The Importance of Consistent Sleep for Memory Retrieval at the Neural Level

Sleep helps us remember the details of past events more clearly. When we sleep, neural mechanisms facilitate the consolidation of memories formed during the waking day. Specifically, memories are temporarily stored in a brain structure called the hippocampus. During the consolidation process, memories are replayed and integrated into long-term storage centers in the neocortex of the brain. Poor sleep impairs sleep-based memory consolidation and memory retrieval. In other words, when our sleep is fragmented, our memory is less clear. 

One way to assess the clarity of a memory is to measure neural similarity, or the overlap between patterns of neural activity.  My colleagues and I presented participants with a series of word pairs to remember while we recorded their neural activity using electroencephalography. We used this task to measure neural activity when participants studied (i.e., encoded) and were tested on (i.e., retrieved) the word pairs. The overlap between their neural patterns for a given word pair at study and test is an index of neural similarity.

Interestingly, we found that sleep quality was associated with neural activity for word pairs that were paired differently. When people had more consistent sleep quality from night-to-night (measured with wrist-worn monitors), they had greater neural similarity when they correctly rejected word pairs that were paired differently. For example, if they saw the pair “wing – clock” during the study period and correctly identified “fork – clock” as a different pairing at test, they demonstrated higher neural similarity. 

There were several strengths of the study. We used an objective measure of sleep quality — wrist-worn monitors. We also measured sleep quality for seven nights, which allows for assessing night-to-night sleep variations. Our participants were racially and ethnically diverse people across the adult lifespan. However, our study was limited by its small, convenience-based sample of participants (74 people) and cross-sectional design. We cannot determine if poorer sleep causes lower neural similarity with this data. 

Taken together, our study suggests that memory integrity, or the ability to clearly remember the details of past events, may be linked with consistent sleep patterns. Thus, in addition to sleeping for enough time, sleep consistency also contributes to better memory retrieval. 

Edited by: Trang Nguyen, Pei-Yin Shih

Engineered bacteria enhanced the current therapeutics in lung cancer

Lung cancer is the second most common type of cancer and is responsible for the most cancer-related death in the U.S. The American Cancer Society reports that more than 235,000 people were diagnosed with lung cancer in 2021. There are three major types of lung cancer: non-small cell lung cancer (85% of cases), small cell lung cancer (10% of cases), and lung carcinoid tumor (5% of cases). The causes of lung cancer include but are not limited to smoking, secondhand smoke, exposure to certain toxins, and family history. The symptoms include cough with blood, chest pain, wheezing, and weight loss when the cancer is in the advanced stage. Depending on the type of lung cancer and what stage it has progressed to, the treatment will be different. Broadly the treatment involves surgical resection, radiation, chemotherapy, targeted therapy and immunotherapy. However, to treat this complex disease researchers are always looking for new and improved treatment modalities.

A research group at Columbia Engineering led by Dr. Dhruba Deb in the lab of professor Tal Danino developed a new therapeutic to treat non-small cell lung cancer (NSCLC) by combining an engineered bacteria with targeted therapy to enhance the treatment efficacy without any additional toxicity in laboratory animal models. This finding was published in Scientific Report on December 13, 2022.

By engineering a toxin named theta (θ) toxin in the bacteria S.typhimurium and by testing the response of a variety of NSCLC cells to this engineered bacteria, the research group found that θ toxin can kill a variety of NSCLC cells even with different genetic background such as mutated growth factor receptor like KRAS or EGFR, the most common mutations found in NSCLC. The research group also administered locally live S.typhimurium expressing theta toxin (Stθ) in NSCLC tumor cells in the mouse model and found a 2.5-fold reduction of tumor growth within a week compared to the control group. 

With the success of testing live S.typhimurium expressing theta toxin (Stθ) in mouse model and no toxicity  found in the peripheral organs, the research group tested whether using the engineered bacteria could enhance the efficacy of the standard of care chemotherapies as well as small molecular inhibitors. To identify potential drugs to combine with Stθ, the authors used RNA-sequencing. This helped to pinpoint which biochemical pathways in NSCLC cells were helping the cells to survive the Stθ treatment. To overcome this ability of NSCLC cells, the researchers blocked those biochemical pathways with drugs and eliminated the NSCLC cells. For example, one of the drugs, MK2206 when paired with Stθ treatment, blocks the NSCLC cells’ ability to survive via biochemical signaling of phosphorylated AKT that promotes survival and growth in response to extracellular signals. Key proteins involved are PI3K (phosphatidylinositol 3-kinase) and Akt (protein kinase B)..  The research group also tested the combination treatment of MK2206 and Stθ in a mouse model. They found that the combination treatment of Stθ bacteria and MK2206 suppressed the tumor growth efficiently compared to treatment with only Stθ or only MK2206. Moreover, with lower dose of bacteria and drug use, they could observe similar treatment results and could possibly avoid  the activation of the immune system caused by high dose of bacteria treatment. Taken together, this combination treatment is a potential therapeutic for the NSCLC. 

There are several limitations in this study that need to be addressed before entering the clinical trial. First, the authors used a small number of animals per cohort in the in vivo study, so they plan to expand their study to assess the overall survival upon treatment. Second, the toxins themselves are not selectively targeted to the cancer cells, so they need to develop a selective delivery method to avoid the systemic toxicity. In the laboratory animal models, the local administration of the live bacteria acted as a selective delivery. But, further studies are necessary to use this live bacteria in human clinical trials. Overall, this study opens up new treatment options for patients diagnosed with the NSCLC.

Reviewed by: Sam Rossano, Margarita T. Angelova

A continuous war between DNA elements shapes genome evolution

While genomes include the totality of genes that determine an organism’s biological identity, genes can only be a tiny fraction of the genome. In humans, and in many other species, DNA contains multiple diverse regions. One example are transposons, or mobile genetic elements, which are parts of DNA that can move in new places in the genome (Figure 1). The “genomic walks” of transposons are potentially harmful, since when they “jump” within a gene, they trigger mutation and loss of that gene. Cells are constantly evolving new strategies to keep transposons under control. In response, transposons adapt and arm themselves with new strategies to escape cellular control.

Sometimes, a mobilized transposon hijacks additional DNA information that it transfers to the new location with itself. When this additional DNA happens to be a gene providing survival advantage, not only the recipient increases its gene pool and survival capacities but the transposon also increases its chances to propagate. This process of transmission of information between different species is termed horizontal gene transfer and represents a major driver of genome evolution across all domains of life. Transposons have been key players in this process.

Figure 1. Schematic representation of a DNA transposon and its movement between two DNA molecules. Image created with BioRender.com.

Transposons can be considered as reminiscent of ancient viral infections that managed to integrate in a host cell genome but later lost the capacity to completely exit the cell. Bacteria have evolved a fascinating mechanism to remember viruses that they have already encountered. This acquired immunity involves a family of DNA sequences named CRISPR and their associated protein partners Cas. The CRISPR sequences are fragments of DNA derived from different viruses that have infected the bacteria and were integrated in the bacterial genome, creating a footprint of previous infections (Figure 2, upper part). When cells are infected, they use the CRISPR catalog and compare it to the invading DNA, helping it to recognize recurrent viruses and destroy them more rapidly. Cas proteins participate in the degradation of the viral DNA. (Figure 2, lower part).

Advances in research showed that the CRISPR-Cas complexes can be modified to edit genes in different organisms. To do this, part of the complex is changed to lead the Cas protein to a gene of interest, instead towards a viral genome. This system of gene editing has already found numerous important applications ranging from basic biology research to disease treatments and development of new technologies. The discovery of CRISPR-Cas was acknowledged with the 2020 Nobel Prize in Chemistry to Emmanuelle Charpentier and Jennifer Doudna.

Figure 2. Schematic representation of the CRISPR-Cas9 adaptive immune system of bacteria. Briefly, upon infection the viral DNA is fragmented and part of it is integrated in a special region of the bacterial genome, the CRISPR locus. CRISPR sequences get copied into shorter RNA molecules that carry parts of sequences identical to the sequence of a certain virus. Once copied, these short RNAs form complexes with Cas proteins and serve as “guides” for them towards potential complementary viral DNAs. The viral DNA is destroyed by the Cas protein if it can hybridize with the short RNA from the CRISPR locus. Image created with BioRender.com. 

Interestingly, researchers have found an intriguing interconnection between transposons and the  CRISPR–Cas defense system. In a striking example a bacterial transposon has hijacked some of the genes of a CRISPR-Cas system and uses those genes for its own propagation in genomes. These transposons are called CRISPR-associated transposons. Such widespread exchange of genes is caused by the never-ending arms race between the transposons and their hosts’ defense systems. In the recent publication of Hoffman and colleagues, five Columbia postdocs Minjoo Kim, Leslie Y. Beh, Jing Wang, Diego R. Gelsinger, and Jerrin Thomas George collaborated and brought significant insight in the functioning of one CRISPR-associated transposon.

In their publication, the authors monitored in detail the formation of  this RNA-guided transposon and its associated complexes, which enabled them to resolve distinct protein recruitment events that take place before the integration of the transposon. They also found that even if initially hundreds of non-desired genomic sites are targeted for integration at the end only few of those sites recruit the whole transposon machinery that is required for integration at this genomic location. This discovery offered insights into how the potential target sites in the host genome are identified, screened and approved for integration, allowing the transposon system to be specific.

To advance the understanding of interactions responsible for the assembly of the transposon associated proteins, the authors determined the structure of one of the interacting proteins, named TnsC and found that it is forming rings of seven molecules of TnsC that can pass DNA through the central pore of the ring. This helps to correctly position DNA for the following integration (Figure 3). The resolved molecular structure also allowed to gain clarity on how TnsC mediates the communication between the proteins in this transposon complex that are responsible on the one hand for the targeting and on the other hand for the integration at a specific genomic location. Their results pointed to TnsC as the proofreading checkpoint that ensures the specific selection of genomic sites for transposition.

Figure 3. Upper part: Model of the 7 molecules of TnsC forming a ring through which DNA is passed. Lower part: Representative experimental result from Hoffmann et al. showing different configurations and views of the DNA-TnsC complex. Figure adapted from the original publication. 

In summary, the paper not only deciphers the molecular specificity of consecutive factor binding to genomic target sites in this interesting process of RNA-guided transposition, but the resolved detailed structure also provides valuable information for the development of future biotechnologies in the field of programmable and specific integration of DNA in desired genomic locations. Such technology differs from the above-described original CRISPR-Cas system currently used for genome editing, because it has the potential to be less mutagenic, as well as because it provides the opportunity to insert much longer pieces of DNA in a desired location. RNA-guided transposases hold tremendous potential for future biotechnological and human therapeutic applications and will without a doubt accelerate novel discoveries. Find out more in the original publication.

Reviewed by: Trang Nguyen & Maaike Schilperoort

A high-tech device to effectively deliver drugs to tumors in the brain

Brain and other nervous system cancer is the 10th leading cause of death for men and women. Around 18,280 adults died from primary brain and central nervous system tumors in the United States in 2020. Glioblastoma is the most common malignant brain and other CNS tumors and median survival is only 12-15 months.

Why is brain tumor hard to treat? It is due to the blood brain barrier (BBB), a specialized network of blood vessels and cells that shields and protects the central nervous system against circulating toxins or pathogens that could cause brain infections. However, the impenetrability of the BBB also makes it difficult to treat tumors in the brain compared  to those in other organs. Patients with brain tumors have to receive higher doses of chemotherapy to penetrate the BBB and ensure an adequate amount of medication reaches into the brain and kills the tumor cells. The higher dose of chemotherapy will lead to the toxicity to the normal cells, which can result in serious side effects and even death of the patient. To overcome the BBB, scientists have tried to develop many different methods to deliver drugs effectively to the brain so that lower doses of chemotherapy can be used.

Over the last decade, Drs. Bruce and Canoll’s laboratory at the Columbia University Medical Center has been developing a new method to directly administer drugs to the site of the brain tumor, which they call convection enhanced delivery (CED). In CED, a small pump is implanted into the abdomen and connected to a thin catheter under the skin. Wireless technology is used to turn the pump on and off and control the flow rate of medicine that seeps in the tumor tissue.

In a recent study with the CED device, Dr. Bruce used topotecan, a drug that is toxic to glioblastoma cells, to treat five patients who were at least 18 years old with recurrent brain tumors. The patients were infused with topotecan for 48 hours, followed by a 5–7 day washout period before the next infusion, with four total infusions. Patients went about their normal routines at home while treatment continued without any severe side effects. After the fourth infusion, the pump was removed and the tumor was resected. This method is in the early phase of clinical trials (phase 1b) and will be expanded to a larger scale of patients due to test the safety and efficacy of the therapy for recurrent glioblastoma. This novel chemotherapy delivery strategy overcomes the limitation of drug delivery in patients with glioma. The results from this study have recently been published in Lancet Oncology 

There are two limitations in this study. First, there is no comparison group for determination of definitive survival benefit. Second, there is no way to assess the disease progression and treatment response due to effects of local drug infusion and surgical resection. However, in the locally delivered therapy (CED method), the authors used patients as their own control by performing pre-therapy and post-therapy MRIs and PET scans. The CED device effectively gets through the BBB to kill the brain tumor so new classes of drugs and targeted compounds could potentially be used such as high-molecular-weight compounds or viruses.

Reviewed by: Pei-Yin Shih, Maaike Schilperoort

How our gut communicates with our brain to drive a preference for fat

Thanksgiving is just around the corner. The buttery sweet potato casserole, mashed potatoes, and gravy on the Thanksgiving dinner table are delicious and irresistible for most of us. Though fat from buttery food provides important building blocks for our body, overconsumption of fatty food could lead to weight gain and obesity-related diseases such as cardiovascular disease. To help keep our health in check, we need a better understanding of how fat consumption changes our desire for fatty food. A recent study led by Dr. Mengtong Li in the laboratory of Dr. Charles Zuker at the Zuckerman Mind Brain and Behavior Institute at Columbia University has started to reveal some insights. 

Previously the research team discovered how sugar preference was established. They found that among the two ways of processing the intake of sugar, taste and gut pathways, the preference for sugar arises from gut and is independent of taste. In line with this finding, the authors discovered that artificial sweeteners do not create a preference because they activate only taste receptors but not the gut pathway.

Built upon what they have learned from sugar preference, the authors first tested if mice have taste-independent preference for fat as well. They gave the mice a choice between oily water and water with artificial sweetener, and they recorded the number of times that the mice licked either of the water bottles as a measurement for preference. They found that the mice predominantly drank from the bottle with oily water two days after exposure to the two choices. Even when the authors directly delivered fat to the gut through surgery, or in mice that did not have taste receptors, the mice could still develop preference for fat. These observations suggested that mice could develop preference for fat through the gut pathway.

Figure 1. The gut-brain axis transfers information of fat intake from the gut to the brain. The orange arrow represents the direction of the information flow. The orange and red dots indicate the activation of the vagus nerve and cNST, respectively. The blue dots represent the hormone cholecystokinin (CCK). The figure was generated using BioRender.

How does the information of fat get transferred to the brain, along the so-called gut-brain axis, and make the mice want fat more than sugar? The authors traced the signals of fat stimuli from gut to brain (Figure 1) through pharmacological and genetic tools. They identified two receptors, G protein-coupled receptors GPR40 and GPR120, that function as fat detectors in the gut. Upon detecting the presence of fat, the gut then releases signaling molecules, including a satiety hormone cholecystokinin, to relay the information to the vagus nerve. Interestingly, while control mice do not have a preference for cherry- versus grape-flavored solutions, the authors were able to create a new preference in experimental mice by artificially activating the subset of vagal neurons that receive cholecystokinin signals from the gut. The vagus nerve travels from gut to brain, and eventually sends the fat signals to the brain region called the caudal nucleus of the solitary tract (cNST) in the brainstem.

Together, the identification of the gut-brain communication might help battle against overindulging in fatty foods. As stress eating could increase the consumption of high calorie foods, it would also be interesting to study how the gut-brain communication is modulated by different emotional states. 

Edited by: Maaike Schilperoort, Trang Nguyen, Sam Rossano

Cleaning Up Data to Spruce Up the Results

Drawing conclusions from scientific studies can be difficult, in part because the data collected may be biased, which leads to a misinterpretation of the data. Let’s say we’re collecting data to investigate how many hours of sleep people get per night, during the week compared to over the weekend. We can ask 100 people their average nightly sleep time on weeknights and on weekends. To avoid bias, or skewing the data toward a particular duration, we should control for a few different factors. For example, we can limit our sample to only ask people 18 years or older, to avoid surveying children who tend to require more sleep than adults. This will avoid introducing a bias in the hours slept per night measure and prevent a trend in the data towards >8 hours a night. 

 

Some biases cannot be totally avoided during data collection. The existence of this unavoidable bias motivates scientists to consider including confounding variables in their data collection. Scientists use covariates when additional variables that change or differ across groups cannot be controlled for. A covariate is a variable that changes with the variable of interest, but isn’t of particular interest or importance for the question at hand. In our example, there are some other variables that may affect the amount of sleep an adult gets. This can include age (a postdoc in their late 20’s with a grant deadline might not get as much sleep as much as a retiree in their 60’s), activity level (strenuous physical activity leads to more sleep for better recovery), and caffeine intake (maybe serial coffee drinkers sacrifice an extra hour of sleep for an extra large cup in the morning). Because these variables may be different for each participant, we can measure them as observed covariates and include them in our statistical analysis.

 

Sometimes, as in the case with many epidemiological or public health studies, it’s difficult to measure or control for these covariates because the studies commonly use observational data from population-based studies which might not measure all potential covariates. In these studies, there may be unmeasured biases in the data that produce confounds, leading to imperfect conclusions in population studies. In our example, maybe we neglect to measure time spent on social media, which can affect someone’s total sleep time (I can’t be the only one who scrolls instagram instead of going to sleep at night…). Time spent on social media would be our unobserved covariate, which contributes to unmeasured bias in our sample. 

 

One way to address the problem of unmeasured bias is to pre-process the data – to fine-tune or clean up the data after it has been collected, but before statistical analysis is performed. In a recent paper, Columbia postdoc Dr. Ilan Cerna-Turoff and colleagues explored the use of a pre-processing method that can be used prior to data analysis in order to reduce the bias introduced by unmeasured covariates in a dataset. 

 

The pre-processing method investigated in this study is called “Full matching incorporating an instrumental variable (IV)” or “Full-IV Matching”, which aims to reduce biases between groups and thereby improve the accuracy of study findings. An instrumental variable (IV) is a measured variable that is unrelated to the covariates but is related to the variable of interest. For our example, an IV could how comfortable participants find their bed – something that is related to the time spent asleep, but isn’t related to the age or amount of coffee consumed. 

 

To apply the Full-IV Matching method, the researchers define an IV and “carve out” moderate values of the variable to focus on the extreme values (highest and lowest) across the range of IV measures, essentially ignoring the center of the data set. With this abridged dataset, the researchers implement a “matching” algorithm that pairs individuals who have similar values in their covariates, but who do not have similar values in their IV. In our example, participants who have similar caffeine intake levels or similar ages would be paired with participants who have the opposite bed-comfort level. This explores how the biases in the dataset change when each measured covariate is individually controlled for. Additionally, the researchers can define how much weight should be given to the unobserved covariate, depending on how much bias may be introduced into the data by this unobserved covariate. 

 

As proof-of-concept, Dr. Cerna-Turoff and colleagues simulated data from a scenario based on the Haitian Violence against Children and Youth Survey. Specifically, data were simulated based on measurements of social characteristics and experiences of young girls in Haiti, who were displaced either to a camp (“exposure” group) or to a wider community (“comparison” group) after the 2010 earthquake. The goal of this simulation experiment was to better understand how the displacement setting may be associated with risk of sexual violence. The researchers simulated data for 5 baseline covariates based on results from the Haitian Violence against Children and Youth Survey: (1) status of restavek (indentureship of poor children for rich families), (2) prior sexual violence, (3) living with parents, (4) age, and (5) social capital, of which the latter is an unobserved covariate. They also generated data for an exposure (camp or community), an outcome (sexual violence against girls), and an IV (earthquake damage severity). The researchers explored how the outcome was affected by the covariates and IV by quantifying the standardized mean difference of the variable across the exposure and comparison groups. A standardized mean difference value close to 0 indicates that the value of the variable was not different across the two groups, suggesting that this variable is not introducing bias into the analysis of group differences. 

 

The results suggest those who were displaced to a camp were at a higher risk of sexual violence than those who were displaced to a wider community, when correcting for all observed covariates. Additionally, the method successfully balanced the groups when correcting for the unobserved covariate of social capital. If not corrected for, differences in social capital might have confounded these results, such that girls with a stronger support network may appear to be at a lower risk. However, using the Full IV Matching method, bias across exposure and comparison groups for the observed covariates and the unobserved covariate of social capital was reduced, suggesting that neither the social capital nor the observed covariates contributed to the difference in risk for sexual violence observed between the two groups. 

 

This study provides a proof-of-concept for a pre-processing method for reducing bias across a data set. The authors mention limitations including the effect of the method on sample size and the ‘bias-variance trade-off’, in which increases in accuracy (less bias) may lead to more noise (higher variability) in the data. Ultimately, this type of methodology can aid in the correction of both observed and unobserved biases in population-based data collection, which has significant implications in epidemiologic studies, where not all sources of bias can be measured effectively.

 

Edited by: Emily Hokett, Pei-Yin Shih, Maaike Schilperoort; Trang Nguyen

A how-to guide for improving the potency of stem cells

You may remember Dolly, the sheep that became famous in the ‘90s as the first mammal to be cloned from an adult cell. Dolly was created through somatic cell nuclear transfer (SCNT), in which the nucleus from a somatic donor cell, i.e., a cell from the body other than a sperm or egg cell, is transferred into an enucleated egg cell. In this case, the donor cell was derived from a sheep’s mammary glands, a medical term for the breasts. The scientists named the cloned sheep Dolly since they could not think of a more impressive pair of mammary glands than Dolly Parton’s, or so the story goes. Aside from generating viable embryos in the laboratory, SCNT can be used to generate human stem cell lines for research and therapeutic purposes. However, this procedure is technically challenging and requires egg cells, which raises ethical concerns.

Artist’s impression of Dolly Parton, the famous American country singer, holding the cloned sheep named after her.
© 2022, Maaike Schilperoort

In 2007, a lab in Kyoto, Japan, found another way of generating human stem cells. The group infected human skin cells with a virus that carried a set of genes known to be important for embryonic stem cells. This resulted in so-called “induced pluripotent stem cells”, or iPSCs, that are functionally identical to embryonic stem cells. Although therapeutically promising, these iPSCs do not have the same potency as the cells generated through SCNT. SCNT generates cells that are totipotent at an early stage, meaning that they can form viable embryos as well as extraembryonic tissues such as the placenta and yolk sack. In contrast, iPSCs are pluripotent and are not able to give rise to extraembryonic tissues. They also have an inferior differentiation potential and lower proliferation rate as compared to totipotent cells.

Efforts have been made by scientists to make embryonic stem cells and iPSCs more totipotent by treating them with small molecule inhibitors, resulting in so-called expanded potential stem cells (EPSCs) that that can give rise to the embryo as well as placenta tissues and thus are more versatile as compared to their pluripotent counterparts. However, the developmental potential of EPSCs is still inferior to true totipotent cells or cells generated through SCNT. To gain insight into how the developmental potential of EPSCs can be improved, Columbia postdoc Vikas Malik and colleagues performed a deep analysis of pluripotent embryonic stem cells vs. the more totipotent EPSCs. They examined gene expression, DNA accessibility, and protein expression, and found some unique genes and proteins that are upregulated in EPSCs as compared to embryonic stem cells, such as Zscan4c, Rara, Zfp281, and UTF1. This pioneering work, published in Life Science Alliance, shows us which genes and proteins to target to generate authentic totipotent stem cells in a petri dish.

The work of Dr. Malik and colleagues has improved our understanding of how to generate totipotent cells outside of the human body without having to deal with the technical and ethical challenges of SCNT. These cells can further improve stem cell therapy through a greater ability to regenerate and repair tissues affected by damage or disease. In addition, totipotent cells are more suitable to study early development and problems of the reproductive system, and are optimal for gene therapy to correct genetic defects that cause disease. As the word indicates, totipotent cells really hold all the power, and could greatly advance scientific knowledge and regenerative medicine.

More information on the pursuit of totipotency can be found in this comprehensive review article by Dr. Malik and his PI Jianlong Wang published in Trends in Genetics.

Reviewed by: Trang Nguyen and Vikas Malik

Lactic acid – a new energy fuel source in brain tumor

What does lactic acid do to the body?

Lactic acid is produced when the body breaks down carbohydrates in low oxygen levels to generate energy. It is mainly found in muscle cells and red blood cells. An example of lactic production is when we perform intense exercise. 

Glucose, glutamine, fatty acids, and amino acids are well-known energy sources for cell growth and division. In the past, lactic acid has been known as a by-product of glycolysis, a process in which glucose is broken down through several enzyme reactions without the involvement of oxygen. However, recent studies showed that lactic acid is a key player in cancer cells to regulate tumor cell growth and division, blood vessel formation, and invasion. The tumor cells prefer to use glycolysis to produce energy and lactic acid despite the abundance of oxygen levels. Lactic acid is an alternative fuel source for glucose-deprived tumors to avoid cell death.

Lactic acid is transported through the membrane via the monocarboxylate transporter 1 (MCT1). A research group at Columbia University led by Dr. Markus Siegelin in the department of Pathology and Cell Biology showed a substantial presence of lactic acid in the citric acid cycle (TCA cycle), a series of chemical reactions to generate energy, in the glioblastoma cells cultured in the nutrient deprivation condition (low glucose and glutamine concentration). When the glucose and/or glutamine concentrations increased, less lactic acid was involved in the TCA-cycle metabolites. The uptaken lactic acid in the TCA-cycle was traced by using a method called C13 carbon tracing and was analyzed by liquid chromatography-mass spectrometry to identify the structure of different molecules. The researchers concluded that lactic acid is used as a fuel source to generate the energy in the brain tumor cells. Furthermore, lactic acid is converted to Actetyl-CoA and contributed to the gene modification in glioblastoma cells (Figure 1). These novel findings were published in a prestigious journal,  Molecular Cell

Figure 1: Role of lactic acid in the epigenetic modification of glioblastoma cells. Lactic acid is transported to the membrane via the monocarboxylate transporter 1 (MCT1) and contributed to the TCA cycle as a fuel source to generate the energy. Lactic acid is converted to Actetyl-CoA and contributed to the gene modification in glioblastoma cells. Suppressing the TCA cycle by using the targeted drug, namely CPI-613 (devimistat) leads to the abrogation of lactic acid in the energy production. The figure was generated by Biorender.

From these findings, the authors proposed to use CPI-613 (devimistat) drug, which targets TCA-cycle metabolites (Figure 1), to  treat glioblastoma cells. Indeed, CPI-613 showed a suppression of cellular viability in vitro of glioblastoma cells and an extension of the animal survival curve in the mouse model. The authors suggested that the combination of CPI-613 with other standard care treatment in glioblastoma such as temozolomide and radiation could be a potential clinical therapy for patients with glioblastoma.

Read more about this exciting finding here:

https://www.sciencedirect.com/science/article/pii/S1097276522006475 

Reviewed by: Pei-Yin Shih, Sam Rossano, Emily Hokett

Alcohol Use Disorder – are we making the right diagnosis?

Do you and your friends enjoy the occasional cocktail or two over the weekend? Maybe we know someone who enjoys the more-than-occasional cocktail. But, at what point do our drinking habits significantly affect our health? Recent studies suggest that 6% of adults in the United States report heavy or high-risk consumption of alcohol, which is defined as an average of more than 7 drinks/week for women and more than 14 drinks/week for men. This high risk-consumption may lead to Alcohol Use Disorder (AUD) if it is repeated for one year or more. AUD is associated with a number of medical and psychiatric problems, and can even increase risk of death in patients who have cancer and cardiovascular disease.

To diagnose AUD, medical and mental health professionals use the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), which explores 11 criteria, including alcohol-related cravings, strains on relationships caused by alcohol use, feeling unable to cut back or stop drinking, dangerous or risky behavior when under the influence of alcohol, etc. Unlike previous versions of the DSM, these AUD diagnoses are divided based on severity, where people who experience 0 or 1 of the diagnostic criteria do not have AUD (no-AUD), 2-3 criteria have mild AUD, 4-5 criteria have moderate AUD, and 6+ have severe AUD. However, it’s not well understood whether other factors like the extent of alcohol use, the degree of cravings or impairments, etc. can help classify mild, moderate, and severe AUD diagnoses. 

Last year, Dr. Zachary L. Mannes, a postdoc in the Department of Epidemiology at Columbia University Mailman School of Public Health and New York State Psychiatric Institute, and colleagues published a study in which they aimed to explore any potential relationships between the severity of AUD (no-AUD, mild, moderate, or severe, based on the DSM-5) and self-reported measures of other factors or “external validators”, such as levels of alcohol craving, functional impairment, and psychiatric conditions. To do this, they collected AUD diagnosis as well as measures of external validators in 588 participants. These validators included alcohol specific validators (i.e. Craving, Problematic Use, Harmful Use, Binge Drinking Frequency), psychiatric validators (i.e. Major Depressive Disorder/MDD and posttraumatic stress disorder/PTSD), and functioning validators (social impairments; physical and mental impairments).

Dr. Mannes and colleagues reported that in this cohort of subjects, participants with alcohol use validators had a significantly greater likelihood of a diagnosis with mild, moderate, and severe AUD than a no-AUD diagnosis. Psychiatric validators like MDD and PTSD had a significantly greater likelihood of a severe AUD diagnosis than no-AUD; this relationship was not seen for either mild or moderate AUD. Participants who had social, physical, and mental impairments had a greater likelihood of having severe AUD than no-AUD, but this was not seen for participants with mild or moderate AUD. When looking within participants with an AUD diagnosis (i.e. excluding a no-AUD diagnosis), participants with many alcohol-specific, psychiatric, and functional validators were more likely to have a severe AUD than either mild or moderate AUD.

Overall, the results of this study support the structure of the DSM-5 diagnosis for AUD, as those diagnoses with mild and moderate AUD had significant associations with alcohol use validators, while those with severe AUD had significant associations with alcohol use, psychiatric and functional validators. In other words, people with severe AUD had a higher likelihood of symptoms that affected other aspects of their lives including impairments in social functioning and presence of psychiatric conditions including MDD and BPD. This study emphasizes the importance of looking at levels of severity in AUD as the current DSM-5 does, as opposed to a binary yes/no diagnosis as older versions of the DSM had incorporated. This study also helps further the understanding of optimal ways to diagnose AUD and may help better understand potential treatment implications for various AUD severity. The study published by Dr. Mannes and colleagues supports and progresses the field of AUD research in order to better understand and characterize the symptoms, comorbidities, and diagnosis of AUD, so that medical professionals can better assist those who are struggling with the disorder. 

Edited by: Trang Nguyen, Maaike Schilperoort

Metastatic cancer cells have unstable DNA which helps them to evade the body’s immune system

Melanoma brain metastasis (MBM) frequently occurs in patients with late stages of melanoma (skin cancer). It is the third leading cause of brain metastases after lung and breast cancers. Cancer cells break away from the primary tumor and travel to the brain through the bloodstream. Despite significant therapeutic advances in the treatment of metastatic cancers, MBM  remains a challenging problem for therapeutic treatment due to the blood brain barrier. The MBM may develop a variety of symptoms that are similar to primary brain tumors such as headache, difficulty walking, or seizures. To provide comprehensive studies of the cells inside melanoma brain metastases, Jana Biermann, a postdoc in Dr. Benjamin Izar’s lab at Columbia University, performed single-cell-sequencing, nucleus RNA-sequencing, and CT scans of 22 treatment-naive MBM and 10 extracranial melanoma metastases that could spur the development of a new generation of therapies (Figure 1).

Figure 1: A comprehensive study of melanoma brain metastasis and extracranial melanoma metastases by performing single-cell genetic analyses of frozen brain samples. snRNA-seq: single nuclei RNA sequencing; TCR-seq: T cells sequencing. Image was created from BioRender based on Figure 1A of the original article that was published in CellPress with title “Dissecting the treatment-naive ecosystem of human melanoma brain metastasis”.

The authors also analyzed the genes expressed in 17 melanoma brain metastases and 10 extracranial melanoma metastases patients. The data revealed unstable DNA in the melanoma brain metastases compared with extracranial melanoma metastases. The unstable DNA triggers signaling pathways that enable the tumor cells to spread around the body and to suppress the body’s natural immune response that normally fights off the tumor cells. The researchers also found that the relocated melanoma cells adopt a neuronal-like state that might help tumor cells adapt and survive after they migrate to the brain. Furthermore, by using CT scans of multiple slices of the tumors, researchers created three-dimensional images of the tumors and uncovered heterogeneity in metabolic and immune pathways within and between tumors. 

The authors also found that the cancer cells in the brain significantly expressed  several genes that are known to promote cancer progression, such as MET and PI3K, while the extracranial melanoma metastases strongly expressed genes related to epithelial cells, which are the cells that cover the inside and outside of the surfaces of your body such as skin and blood vessels. Understanding these pathways will help for the therapeutic targets. 

A limitation of the study is that the authors did not compare melanoma brain metastasis and extracranial melanoma metastases within the same patients, which could have introduced variability in their dataset. Nevertheless, the atlas that they built provides a foundation for further mechanistic studies on how different perturbations could influence brain metastasis ecosystems.

Reviewed by: Pei-Yin Shih, Sam Rossano, Maaike Schilperoort

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