Pub Date : 2026-01-01Epub Date: 2025-12-29DOI: 10.1016/j.csbj.2025.12.027
Karl Paygambar, Roude Jean-Marie, Mallek Mziou-Sallami, Vincent Meyer
Optimizing genomic-based molecular subtyping is key to promote personalized medicine. While neural networks face setbacks regarding tabular data modeling, deep learning has undergone groundbreaking advances across multiple domains, catalyzing further breakthroughs across AI applications. New neural network architectures exhibit enhanced performance, efficiency, and robustness, which could benefit the genomic use-case.
In this study we introduce SubNExT, an optimized shallow CNN with a ConvNeXt backbone using t-SNE and DeepInsight 2D-converted gene expression for breast cancer subtyping. It was compared with other modelization strategies for gene expression data, by optimizing a Transformer, an MLP and XGBoost for unconverted values, a 1D CNN (NeXt-TDNN) for ordered values, and a ViT as an alternative for 2D-converted expression. During evaluation, SubNExT obtains an accuracy of 87.12%, matching the state-of-the-art XGBoost and its 87.24% acc at the top of the benchmark. SubNExT manages this performance with just 76k parameters and the shortest training time, as well as the best stability and robustness among all considered approaches.
By providing accurate, efficient and robust molecular subtyping of breast cancer using gene expression data, SubNExT and its design principles catalyze deep learning adoption in oncogenomics.
{"title":"SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping","authors":"Karl Paygambar, Roude Jean-Marie, Mallek Mziou-Sallami, Vincent Meyer","doi":"10.1016/j.csbj.2025.12.027","DOIUrl":"10.1016/j.csbj.2025.12.027","url":null,"abstract":"<div><div>Optimizing genomic-based molecular subtyping is key to promote personalized medicine. While neural networks face setbacks regarding tabular data modeling, deep learning has undergone groundbreaking advances across multiple domains, catalyzing further breakthroughs across AI applications. New neural network architectures exhibit enhanced performance, efficiency, and robustness, which could benefit the genomic use-case.</div><div>In this study we introduce SubNExT, an optimized shallow CNN with a ConvNeXt backbone using t-SNE and DeepInsight 2D-converted gene expression for breast cancer subtyping. It was compared with other modelization strategies for gene expression data, by optimizing a Transformer, an MLP and XGBoost for unconverted values, a 1D CNN (NeXt-TDNN) for ordered values, and a ViT as an alternative for 2D-converted expression. During evaluation, SubNExT obtains an accuracy of 87.12%, matching the state-of-the-art XGBoost and its 87.24% acc at the top of the benchmark. SubNExT manages this performance with just 76k parameters and the shortest training time, as well as the best stability and robustness among all considered approaches.</div><div>By providing accurate, efficient and robust molecular subtyping of breast cancer using gene expression data, SubNExT and its design principles catalyze deep learning adoption in oncogenomics.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 412-421"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-29DOI: 10.1016/j.csbj.2025.12.019
Isabel von der Decken , Hamid Azimi , Anna Lauber-Biason
Despite advances in understanding genetic causes of DSD (differences of sex development), the molecular cause remains unknown for over half of affected individuals. Next-generation sequencing (NGS) has improved diagnosis, but interpreting results can be challenging, especially when no known DSD gene mutations are found, or only variants of unknown significance appear. Identifying new genes involved in sex development from whole exome sequencing (WES) alone is difficult. To overcome this, we introduce “GONAD-ResNet,” a residual convolutional neural network designed to predict novel DSD-associated genes by learning complex patterns in time-dependent single-cell gene expression data. When applied to WES data from six patients (three XX, three XY) with DSD, GONAD-ResNet prioritized genes with expression profiles similar to known DSD genes while disregarding ubiquitous or irrelevant genes. This narrowed the list of potential candidates from around 1000 to a few promising novel genes per patient. This innovative approach accelerates the discovery of new DSD-related genes, opening new research avenues and potentially improving patient outcomes.
{"title":"The potential of deep learning on the discovery of new genes implicated in differences of sex development","authors":"Isabel von der Decken , Hamid Azimi , Anna Lauber-Biason","doi":"10.1016/j.csbj.2025.12.019","DOIUrl":"10.1016/j.csbj.2025.12.019","url":null,"abstract":"<div><div>Despite advances in understanding genetic causes of DSD (differences of sex development), the molecular cause remains unknown for over half of affected individuals. Next-generation sequencing (NGS) has improved diagnosis, but interpreting results can be challenging, especially when no known DSD gene mutations are found, or only variants of unknown significance appear. Identifying new genes involved in sex development from whole exome sequencing (WES) alone is difficult. To overcome this, we introduce “GONAD-ResNet,” a residual convolutional neural network designed to predict novel DSD-associated genes by learning complex patterns in time-dependent single-cell gene expression data. When applied to WES data from six patients (three XX, three XY) with DSD, GONAD-ResNet prioritized genes with expression profiles similar to known DSD genes while disregarding ubiquitous or irrelevant genes. This narrowed the list of potential candidates from around 1000 to a few promising novel genes per patient. This innovative approach accelerates the discovery of new DSD-related genes, opening new research avenues and potentially improving patient outcomes.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 221-234"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-31DOI: 10.1016/j.csbj.2025.12.031
Yi Zhang , Xinming Zhang , Cheng Chen , Bing Li , Yao Lu , Xin Ma , Yunfeng Yang
Background
Cellular senescence is a key driver of aging and chronic diseases. However, accurately identifying senescent cells is challenging due to limitations of conventional biomarkers and senescence heterogeneity. Transcriptome-wide analyses offer powerful tools for deciphering cellular states. Yet, there is a critical gap in computational frameworks for senescence assessment from transcriptomic data.
Methods
We developed the seneR package, which includes functions such as calculating senescence identity scores (SID scores), assessing senescence-related phenotypes, and plotting senescence trajectories, and provides an interactive Shiny interface. We applied seneR to transcriptome datasets from human islets and chondrocytes to investigate the role of senescence in Type 2 Diabetes (T2D) and osteoarthritis (OA). Additionally, in vitro validation confirmed phentolamine (PM)'s potential to delay chondrocyte senescence.
Results
seneR accurately identified senescent cells and revealed senescence-related phenotypes in transcriptome datasets. In T2D, SID scores were significantly higher in elderly islets. Senescent islet cells exhibited diminished responsiveness to nutrient stimuli, linking senescence to impaired insulin secretion. In OA, seneR identified SLPI as a molecule strongly associated with chondrocyte senescence, with PM treatment reducing SID scores. Trajectory analysis revealed chondrocyte senescence progression and potential therapeutic targets. In vitro experiments, PM reversed both IL-1β- and H₂O₂-induced chondrocyte senescence.
Conclusion
Our study demonstrates that seneR is a valuable tool for assessing cellular senescence from transcriptomic data, revealing key phenotypes and potential therapeutic targets in T2D and OA. The identification of SLPI as a senescence-associated molecule and the therapeutic potential of PM highlights the utility of our approach in understanding senescence-related diseases.
{"title":"seneR: An R package for comprehensive senescence assessment and its application in type 2 diabetes and osteoarthritis","authors":"Yi Zhang , Xinming Zhang , Cheng Chen , Bing Li , Yao Lu , Xin Ma , Yunfeng Yang","doi":"10.1016/j.csbj.2025.12.031","DOIUrl":"10.1016/j.csbj.2025.12.031","url":null,"abstract":"<div><h3>Background</h3><div>Cellular senescence is a key driver of aging and chronic diseases. However, accurately identifying senescent cells is challenging due to limitations of conventional biomarkers and senescence heterogeneity. Transcriptome-wide analyses offer powerful tools for deciphering cellular states. Yet, there is a critical gap in computational frameworks for senescence assessment from transcriptomic data.</div></div><div><h3>Methods</h3><div>We developed the seneR package, which includes functions such as calculating senescence identity scores (SID scores), assessing senescence-related phenotypes, and plotting senescence trajectories, and provides an interactive Shiny interface. We applied seneR to transcriptome datasets from human islets and chondrocytes to investigate the role of senescence in Type 2 Diabetes (T2D) and osteoarthritis (OA). Additionally, in vitro validation confirmed phentolamine (PM)'s potential to delay chondrocyte senescence.</div></div><div><h3>Results</h3><div>seneR accurately identified senescent cells and revealed senescence-related phenotypes in transcriptome datasets. In T2D, SID scores were significantly higher in elderly islets. Senescent islet cells exhibited diminished responsiveness to nutrient stimuli, linking senescence to impaired insulin secretion. In OA, seneR identified SLPI as a molecule strongly associated with chondrocyte senescence, with PM treatment reducing SID scores. Trajectory analysis revealed chondrocyte senescence progression and potential therapeutic targets. In vitro experiments, PM reversed both IL-1β- and H₂O₂-induced chondrocyte senescence.</div></div><div><h3>Conclusion</h3><div>Our study demonstrates that seneR is a valuable tool for assessing cellular senescence from transcriptomic data, revealing key phenotypes and potential therapeutic targets in T2D and OA. The identification of SLPI as a senescence-associated molecule and the therapeutic potential of PM highlights the utility of our approach in understanding senescence-related diseases.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 192-201"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-08DOI: 10.1016/j.csbj.2025.12.002
Julia Malec , G.Brian Golding , Lucian Ilie
Background
The advent of protein embeddings has revolutionized bioinformatics by providing contextual representations that capture functional and evolutionary patterns. They have become, alongside sequence alignments, the cornerstone of bioinformatics. Embeddings cannot replace alignments but they can greatly help improve their quality. While embedding-based improvements have been considered for global alignments, the more important counterpart, local alignments, has not been studied thoroughly. Our goal is to identify the most accurate local alignment algorithm for protein sequences.
Results
We introduce a new scoring function into our previous E-score algorithm by using Ankh embeddings. We prove that the resulting algorithm produces the most accurate local alignments of protein sequences using a new comprehensive framework that enables thorough evaluation of local alignment quality. We design a new algorithm for local alignment extraction, localization and quality evaluation and employ five distance metrics to evaluate the similarity with the true alignment. We also build multiple datasets, using both natural and inserted sequences, from the Conserved Domain Database, BAliBASE, and GPCRdb. We perform over two and a half million tests to compare the new algorithm with the best BLOSUM matrices, specialized GPCRtm matrices, and top programs, such as PEbA, DEDAL, vcMSA and pLM-BLAST. Our testing also reveals interesting insights into the behaviour of various protein language models as some of them perform much better on natural sequences compared to artificial ones obtained by inserting domains into random protein sequences. Also, while some models combine to produce better results, Ankh does not combine well with other embeddings.
Conclusions
The new, Ankh-score-based, program is clearly superior to all existing methods. New light shed on the protein embeddings can guide future improvements. In order to facilitate the use of the new method and protocol, they are freely available as a web server at e-score.csd.uwo.ca and as source code at github.com/lucian-ilie/E-score.
{"title":"Protein embeddings and local alignments","authors":"Julia Malec , G.Brian Golding , Lucian Ilie","doi":"10.1016/j.csbj.2025.12.002","DOIUrl":"10.1016/j.csbj.2025.12.002","url":null,"abstract":"<div><h3>Background</h3><div>The advent of protein embeddings has revolutionized bioinformatics by providing contextual representations that capture functional and evolutionary patterns. They have become, alongside sequence alignments, the cornerstone of bioinformatics. Embeddings cannot replace alignments but they can greatly help improve their quality. While embedding-based improvements have been considered for global alignments, the more important counterpart, local alignments, has not been studied thoroughly. Our goal is to identify the most accurate local alignment algorithm for protein sequences.</div></div><div><h3>Results</h3><div>We introduce a new scoring function into our previous E-score algorithm by using Ankh embeddings. We prove that the resulting algorithm produces the most accurate local alignments of protein sequences using a new comprehensive framework that enables thorough evaluation of local alignment quality. We design a new algorithm for local alignment extraction, localization and quality evaluation and employ five distance metrics to evaluate the similarity with the true alignment. We also build multiple datasets, using both natural and inserted sequences, from the Conserved Domain Database, BAliBASE, and GPCRdb. We perform over two and a half million tests to compare the new algorithm with the best BLOSUM matrices, specialized GPCRtm matrices, and top programs, such as PEbA, DEDAL, vcMSA and pLM-BLAST. Our testing also reveals interesting insights into the behaviour of various protein language models as some of them perform much better on natural sequences compared to artificial ones obtained by inserting domains into random protein sequences. Also, while some models combine to produce better results, Ankh does not combine well with other embeddings.</div></div><div><h3>Conclusions</h3><div>The new, Ankh-score-based, program is clearly superior to all existing methods. New light shed on the protein embeddings can guide future improvements. In order to facilitate the use of the new method and protocol, they are freely available as a web server at <span>e-score.csd.uwo.ca</span> and as source code at <span>github.com/lucian-ilie/E-score</span>.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 24-37"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-14DOI: 10.1016/j.csbj.2026.01.009
Rong Xu, Guihua Cao, Liming Hou, Wei Fu, Chenting Bi, Xu Li, Xiaoming Wang
Bisdemethoxycurcumin (BDMC), a natural derivative of curcumin with improved solubility and stability, has shown potential cardioprotective properties. This study investigated the efficacy and underlying mechanisms of BDMC in heart failure with preserved ejection fraction (HFpEF) using both in vivo and in vitro models. The HFpEF mouse model was established using a high-fat diet and L-NAME. BDMC treatment improved cardiac function, attenuated myocardial fibrosis, and exhibited antioxidant effects. Mechanistically, integrated network pharmacology and proteomics identified TGFBR1 as a potential target. BDMC inhibited cardiac fibroblast activation by suppressing TGFBR1 expression and SMAD2/3 phosphorylation. Molecular docking and dynamics simulations confirmed stable binding between BDMC and TGFBR1. These findings demonstrate that BDMC mitigates myocardial fibrosis in HFpEF, primarily by competitively inhibiting the binding of TGF-β and TGFBR1, achieving the effect of inhibiting cardiac fibroblast activation.
{"title":"Bisdemethoxycurcumin attenuates myocardial fibrosis in heart failure with preserved ejection fraction by targeting TGFBR1 and oxidative stress","authors":"Rong Xu, Guihua Cao, Liming Hou, Wei Fu, Chenting Bi, Xu Li, Xiaoming Wang","doi":"10.1016/j.csbj.2026.01.009","DOIUrl":"10.1016/j.csbj.2026.01.009","url":null,"abstract":"<div><div>Bisdemethoxycurcumin (BDMC), a natural derivative of curcumin with improved solubility and stability, has shown potential cardioprotective properties. This study investigated the efficacy and underlying mechanisms of BDMC in heart failure with preserved ejection fraction (HFpEF) using both <em>in vivo</em> and <em>in vitro</em> models. The HFpEF mouse model was established using a high-fat diet and <span>L</span>-NAME. BDMC treatment improved cardiac function, attenuated myocardial fibrosis, and exhibited antioxidant effects. Mechanistically, integrated network pharmacology and proteomics identified TGFBR1 as a potentia<u>l</u> target. BDMC inhibited cardiac fibroblast activation by suppressing TGFBR1 expression and SMAD2/3 phosphorylation. Molecular docking and dynamics simulations confirmed stable binding between BDMC and TGFBR1. These findings demonstrate that BDMC mitigates myocardial fibrosis in HFpEF, primarily by competitively inhibiting the binding of TGF-β and TGFBR1, achieving the effect of inhibiting cardiac fibroblast activation.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 422-435"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-28DOI: 10.1016/j.csbj.2025.12.025
Shuangmei Tian , Ziyu Zhao , Meharie G. Kassie , Fangyuan Zhang , Beibei Ren , Degeng Wang
The microRNA (miRNA) induced silencing complex (miRISC) is the targeting apparatus and arguably the rate-limiting step of the miRNA-mediated regulatory subsystem – a major noise reducing, though metabolically costly, mechanism. Recently, we reported that miRISC channels miRNA-mediated regulatory activity back onto its own mRNAs to form negative self-feedback loops, a noise-reduction technique in engineering and synthetic/systems biology. In this paper, our mathematical modeling predicts that mRNA expression noise exhibits a negative correlation with the degradation rate (Kdeg) and is attenuated by self-feedback control of degradation. We also calculated Kdeg and expression noise of mRNAs detected in a total-RNA single-cell RNA-seq (scRNA-seq) dataset. As predicted, miRNA-targeted mRNAs exhibited higher Kdeg values accompanied by reduced cell-to-cell expression noise, confirming the operational trade-off between noise suppression and the increased metabolic/energetic costs associated with producing these mRNAs subjected to accelerated degradation and translational inhibition. Moreover, consistent with the Kdeg self-feedback control model, miRISC mRNAs (AGO1/2/3 and TNRC6A/B/C) exhibited further reduced expression noise. In summary, mathematical-modeling and total-RNA scRNA-seq data-analyses provide evidence that negative self-feedback regulation of mRNA degradation reinforces miRISC, the core machinery of the miRNA-mediated noise-reduction subsystem. To our knowledge, this is the first study to concurrently analyze mRNA degradation dynamics and expression noise, and to demonstrate noise reduction by direct self-feedback regulation of mRNA degradation.
{"title":"Enhanced MiRISC expression noise reduction by self-feedback regulation of mRNA degradation","authors":"Shuangmei Tian , Ziyu Zhao , Meharie G. Kassie , Fangyuan Zhang , Beibei Ren , Degeng Wang","doi":"10.1016/j.csbj.2025.12.025","DOIUrl":"10.1016/j.csbj.2025.12.025","url":null,"abstract":"<div><div>The microRNA (miRNA) induced silencing complex (miRISC) is the targeting apparatus and arguably the rate-limiting step of the miRNA-mediated regulatory subsystem – a major noise reducing, though metabolically costly, mechanism. Recently, we reported that miRISC channels miRNA-mediated regulatory activity back onto its own mRNAs to form negative self-feedback loops, a noise-reduction technique in engineering and synthetic/systems biology. In this paper, our mathematical modeling predicts that mRNA expression noise exhibits a negative correlation with the degradation rate (K<sub>deg</sub>) and is attenuated by self-feedback control of degradation. We also calculated K<sub>deg</sub> and expression noise of mRNAs detected in a total-RNA single-cell RNA-seq (scRNA-seq) dataset. As predicted, miRNA-targeted mRNAs exhibited higher K<sub>deg</sub> values accompanied by reduced cell-to-cell expression noise, confirming the operational trade-off between noise suppression and the increased metabolic/energetic costs associated with producing these mRNAs subjected to accelerated degradation and translational inhibition. Moreover, consistent with the K<sub>deg</sub> self-feedback control model, miRISC mRNAs (AGO1/2/3 and TNRC6A/B/C) exhibited further reduced expression noise. In summary, mathematical-modeling and total-RNA scRNA-seq data-analyses provide evidence that negative self-feedback regulation of mRNA degradation reinforces miRISC, the core machinery of the miRNA-mediated noise-reduction subsystem. To our knowledge, this is the first study to concurrently analyze mRNA degradation dynamics and expression noise, and to demonstrate noise reduction by direct self-feedback regulation of mRNA degradation.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 179-191"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-06DOI: 10.1016/j.csbj.2026.01.002
Sung-Yeon Lee, Seongwon Ma, Sangjun Davie Jeon, Hyoju Kim, Beomjoon Jo, Seung-Hoon Han, Eunsoo Jang, Jimin Lee, Yong-Kyu Lee, Dasom Lee
CRISPR/Cas9 has transformed gene editing, enabling precise genetic modifications across species. However, existing sgRNA design prediction models based on in vitro data are difficult to generalize to in vivo contexts. In particular, approaches based on single-sgRNA design require additional filtering of in-frame mutations, which is inefficient in terms of both time and cost. In this study, we developed the first mammalian in vivo-trained prediction model to evaluate the efficiency of a dual-sgRNA-based exon deletion strategy. Using 230 editing outcomes of postnatal viable individuals, eight prediction models were constructed and evaluated based on generalized linear models and Random Forests. The final selected model, a Combined GLM, integrated the DeepSpCas9 score with k-mer sequence features, achieving an AUC of 0.759 (95 % Confidence Interval: 0.697–0.821). Motif analysis revealed that CC sequences were associated with high efficiency and TT sequences were associated with low editing efficiency. This study demonstrates that integrating sequence-based features with existing design scores can improve sgRNA efficiency prediction in vivo. The proposed framework can be applied to the development of next-generation sgRNA design tools, with implications for gene therapy, effective animal model generation, and precision genome engineering.
{"title":"Empirical optimization of dual-sgRNA design for in vivo CRISPR/Cas9-mediated exon deletion in mice","authors":"Sung-Yeon Lee, Seongwon Ma, Sangjun Davie Jeon, Hyoju Kim, Beomjoon Jo, Seung-Hoon Han, Eunsoo Jang, Jimin Lee, Yong-Kyu Lee, Dasom Lee","doi":"10.1016/j.csbj.2026.01.002","DOIUrl":"10.1016/j.csbj.2026.01.002","url":null,"abstract":"<div><div>CRISPR/Cas9 has transformed gene editing, enabling precise genetic modifications across species. However, existing sgRNA design prediction models based on in vitro data are difficult to generalize to in vivo contexts. In particular, approaches based on single-sgRNA design require additional filtering of in-frame mutations, which is inefficient in terms of both time and cost. In this study, we developed the first mammalian in vivo-trained prediction model to evaluate the efficiency of a dual-sgRNA-based exon deletion strategy. Using 230 editing outcomes of postnatal viable individuals, eight prediction models were constructed and evaluated based on generalized linear models and Random Forests. The final selected model, a Combined GLM, integrated the DeepSpCas9 score with k-mer sequence features, achieving an AUC of 0.759 (95 % Confidence Interval: 0.697–0.821). Motif analysis revealed that CC sequences were associated with high efficiency and TT sequences were associated with low editing efficiency. This study demonstrates that integrating sequence-based features with existing design scores can improve sgRNA efficiency prediction in vivo. The proposed framework can be applied to the development of next-generation sgRNA design tools, with implications for gene therapy, effective animal model generation, and precision genome engineering.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 355-362"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-29DOI: 10.1016/j.csbj.2025.12.023
Silvia Tapia-Gonzalez , Josué García Yagüe , George E. Barreto
Major depressive disorder (MDD) is a multifactorial mental health condition involving genetic, environmental, and neurobiological factors. Conventional antidepressants such as fluoxetine, a selective serotonin reuptake inhibitor, require weeks to exert therapeutic effects, whereas ketamine and esketamine act rapidly via glutamatergic modulation. These drugs may also converge on the inhibition of glycogen synthase kinase 3 beta (GSK3B) as a key mechanism for their antidepressant effects, increasing neuroplasticity, synaptic transmission, and neuronal survival through upregulation of brain-derived neurotrophic factor (BDNF). Part of the antidepressant effects of ketamine also seems to depend on opioid receptor activation. Despite recent progress, variability in antidepressant response in MDD remains unclear. This work explores, via meta-analysis and network fragility analysis, key molecular mechanisms in MDD, how these drugs exert actions, and highlights potential therapeutic targets for MDD. We performed a network pharmacology approach to unravel the key cellular processes involved in MDD, including altered synaptic plasticity, neurogenesis, apoptosis, and neuroinflammation. Second, we explored the therapeutic role of these treatments on these altered cellular processes. By integrating drug-target data with MDD-associated genes, we identified the opioid receptor mu 1 (OPRM1), epidermal growth factor receptor (EGFR) and GSK3B as key druggable targets. Network analysis further suggested that nuclear factor kappa B (NFKB) may regulate all three, positioning it as a central node linking inflammation, synaptic plasticity, and neuronal metabolism in MDD. We hypothesize that targeted modulation of these genes may optimize the therapeutic efficacy, while NFKB emerges as a promising candidate biomarker for guiding treatment strategies in MDD.
重度抑郁症（MDD）是一种涉及遗传、环境和神经生物学因素的多因素精神健康状况。传统的抗抑郁药，如氟西汀，一种选择性血清素再摄取抑制剂，需要数周才能发挥治疗效果，而氯胺酮和艾氯胺酮通过谷氨酸调节迅速起作用。这些药物也可能集中于抑制糖原合成酶激酶3 β (GSK3B)，这是其抗抑郁作用的关键机制，通过上调脑源性神经营养因子（BDNF）来增加神经可塑性、突触传递和神经元存活。氯胺酮的部分抗抑郁作用似乎也依赖于阿片受体的激活。尽管最近取得了进展，但抑郁症患者抗抑郁反应的变异性仍不清楚。本研究通过荟萃分析和网络脆弱性分析，探讨了MDD的关键分子机制，这些药物如何发挥作用，并强调了MDD的潜在治疗靶点。我们采用网络药理学方法来揭示与MDD相关的关键细胞过程，包括突触可塑性改变、神经发生、细胞凋亡和神经炎症。其次，我们探索了这些治疗对这些改变的细胞过程的治疗作用。通过整合药物靶点数据和mdd相关基因，我们确定了阿片受体mu 1 （OPRM1）、表皮生长因子受体（EGFR）和GSK3B作为关键的可药物靶点。网络分析进一步表明，核因子κ B （NFKB）可能调节这三者，将其定位为MDD中连接炎症、突触可塑性和神经元代谢的中心节点。我们假设这些基因的靶向调节可能会优化治疗效果，而NFKB则成为指导MDD治疗策略的有希望的候选生物标志物。
{"title":"Network pharmacology of cellular targets in major depressive disorder and differential mechanisms of fluoxetine, ketamine and esketamine","authors":"Silvia Tapia-Gonzalez , Josué García Yagüe , George E. Barreto","doi":"10.1016/j.csbj.2025.12.023","DOIUrl":"10.1016/j.csbj.2025.12.023","url":null,"abstract":"<div><div>Major depressive disorder (MDD) is a multifactorial mental health condition involving genetic, environmental, and neurobiological factors. Conventional antidepressants such as fluoxetine, a selective serotonin reuptake inhibitor, require weeks to exert therapeutic effects, whereas ketamine and esketamine act rapidly via glutamatergic modulation. These drugs may also converge on the inhibition of glycogen synthase kinase 3 beta (<em>GSK3B</em>) as a key mechanism for their antidepressant effects, increasing neuroplasticity, synaptic transmission, and neuronal survival through upregulation of brain-derived neurotrophic factor (<em>BDNF)</em>. Part of the antidepressant effects of ketamine also seems to depend on opioid receptor activation. Despite recent progress, variability in antidepressant response in MDD remains unclear. This work explores, via meta-analysis and network fragility analysis, key molecular mechanisms in MDD, how these drugs exert actions, and highlights potential therapeutic targets for MDD. We performed a network pharmacology approach to unravel the key cellular processes involved in MDD, including altered synaptic plasticity, neurogenesis, apoptosis, and neuroinflammation. Second, we explored the therapeutic role of these treatments on these altered cellular processes. By integrating drug-target data with MDD-associated genes, we identified the opioid receptor mu 1 (<em>OPRM1</em>), epidermal growth factor receptor (<em>EGFR</em>) and <em>GSK3B</em> as key druggable targets. Network analysis further suggested that nuclear factor kappa B (<em>NFKB</em>) may regulate all three, positioning it as a central node linking inflammation, synaptic plasticity, and neuronal metabolism in MDD. We hypothesize that targeted modulation of these genes may optimize the therapeutic efficacy, while <em>NFKB</em> emerges as a promising candidate biomarker for guiding treatment strategies in MDD.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 235-249"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.csbj.2025.12.034
Juan José Oropeza-Valdez , Cristian Padron-Manrique , Jorge E. Arellano-Villavicencio , Aarón Vázquez-Jiménez , Laura E. Hernández-Juárez , Xavier Soberon , María de Lourdes Reyes-Escogido , Rodolfo Guardado-Mendoza , Osbaldo Resendis-Antonio
Prediabetes confers a high risk of progressing to type 2 diabetes mellitus (T2DM). While metformin, a first-line T2DM therapy, improves glycemic control in prediabetes, its effects on the gut microbiota and host metabolic shifts remain poorly understood. Here, we applied a genome-scale community metabolic modeling to build a personalized “digital microbiota” for analyzing the metabolic activity of gut microbes in 106 samples of Mexican prediabetic patients, distributed among patients without treatment and patients treated with metformin over baseline, 6 and 12 months. To contrast microbial metabolic activity across groups and explore how diet modulates it, we simulated computationally the microbial metabolic fluxes under Western, Mediterranean, and traditional Milpa diets across the three groups. As expected, in general terms in silico dietary intervention changes the metabolic responses in the microbiota profiles among the stages, suggesting specific combinations of diets that favor the production of relevant metabolites for wellness, such as amino sugars, short-chain fatty acids, and bile acid exchange fluxes. Furthermore, by selecting two individuals across the entire time as case studies, we provide a proof of concept for in silico personalized diet design. These examples illustrate how the concept of personalized digital microbiota could be leveraged to optimize dietary strategies and potentially improve outcomes in prediabetic patients.
{"title":"Digital modeling of metformin and diet interactions on gut-microbiota metabolism in prediabetic patients","authors":"Juan José Oropeza-Valdez , Cristian Padron-Manrique , Jorge E. Arellano-Villavicencio , Aarón Vázquez-Jiménez , Laura E. Hernández-Juárez , Xavier Soberon , María de Lourdes Reyes-Escogido , Rodolfo Guardado-Mendoza , Osbaldo Resendis-Antonio","doi":"10.1016/j.csbj.2025.12.034","DOIUrl":"10.1016/j.csbj.2025.12.034","url":null,"abstract":"<div><div>Prediabetes confers a high risk of progressing to type 2 diabetes mellitus (T2DM). While metformin, a first-line T2DM therapy, improves glycemic control in prediabetes, its effects on the gut microbiota and host metabolic shifts remain poorly understood. Here, we applied a genome-scale community metabolic modeling to build a personalized “digital microbiota” for analyzing the metabolic activity of gut microbes in 106 samples of Mexican prediabetic patients, distributed among patients without treatment and patients treated with metformin over baseline, 6 and 12 months. To contrast microbial metabolic activity across groups and explore how diet modulates it, we simulated computationally the microbial metabolic fluxes under Western, Mediterranean, and traditional Milpa diets across the three groups. As expected, in general terms <em>in silico</em> dietary intervention changes the metabolic responses in the microbiota profiles among the stages, suggesting specific combinations of diets that favor the production of relevant metabolites for wellness, such as amino sugars, short-chain fatty acids, and bile acid exchange fluxes. Furthermore, by selecting two individuals across the entire time as case studies, we provide a proof of concept for <em>in silico</em> personalized diet design. These examples illustrate how the concept of personalized digital microbiota could be leveraged to optimize dietary strategies and potentially improve outcomes in prediabetic patients.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"31 ","pages":"Pages 250-262"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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