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Small brain region linked to schizophrenia risk through unique gene changes

New research published in the American Journal of Psychiatry provides new molecular insights into the role of the habenula, a pea-sized brain region that helps regulate motivation and mood, in contributing to the risk of schizophrenia. A team of researchers at Lieber Institute for Brain Development and Johns Hopkins found that many schizophrenia-related molecular changes appear to be specific to this region, suggesting the habenula could be a potential target for future treatments.

Schizophrenia is a heritable disorder, and a combination of multiple genetic variants contributes to it. This study sought to understand how molecular changes in the habenula region of the brain contribute to the development of . The authors note that they focused on the habenula because of its “emerging role in and functional influence on neurotransmitter systems impacted in schizophrenia.”

The study team, led by Ege A. Yalcinbas, Ph.D., used cutting-edge molecular techniques to analyze postmortem human brains, resulting in the creation of the first cell-by-cell and within-cell gene expression map of the human habenula (Hb). They then compared from 35 individuals with schizophrenia and 33 nonpsychiatric donors.

Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area

An amazing paper (link:) where functional ultrasound imaging (fUSI) is used to explore how brain activity in the lateral intraparietal cortex (LIP) can predict visual saccades (eye movements) in two monkeys. An impressive array of computational analyses are used to extract insights from the imaged regions. Indeed, predictive models developed by the authors remained fairly stable over the course of up to 900 days! I happen to know two of the authors (Sumner L Norman and Mikhail Shapiro): congratulations to them and their colleagues on this excellent publication!

Our results demonstrate that PPC contains subregions tuned to different directions. These tuned voxels were predominately within LIP and grouped into contiguous mesoscopic subpopulations. Multiple subpopulations existed within a given coronal plane, i.e., there were multiple preferred directions in each plane. A rough topography exists where anterior LIP had more voxels tuned to contralateral downwards saccades and posterior LIP had more voxels tuned to contralateral upwards saccades. These populations remained stable across more than 100–900 days.

We observed large effect sizes with changes in CBV on the order of 10–30% from baseline activity (Fig. 3). This is much larger than observed with BOLD fMRI where the effect size was ~0.4–2% on similar saccade-based event-related tasks27,32. Our results support a growing evidence base that establishes fUSI as a sensitive neuroimaging technique for detecting mesoscopic functional activity in a diversity of model organisms, including pigeons, rats, mice, nonhuman primates, ferrets, and infant and adult humans23,24,25,33,34,35,36,37,38,39,40.

Several studies have reported a patchiness in direction selectivity with many neighboring neurons tuned to approximately the same direction followed by an abruption to a patch of a different preferred direction13,14,41. These results match very closely with the results observed in this study where we found clusters within LIP tightly tuned to one direction with differently tuned clusters in close proximity within a given plane. These results further emphasize the high spatial resolution of fUSI for functional mapping of neuronal activity. These results also closely match a previous study that used fUSI to identify the tonotopic mapping of the auditory cortex and inferior colliculus in awake ferrets where the authors found a functional resolution of 100 µm for voxel responsiveness and 300 µm for voxel frequency tuning34.

Turning on an immune pathway in tumors could lead to their destruction

Activating this , known as the cGAS-STING pathway, worked even better when combined with existing immunotherapy drugs known as checkpoint blockade inhibitors, in a study of mice. That dual treatment was successfully able to control tumor growth.

The researchers turned on the cGAS-STING pathway in immune cells using messenger RNA delivered to . This approach may avoid the of delivering large doses of a STING activator, and takes advantage of a natural process in the body. This could make it easier to develop a treatment for use in patients, the researchers say.

New Artificial Neurons Physically Replicate the Brain

A breakthrough in neuromorphic computing could lower the energy consumption of chips and accelerate progress toward artificial general intelligence (AGI). Researchers from the USC Viterbi School of Engineering and the School of Advanced Computing have created artificial neurons that closely mimic

Innovative Treatment Regrows 90% of Lost Hair

Hair loss affects millions of people worldwide. Although treatments do exist, these solutions are costly and not always effective. Looking for a more lasting and effective solution, scientists have turned their attention to understanding the molecular mechanisms that regulate hair growth, leading to a new frontier in hair regeneration: dermal exosomes.

Experts feared a disease rebound after COVID-19—it didn’t happen

As the COVID-19 lockdown in 2020 stretched on, scientists watched for all sorts of unintended effects, from social to economic to environmental.

But the experts who predict wondered specifically whether other than COVID-19 would surge after the prolonged isolation of the population. Would cause us to have less immunity to common diseases? Would those diseases rebound with deadly consequences?

In a paper published in Science, the University of Georgia’s Tobias Brett and Pejman Rohani explored which infectious diseases were impacted by COVID-19 control measures and, of those, which rebounded. They found airborne diseases were most likely to rebound—but not as much as some feared. Surprisingly, the incidence of sexually transmitted diseases remained low, even long after -era behaviors changed.

Functionally dominant hotspot mutations of mitochondrial ribosomal RNA genes in cancer

To study selection for somatic single nucleotide variants (SNVs) in tumor mtDNA, we identified somatic mtDNA variants across primary tumors from the GEL cohort (n = 14,106). The sheer magnitude of the sample size in this dataset, in conjunction with the high coverage depth of mtDNA reads (mean = 15,919×), enabled high-confidence identification of mtDNA variants to tumor heteroplasmies of 5%. In total, we identified 18,104 SNVs and 2,222 indels (Supplementary Table 1), consistent with previously reported estimates of approximately one somatic mutation in every two tumors1,2,3. The identified mutations exhibited a strand-specific mutation signature, with a predominant occurrence of CT mutations on the heavy strand and TC on the light strand in the non-control region that was reversed in the control region2 (Extended Data Fig. 1a, b). These mutations occur largely independently of known nuclear driver mutations, with the exception of a co-occurrence of TP53 mutation and mtDNA mutations in breast cancer (Q = 0.031, odds ratio (OR) = 1.43, chi-squared test) (Extended Data Fig. 2a and Supplementary Table 4).

Although the landscape of hotspot mutations in nuclear-DNA-encoded genes is relatively well described, a lack of statistical power has impeded an analogous, comprehensive analysis in mtDNA16,17. To do so, we applied a hotspot detection algorithm that identified mtDNA loci demonstrating a mutation burden in excess of the expected background mutational processes in mtDNA (Methods). In total, we recovered 138 unique statistically significant SNV hotspots (Q 0.05) across 21 tumor lineages (Fig. 1a, b and Supplementary Table 2) and seven indel hotspots occurring at homopolymeric sites in complex I genes, as previously described by our group (Extended Data Fig. 2b and Supplementary Table 3). SNV hotspots affected diverse genetic elements, including protein-coding genes (n = 96 hotspots, 12 of 13 distinct genes), tRNA genes (n = 8 hotspots, 6 of 22 distinct genes) and rRNA genes (n = 34 hotspots, 2 of 2 genes) (Fig. 1b, c, e).

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