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Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex.

1. To infer relative roles of cortical areas at different stages of the ventral visual pathway, we quantitatively examined visual responses of cells in V2, V4, the posterior part of the inferotemporal cortex (posterior IT), and the anterior part of the inferotemporal cortex (anterior IT), using anesthetized macaque monkeys. 2. The critical feature for the activation was first determined for each recorded cell by using a reduction method. We started from images of three-dimensional complex objects and simplified the image of effective stimuli step by step by eliminating a part of the features present in the image. The simplest feature that maximally activated the cell was determined as the critical feature. The response to the critical feature was then compared with responses of the same cell to a routine set of 32 simple stimuli, which included white and black bars of four different orientations and squares or spots of four different colors. 3. Cells that responded maximally to particular complex object features were found in posterior IT and V4 as well as in anterior IT. The cells in posterior IT and V4 were, however, different from the cells in anterior IT in that many of them responded to some extent to some simple features, that the size of the receptive field was small, and that they intermingled in single penetrations with cells that responded maximally to some simple features. The complex critical features in posterior IT and V4 varied; they consisted of complex shapes, combinations of a shape and texture, and combinations of a shape and color. 4. We suggest that local neuronal networks in V4 and posterior IT play an essential role in the formation of selective responses to complex object features.

Animals↗

Lipopolysaccharide/interferon-gamma and not transforming growth factor beta inhibits retinal microglial migration from retinal explant.

BACKGROUND: /aims: The retina possesses a rich network of CD45(+) positive myeloid derived cells that both surround inner retinal vessels and lie within the retina (microglia). Microglia migrate and accumulate in response to neurodegeneration and inflammation. Although microglia express MHC class II, their role remains undefined. The aims of this study are to investigate changes in human microglia phenotype, migration, and activation status in response to pro-inflammatory and anti-inflammatory stimulation. METHODS: Donor eyes were obtained from the Bristol Eye Bank with consent and whole retina was removed. 5 mm retinal trephines were cultured in glucose enhanced RPMI on cell culture insert membranes for up to 72 hours. The effects of lipopolysaccharide/interferon-gamma (LPS/IFNgamma) and transforming growth factor beta inhibits (TGFbeta) stimulation, alone or in combination, on migration, phenotype, and activation status (iNOS expression) of microglia were studied using immunofluorescence and cytokine analysis by ELISA. RESULTS: CD45(+) MHC class II(+) retinal microglia were observed within retinal explants, and in culture microglia readily migrated, adhered to culture membrane, downregulated MHC class II expression, and produced interleukin 12 (IL-12) and tumour necrosis factor alpha (TNFalpha). Following LPS/IFNgamma stimulation microglia remained MHC class II(-) iNOS(-), and secreted IL-10. Migration was suppressed and this could be reversed by neutralising IL-10 activity. TGFbeta did not affect ability of microglia to migrate and was unable to reverse LPS/IFNgamma induced suppression. CONCLUSIONS: Microglia readily migrate from retinal explants and are subsequently MHC class II(-), iNOS(-), and generate IL-12. In response to LPS/IFNgamma microglia produce IL-10, which inhibits both their migration and activation. TGFbeta was unable to counter LPS/IFNgamma effects. The data infer that microglia respond coordinately, dependent upon initial cytokine stimulation, but paradoxically respond to classic myeloid activation signals.

Cell Adhesion↗

Population-scale detection of methylation outliers from long-read genome sequencing.

BACKGROUND: Aberrant DNA methylation can mediate the functional effects of rare genetic variation and contribute to imprinting disorders, repeat expansion diseases, and other pathogenic regulatory mechanisms. Long-read sequencing technologies now enable genome-wide detection of CpG methylation alongside genetic variation from a single assay. However, methods for systematic identification and interpretation of methylation outliers from long-read sequencing data remain limited. METHODS: We developed METAFORA, a computational workflow for detecting methylation outlier regions from PacBio and Oxford Nanopore long-read sequencing data. METAFORA constructs population-level methylation references, segments the genome into correlated CpG blocks, infers technical and biological sources of variation through hidden factor estimation, models uncertainty due to variable depth sequencing, and computes covariate-adjusted methylation outlier scores for individual samples. We applied METAFORA across large long-read sequencing cohorts and integrated methylation outliers with multi-omic data. METAFORA is implemented as a snakemake workflow available at https://github.com/tjense25/METAFORA. RESULTS: METAFORA identified methylation outlier regions associated with rare structural variants, tandem repeat expansions, and imprinting abnormalities. We found outlier regions were enriched for molecular outliers across transcriptomic and chromatin accessibility datasets, supporting their functional relevance in gene regulation. In a representative case, METAFORA identified an imprinting defect affecting the GNAS locus associated with an STX16 deletion. CONCLUSIONS: METAFORA enables scalable detection and interpretation of methylation outliers from long-read sequencing data and provides a framework for integrating epigenetic outliers with genomic and multi-omic analyses. These approaches may improve interpretation of rare regulatory variation and support discovery of clinically relevant epigenetic abnormalities in genomic medicine.

DNA methylation↗

Bottom-up and top-down dynamics in visual cortex.

A key emergent property of the primary visual cortex (V1) is the orientation selectivity of its neurons. Recent experiments demonstrate remarkable bottom-up and top-down plasticity in orientation networks of the adult cortex. The basis for such dynamics is the mechanism by which orientation tuning is created and maintained, by integration of thalamocortical and intracortical inputs. Intracellular measurements of excitatory and inhibitory synaptic conductances reveal that excitation and inhibition balance each other at all locations in the cortex. This balance is particularly critical at pinwheel centers of the orientation map, where neurons receive intracortical input from a wide diversity of local orientations. The orientation tuning of neurons in adult V1 changes systematically after short-term exposure to one stimulus orientation. Such reversible physiological shifts in tuning parallel the orientation tilt aftereffect observed psychophysically. Neurons at or near pinwheel centers show pronounced changes in orientation preference after adaptation with an oriented stimulus, while neurons in iso-orientation domains show minimal changes. Neurons in V1 of alert, behaving monkeys also exhibit short-term orientation plasticity after very brief adaptation with an oriented stimulus, on the time scale of visual fixation. Adaptation with stimuli that are orthogonal to a neuron's preferred orientation does not alter the preferred orientation but sharpens orientation tuning. Thus, successive fixation on dissimilar image patches, as happens during natural vision, combined with mechanisms of rapid cortical plasticity, actually improves orientation discrimination. Finally, natural vision involves judgements about where to look next, based on an internal model of the visual world. Experiments in behaving monkeys in which information about future stimulus locations can be acquired in one set of trials but not in another demonstrate that V1 neurons signal the acquisition of internal representations. Such Bayesian updating of responses based on statistical learning is fundamental for higher level vision, for deriving inferences about the structure of the visual world, and for the regulation of eye movements.

Adaptation, Psychological↗

Metareasoning and meta-level learning in a hybrid knowledge-based architecture.

Ahybrid knowledge-based architecture integrates different problem solvers for the same (sub)task through a control unit operating at a meta-level, the metareasoner, which coordinates the use of, and the communication between, the different problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge domain view operated by the inference mechanism in order to perform the (sub)task. Important issues in a hybrid system are the metareasoning and learning aspects. Metareasoning encompasses the functions performed by the metareasoner, while learning reflects the ability of the system to evolve on the basis of its experiences in problem solving. Learning occurs at different levels, learning at the meta-level and learning at the level of the specific problem solvers. Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks. Meta-level tasks include the initial planning of problem solving strategies and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving. In this paper we concentrate on metareasoning and meta-level learning in the context of a hybrid architecture. The theoretical arguments presented in the paper are demonstrated in practice through a hybrid knowledge-based prototype system for the domain of breast cancer histopathology.

Algorithms↗

Biallelic variants in RNU2-2 cause the most prevalent known recessive neurodevelopmental disorder.

We recently showed that mutations in RNU4-2 and RNU2-2, two genes that are transcribed into small nuclear RNA (snRNA) components of the major spliceosome, are prevalent causes of dominant neurodevelopmental disorders (NDDs). By genetic association comparing 12,776 NDD cases with 56,064 controls, we now demonstrate the existence of a recessive form of RNU2-2 syndrome that, in England, is even more common than the dominant form. We inferred log Bayes factors for dominant and recessive models of association of 14.0 and 18.2, respectively, and observed 17 rare variants with a posterior probability of pathogenicity conditional on recessive association >0.8. This conservative threshold identified 18 probands (all with unaffected parents) and five affected siblings, each carrying two alleles in trans at these variants. A relaxed threshold of >0.6 identified a further 13 candidate probands. We estimate that recessive RNU2-2 syndrome accounts for 7-10% of families with a diagnosed recessive NDD, and is 36-62% as prevalent as the dominant RNU4-2-related disorder ReNU syndrome. We identified a further seven cases in five pedigrees in two replication collections. Cases are characterized by intellectual disability, global developmental delay and seizures. The variants are predicted to destabilize stem loops and binding domains of the U2-2 snRNA that contribute to spliceosome quaternary structure, intron recognition and catalytic function. Despite this, whole-blood derived RNA-seq data from three patients did not reveal splicing defects, in line with previous analogous observations for dominant RNU2-2 syndrome.

Journal Article↗

Lysozyme gene expression by hemocytes of Pacific white shrimp, Litopenaeus vannamei, after injection with Vibrio.

The purpose of this study was to quantify the gene expression of lysozyme, an important antibacterial protein produced by shrimp hemocytes, within tissues of Litopenaeus vannamei Boone in response to a pathogen challenge. We quantified lysozyme transcripts with a real-time PCR method and used these data, along with total hemocyte counts, to infer patterns of hemocyte trafficking during the immune response. Transcript expression was detected by in situ hybridization of mRNA in circulating hemocytes, and within tissues with high hemocyte concentrations. Lysozyme gene expression was monitored in 5 tissues and in circulating hemocytes for 48 h following challenge with the shrimp pathogen Vibrio campbellii Baumann. The results suggest that lysozyme is expressed in most if not all hemocytes in circulation and in peripheral tissues. Injection with V. campbellii produced a significant decrease in transcriptional signal in circulating hemocytes and peripheral tissues 4 h after injection. Over the same early time period lysozyme signal increased significantly in the muscle at the site of injection and remained high for the duration of the time-course, suggesting that hemocytes are recruited to the site of injection early during the course of the immune response. After 4 h, lysozyme signal increased in circulating hemocytes and tissues, with a return to control levels noted for all tissues except the muscle at the site of injection.

Animals↗

Digital imaging at a community hospital: implications for hospital stays and teleradiology.

Two key questions for the cost-effectiveness of digital imaging concern its likely impact on average inpatient stays and the prevalence of applications for teleradiology. Little empirical evidence is available because of the state of the technology, but useful evidence can be drawn from conventional imaging departments. This paper reports a survey of admitting physicians and draws inferences about the likely effects of conversion to digital imaging. Published cost estimates for this Canadian community hospital show that conversion to digital imaging is likely to increase medical imaging costs despite shorter examination times. However, digital imaging could produce net savings hospital-wide by improving the efficiency of diagnosis and treatment sufficiently to reduce the length of patient stays. In this sample, no inpatient medical imaging examinations lengthened a stay in a way likely to be averted by digital imaging (95% confidence interval 0% to 2%), and probability testing rejects the hypothesis that digital imaging will reduce hospital stays sufficiently to produce net savings at this hospital. Based on these results, it appears premature to assume that digital imaging will significantly reduce length of stay for most inpatients. On teleradiology, physicians either exchanged films with other medical imaging centres or indicated they would have sent or received teleradiology images for 17% of inpatient medical imaging examinations (95% confidence interval 10-24%). Since outpatient procedures comprise 60% of the medical imaging workload at Victoria General Hospital, further study would be required to estimate overall use of teleradiology.

Analog-Digital Conversion↗

Interleukin-18 binding protein in acute graft versus host disease and engraftment following allogeneic peripheral blood stem cell transplants.

Dysregulation of the cytokine network plays an important role in graft-versus-host disease (GVHD). Interleukin-18 (IL-18) is an obligatory cytokine for interferon-gamma (IFN-gamma) production and IFN-gamma and sIFN-gammaR are elevated in patients with GVHD. Because IL-18 binding protein (IL-18BP) is an inhibitor of IL-18-mediated IFN-gamma production, we evaluated IL-18BP levels in patients undergoing allogeneic peripheral blood stem cell transplantation (PBSCT). IL-18BP levels were assessed in 14 patients on day -10 (before conditioning), on the day of transplant, on the day of engraftment, and during transplant-related complications. A comparison of the kinetics of IL-18BP and soluble(s) IL-6R, sIFN-gammaR, IL-18 serum levels was performed. IL-18BP levels were assessed by specific monoclonal antibodies in a double-sandwich enzyme-linked immunosorbent assay (ELISA). In all patients IL-18BP levels decreased during conditioning and increased in parallel with engraftment (p < 0.05). Accordingly, during rejection, IL-18BP serum levels remained low and similar to pretransplant levels. The mean elevation of IL-18BP detected in association to acute GVHD was significantly higher in comparison to normal engraftment (p < 0.05). A correlation between IL-18BP, sIFNgammaR, and sIL-6R serum levels was found in all patients. No correlation between IL-18 and IL-18BP serum levels was found in patients undergoing uneventful PBSCT and rejection, whereas a marked increase in both IL-18 and IL-18BP levels was detected during acute GVHD (p < 0.01). Our data suggest that the dysregulation of IL-18 and IL-18BP may be important in the pathophysiology of transplant-related complications. Furthermore, because preliminary data from our group show that IL-18 blockage ameliorates GVHD in murine models, it is inferred that these cytokines may represent potential targets in the development of new therapeutic strategies in acute GVHD.

Acute Disease↗

Gut microbiota dynamics and metabolic pathways associated with bleomycin-induced pulmonary fibrosis progression.

BACKGROUND: Pulmonary fibrosis (PF) is a progressive respiratory disease characterized by epithelial injury, aberrant repair and excessive extracellular matrix deposition. Although the gut-lung axis is increasingly implicated in respiratory disorders, stage-resolved characterization of gut microbiota taxonomic and functional potential during PF development is limited. METHODS: We established a bleomycin-induced murine PF model and performed cross-sectional shotgun metagenomic sequencing of fecal samples from separate cohorts at three defined stages: baseline (control), day 7 (early fibrosis; M7), and day 14 (established fibrosis; M14). Microbial taxonomy, alpha/beta diversity, and predicted functional capacity were inferred using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Carbohydrate-Active enZymes (CAZy) annotations; associations were assessed using Procrustes and Spearman correlation analyses. RESULTS: Histopathology and immunohistochemistry confirmed progressive fibrogenesis with increased TGF-&#x3b2;1 and &#x3b1;-SMA expression. Compared with baseline, bleomycin-treated groups exhibited stage-specific shifts in gut microbial composition, including depletion of mucin-associated taxa (e.g., Prevotella, Akkermansia muciniphila) and expansion of Muribaculaceae- and Clostridiaceae-affiliated taxa. Alpha and beta diversity metrics differed across groups. KEGG/CAZy-based annotations revealed predicted, stage-dependent changes in microbial metabolic potential, including early reductions in pathways related to amino acid and glycan metabolism (M7) and later increases in predicted starch/sucrose catabolism, phosphotransferase system (PTS) representation, and secondary bile acid biosynthesis (M14). Correlation analyses linked compositional shifts to these predicted functional changes. CONCLUSION: In a stage-resolved, cross-sectional study, bleomycin-associated pulmonary fibrosis was accompanied by compositional and predicted functional alterations in the gut microbiota. These data identify candidate taxa and predicted pathways for follow-up mechanistic testing, but functional (metabolomic) and causality experiments are required to confirm whether and how microbial changes contribute to PF pathogenesis.

Animals↗

A dynamic neuro-fuzzy model providing bio-state estimation and prognosis prediction for wearable intelligent assistants.

BACKGROUND: Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs). METHODS: The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. RESULTS: The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. CONCLUSION: A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective.

Decision Support Techniques↗

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.

MOTIVATION: Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. RESULTS: We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case. AVAILABILITY AND IMPLEMENTATION: The hypeR-GEM R package, documentation, and workflow examples are freely available at https://github.com/montilab/hypeR-GEM and archived at https://doi.org/10.5281/zenodo.20586748.

Metabolomics↗

Factorial hidden Markov models and the generalized backfitting algorithm.

Previous researchers developed new learning architectures for sequential data by extending conventional hidden Markov models through the use of distributed state representations. Although exact inference and parameter estimation in these architectures is computationally intractable, Ghahramani and Jordan (1997) showed that approximate inference and parameter estimation in one such architecture, factorial hidden Markov models (FHMMs), is feasible in certain circumstances. However, the learning algorithm proposed by these investigators, based on variational techniques, is difficult to understand and implement and is limited to the study of real-valued data sets. This chapter proposes an alternative method for approximate inference and parameter estimation in FHMMs based on the perspective that FHMMs are a generalization of a well-known class of statistical models known as generalized additive models (GAMs; Hastie & Tibshirani, 1990). Using existing statistical techniques for GAMs as a guide, we have developed the generalized backfitting algorithm. This algorithm computes customized error signals for each hidden Markov chain of an FHMM and then trains each chain one at a time using conventional techniques from the hidden Markov models literature. Relative to previous perspectives on FHMMs, we believe that the viewpoint taken here has a number of advantages. First, it places FHMMs on firm statistical foundations by relating them to a class of models that are well studied in the statistics community, yet it generalizes this class of models in an interesting way. Second, it leads to an understanding of how FHMMs can be applied to many different types of time-series data, including Bernoulli and multinomial data, not just data that are real valued. Finally, it leads to an effective learning procedure for FHMMs that is easier to understand and easier to implement than existing learning procedures. Simulation results suggest that FHMMs trained with the generalized backfitting algorithm are a practical and powerful tool for analyzing sequential data.

Algorithms↗

Interpretation of surface EMGs in children with cerebral palsy: An initial study using a fuzzy expert system.

Surface EMG detected simultaneously at different muscles has become an important tool for analysing the gait of children with cerebral palsy (CP), as it offers essential information about muscular coordination. However, the interpretation of surface EMG is a difficult task that assumes extensive knowledge and experience. As such, this noninvasive procedure is not frequently used in the general clinical routine. An Artificial Intelligence (AI) system for interpreting surface EMG signals and the resulting muscular coordination patterns could overcome these limitations. To support such interpretation, an expert system based on fuzzy inference methodology was developed. The knowledge-base of the system implemented 15 rules, from which the fuzzy inference methodology performs a prediction of the effectiveness of the muscular coordination during gait. Our aim was to assess the feasibility and value of such an expert system in clinical applications. Surface EMG signals were recorded from the tibialis anterior, soleus muscle, and gastrocnemius muscles of children with CP to assess muscular coordination patterns of ankle movement during gait. Nineteen children underwent 114 surface EMG measurements. Simultaneously, the gait cycles of each patient were determined using foot switches and videotapes. From the EMG signals, the effectiveness of the ankle movement was predicted by the expert system, and predictions were classified using a three-point ordinal scale. In 91 cases (80%), the clinical findings matched the predictions of the expert system. In 23 cases (20%) the predictions of the expert system differed from the clinical findings with 12 cases revealing worse and 11 cases revealing better results in comparison to the clinical findings. As this study is a first attempt to verify the feasibility and correctness of this expert system, the results are promising. Further study is required to assess the correlation with the kinematic data and to include the whole leg.

Cerebral Palsy↗

Can connectionist models of phonology assembly account for phonology?

Connectionist models have gained considerable success as accounts of how printed words are named. Their success challenges the view of grapheme-to-phoneme correspondences (GPCs) as rules. By extension, however, this challenge is sometimes interpreted also as evidence against linguistic rules and variables. This inference tacitly assumes that the generalizations inherent in reading (specifically, GPCs) are similar in their scope to linguistic generalizations and that they are each reducible to token associations. I examine this assumption by comparing the scope of generalizations required for mapping graphemes to phonemes and several linguistic phonological generalizations. Marcus (1998b) distinguishes between two types of generalizations: those that fall within a model's training space and those that exceed it. The scope of generalizations is determined by the model's representational choices--specifically, the implementation of operations over mental variables. An analysis of GPCs suggests that such generalizations do not appeal to variables; hence, they may not exceed the training space. Likewise, certain phonological regularities, such as syllable phonotactic constraints and place assimilation, may be captured by an associative process. In contrast, other phonological processes appeal to variables; hence, such generalizations potentially exceed the training space. I discuss one such case, the obligatory contour principle. I demonstrate that speakers conform to this constraint and that their behavior is inexplicable by the statistical structure of the language. This analysis suggests that, unlike GPCs, phonological generalizations may exceed the training space. Thus, despite their success in modeling GPCs, eliminative connectionist models of phonology assembly may be unable to provide a complete account for phonology. To the extent that reading is subject to phonological constraints, its modeling may require implementing operations over variables.

Humans↗

FIGENIX: intelligent automation of genomic annotation: expertise integration in a new software platform.

BACKGROUND: Two of the main objectives of the genomic and post-genomic era are to structurally and functionally annotate genomes which consists of detecting genes' position and structure, and inferring their function (as well as of other features of genomes). Structural and functional annotation both require the complex chaining of numerous different software, algorithms and methods under the supervision of a biologist. The automation of these pipelines is necessary to manage huge amounts of data released by sequencing projects. Several pipelines already automate some of these complex chaining but still necessitate an important contribution of biologists for supervising and controlling the results at various steps. RESULTS: Here we propose an innovative automated platform, FIGENIX, which includes an expert system capable to substitute to human expertise at several key steps. FIGENIX currently automates complex pipelines of structural and functional annotation under the supervision of the expert system (which allows for example to make key decisions, check intermediate results or refine the dataset). The quality of the results produced by FIGENIX is comparable to those obtained by expert biologists with a drastic gain in terms of time costs and avoidance of errors due to the human manipulation of data. CONCLUSION: The core engine and expert system of the FIGENIX platform currently handle complex annotation processes of broad interest for the genomic community. They could be easily adapted to new, or more specialized pipelines, such as for example the annotation of miRNAs, the classification of complex multigenic families, annotation of regulatory elements and other genomic features of interest.

Automation↗

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR&#xa0;=&#xa0;1.29 [1.18-1.42]), diabetes (HR&#xa0;=&#xa0;1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR&#xa0;=&#xa0;1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR&#xa0;=&#xa0;1.45 [1.03-2.05]), diabetes (OR&#xa0;=&#xa0;1.01 [1.01-1.02]) and COPD (OR&#xa0;=&#xa0;1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged &#x2264;55&#xa0;years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans↗

Crystal structure of lactaldehyde dehydrogenase from Escherichia coli and inferences regarding substrate and cofactor specificity.

Aldehyde dehydrogenases catalyze the oxidation of aldehyde substrates to the corresponding carboxylic acids. Lactaldehyde dehydrogenase from Escherichia coli (aldA gene product, P25553) is an NAD(+)-dependent enzyme implicated in the metabolism of l-fucose and l-rhamnose. During the heterologous expression and purification of taxadiene synthase from the Pacific yew, lactaldehyde dehydrogenase from E. coli was identified as a minor (</=5%) side-product subsequent to its unexpected crystallization. Accordingly, we now report the serendipitous crystal structure determination of unliganded lactaldehyde dehydrogenase from E. coli determined by the technique of multiple isomorphous replacement using anomalous scattering at 2.2 A resolution. Additionally, we report the crystal structure of the ternary enzyme complex with products lactate and NADH at 2.1 A resolution, and the crystal structure of the enzyme complex with NADPH at 2.7 A resolution. The structure of the ternary complex reveals that the nicotinamide ring of the cofactor is disordered between two conformations: one with the ring positioned in the active site in the so-called hydrolysis conformation, and another with the ring extended out of the active site into the solvent region, designated the out conformation. This represents the first crystal structure of an aldehyde dehydrogenase-product complex. The active site pocket in which lactate binds is more constricted than that of medium-chain dehydrogenases such as the YdcW gene product of E. coli. The structure of the binary complex with NADPH reveals the first view of the structural basis of specificity for NADH: the negatively charged carboxylate group of E179 destabilizes the binding of the 2'-phosphate group of NADPH sterically and electrostatically, thereby accounting for the lack of enzyme activity with this cofactor.

Aldehyde Oxidoreductases↗