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Biology subjects

Kohno, T.

Publications and source records attributed to Kohno, T..

11 recordsLinked to original sources

DNAS-Bench: Deterministic Nucleic Acid Screener Benchmarking

The rapid growth of biotechnology manufacturing for synthetic DNA and proteins has raised concerns that adversaries could exploit commercial synthesis pipelines to create biological weapons. Without effective safeguards, an attacker could seek regulated genetic sequences from synthesis providers; while synthetic DNA is not itself a pathogen or toxin, access to such sequences can lower barriers to downstream misuse, motivating robust order-time screening. To mitigate this risk, Biosecurity Screening Software (BSS) systems have been developed to flag potentially malicious synthesis orders. Here, we propose one of the first deterministic benchmarks for evaluating the robustness of Biosecurity Screening Software. Our framework enables systematic testing of BSS behaviors and potential on specific nucleic-acid sequences and on targeted regions of malicious genomes. Our framework allows for insights into what is being flagged as malicious in BSSs, leading to potential discussions if specific BSS is fit for a specific manufacturing pipeline. We additionally introduce a dataset of manipulated genomes derived from the HHS and USDA Select Agents and Toxins List. When evaluated on this dataset, SeqScreen flags 42% of the sequences as malicious, while Commec flags 10.2%. Across a range of manipulation strategies, we find that simple manipulations, such as padding sequences by adding a repeated nucleotides at 1.5 times the original length, perform nearly as well as more targeted methods, such as embedding malicious sequences within benign genomic context. Padding-based methods trail embedding-based methods by only 0.75 percentage points in average detection rate. Consistent with prior reports from BSS developers and studies, we observe a sharp drop in detection rate when input sequence length falls below a critical threshold, typically between 50 and 100 base pairs (bp). Under our threat model, this implies that an adversary can bypass most existing safeguards by splitting a target genome into fragments shorter than 50 bp. Fragment-level analysis further reveals that some toxin regions evade detection entirely by SeqScreen, while other malicious genomes remain detectable even when fragmented into 30-50 base-pair segments. We open-source this benchmark to support reproducible evaluation of BSS robustness and to inform the development of next-generation biosecurity screening tools (https://github.com/HenryCWong/DNAS-Bench). For ethical concerns we only open-source the framework while the data is available upon request.

bioinformatics↗

A Closer-to-Brain Heterosynaptic Learning Rule for Spatiotemporal Spike Pattern Detection with Low-Resolution Synapse

The brain is believed to process information efficiently in a different manner from deep learning-based artificial intelligence (AI). Brain-like next-generation AI is gaining attention owing to its potential to perform human-like, highly adaptive, robust, and power-efficient computation. To realize such AI, one crucial approach is the bottom-up implementation of the neuronal systems, capturing their electrophysiological characteristics in electronic circuits. However, this neuromorphic approach generally focuses on simplified neuronal models that do not refer to many biological findings. Developing closer-to-brain models is a natural direction that serve as a fundamental computing model for next-generation AI. One of the constraints of neuromorphic circuits is the bit resolution of synaptic efficacy memory, as the memory footprint scales with it precision. Although low-resolution synaptic efficacy is essential for minimizing memory circuit footprint and energy consumption, it generally leads to performance degradation in many tasks such as the spatio-temporal spike pattern detection. This study proposed a closer-to-brain learning rule that incorporates heterosynaptic plasticity (HP) induced by glutamate spillover. It is demonstrated that our model mitigates the performance degradation associated with low-bit resolution synaptic efficacy, achieving the pattern detection success rate with 3-bit resolution synaptic efficacy, which is comparable to 64-bit floating-point precision. Furthermore, the findings of the study indicate that HP based model accelerates the convergence of the synaptic effcacy and effectively potentiates the synapses relevant to the pattern detection while suppressing irrelevant ones, thereby promoting a bimodal distribution of synaptic efficacies. These findings may provide a basic framework for constructing an energy-efficient, brain-like next-generation AI that maintains high performance under hardware constraints.

neuroscience↗

Dopamine Modulation of Spike-Timing-Dependent Plasticity for Spatio-Temporal Spike Pattern Detection in Single Neurons

AO_SCPLOWBSTRACTC_SCPLOWDopamine (DA) is an important neuromodulator that has been suggested to play key roles in a range of neuropsychiatric disorders. However, its computational impact at the neural circuit level has not been elucidated. Here, we extended a spike-timing-dependent plasticity (STDP)-based single-neuron spatio-temporal pattern detection model by incorporating a DA input and DA-type STDP modulation. We analyzed the direct effect of the DA-type STDP curve and the effect of DA release. We found that the DhA-type STDP curve accelerates learning but may promote coarse potentiation of the synaptic efficacy, which leads to false detections. It was also shown that DA can improve the pattern-detection performance up to a 44 % success rate (2.44 x control) with no false detection, but a DA release with an excessive amount or with too short a delay induces false detection. These results suggest that DA can maintain the pattern detection ability only when released within a limited concentration and a sufficient delay.

neuroscience↗

Twinfilin-1 phosphorylation in reelin signaling regulates actin dynamics and spine development

Reelin is an extracellular glycoprotein essential for neuronal migration, spine development, and synaptic plasticity. Impaired reelin signaling is linked to neurological disorders, including schizophrenia and autism. While reelin mutant (reeler) mice exhibit behavioral deficits associated with impaired spine formation, the underlying molecular mechanisms remain unclear. We identified Twinfilin-1 (Twf1) as a downstream effector of reelin signaling via phosphoproteomic analysis, based on its reduced tyrosine phosphorylation in reeler mice. We found that Src regulated Twf1 phosphorylation at tyrosine 309, and reelin stimulation increased Twf1 phosphorylation in neurons, an effect blocked by the Src inhibitor PP2. A phospho-resistant Twf1 mutant (Twf1 Y309F) showed reduced capping protein binding and a lower F/G-actin ratio. Twf1Y309F mice exhibited cognitive deficits, reduced spine density, smaller spine head size, and a decreased F/G-actin ratio in synaptosomes. These findings highlight Twf1 phosphorylation as a key component of reelin signaling involved in actin remodeling and spine development.

neuroscience↗

A lightweight data-driven spiking neural network model of Drosophila olfactory nervous system with dedicated hardware support

Data-driven spiking neural network (SNN) models are vital for understanding the brains information processing at the cellular and synaptic level. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand significant computational resources. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model implemented on an entry-level field-programmable gate array successfully reproduced the functions and characteristic spiking activities of different neuron types. Our approach thus provides a foundation for constructing lightweight in silico models that are critical for investigating the brains information processing mechanisms at the cellular and synaptic level through an analysis-by-construction approach and applicable to edge artificial intelligence (AI) systems.

neuroscience↗

Doctoring Direct-to-Consumer Genetic Tests with DNA Spike-Ins

Direct-to-consumer (DTC) genetic testing companies have provided personal genotyping services to millions of customers. Customers mail saliva samples to DTC service providers to have their genotypes analyzed and receive back their raw genetic data. Both consumers and the DTC companies use the results to perform ancestry analyses, relative matching, trait prediction, and estimate predisposition to disease, often relying on genetic databases composed of the data from millions of other DTC-genotyped individuals. While the digital integrity risks to this type of data have been explored, we considered whether data integrity issues could manifest upstream of data generation through physical manipulation of DNA samples themselves, for example by adding synthetic DNA to a saliva sample ("spiked samples") prior to sample processing by a DTC company. Here, we investigated the feasibility of this scenario within the standard DTC genetic testing pipeline. Starting with the purchase of off-the-shelf DTC genetic testing kits, we found that synthetic DNA can be used to precisely manipulate the results of saliva samples genotyped by a popular DTC genetic testing service and that this method can be used to modify arbitrary single nucleotide polymorphisms (SNPs) in multiplex to create customized doctored genetic profiles. This capability has implications for the use of DTC-generated results and the outcomes of their downstream analyses.

genetics↗

Multi-state structure determination and dynamics analysis reveals a new ubiquitin-recognition mechanism in yeast ubiquitin C-terminal hydrolase

Despite accumulating evidence that protein dynamics is indispensable for understanding the structural basis of biological activities, it remains challenging to visualize the spatial description of the dynamics and to associate transient conformations with their molecular functions. We have developed a new NMR protein structure determination method for the inference of multi-state conformations using multiple types of NMR data, including paramagnetic NMR and residual dipolar couplings, as well as conventional NOEs. Integration of these data in the structure calculation permits delineating accurate ensemble structures of biomacromolecules. Applying the method to the protein yeast ubiquitin hydrolase 1 (YUH1), we find large dynamics of its N-terminus and crossover loop surrounding the active site for ubiquitin-recognition and proteolysis. The N-terminus gets into and out of the crossover loop, suggesting their underlying functional significance. Our results, including those from biochemical analysis, show that large motion surrounding the active site contributes strongly to the efficiency of the enzymatic activity.

biophysics↗

Systematic analysis of brain lactate and pH levels in 65 animal models related to neuropsychiatric conditions

Increased levels of lactate, an end-product of glycolysis, have been proposed as a potential surrogate marker for metabolic changes during neuronal excitation. These changes in lactate levels can result in decreased brain pH, which has been implicated in patients with various neuropsychiatric disorders. We previously demonstrated that such alterations are commonly observed in five mouse models of schizophrenia, bipolar disorder, and autism, suggesting a shared endophenotype among these disorders rather than mere artifacts due to medications or agonal state. However, there is still limited research on this phenomenon in animal models, leaving its generality across other disease animal models uncertain. Moreover, the association between changes in brain lactate levels and specific behavioral abnormalities remains unclear. To address these gaps, the International Brain pH Project Consortium investigated brain pH and lactate levels in 109 strains/conditions of 2,294 animals with genetic and other experimental manipulations relevant to neuropsychiatric disorders. Systematic analysis revealed that decreased brain pH and increased lactate levels were common features observed in multiple models of depression, epilepsy, Alzheimers disease, and some additional schizophrenia models. While certain autism models also exhibited decreased pH and increased lactate levels, others showed the opposite pattern, potentially reflecting subpopulations within the autism spectrum. Furthermore, utilizing large-scale behavioral test battery, a multivariate cross-validated prediction analysis demonstrated that poor working memory performance was predominantly associated with increased brain lactate levels. Importantly, this association was confirmed in an independent cohort of animal models. Collectively, these findings suggest that altered brain pH and lactate levels, which could be attributed to dysregulated excitation/inhibition balance, may serve as transdiagnostic endophenotypes of debilitating neuropsychiatric disorders characterized by cognitive impairment, irrespective of their beneficial or detrimental nature.

neuroscience↗

Physiological significance of proteolytic processing of Reelin revealed by cleavage-resistant Reelin knock-in mice

Reelin is a secreted protein that plays versatile roles in neuronal development and function, and hypoactivity of Reelin is implicated in many neuropsychiatric disorders. The strength of Reelin signaling is regulated by proteolytic processing, but its importance in vivo is not yet fully understood. Here, we generated Reelin knock-in (PA-DV KI) mice in which the key cleavage site of Reelin was abolished by mutation. As expected, the cleavage of Reelin was severely abrogated in the cerebral cortex and hippocampus of PA-DV KI mice. The amount of Dab1, whose degradation is induced by Reelin signaling, decreased in these tissues, indicating that the signaling strength of Reelin was augmented. The brains of PA-DV KI mice were largely structurally normal, but unexpectedly, the hippocampal layer was disturbed. This phenotype was ameliorated in hemizygote PA-DV KI mice, indicating that excess Reelin signaling is detrimental to hippocampal layer formation. The neuronal dendrites of PA-DV KI mice had more branches and were elongated compared to wild-type mice. These results present the first direct evidence of the physiological importance of Reelin cleavage and suggest that inhibition of Reelin cleavage would counteract neuropsychiatric disorders without causing severe systemic side effects.

neuroscience↗

Long read sequencing reveals a novel class of structural aberrations in cancers: identification and characterization of cancerous local amplifications

Here we report identification of a new class of local structural aberrations in lung cancers. The whole-genome sequencing of cell lines using a long read sequencer, PromethION, demonstrated that typical cancerous mutations, such as point mutations, large deletions and gene fusions can be detected also on this platform. Unexpectedly, we revealed unique structural aberrations consisting of complex combinations of local duplications, inversions and micro deletions. We further analyzed and found that these mutations also occur in vivo, even in key cancer-related genes. These mutations may elucidate the molecular etiology of patients for whom causative cancerous events and therapeutic strategies remain elusive.

genomics↗

Exploiting Index Cross-Talk to Modify Variant Calls

Modern next-generation DNA sequencers support multiplex sequencing to improve throughput and decrease costs. This is done by pooling and sequencing samples together in parallel, which are later demultiplexed according to their unique indexes1, 2. When reads are assigned to the wrong index, called index cross-talk, information is leaked between samples3-6. This creates a physical information side-channel, a well known class of vulnerabilities in information security7-10, that may be used to modify downstream results. Here we demonstrate the feasibility of such an attack through the use of a separately indexed library that causes a wild-type human exome to be misclassified as heterozygous at the sickle-cell locus. Simple methods can be used to minimize or detect attempts to modify genetic variants using this side-channel, such as filtering by read quality or finding outliers in read coverage. To further minimize this risk we recommend the use of new library preparation methods that reduce index cross-talk, like unique dual indexes11, 12, whenever samples are sequenced together in important applications. Biotechnology that interfaces molecular and digital information, like DNA sequencers, may have security risks typically associated with information systems, including the side-channel vulnerability described in this study. We encourage the community to consider the security of genomics-information pipelines before they reach mass adoption.

bioengineering↗