National Academy of Sciences earlier this month. It was funded by Intellectual Ventures and the state of Texas via TcSUH and other foundations. Chu, founding director and chief scientist at TcSUH, previously made the breakthrough discovery that the material YBCO reaches superconductivity at minus 93 K in 1987. This helped begin a global competition to develop high-temperature superconductors.

“Transmitting electricity in the grid loses about 8% of the electricity,” Chu, who’s also a professor of physics at UH and the paper’s senior author, said in a news release. “If we conserve that energy, that’s billions of dollars of savings and it also saves us lots of effort and reduces environmental impacts.”

Chu and his team used a technique known as pressure quenching, which has been adapted from techniques used to create diamonds. With pressure quenching, researchers first apply intense pressure to the material to enhance its superconducting properties and raise its transition temperature.

Next, researchers are targeting ambient-pressure, room-temperature superconductivity of around 300 K. In a companion PNAS paper, Chu and Deng point to pressure quenching as a promising approach to help bridge the gap between current results and that goal.

“Room-temperature superconductivity has been seen as a ‘holy grail’ by scientists for over a century,” Rohit Prasankumar, director of superconductivity research at Intellectual Ventures, said in the release. “The UH team’s result shows that this goal is closer than ever before. However, the distance between the new record set in this study and room temperature is still about 140 C. Closing this gap will require concerted, intentional efforts by the broader scientific community, including materials scientists, chemists, and engineers, as well as physicists.”

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This article originally appeared on EnergyCapitalHTX.com.