Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain.

PURPOSE: The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS: 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS: Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS: Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Reconciling multiple connectivity scores for drug repurposing.

The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular 'signature' with minimal side effects. This principle was defined and popularized by the influential 'connectivity map' study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug 'connectivity score.' Over the past 15 years, several studies have proposed variations in calculating connectivity scores toward improving accuracy and robustness in light of massive growth in reference drug profiles. However, these variations have been formulated inconsistently using various notations and terminologies even though they are based on a common set of conceptual and statistical ideas. Therefore, we present a systematic reconciliation of multiple disease-drug similarity metrics ($ES$, $css$, $Sum$, $Cosine$, $XSum$, $XCor$, $XSpe$, $XCos$, $EWCos$) and connectivity scores ($CS$, $RGES$, $NCS$, $WCS$, $Tau$, $CSS$, $EMUDRA$) by defining them using consistent notation and terminology. In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily. To facilitate the continuous and transparent integration of newer methods, this article will be available as a live document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub repository (https://github.com/jravilab/connectivity_scores) that any researcher can build on and push changes to.

Variability of Single Pulse Electrical Stimulation Responses Recorded with Intracranial Electroencephalography in Epileptic Patients.

Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and threshold value Z = 3, correlation between measurements drops almost to zero. In general, it remains below 0.5 when the threshold is smaller than Z = 4 or the stimulating current intensity is 1 mA. It grows with an increase of either of these factors. Variability is independent of interictal spiking rates in the stimulating and recording sites.We conclude that responses to SEEG stimulation in the human brain are variable, i.e. in a subject at rest, two stimulation trains performed at the same electrode contacts and with the same protocol can give discrepant results. Our findings highlight an advantage of probabilistic interpretation of such results even in the context of a single individual.

In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data.

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system still lacking a cure. Treatment typically focuses on slowing the progression and managing MS symptoms. Single-cell transcriptomics allows the investigation of the immune system-the key player in MS onset and development-in great detail increasing our understanding of MS mechanisms and stimulating the discovery of the targets for potential therapies. Still, de novo drug development takes decades; however, this can be reduced by drug repositioning. A promising approach is to select potential drugs based on activated or inhibited genes and pathways. In this study, we explored the public single-cell RNA data from an experiment with six patients on single-cell RNA peripheral blood mononuclear cells (PBMC) and cerebrospinal fluid cells (CSF) of patients with MS and idiopathic intracranial hypertension. We demonstrate that AIM2 inflammasome, SMAD2/3 signaling, and complement activation pathways are activated in MS in different CSF and PBMC immune cells. Using genes from top-activated pathways, we detected several promising small molecules to reverse MS immune cells' transcriptomic signatures, including AG14361, FGIN-1-27, CA-074, ARP 101, Flunisolide, and JAK3 Inhibitor VI. Among these molecules, we also detected an FDA-approved MS drug Mitoxantrone, supporting the reliability of our approach.

Identifying FDA-approved drugs with multimodal properties against COVID-19 using a data-driven approach and a lung organoid model of SARS-CoV-2 entry.

BACKGROUND: Vaccination programs have been launched worldwide to halt the spread of COVID-19. However, the identification of existing, safe compounds with combined treatment and prophylactic properties would be beneficial to individuals who are waiting to be vaccinated, particularly in less economically developed countries, where vaccine availability may be initially limited. METHODS: We used a data-driven approach, combining results from the screening of a large transcriptomic database (L1000) and molecular docking analyses, with in vitro tests using a lung organoid model of SARS-CoV-2 entry, to identify drugs with putative multimodal properties against COVID-19. RESULTS: Out of thousands of FDA-approved drugs considered, we observed that atorvastatin was the most promising candidate, as its effects negatively correlated with the transcriptional changes associated with infection. Atorvastatin was further predicted to bind to SARS-CoV-2's main protease and RNA-dependent RNA polymerase, and was shown to inhibit viral entry in our lung organoid model. CONCLUSIONS: Small clinical studies reported that general statin use, and specifically, atorvastatin use, are associated with protective effects against COVID-19. Our study corroborrates these findings and supports the investigation of atorvastatin in larger clinical studies. Ultimately, our framework demonstrates one promising way to fast-track the identification of compounds for COVID-19, which could similarly be applied when tackling future pandemics.

A connectivity mapping approach predicted acetylsalicylic acid (aspirin) to induce osteo/odontogenic differentiation of dental pulp cells.

AIM: To use connectivity mapping, a bioinformatics approach, to identify compounds that could induce odontogenic differentiation of dental pulp cells (DPCs) and to experimentally validate this effect. A subsidiary aim was to investigate the anti-inflammatory effect of any identified compound. METHODOLOGY: The Gene Expression Omnibus (GEO) database was searched for microarray data sets assessing odontogenic differentiation of human DPCs. An odontogenic gene expression signature was generated by differential expression analysis. The statistical significant connectivity map (ssCMap) method was used to identify compounds with a highly correlating gene expression pattern. DPCs were treated with the compound identified, and osteo/odontogenic differentiation was assessed by Alizarin red staining, alkaline phosphatase activity and expression of osteo/odontogenic genes ALPL, RUNX2, COL1A1, DSPP, DMP1 and SPP1 by RT-PCR. The anti-inflammatory effect of the compound was assessed using an ex vivo pulpitis model, and cytokine levels were measured with multiplex assay. Means were compared using the t-test or ANOVA followed by a Bonferroni post hoc test with the level of significance set at P ≤ 0.05. RESULTS: The GEO database search identified a specific gene expression signature for osteo/odontogenic differentiation. Analysis using ssCMap found that acetylsalicylic acid [(ASA)/aspirin] was the drug with the strongest correlation with that gene signature. The treatment of DPCs with 0.05 mmol L-1 ASA showed increased alkaline phosphatase activity (P < 0.001), mineralization (P < 0.05), and increased the expression of the osteo/odontogenic genes, DMP1 and DSPP (P < 0.05). Low concentration (0.05 mmol L-1 ) ASA reduced inflammatory cytokines IL-6 (P < 0.001), CCL21 (P < 0.05) and MMP-9 (P < 0.05) in an ex vivo pulpitis model. CONCLUSIONS: Connectivity mapping, a web-based informatics method, was successfully used to identify aspirin as a candidate drug that could modulate the differentiation of DPCs. Aspirin was shown to induce odontogenic differentiation in DPCs in vitro and this, together with its anti-inflammatory effects, makes it a potential candidate for vital pulp therapies.

Connectivity mapping (ssCMap) to predict A20-inducing drugs and their antiinflammatory action in cystic fibrosis.

Cystic fibrosis (CF) lung disease is characterized by chronic and exaggerated inflammation in the airways. Despite recent developments to therapeutically overcome the underlying functional defect in the cystic fibrosis transmembrane conductance regulator, there is still an unmet need to also normalize the inflammatory response. The prolonged and heightened inflammatory response in CF is, in part, mediated by a lack of intrinsic down-regulation of the proinflammatory NF-κB pathway. We have previously identified reduced expression of the NF-κB down-regulator A20 in CF as a key target to normalize the inflammatory response. Here, we have used publicly available gene array expression data together with a statistically significant connections' map (sscMap) to successfully predict drugs already licensed for the use in humans to induce A20 mRNA and protein expression and thereby reduce inflammation. The effect of the predicted drugs on A20 and NF-κB(p65) expression (mRNA) as well as proinflammatory cytokine release (IL-8) in the presence and absence of bacterial LPS was shown in bronchial epithelial cells lines (16HBE14o-, CFBE41o-) and in primary nasal epithelial cells from patients with CF (Phe508del homozygous) and non-CF controls. Additionally, the specificity of the drug action on A20 was confirmed using cell lines with tnfαip3 (A20) knockdown (siRNA). We also show that the A20-inducing effect of ikarugamycin and quercetin is lower in CF-derived airway epithelial cells than in non-CF cells.

In vitro and clinical data analysis of Osteopontin as a prognostic indicator in colorectal cancer.

Osteopontin (OPN) has been shown to promote colorectal cancer (CRC) progression; however, the mechanism of OPN-induced CRC progression is largely unknown. In this study, we found that OPN overexpression led to enhanced anchorage-independent growth, cell migration and invasion in KRAS gene mutant cells but to a lesser extent in KRAS wild-type cells. OPN overexpression also induced PI3K signalling, expression of Snail and Matrix metallopeptidase 9 (MMP9), and suppressed the expression of E-cadherin in KRAS mutant cells. In human CRC specimens, a high-level expression of OPN significantly predicted poorer survival in CRC patients and OPN expression was positively correlated with MMP9 expression, and negatively correlated with E-cadherin expression. Furthermore, we have found that 15 genes were co-upregulated in OPN highly expression CRC and a list of candidate drugs that may have potential to reverse the secreted phosphoprotein 1 (SPP1) gene signature by connectivity mapping. In summary, OPN is a potential prognostic indicator and therapeutic target for colon cancer.

Probabilistic functional tractography of the human cortex revisited.

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

A combined connectivity mapping and pharmacoepidemiology approach to identify existing medications with breast cancer causing or preventing properties.

PURPOSE: We applied a novel combined connectivity mapping and pharmacoepidemiological approach to identify medications that alter breast cancer risk. METHODS: The connectivity mapping process identified 6 potentially cancer-causing (meloxicam, azithromycin, rizatriptan, citalopram, rosiglitazone, and verapamil) and 4 potentially cancer-preventing (bendroflumethiazide, sertraline, fluvastatin, and budesonide) medications that were suitable for pharmacoepidemiological investigation. Within the UK Clinical Practice Research Datalink, we matched 45,147 breast cancer cases to 45,147 controls based on age, year, and general practice. Medication use was determined from electronic prescribing records. We used conditional logistic regression to calculate odds ratios (ORs) for the association between medication use and cancer risk after adjustment for comorbidities, lifestyle factors, deprivation, and other medication use. RESULTS: Bendroflumethiazide was associated with increased breast cancer risk (OR: 1.11; 95% CI: 1.06, 1.15); however the connectivity mapping exercise predicted that this medication would reduce risk. There were no statistically significant associations for any of the other candidate medications, with ever use ORs ranging from 0.93 (95% CI: 0.78, 1.11) for azithromycin to 1.16 (95% CI: 0.99, 1.37) for verapamil. CONCLUSIONS: In this instance, our combined connectivity mapping and pharmacoepidemiological approach did not identify any additional medications that were substantially associated with breast cancer risk. This could be due to limitations in the connectivity mapping, such as implausible dosage requirements, or the pharmacoepidemiology, such as residual confounding.

An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping.

BACKGROUND: Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer). RESULTS: We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs. CONCLUSIONS: A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics.

BACKGROUND: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. RESULTS: We describe QUADrATiC ( http://go.qub.ac.uk/QUADrATiC ), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts. CONCLUSIONS: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping.

BACKGROUND: Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user's perspective. RESULTS: We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a "gene signature progression" method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin. CONCLUSIONS: In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.

Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer.

BACKGROUND: Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients. METHODS: We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature "matches" the "reference" signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines. RESULTS: Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability. CONCLUSIONS: Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.

The use of gene arrays and corresponding connectivity mapping (Cmap) to identify novel anti-ageing ingredients.

OBJECTIVE: The need for effective 'anti-ageing' treatments, in particular for the management of photodamaged skin, prompted us to develop a novel method to identify new active ingredients. The model utilized a gene profiling study with corresponding connectivity mapping (Cmap) to identify novel anti-ageing compounds using all-trans retinoic acid (RA) as the lead compound due to its beneficial effect on photodamaged skin and skin firmness. METHOD: A vehicle-controlled clinical study including nine healthy Caucasian female volunteers aged 57 ± 7 (mean ± SEM) exhibiting photodamage on their lower outer forearms was conducted. The volunteers applied RA once daily on photodamaged skin for 7 days, and biopsies were subjected to Affymetrix gene arrays. Connectivity mapping (Cmap), examining hundreds of gene expression profiles, was run on the gene signature of RA-treated photodamaged skin to identify small bioactive compounds. RESULTS: Affymetrix gene array identified 19 genes significantly differentially expressed after application of RA. Corresponding Cmap analysis revealed six natural bioactive compounds including N-acetyl aspartic acid (A-A-A) showing similar activity to RA on the differentially expressed genes identified. CONCLUSION: Based on RA mimicking gene array activity, potential use within skincare on molecular size, safety assessment and sourcing, we identified the natural amino acid, A-A-A as a potential candidate to treat ageing skin.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database.

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling.

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

Brain tissue modifications induced by cholinergic therapy in Alzheimer's disease.

A previous preliminary investigation based on a novel MRI approach to map anatomical connectivity revealed areas of increased connectivity in Alzheimer's disease (AD) but not in mild cognitive impairment patients. This prompted the hypothesis tested here, that these areas might reflect phenomena of brain plasticity driven by acetylcholinesterase inhibitors (AChEIs). Thirty-eight patients with probable AD (19 under medication with AChEIs and 19 drug-naïve) were recruited together with 11 healthy controls. All subjects had MRI scanning at 3T, including volumetric and diffusion-weighted scans. Probabilistic tractography was used to initiate streamlines from all parenchymal voxels, and anatomical connectivity maps (ACMs) were obtained by counting, among the total number of streamlines initiated, the fraction passing through each brain voxel. After normalization into standard space, ACMs were used to test for between-group comparisons, and for interactions between the exposure to AChEIs and global level of cognition. Patients with AD had reduced ACM values in the fornix, cingulum, and supramarginal gyri. The ACM value was strongly associated with the AChEI dosage-x-duration product in the anterior limb (non-motor pathway) of the internal capsule. Tractography from this region identified the anterior thalamic radiation as the main white matter (WM) tract passing through it. The reduced connectivity in WM bundles connecting the hippocampi with the rest of the brain (fornix/cingulum) suggests a possible mechanism for the spread of AD pathology. An intriguing explanation for the interaction between AChEIs and ACM is related to the mechanisms of brain plasticity, partially driven by neurotrophic properties of acetylcholine replacement.

Anatomical brain connectivity can assess cognitive dysfunction in multiple sclerosis.

BACKGROUND: Brain disconnection plays a major role in determining cognitive disabilities in multiple sclerosis (MS). We recently developed a novel diffusion-weighted magnetic resonance imaging (DW-MRI) tractography approach, namely anatomical connectivitity mapping (ACM), that quantifies structural brain connectivity. OBJECTIVE: Use of ACM to assess structural connectivity modifications in MS brains and ascertain their relationship with the patients' Paced-Auditory-Serial-Addition-Test (PASAT) scores. METHODS: Relapsing-remitting MS (RRMS) patients (n = 25) and controls (n = 25) underwent MRI at 3T, including conventional images, T1-weighted volumes and DW-MRI. Volumetric scans were coregistered to fractional anisotropy (FA) images, to obtain parenchymal FA maps for both white and grey matter. We initiated probabilistic tractography from all parenchymal voxels, obtaining ACM maps by counting the number of streamlines passing through each voxel, then normalizing by the total number of streamlines initiated. The ACM maps were transformed into standard space, for statistical use. RESULTS: RRMS patients had reduced grey matter volume and FA, consistent with previous literature. Also, we showed reduced ACM in the thalamus and in the head of the caudate nucleus, bilaterally. In our RRMS patients, ACM was associated with PASAT scores in the corpus callosum, right hippocampus and cerebellum. CONCLUSIONS: ACM opens a new perspective, clarifying the contribution of anatomical brain disconnection to clinical disabilities in MS.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.