School of Biological Sciences

Cats always land on their feet, but what makes them so agile? Their unique sense of balance has more in common with humans than it may appear. Researchers at the Georgia Institute of Technology are studying cat locomotion to better understand how the spinal cord works to help humans with partial spinal cord damage walk and maintain balance.

Using a mix of experimental studies and computational models, the researchers show that somatosensory feedback, or neural signals from specialized sensors throughout a cat’s body, help inform the spinal cord about the ongoing movement and coordinate the four limbs to keep cats from falling when they encounter obstacles. Research suggests that with those motion-related sensory signals the animal can walk even if the connection between the spinal cord and the brain is partially fractured.

Understanding the mechanisms of this type of balance control is particularly relevant to older people who often have balance issues and can injure themselves in falls. Eventually, the researchers hope this could bring new understanding to somatosensory feedback’s role in balance control. It could also lead to progress in spinal cord injury treatment because the research suggests activation of somatosensory neurons can improve spinal neural networks’ function below the site of spinal cord damage.

“We have been interested in the mechanisms that make it possible to reactivate injured networks in the spinal cord,” said School of Biological Sciences Professor Boris Prilutsky. “We know from previous studies that somatosensory feedback from moving legs helps activate spinal networks that control locomotion, enabling stable movement.”

The researchers presented their findings in “Sensory Perturbations From Hindlimb Cutaneous Afferents Generate Coordinated Functional Responses in All Four Limbs During Locomotion in Intact Cats” in the journal eNeuro.

Coordinated Cats

Although genetically modified mouse models have recently become dominant in neural control of locomotion research, the cat model offers an important advantage. When they move, mice remain crouched, meaning they are less likely to have balance problems even if somatosensory feedback fails. Humans and cats, on the other hand, cannot maintain balance or even move if they lose sensory information about limb motion. This suggests that larger species, like cats and humans, might have a different organization of spinal neural network controlling locomotion compared to rodents.

Georgia Tech partnered with researchers at the University of Sherbrooke in Canada and Drexel University in Philadelphia to better understand how signals from sensory neurons coordinate movements of the four legs. The Sherbrooke lab trained cats to walk on a treadmill at a pace consistent with human gait and then used electrodes to stimulate their sensory nerve.

The researchers focused on the sensory nerve that transmits touch sensation from the top of the foot to the spinal cord. By electrically stimulating this nerve, researchers mimicked hitting an obstacle and saw how the cats stumbled and corrected their movement in response. Stimulations were applied in four periods of the walking cycle: mid-stance, stance-to-swing transition, mid-swing, and swing-to-stance transition. From this, they learned that mid-swing and the stance-to-swing transition were the most significant periods because the stimulation increased activity in muscles that flex the knee and hip joints, joint flexion and toe height, step length, and step duration of the stimulated limb.

“In order to maintain balance, the animal must coordinate movement of the other three limbs, otherwise it would fall,” Prilutsky said. “We found that stimulation of this nerve during the swing phase increases the duration of the stance phase of the other limbs and improves stability.”

In effect, when the cat stumbles during the swing phase, the sensation triggers spinal reflexes that ensure the three other limbs stay on the ground and keep the cat upright and balanced, while the swing limb steps over the obstacle.

Computational Cats

With these Canadian lab experiments, the researchers at Georgia Tech and Drexel University are using observations to develop a computational model of the cat’s musculoskeletal and spinal neural control systems. The data gathered are used to compute somatosensory signals related to length, velocity, and produced force of muscles, as well as pressure on the skin in all limbs. This information forms motion sensations in the animal’s spinal cord and contributes to interlimb coordination by the spinal neuronal networks.

“To help treat any disease, we need to understand how the intact system works,” Prilutsky said. “That was one reason why this study was performed, so we could understand how the spinal networks coordinate limb movements and develop a realistic computational model of spinal control of locomotion. This will help us know better how the spinal cord controls locomotion.”

CITATION: Merlet AN, Jéhannin P, Mari S, Lecomte CG, Audet J, Harnie J, Rybak IA, Prilutsky BI, Frigon A (2022) Sensory Perturbations from Hindlimb Cutaneous Afferents Generate Coordinated Functional Responses in All Four Limbs during Locomotion in Intact Cats. eNeuro 9: 0178-22.

DOI: 10.1523/ENEURO.0178-22.2022

Cats always land on their feet, but what makes them so agile? Their unique sense of balance has more in common with humans than it may appear. Researchers at the Georgia Institute of Technology are studying cat locomotion to better understand how the spinal cord works to help humans with partial spinal cord damage walk and maintain balance.

Using a mix of experimental studies and computational models, the researchers show that somatosensory feedback, or neural signals from specialized sensors throughout a cat’s body, help inform the spinal cord about the ongoing movement and coordinate the four limbs to keep cats from falling when they encounter obstacles. Research suggests that with those motion-related sensory signals the animal can walk even if the connection between the spinal cord and the brain is partially fractured.

Understanding the mechanisms of this type of balance control is particularly relevant to older people who often have balance issues and can injure themselves in falls. Eventually, the researchers hope this could bring new understanding to somatosensory feedback’s role in balance control. It could also lead to progress in spinal cord injury treatment because the research suggests activation of somatosensory neurons can improve spinal neural networks’ function below the site of spinal cord damage.

“We have been interested in the mechanisms that make it possible to reactivate injured networks in the spinal cord,” said School of Biological Sciences Professor Boris Prilutsky. “We know from previous studies that somatosensory feedback from moving legs helps activate spinal networks that control locomotion, enabling stable movement.”

The researchers presented their findings in “Sensory Perturbations From Hindlimb Cutaneous Afferents Generate Coordinated Functional Responses in All Four Limbs During Locomotion in Intact Cats” in the journal eNeuro.

Coordinated Cats

Although genetically modified mouse models have recently become dominant in neural control of locomotion research, the cat model offers an important advantage. When they move, mice remain crouched, meaning they are less likely to have balance problems even if somatosensory feedback fails. Humans and cats, on the other hand, cannot maintain balance or even move if they lose sensory information about limb motion. This suggests that larger species, like cats and humans, might have a different organization of spinal neural network controlling locomotion compared to rodents.

Georgia Tech partnered with researchers at the University of Sherbrooke in Canada and Drexel University in Philadelphia to better understand how signals from sensory neurons coordinate movements of the four legs. The Sherbrooke lab trained cats to walk on a treadmill at a pace consistent with human gait and then used electrodes to stimulate their sensory nerve.

The researchers focused on the sensory nerve that transmits touch sensation from the top of the foot to the spinal cord. By electrically stimulating this nerve, researchers mimicked hitting an obstacle and saw how the cats stumbled and corrected their movement in response. Stimulations were applied in four periods of the walking cycle: mid-stance, stance-to-swing transition, mid-swing, and swing-to-stance transition. From this, they learned that mid-swing and the stance-to-swing transition were the most significant periods because the stimulation increased activity in muscles that flex the knee and hip joints, joint flexion and toe height, step length, and step duration of the stimulated limb.

“In order to maintain balance, the animal must coordinate movement of the other three limbs, otherwise it would fall,” Prilutsky said. “We found that stimulation of this nerve during the swing phase increases the duration of the stance phase of the other limbs and improves stability.”

In effect, when the cat stumbles during the swing phase, the sensation triggers spinal reflexes that ensure the three other limbs stay on the ground and keep the cat upright and balanced, while the swing limb steps over the obstacle.

Computational Cats

With these Canadian lab experiments, the researchers at Georgia Tech and Drexel University are using observations to develop a computational model of the cat’s musculoskeletal and spinal neural control systems. The data gathered are used to compute somatosensory signals related to length, velocity, and produced force of muscles, as well as pressure on the skin in all limbs. This information forms motion sensations in the animal’s spinal cord and contributes to interlimb coordination by the spinal neuronal networks.

“To help treat any disease, we need to understand how the intact system works,” Prilutsky said. “That was one reason why this study was performed, so we could understand how the spinal networks coordinate limb movements and develop a realistic computational model of spinal control of locomotion. This will help us know better how the spinal cord controls locomotion.”

CITATION: Merlet AN, Jéhannin P, Mari S, Lecomte CG, Audet J, Harnie J, Rybak IA, Prilutsky BI, Frigon A (2022) Sensory Perturbations from Hindlimb Cutaneous Afferents Generate Coordinated Functional Responses in All Four Limbs during Locomotion in Intact Cats. eNeuro 9: 0178-22.

DOI: 10.1523/ENEURO.0178-22.2022

Though it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.

In a new paper published in eLife, researchers in the School of Biological Sciences and the School of Computer Science have shown that AF2Complex may be able to lend a hand.

Building on the models of DeepMind’s AlphaFold 2, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex — short for AlphaFold 2 Complex — is a deep learning tool designed to predict the physical interactions of multiple proteins. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.

“We essentially conduct computational experiments that try to figure out the atomic details of supercomplexes (large interacting groups of proteins) important to biological functions,” explained Jeffrey Skolnick, Regents’ Professor and Mary and Maisie Gibson Chair in the School of Biological Sciences, and one of the corresponding authors of the study. With AF2Complex, which was developed last year by the same research team, it’s “like using a computational microscope powered by deep learning and supercomputing.”

In their latest study, the researchers used this ‘computational microscope’ to examine a complicated protein synthesis and transport pathway, hoping to clarify how proteins in the pathway interact to ultimately transport a newly synthesized protein from the interior to the outer membrane of the bacteria — and identify players that experiments might have missed. Insights into this pathway may identify new targets for antibiotic and therapeutic design while providing a foundation for using AF2Complex to computationally expedite this type of biology research as a whole.

Computing complexes

Created by London-based artificial intelligence lab DeepMind, AlphaFold 2 is a deep learning tool able to generate accurate predictions about the three-dimensional structure of single proteins using just their building blocks, amino acids. Taking things a step further, AF2Complex uses these structures to predict the likelihood that proteins are able to interact to form a functional complex, what aspects of each structure are the likely interaction sites, and even what protein complexes are likely to pair up to create even larger functional groups called supercomplexes.

“The successful development of AF2Complex earlier this year makes us believe that this approach has tremendous potential in identifying and characterizing the set of protein-protein interactions important to life,” shared Mu Gao, a senior research scientist at Georgia Tech. “To further convince the broad molecular biology community, we [had to] demonstrate it with a more convincing, high impact application.”

The researchers chose to apply AF2Complex to a pathway in Escherichia coli (E. coli), a model organism in life sciences research commonly used for experimental DNA manipulation and protein production due to its relative simplicity and fast growth.

To demonstrate the tool’s power, the team examined the synthesis and transport of proteins that are essential for exchanging nutrients and responding to environmental stressors: outer membrane proteins, or OMPs for short. These proteins reside on the outermost membrane of gram-negative bacteria, a large family of bacteria characterized by the presence of inner and outer membranes, like E. coli. However, the proteins are created inside the cell and must be transported to their final destinations.

“After more than two decades of experimental studies, researchers have identified some of the protein complexes of key players, but certainly not all of them,” Gao explained. AF2Complex “could enable us to discover some novel and interesting features of the OMP biogenesis pathway that were missed in previous experimental studies.”

New insights

Using the Summit supercomputer at the Oak Ridge National Laboratory, the team, which included computer science undergraduate Davi Nakajima An, put AF2Complex to the test. They compared a few proteins known to be important in the synthesis and transport of OMPs to roughly 1,500 other proteins — all of the known proteins in E. coli’s cell envelope — to see which pairs the tool computed as most likely to interact, and which of those pairs were likely to form supercomplexes.

To determine if AF2Complex’s predictions were correct, the researchers compared the tool’s predictions to known experimental data. “Encouragingly,” said Skolnick, “among the top hits from computational screening, we found previously known interacting partners.” Even within those protein pairs known to interact, AF2Complex was able to highlight structural details of those interactions that explain data from previous experiments, lending additional confidence to the tool’s accuracy.

In addition to known interactions, AF2Complex predicted several unknown pairs. Digging further into these unexpected partners revealed details on what aspects of the pairs might interact to form larger groups of functional proteins, likely active configurations of complexes that have previously eluded experimentalists, and new potential mechanisms for how OMPs are synthesized and transported.

“Since the outer membrane pathway is both vital and unique to gram-negative bacteria, the key proteins involved in this pathway could be novel targets for new antibiotics,” said Skolnick. “As such, our work that provides molecular insights about these new drug targets might be valuable to new therapeutic design.”

Beyond this pathway, the researchers are hopeful that AF2Complex could mean big things for biology research.

“Unlike predicting structures of a single protein sequence, predicting the structural model of a supercomplex can be very complicated, especially when the components or stoichiometry of the complex is unknown,” Gao noted. “In this regard, AF2Complex could be a new computational tool for biologists to conduct trial experiments of different combinations of proteins,” potentially expediting and increasing the efficiency of this type of biology research as a whole.

AF2Complex is an open-source tool available to the public and can be downloaded here.

This work was supported in part by the DOE Office of Science, Office of Biological and Environmental Research (DOE DE-SC0021303) and the Division of General Medical Sciences of the National Institute Health (NIH R35GM118039). DOI: https://doi.org/10.7554

The Institute for Advanced Study (IAS) recently awarded Georgia Tech Bioinformatics Graduate Program Ph.D. student Breanna Shi the Advanced Graduate Ambassadorship from their Women and Mathematics program. As part of this award, Shi will organize a workshop to address equity by using her math background to help other underrepresented graduate students across Georgia Tech’s College of Sciences learn and apply math and computational methods in their research.

Associate Professor of Biological Sciences and Biological Sciences Graduate Committee Chair Ingeborg Schmidt-Krey first met Shi during her recruitment into the Bioinformatics (BINF) Ph.D. program, which is directed by Professor King Jordan, and is one of five Ph.D. programs and two M.S. programs in the School of Biological Sciences at Tech. Shi impressed Schmidt-Krey during her second semester as an engaged student — particularly in research ethics discussions.

“Bree’s background in mathematics coupled with her passion for applying mathematical approaches to biological research made her a fantastic match for such an interdisciplinary program,” said Schmidt-Krey. “Bree has a particular interest in using machine learning in her research and making her skills accessible to other students via her symposium, outreach activities, and teaching.”

Shi first contacted Biological Sciences Associate Professor Patrick McGrath about coming to Georgia Tech to join the Bioinformatics program. “With her mathematical background and interest in genomics, I thought that she would be a great match for this program and Georgia Tech in general,” McGrath said.

“Now in her second year, Breanna is fully participating in our lab’s research,” he added. “She’s using her skills in machine learning and computational biology to understand the evolution of behavior in Lake Malawi cichlids, a large flock of species that have evolved a variety of new social behaviors.”

Schmidt-Krey shared that Shi advocates for graduate students via the Georgia Tech Student Government Association (SGA), is working towards the Tech to Teaching certificate in preparation for her plans to become a professor, participates in several underrepresented minority recruitment activities, and is an instructor in the VIP program, where she will involve undergraduates from various backgrounds in her research.

“Bree is currently supported by a GEM Fellowship and GAANN award. Bree's symposium impressively shows a second-year Ph.D. student's initiative and commitment to equity in our community.”

Shi has also been awarded the Graduate Fellowship for STEM Diversity (GFSD) and the Graduate Retaining Inspirational Scholars in Technology and Engineering (Grad RISE) from Georgia Tech’s Center for Engineering Education and Diversity (CEED).

“Bree’s research includes looking for particular neurons in the brain that are activated during reproductive behaviors,” said McGrath, who is now Shi’s advisor.

McGrath added that Shi is also passionate about using new technologies to study aggression behaviors, simulating virtual fish to induce and learn from aggressive behaviors in other fish.

Currently overseeing a large group of undergraduate and master’s students, Shi is also passionate about mentorship, adding that she became interested in education research through her time with the Georgia Tech Center for Teaching and Learning.

“While I was initially nervous about having her overseeing so many students so early in her career, Breanna has really done an outstanding job of overseeing this group,” McGrath shared. “Her goal is to become an academic professor, so it's great to see her display these skills. I am very proud of what Breanna has accomplished in such a short time.”

Shi’s mentorship will continue with the IAS workshop. “[The workshop] will be a partnership with Christin Salley, a third year Ph.D. student in Civil Engineering who is also a GEM fellow,” Shi said.

“Our hope is to get graduate students interested in using mathematics and computer science into their research,” Shi said. “As diversity fellows, Christin and I are making it a priority to include students from diverse groups and to facilitate mentoring.”

They also hope to provide a collaborative environment where students can network and learn. “Our goal is to host this event annually,” Shi added.

Shi, who has two degrees in mathematics, has been interested to understand why some students find math and computer sciences (CS) difficult to master. She hopes to employ a few non-traditional techniques that will allow students to feel less resistant and more understanding of the subjects. “We hope to provide greater outcomes for the participants than their past experiences with math and CS.”