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Results for “molecular biology”

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At least 307 records · Page 17Linked to original sources

bioRxiv2022

A bioengineered probiotic for the oral delivery of a peptide Kv1.3 channel blocker to treat rheumatoid arthritis

Engineered microbes for the delivery of biologics is a promising avenue for the treatment of various conditions such as chronic inflammatory disorders and metabolic disease. In this study, we developed a genetically engineered probiotic delivery system that delivers the small molecular biologic to the intestinal tract with high efficacy and minimized side effects. We constructed an inducible system in the probiotic Lactobacillus reuteri to secret functional Kv1.3 potassium blocker ShK-235 (LrS235). We show that LrS235 is capable of blocking Kv1.3 currents and preferentially inhibiting human T effector memory (TEM) cells proliferation in vitro. A single oral gavage of healthy rats with LrS235 resulted in adequate functional ShK-235 in the circulation to reduce inflammation in a delayed-type hypersensitivity model of atopic dermatitis mediated by TEM cells. Furthermore, the daily oral gavage of LrS235 dramatically reduced clinical signs of disease and joint inflammation in rats with a model of rheumatoid arthritis without eliciting immunogenicity against ShK-235. This work demonstrates the efficacy of using probiotic L. reuteri as a novel oral delivery platform for the small molecule ShK-235, and provides a efficacious strategy to deliver other biologics with great translational potential. Significance StatementNew therapeutics that combine efficacy with limited side effects and can be delivered non-invasively are needed to adequately treat patients with rheumatoid arthritis (RA) and other autoimmune diseases. Kv1.3 channel-expressing CCR7- effector memory T (TEM) lymphocytes are significant players in the pathogenesis of multiple autoimmune diseases and blocking Kv1.3 reduces disease severity in rat models of RA and patients with plaque psoriasis. However, peptide therapeutics require repeated injections, reducing patient compliance. We used a bioengineered Lactobacillus reuteri as an oral delivery method of a Kv1.3 blocker for immunomodulation in rat models of atopic dermatitis and RA. This study demonstrates a novel approach for the non-invasive delivery of peptide-based therapeutics for the oral treatment of chronic inflammatory diseases.

synthetic biology↗

bioRxiv2017

Methods for chair restraint and training of the common marmoset on oculomotor tasks

The oculomotor system is the most thoroughly understood sensorimotor system in the brain, due in large part to electrophysiological studies carried out in macaque monkeys trained to perform ocuolomotor tasks. A disadvantage of the macaque model is that many cortical oculomotor areas of interest lie within sulci, making high-density array and laminar recordings impractical. Further, many techniques of molecular biology developed in rodents, such as transgenic animals and optogenetic manipulation of neuronal subtypes, are limited in this species. The common marmoset (Callithrix jacchus) may potentially bridge the gap between systems neuroscience in macaques and molecular techniques, and additionally possesses a smooth cortex allowing easy access to frontoparietal oculomotor areas. To date, techniques for restraint and training of these animals to perform oculomotor tasks remain in an early stage of development. Here we provide details of a custom-designed chair for restraint of marmosets, a combination head restraint/recording chamber providing stability suitable for eye movement and neural recordings, and a training protocol for oculomotor tasks. As proof-of-principle, we report the results of a psychophysical study in marmosets trained to perform a saccade task using these methods, showing that, as in rhesus and humans, marmosets exhibit a \"gap effect\" - a decrease in reaction time when the fixation stimulus is removed prior to the onset of a visual saccade target. These results provide evidence that the common marmoset is a suitable model for neurophysiogical investigations of oculomotor control.\n\nNEW AND NOTEWORTHYThe ability to carry out neuronal recordings in behaving primates has provided a wealth of information regarding the neural circuits underlying the control of eye movements. Such studies require restraint of the animal within a primate chair, head fixation, methods of acclimating the animals to this restraint, and the use of operant conditioning methods for training on oculomotor tasks. In contrast to the macaque model, relatively few studies have reported in detail methods for use in the common marmoset. Here we detail custom-designed equipment and methods by which we have used to successfully train head-restrained marmosets to perform basic oculomotor tasks.

neuroscience↗

bioRxiv2026

Particle Biology: A Perspective on a First-Principles Theory of Life

This Perspective formally proposes Particle Biology as a unifying theoretical framework to address the critical bottleneck in current life science research. Current life science research has reached a critical bottleneck. While the field has advanced to the study of 3D genomic spatial configurations and chromosomal organization, it remains largely descriptive and confined to the macromolecular level. This approach lacks a first-principles understanding of the underlying physical forces that drive biological processes. This Perspective formally proposes Particle Biology as a unifying theoretical framework. We establish an axiomatic system positing that life phenomena are fundamentally emergent spatiotemporal patterns of electromagnetic forces among atoms, electrons, and nuclei operating far from thermodynamic equilibrium. By defining biological states through the Biological Hamiltonian and mapping biochemical pathways to multidimensional Potential Energy Surfaces (PES), we bridge the gap between descriptive biology and predictive physics. We categorize core research technologies into three modalities--seeing, computing, and controlling particles--facilitated by advancements in Cryo-EM, AlphaFold 3, and Boron Neutron Capture Therapy (BNCT). Ultimately, the trajectory of molecular biology has evolved from cells to DNA and onto the 3D spatial genome, yet it cannot go deeper within current paradigms. The next logical evolution is to move beyond the macromolecular bottleneck to focus on the electromagnetic interactions between atoms and ions--the true Particle Biology level--to redefine disease and intervention.

biophysics↗

bioRxiv2021

OPTIMIZED PROTOCOL FOR CYANOBACTERIAL 16S rRNA ANALYSIS IN DANUBE DELTA LAKES

Molecular biology protocols have been more and more accessible to researchers for ecological investigations, however, these protocols always require optimization steps for the analysis of specific types of samples. The purpose of this study was to optimize a molecular protocol for the analysis of cyanobacterial 16S rRNA in Danube Delta shallows lakes. In this regard, several commercial DNA extraction kits were tested in comparison with potassium ethyl xanthogenate extraction method on different matrices. The obtained DNA was further used for 16S rRNA PCR optimization. Finally, an optimized protocol is proposed for the molecular analysis of cyanobacteria group in freshwater samples. The best DNA extraction method was the potassium xanthogenate extraction from dried cyanobacterial biomass. A dynamic in total genomic eDNA was observed, reflecting the seasonal difference in phytoplankton biomass from the studied lakes. The PCR protocol optimized by us can be successfully applied for the identification of a broad range of cyanobacterial genetic markers.

microbiology↗

bioRxiv2018

Comparison of single gene and module-based methods for modeling gene regulatory networks

Gene regulatory networks describe the regulatory relationships among genes, and developing methods for reverse engineering these networks are an ongoing challenge in computational biology. The majority of the initially proposed methods for gene regulatory network discovery create a network of genes and then mine it in order to uncover previously unknown regulatory processes. More recent approaches have focused on inferring modules of co-regulated genes, linking these modules with regulator genes and then mining them to discover new molecular biology.\n\nIn this work we analyze module-based network approaches to build gene regulatory networks, and compare their performance to the well-established single gene network approaches. In particular, we focus on the problem of linking genes with known regulatory genes. First, modules are created iteratively using a regression approach that links co-expressed genes with few regulatory genes. After the modules are built, we create bipartite graphs to identify a set of target genes for each regulatory gene. We analyze several methods for uncovering these modules and show that a variational Bayes approach achieves significant improvement with respect to previously used methods for module creation on both simulated and real data. We also perform a topological and gene set enrichment analysis and compare several module-based approaches to single gene network approaches where a graph is built from the gene expression profiles without clustering genes in modules. We show that the module-based approach with variational Bayes outperforms all other methods and creates regulatory networks with a significantly higher rate of enriched molecular pathways.\n\nThe code is written in R and can be downloaded from https://github.com/mikelhernaez/linker.

systems biology↗

bioRxiv2021

Portable low-cost optical density meter

Measuring optical density (OD) is a very common technique in biological laboratories to determine the concentration of a substance in solution or of bacteria (or microscopic particles) in suspension. For example, bacterial cultures engineered to produce (express) a protein or compound of interest are a workhorse of modern molecular biology laboratories. Commonly, the expression of the product is triggered (induced) by a chemical signal added to the culture at the proper time in the growth curve of the culture (typically in the middle of the exponential growth phase, at an OD value of [~]0.6). The most common tool for measuring OD is a spectrophotometer. However, most spectrophotometers are sophisticated, non-portable and expensive laboratory instruments, costing tens of thousands of dollars. Even a very low cost spectrophotometer for educational use costs at least US$1,000. Because of the cost, even well resourced labs have only one instrument, which becomes a bottleneck when multiple bacterial cultures need to be monitored simultaneously. The problem is more acute in developing countries, where multiple labs have to share a single spectrophotometer, or theres no such instrument at all. Having a cheap and simple device to measure OD would enable multiple people in a laboratory to monitor their bacterial cultures independently, even in resource-limited settings. At the same time, a portable OD meter could be useful for field work. Here we present the detailed build instructions and characterization of a very simple OD meter that costs only US$60, and can measure OD values from [~]0.05 to 2.0. Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@721005org.highwire.dtl.DTLVardef@79c5d3org.highwire.dtl.DTLVardef@aabb95org.highwire.dtl.DTLVardef@1016f09org.highwire.dtl.DTLVardef@120f3e2_HPS_FORMAT_FIGEXP M_TBL C_TBL

bioengineering↗

bioRxiv2021

Ribosomal protein database profiling lends clarity to ribosomal protein evolution and mass distribution

Existence of theoretical ribosomal protein mass fingerprint as well as utility of ribosomal protein as biomarkers in mass spectrometry microbial identification suggests phylogenetic significance for this class of proteins. To serve the above two functions, facile means of identifying and extracting important attributes of ribosomal proteins from proteome data file of microbial species must be found. Additionally, there is a need to calculate important properties of ribosomal proteins such as molecular weight and nucleotide sequence based on amino acid sequence information from FASTA proteome file. This work sought to support the above endeavour through developing a MATLAB software that extracts the amino acid sequence information of all ribosomal proteins from the FASTA proteome datafile of a microbial species downloaded from UniProt. Built-in functions in MATLAB are subsequently employed to calculate important properties of extracted ribosomal proteins such as number of amino acid residue, molecular weight and nucleotide sequence. All information above are output, as a database, to an Excel file for ease of storage and retrieval. Data available from the analysis of an Escherichia coli K-12 proteome revealed that the bacterium possess a total of 59 ribosomal proteins distributed between the large and small ribosome subunits. The ribosomal protein ranges in sequence length from 38 (50S ribosomal protein L36) to 557 (30S ribosomal protein S1). In terms of molecular weight distribution, the profiled ribosomal proteins range in weight from 4364.305 Da (50S ribosomal protein L36) to 61157.66 Da (30S ribosomal protein S1). More important, analysis of the distribution of the molecular weight of different ribosomal proteins in E. coli reveals a smooth curve that suggests strong co-evolution of ribosomal protein sequence and mass given the tight constraints that a functional ribosome presents. Finally, cluster analysis reveals a preponderance of small ribosomal proteins compared to larger ones, which remains to be a mystery to evolutionary biologists. Overall, the information encapsulated in the ribosomal protein database should find use in gaining a better appreciation for the molecular weight distribution of ribosomal proteins in a species, as well as delivering information for using ribosomal protein biomarkers in identifying particular microbial species in mass spectrometry microbial identification. Subject areasbiochemistry, microbiology, bioinformatics, biotechnology, molecular biology, Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/465821v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@17b8205org.highwire.dtl.DTLVardef@195517eorg.highwire.dtl.DTLVardef@1940a0org.highwire.dtl.DTLVardef@1e1ea47_HPS_FORMAT_FIGEXP M_FIG C_FIG Short descriptionSmooth function describes the distribution of molecular weight of ribosomal proteins in Escherichia coli, which suggests strong co-evolution pressure that fine-tunes the molecular weight of individual proteins with the constraint coming from the overall structure of the ribosome that needs to deliver a consistent function to the living cell. Multitude of ribosomal proteins with different roles in the ribosomal proteins under tight co-evolution pressure likely engender the observed smooth curve in the above plot. O_TEXTBOXHighlightsO_LIAutomated MATLAB software for extracting ribosomal protein sequence from FASTA proteome file of a microbial species was developed. C_LIO_LIIn-built functions were used to calculate nucleotide sequence, number of residues and molecular weight of each of the extracted ribosomal protein C_LIO_LIAll extracted and calculated information were encapsulated as a ribosomal protein database for output to an Excel file for ease of storage and retrieval. C_LIO_LIInformation in database could be useful for theoretical ribosomal protein mass fingerprint or help assign ribosomal protein biomarker peaks in matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) microbial identification. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2022

Episomal editing of synthetic constructs in yeast using CRISPR

Use of synthetic genomics to design and build "big" DNA has revolutionized our ability to answer fundamental biological questions by employing a bottom-up approach. S. cerevisiae, or budding yeast, has become the major platform to assemble large synthetic constructs thanks to its powerful homologous recombination machinery and the availability of well-established molecular biology techniques. However, efficiently and precisely introducing designer variations to episomal assemblies remains challenging. Here, we describe CRISPR Engineering of EPisomes in Yeast, or CREEPY, for rapid engineering of mammalian DNA constructs larger than 100 kb. We demonstrate that editing of circular episomes presents unique challenges compared to modifying native yeast chromosomes with CRISPR. After optimizing CREEPY for episomal editing, we achieve efficient simplex and multiplex editing as demonstrated by engineering a mouse Sox2-harboring episome.

synthetic biology↗

bioRxiv2026

Methods for Molecular Recognition Computing

The softmax attention mechanism in transformer architectures (Vaswani et al., 2017) is mathematically identical to the Boltzmann distribution governing molecular binding at thermal equilibrium (Boltzmann, 1877). Luces Choice Axiom (1959) establishes this function - which we term the convergence equation - as the unique function satisfying five axioms of competitive selection: positivity, normalization, unrestricted domain, rank preservation, and independence of irrelevant alternatives. We show that five additional architecture conditions - discrete intermolecular contacts, bilinear energy decomposition, finite competitor pools, thermal equilibrium, and stochastic selection - are satisfied by at least ten biological molecular recognition systems and together prescribe a complete neural architecture: dual encoders, cross-attention, InfoNCE contrastive training, symmetric loss, learned temperature, and cross-attentive decoder. We term this architecture a Specificity Foundation Model (SFM) and specify it for antibody-antigen, TCR-peptide-MHC, transcription factor-DNA, microRNA-mRNA, enzyme-substrate, CRISPR guide RNA-DNA, drug-target, peptide-MHC, receptor-ligand, and RNA-binding protein-RNA recognition. The first implementation (CALM; Lee et al., 2026) achieves antibody-antigen retrieval from approximately 4,000 training pairs with [~]100,000-fold greater data efficiency than comparable contrastive architectures trained without the physics derivation. We classify this as Level 3 architecture-physics alignment and derive three further theoretical results: an exponential scaling law for retrieval accuracy as a function of training data diversity (the MRC scaling law), a two-parameter affinity calibration framework connecting contrastive scores to binding free energies, and a hybrid recursive learning framework for cross-modal reinforcement learning with orthogonal verification. The failure conditions of the framework are analyzed in terms of the validity of equilibrium thermodynamics for molecular binding and the convergence properties of gradient-based parameter estimation.

synthetic biology↗

bioRxiv2024

Language Models for Molecular Dynamics

Molecular Dynamics (MD) simulations provide accurate descriptions of the motions of molecular systems, yet their computational demands pose significant challenges in applications in molecular biology and materials science. Given the success of deep learning methods in a wide range of fields, a timely question concerns whether these methods could be leveraged to improve the efficiency of MD simulations. To investigate this possibility, we introduce Molecular Dynamics Language Models (MDLMs), to enable the generation of MD trajectories. In the present implementation, an MDLM is trained on a short classical MD trajectory of a protein, where structural accuracy is maintained through kernel density estimations derived from extensive MD datasets. We illustrate the application of this MDLM in the case of the determination of the free energy landscape a small protein, showing that this approach makes it possible to discover conformational states undersampled in the training data. These results provide initial evidence for the use of language models for the efficient implementation of molecular dynamics.

bioinformatics↗

bioRxiv2019

Complex+: Aided Decision-Making for the Study of Protein Complexes

Proteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective - motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

bioinformatics↗

bioRxiv2025

DNA framework array enables ultra-high throughput DNA synthesis

High-throughput DNA synthesis has revolutionized synthetic biology, molecular diagnostics, genome engineering, and DNA data storage by enabling the scalable and precise construction of nucleotide-based information systems. However, existing high-throughput synthesis technologies rely on top-down fabrication strategies to maximize throughput, yet they are inherently constrained by physical limitations. These constraints make it exceedingly difficult to achieve single-molecule level synthesis control that is essential for advancing next-generation DNA synthesis. Here, we present a DNA framework-based bottom-up enzymatic synthesis strategy that enables single-molecule level control of DNA synthesis with high-throughput. Leveraging the nanoscale addressability of DNA frameworks, we achieved a synthesis site pitch of 10.9 nm--183-fold smaller than that of previously reported electrode arrays. Theoretical projections suggest that this approach could scale synthesis throughput to 5.3 x 1011 sequences per square centimeter, representing a four-order-of-magnitude improvement over existing synthesis arrays. This approach lays the foundation for gigabyte-scale DNA writing and offers a highly accessible pathway toward large-scale DNA synthesis for emerging applications such as molecular data storage.

synthetic biology↗

bioRxiv2021

Computational modeling of molecular structure of microRNA inhibitors selected against microRNA over-expressed in thyroid cancer

IntroductionThyroid cancer is the most prevalent malignant neoplasm of endocrine system and advances in thyroid molecular biology studies demonstrate that microRNAs (miRNAs) seem to play a fundamental role in tumor triggering and progression. The miRNAs inhibitors are nucleic acid-based molecules that blockade miRNAs function, making unavailable for develop their usual function, also acting as gene expression controlling molecules. ObjectiveTo develop in silico projection of molecular structure of miRNA inhibitors against miRNA over-expressed in thyroid cancer. MethodsWe conducted a search of the nucleotide sequence of 12 miRNAs already defined as inhibitors against miRNA over-expressed in thyroid cancer, realizing in silico projection of the molecular structure of following miRNAs: miRNA-101, miRNA-126, miRNA-126-3p, miRNA-141, miRNA-145, miRNA-146b, miRNA-206, miRNA-3666, miRNA-497, miRNA-539, miRNA-613, and miRNA-618. The nucleotides were selected using GenBank that is the NIH genetic sequence database. The sequences obtained were aligned with the Clustal W multiple alignment algorithms. For the molecular modeling, the structures were generated with the RNAstructure, a fully automated miRNAs structure modelling server, accessible via the Web Servers for RNA Secondary Structure Prediction. ResultsWe demonstrated a search for nucleotide sequence and the projection of the molecular structure of the following miRNA inhibitors against miRNA over-expressed in thyroid cancer: miRNA-101, miRNA-126, miRNA-126-3p, miRNA-141, miRNA-145, miRNA-146b, miRNA-206, miRNA-3666, miRNA-497, miRNA-539, miRNA-613, and miRNA-618. ConclusionIn this study we show in silico secondary structures projection of selected of 12 miRNA inhibitors against miRNA over-expressed in thyroid cancer through computational biology.

cancer biology↗

bioRxiv2021

PRDM9 losses in vertebrates are coupled to the loss of at least three other meiotic genes

In most mammals and likely throughout vertebrates, the gene PRDM9 specifies the locations of meiotic double strand breaks; in mice and humans at least, it also aids in their repair. For both roles, many of the molecular partners remain unknown. Here, we take a phylogenetic approach to identify genes that may be interacting with PRDM9, by leveraging the fact that PRDM9 arose before the origin of vertebrates, but was lost many times, either partially or entirely--and with it, its role in recombination. As a first step, we characterize PRDM9 domain composition across 446 vertebrate species, inferring at least thirteen independent losses. We then use the interdigitation of PRDM9 orthologs across vertebrates to test whether it co-evolved with any of 241 candidate genes co-expressed with PRDM9 in mice or associated with recombination phenotypes in mammals. Accounting for the phylogenetic relationship among species, we find two genes whose presence and absence is unexpectedly coincident with that of PRDM9: ZCWPW1, which was recently shown to facilitate double strand break repair, and its paralog ZCWPW2, as well as more tentative evidence for TEX15 and FBXO47. ZCWPW2 is expected to be recruited to sites of PRDM9 binding; its tight coevolution with PRDM9 across vertebrates suggests that it is a key interactor, with a role either in recruiting the recombination machinery or in double strand break repair. Author SummaryOur understanding of meiotic recombination in mammals has seen great progress over the past 15 years, spurred in part by the convergence of lines of evidence from molecular biology, statistical genetics and evolutionary biology. We now know that in most mammals and likely in many vertebrates, the gene PRDM9 specifies the location of meiotic double strand breaks and that in mice and humans at least, it also aids in their repair. For both roles, however, many of the molecular partners remain unknown. To search for these, we take a phylogenetic approach, leveraging the fact that the complete PRDM9 has been lost at least thirteen times in vertebrates and thus that its presence is interdigitated across species. By this approach, we identify two genes whose presence or absence across vertebrates is coupled to the presence or absence of PRDM9, ZCWPW1 and ZCWPW2, as well as two genes for which the evidence is weaker, TEX15 and FBXO47. ZCWPW1 was recently shown to be recruited to sites of PRDM9 binding and to aid in the repair of double strand breaks. ZCWPW2 is likely recruited to sites of PRDM9 binding as well; its tight coevolution with PRDM9 across vertebrates suggests that it plays an important role either in double strand break formation, potentially as the missing link that recruits the recombination machinery to sites of PRDM9 binding, or in double strand break repair.

evolutionary biology↗

bioRxiv2025

Multimodal approach to characterize surgically removed epileptogenic zone from patients with focal drug-resistant epilepsy: from operating room to wet lab

ObjectiveWe have established a comprehensive sample handling protocol designed for the multiscale assessment of epileptogenic tissue. This protocol aims to identify novel therapeutic targets and enhance the diagnosis and stratification of patients with drug-resistant epilepsy, thereby optimizing their treatment with anti-seizure medications and surgical interventions. MethodsPatients with drug-resistant focal epilepsy, recommended for surgical treatment, are recruited after detailed multidisciplinary preoperative evaluation at the Epilepsy Center at Kuopio University Hospital in Finland. A day before the resective surgery, patients undergo magnetic resonance imaging (MRI) including advanced methodologies. During the surgery, each piece of resected tissue is placed under oxygenation on ice-cold artificial cerebral spinal fluid-solution. The pieces are then immediately transported to the laboratory, assessed by a neuropathologist, and sliced for both clinical diagnosis and research. Two adjacent slices are provided for research and are sent to the University of Eastern Finland. ResultsThe developed sample handling protocol provides the opportunity for detailed characterization of the tissue from the same patient using emerging imaging, electrophysiology, and molecular biology technologies. We have optimized the conditions for preserving the resected tissue alive for electrophysiological measurements and simultaneously making possible ex vivo studies including multi-omics acquisition, electron microscopy, histology, and MRI. Our protocol enables the mapping of functional readouts to structural and molecular alterations in human tissue. Our goal is to integrate multimodal data and co-register the resected tissues within the whole brains in vivo MRI space. This approach aims to enhance the characterization and localization of epileptogenic zones and refine surgical treatment targets by identifying abnormalities in global connectivity and structural patterns. SignificanceWe have successfully developed a systematic protocol for the collection and analysis of multimodal data. This protocol aims to elucidate the structural, functional, and molecular characteristics that render tissue epileptogenic, thereby enhancing the diagnosis and subsequent care of patients with epilepsy. Key pointsO_LIIntroducing a systematic sample handling protocol to assess tissue epileptogenicity at structural, functional and molecular levels. C_LIO_LIA multimodal approach integrating advanced technology with detailed characterization of epileptogenic tissue properties to obtain patient-specific data. C_LIO_LICorrelation of data from preoperative MRI and resected tissue to predict tissue pathology from clinical MRI. C_LI

neuroscience↗

bioRxiv2026

Integrated metabolomics and proteomics from voxelated cortical hemispheres of adult rhesus monkeys

The spatial organization of molecular networks across cortex likely contributes to differences in local circuit vulnerability in aging and Alzheimers disease; yet many existing molecular datasets sacrifice spatial structure, sampling only a handful of regions per brain. Here, we present a framework for generating spatially registered, paired metabolomic and proteomic maps across an entire cortical hemisphere of an adult rhesus monkey, at millimeter resolution. One hemisphere each from two animals was harvested under controlled conditions, approximately flattened, and hand dissected at different sampling resolutions (roughly 2.5 and 4 mm/side) into tissue voxels. Each voxel was split after homogenization and extraction to provide matched aliquots for targeted metabolomics and deep untargeted proteomics. To handle these high dimensional data, we developed PChclust, a principal component guided feature clustering algorithm. For cross omic integration, we developed a spatially regularized sparse canonical correlation analysis (sr-sCCA), which incorporates spatial neighborhood structure via graph Laplacian smoothing. We recover meaningful biology: Molecular similarity between neighboring voxels decayed with distance in both modalities, confirming that voxelation captures spatially organized biological variance. The sr-sCCA identified joint proteome-metabolome components with coherent cortical gradients that were conserved across animals. Pathway enrichment analysis recovered brain relevant ontologies and reconstructed complete metabolic circuits from single voxels.

neuroscience↗

bioRxiv2017

Evolution And Multiple Roles Of The Pancrustacea Specific Transcription Factor zelda In Insects

Gene regulatory networks (GRN) evolve as a result of the coevolutionary process acting on transcription factors and the cis-regulatory modules (CRMs) they bind. The zinc-finger transcription factor (TF) zelda (zld) is essential for maternal zygotic transition (MZT) in Drosophila melanogaster, where it directly binds over thousand CRMs to regulate chromatin accessibility. D. melanogaster displays a long germ type of embryonic development, where all segments are simultaneously generated along the whole egg. However, it remains unclear if zld is also involved in MZT of short-germ insects (including those from basal lineages) or in other biological processes. Here we show that zld is an innovation of the Pancrustacea lineage, being absent in more distant arthropods (e.g. chelicerates) and other organisms. To better understand zlds ancestral function, we thoroughly investigated its roles in a short-germ beetle, Tribolium castaneum, using molecular biology and computational approaches. Our results demonstrate roles for zld not only during the MZT, but also in posterior segmentation and patterning of imaginal disc derived structures. Further, we also demonstrate that zld is critical for posterior segmentation in the hemipteran Rhodnius prolixus, indicating this function predates the origin of holometabolous insects and was subsequently lost in long-germ insects. Our results unveil new roles of zld in maintaining pluripotent state of progenitor cells at the posterior region and suggest that changes in expression of zld (and probably other pioneer TFs) are critical in the evolution of insect GRNs.

genetics↗

bioRxiv2026

Benchmarking spatial interpolation methods for brain maps

The human brain is a unique biological space that hosts complex processes unfolding at multiple scales. To study these processes, an abundance of imaging technologies evolved over many decades to produce large-scale, dense mappings of structural and functional features. In parallel, a rich universe of techniques for cellular and molecular biology supplies us with fine-scale, highly specific and reliable measurements in sparse tissue samples. To represent cortical processes integratively across scales, spatial interpolation is necessary for bridging dense and sparse data. The absence of a field consensus for realistic interpolation of features over the whole brain prompts a comprehensive comparison of existing frameworks from the broader scientific literature. Here, we benchmark the performance of multiple deterministic (Inverse Distance Weighting, K-Nearest Neighbours, and Radial Basis Function) and stochastic (Spatially-Weighted Regression, Ordinary Kriging, and Regression Kriging) strategies first with simulated or empirical ground truths. We then demonstrate two use cases with de novo sparse brain data (intracranial EEG and microarray gene expression). In these experiments, we investigate how differences in data characteristics, such as spatial dependency structure and sampling distribution, impact the performance of different interpolation methods. Throughout the results, we consistently find that maps interpolated through spatially-informed stochastic frameworks such as Ordinary Kriging and Regression Kriging are more accurate and biologically realistic across geometric constraints, data modalities, and sampling conditions. This invites continued development of spatially-informed statistical frameworks for analyzing brain data and, more fundamentally, the biological processes that produce them.

neuroscience↗