FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization.

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .

Molecular characterization of depression trait and state.

Major depressive disorder (MDD) is a brain disorder often characterized by recurrent episode and remission phases. The molecular correlates of MDD have been investigated in case-control comparisons, but the biological alterations associated with illness trait (regardless of clinical phase) or current state (symptomatic and remitted phases) remain largely unknown, limiting targeted drug discovery. To characterize MDD trait- and state-dependent changes, in single or recurrent depressive episode or remission, we generated transcriptomic profiles of subgenual anterior cingulate cortex of postmortem subjects in first MDD episode (n = 20), in remission after a single episode (n = 15), in recurrent episode (n = 20), in remission after recurring episodes (n = 15) and control subject (n = 20). We analyzed the data at the gene, biological pathway, and cell-specific molecular levels, investigated putative causal events and therapeutic leads. MDD-trait was associated with genes involved in inflammation, immune activation, and reduced bioenergetics (q < 0.05) whereas MDD-states were associated with altered neuronal structure and reduced neurotransmission (q < 0.05). Cell-level deconvolution of transcriptomic data showed significant change in density of GABAergic interneurons positive for corticotropin-releasing hormone, somatostatin, or vasoactive-intestinal peptide (p < 3 × 10-3). A probabilistic Bayesian-network approach showed causal roles of immune-system-activation (q < 8.67 × 10-3), cytokine-response (q < 4.79 × 10-27) and oxidative-stress (q < 2.05 × 10-3) across MDD-phases. Gene-sets associated with these putative causal changes show inverse associations with the transcriptomic effects of dopaminergic and monoaminergic ligands. The study provides first insights into distinct cellular and molecular pathologies associated with trait- and state-MDD, on plasticity mechanisms linking the two pathologies, and on a method of drug discovery focused on putative disease-causing pathways.

Integrin alpha 6 is upregulated and drives hepatocellular carcinoma progression through integrin α6ß4 complex.

Integrin α6 (ITGA6) forms integrin receptors with either integrin ß1 (ITGB1) or integrin ß4 (ITGB4). How it functions to regulate hepatocellular carcinoma (HCC) progression is not well-elucidated. We found that ITGA6 RNA and protein expression levels are significantly elevated in human HCC tissues in comparison with paired adjacent nontumor tissues by RNA sequencing, RT-qPCR, Western blotting and immunofluorescence staining. Stable knockdown of ITGA6 with different ITGA6 shRNA expression lentivectors significantly inhibited proliferation, migration and anchorage-independent growth of HCC cell lines in vitro, and xenograft tumor growth in vivo. The inhibition of anchorage-dependent and -independent growth of HCC cell lines was also confirmed with anti-ITGA6 antibody. ITGA6 knockdown was shown to induce cell-cycle arrest at G0/G1 phase. Immunoprecipitation assay revealed apparent interaction of ITGA6 with ITGB4, but not ITGB1. Expression studies showed that ITGA6 positively regulates the expression of ITGB4 with no or negative regulation of ITGB1 expression. Finally, while high levels of ITGA6 and ITGB4 together were associated with significantly worse survival of HCC patients in TCGA data set, the association was not significant for high levels of ITGA6 and ITGB1. In conclusion, ITGA6 is upregulated in HCC tumors and has a malignant promoting role in HCC cells through integrin α6ß4 complex. Thus, integrin α6ß4 may be a therapeutic target for treating patients with HCC.

Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data.

OBJECTIVES: Sepsis is a life-threatening response to infection that causes tissue damage, organ failure, and death. Effective early prediction of sepsis would improve patients' diagnosis and reduce the cost associated with late-stage sepsis infection by applying appropriate early intervention. However, effective early prediction is challenging because sepsis biomarkers are neither obvious nor definitive, and sepsis datasets are heavily imbalanced against positive diagnosis of sepsis while containing significant missing values. Early prediction of sepsis in ICUs using clinical data is the objective of the PhysioNet/Computing in Cardiology Challenge 2019. DESIGN: In this article, we proposed a machine learning algorithm to aid in the early detection of sepsis. SETTING: We applied linear interpolation and implemented a sample weighted AdaBoost model to predict sepsis 6 hours before clinical diagnosis. PATIENTS: Medical data contains more than 40,000 patients gathered from three geographically distinct U.S. hospital systems that consisted of a combination of hourly vital sign, lab values, and static patient descriptions. INTERVENTIONS: The challenge metric, however, did not directly reward models for their generalizability across institutions. MEASUREMENTS AND MAIN RESULTS: The article is evaluated using a new metric called Utility Score that is defined as Official scoring criteria. Our approach was among the top 10% of entries to the Challenge on a hidden test set. CONCLUSIONS: Herein, we demonstrate that our proposed approach was the most effective of the Challenge entrants when such generalizability is explicitly accounted for in model evaluation.

C3HeB/FeJ Mice mimic many aspects of gene expression and pathobiological features of human hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a deadly cancer, underscoring the need for relevant preclinical models. Male C3HeB/FeJ mice model spontaneous HCC with some hepatocarcinogenesis susceptibility loci corresponding to syntenic regions of human chromosomes altered in HCC. We tested other properties of C3HeB/FeJ tumors for similarity to human HCC. C3HeB/FeJ tumors were grossly visible at 4 months of age, with prevalence and size increasing until about 11 months of age. Histologic features shared with human HCC include hepatosteatosis, tumor progression from dysplasia to poorly differentiated, vascular invasion, and trabecular, oncocytic, vacuolar, and clear cell variants. More tumor cells displayed cytoplasmic APE1 staining versus normal liver. Ultrasound effectively detected and monitored tumors, with 85.7% sensitivity. Over 5000 genes were differentially expressed based on the GSE62232 and GSE63898 human HCC datasets. Of these, 158 and 198 genes, respectively, were also differentially expressed in C3HeB/FeJ. Common cancer pathways, cell cycle, p53 signaling and other molecular aspects, were shared between human and mouse differentially expressed genes. We established eigengenes that distinguish HCC from normal liver in the C3HeB/FeJ model and a subset of human HCC. These features extend the relevance and improve the utility of the C3HeB/FeJ line for HCC studies.

Inferring clonal composition from multiple sections of a breast cancer.

Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.

Systematic analysis of microorganisms' metabolism for selective targeting.

Selective drugs with a relatively narrow spectrum can reduce the side effects of treatments compared to broad-spectrum antibiotics by specifically targeting the pathogens responsible for infection. Furthermore, combating an infectious pathogen, especially a drug-resistant microorganism, is more efficient by attacking multiple targets. Here, we combined synthetic lethality with selective drug targeting to identify multi-target and organism-specific potential drug candidates by systematically analyzing the genome-scale metabolic models of six different microorganisms. By considering microorganisms as targeted or conserved in groups ranging from one to six members, we designed 665 individual case studies. For each case, we identified single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets that are lethal for targeted microorganisms and neutral for conserved ones. As expected, the number of obtained solutions for each case depends on the genomic similarity between the studied microorganisms. Mapping the identified potential drug targets to their corresponding pathways highlighted the importance of key subsystems such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and the nucleotide salvage pathway. To assist in the validation and further investigation of our proposed potential drug targets, we introduced two sets of targets that can theoretically address a substantial portion of the 665 cases. We expect that the obtained solutions provide valuable insights into designing narrow-spectrum drugs that selectively cause system-wide damage only to the target microorganisms.

A Meta-analysis of Transcriptome Data to Investigate the Effect of Soy Isoflavones on Breast Cancer Cell.

Background: Breast cancer ranks as the second highest cause of cancer-linked deaths in women, with varying rates between Western and Asian countries. The consumption of phytoestrogens can influence breast cancer occurrence. Objective: To comprehend how soy isoflavones impact breast cancer cells, we conducted a meta-analysis, combining gene expression data from multiple studies. This approach aimed to identify crucial transcriptional characteristics driving breast cancer cell response to soy phytoestrogens. Materials and Methods: The gene expression profiles obtained from the Gene Expression Omnibus and Array Express and were grouped into control and isoflavones exposure conditions. We performed a meta-analysis based on the effect size combination method to identify the differentially expressed genes (DEGs). In addition, we performed Gene Ontology (GO) enrichment analysis, pathway analysis, weighted gene co-expression network analysis (WGCNA) and recursive support vector machine (R-SVM) algorithm. Results: Based on this meta-analysis, we identified 3,890 DEGs, of which 2,173 were up-regulated and 1,717 were down-regulated. For example, SGCG, PLK2, and TBC1D9 were the most highly down-regulated genes and EGR3, WISP2, and FKBP4 were the most highly expressed genes in the isoflavones exposure condition. The functional enrichment and pathway analysis were revealed "cell division" and "cell cycle" among the most enriched terms. Among the identified DEGs, 269 transcription factor (TF) genes belonged to 42 TF families, where the C2H2 ZF, bZIP, and bHLH were the most prominent families. We also employed the R-SVM for detecting the most important genes to classify samples into isoflavones exposure and control conditions. It identified a subset of 100 DEGs related to regulation of cell growth, response to estradiol, and intermediate ribonucleoside monophosphate in the purine (IMP) metabolic process. Moreover, the WGCNA separated the DEGs into five discrete modules strongly enriched for genes involved in cell division, DNA replication, embryonic digit morphogenesis, and cell-cell adhesion. Conclusion: Our analysis provides evidence suggesting that isoflavone affects various mechanisms in cells, including pathways associated with NF-κB, Akt, MAPK, Wnt, Notch, p53, and AR pathways, which can lead to the induction of apoptosis, the alteration of the cell cycle, the inhibition of angiogenesis, and interference in the redox state of cells. These findings can shed light on the molecular mechanisms that underlie the response of breast cancer cells to isoflavones.

Nanopore-based DNA long-read sequencing analysis of the aged human brain.

Aging disrupts cellular processes such as DNA repair and epigenetic control, leading to a gradual buildup of genomic alterations that can have detrimental effects in post-mitotic cells. Genomic alterations in regions of the genome that are rich in repetitive sequences, often termed "dark loci," are difficult to resolve using traditional sequencing approaches. New long-read technologies offer promising avenues for exploration of previously inaccessible regions of the genome. Using nanopore-based long-read whole-genome sequencing of DNA extracted from aged 18 human brains, we identify previously unreported structural variants and methylation patterns within repetitive DNA, focusing on transposable elements ("jumping genes") as crucial sources of variation, particularly in dark loci. Our analyses reveal potential somatic insertion variants and provides DNA methylation frequencies for many retrotransposon families. We further demonstrate the utility of this technology for the study of these challenging genomic regions in brains affected by Alzheimer's disease and identify significant differences in DNA methylation in pathologically normal brains versus those affected by Alzheimer's disease. Highlighting the power of this approach, we discover specific polymorphic retrotransposons with altered DNA methylation patterns. These retrotransposon loci have the potential to contribute to pathology, warranting further investigation in Alzheimer's disease research. Taken together, our study provides the first long-read DNA sequencing-based analysis of retrotransposon sequences, structural variants, and DNA methylation in the aging brain affected with Alzheimer's disease neuropathology.

Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis.

One challenge in applying bioinformatic tools to clinical or biological data is high number of features that might be provided to the learning algorithm without any prior knowledge on which ones should be used. In such applications, the number of features can drastically exceed the number of training instances which is often limited by the number of available samples for the study. The Lasso is one of many regularization methods that have been developed to prevent overfitting and improve prediction performance in high-dimensional settings. In this paper, we propose a novel algorithm for feature selection based on the Lasso and our hypothesis is that defining a scoring scheme that measures the "quality" of each feature can provide a more robust feature selection method. Our approach is to generate several samples from the training data by bootstrapping, determine the best relevance-ordering of the features for each sample, and finally combine these relevance-orderings to select highly relevant features. In addition to the theoretical analysis of our feature scoring scheme, we provided empirical evaluations on six real datasets from different fields to confirm the superiority of our method in exploratory data analysis and prediction performance. For example, we applied FeaLect, our feature scoring algorithm, to a lymphoma dataset, and according to a human expert, our method led to selecting more meaningful features than those commonly used in the clinics. This case study built a basis for discovering interesting new criteria for lymphoma diagnosis. Furthermore, to facilitate the use of our algorithm in other applications, the source code that implements our algorithm was released as FeaLect, a documented R package in CRAN.

Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays.

MOTIVATION: Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted. RESULTS: To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis). AVAILABILITY: The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: rbrinkman@bccrc.ca.

FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization.

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

The Spectrum of Alzheimer-Type Pathology in Cognitively Normal Individuals.

BACKGROUND: The strongest risk factor for the development of Alzheimer's disease (AD) is age. The progression of Braak stage and Thal phase with age has been demonstrated. However, prior studies did not include cognitive status. OBJECTIVE: We set out to define normative values for Alzheimer-type pathologic changes in individuals without cognitive decline, and then define levels that would qualify them to be resistant to or resilient against these changes. METHODS: Utilizing neuropathology data obtained from the National Alzheimer's Coordinating Center (NACC), we demonstrate the age-related progression of Alzheimer-type pathologic changes in cognitively normal individuals (CDR = 0, n = 542). With plots generated from these data, we establish standard lines that may be utilized to measure the extent to which an individual's Alzheimer-type pathology varies from the estimated normal range of pathology. RESULTS: Although Braak stage and Thal phase progressively increase with age in cognitively normal individuals, the Consortium to Establish a Registry for Alzheimer's Disease neuritic plaque score and Alzheimer's disease neuropathologic change remain at low levels. CONCLUSION: These findings suggest that an increasing burden of neuritic plaques is a strong predictor of cognitive decline, whereas, neurofibrillary degeneration and amyloid-ß (diffuse) plaque deposition, both to some degree, are normal pathologic changes of aging that occur in almost all individuals regardless of cognitive status. Furthermore, we have defined the amount of neuropathologic change in cognitively normal individuals that would qualify them to be "resilient" against the pathology (significantly above the normative values for age, but still cognitively normal) or "resistant" to the development of pathology (significantly below the normative values for age).

"iNETgrate": integrating DNA methylation and gene expression data in a single gene network.

Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package.

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Senolytic therapy to modulate the progression of Alzheimer's Disease (SToMP-AD) - Outcomes from the first clinical trial of senolytic therapy for Alzheimer's disease.

Cellular senescence has been identified as a pathological mechanism linked to tau and amyloid beta (Aß) accumulation in mouse models of Alzheimer's disease (AD). Clearance of senescent cells using the senolytic compounds dasatinib (D) and quercetin (Q) reduced neuropathological burden and improved clinically relevant outcomes in the mice. Herein, we conducted a vanguard open-label clinical trial of senolytic therapy for AD with the primary aim of evaluating central nervous system (CNS) penetrance, as well as exploratory data collection relevant to safety, feasibility, and efficacy. Participants with early-stage symptomatic AD were enrolled in an open-label, 12-week pilot study of intermittent orally-delivered D+Q. CNS penetrance was assessed by evaluating drug levels in cerebrospinal fluid (CSF) using high performance liquid chromatography with tandem mass spectrometry. Safety was continuously monitored with adverse event reporting, vitals, and laboratory work. Cognition, neuroimaging, and plasma and CSF biomarkers were assessed at baseline and post-treatment. Five participants (mean age: 76±5 years; 40% female) completed the trial. The treatment increased D and Q levels in the blood of all participants ranging from 12.7 to 73.5 ng/ml for D and 3.29-26.30 ng/ml for Q. D levels were detected in the CSF of four participants ranging from 0.281 to 0.536 ng/ml (t(4)=3.123, p=0.035); Q was not detected. Treatment was well-tolerated with no early discontinuation and six mild to moderate adverse events occurring across the study. Cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment. CNS levels of IL-6 and GFAP increased from baseline to post-treatment (t(4)=3.913, p=008 and t(4)=3.354, p=0.028, respectively) concomitant with decreased levels of several cytokines and chemokines associated with senescence, and a trend toward higher levels of Aß42 (t(4)=-2.338, p=0.079). Collectively the data indicate the CNS penetrance of D and provide preliminary support for the safety, tolerability, and feasibility of the intervention and suggest that astrocytes and Aß may be particularly responsive to the treatment. While early results are promising, fully powered, placebo-controlled studies are needed to evaluate the potential of AD modification with the novel approach of targeting cellular senescence.

Senolytic therapy in mild Alzheimer's disease: a phase 1 feasibility trial.

Cellular senescence contributes to Alzheimer's disease (AD) pathogenesis. An open-label, proof-of-concept, phase I clinical trial of orally delivered senolytic therapy, dasatinib (D) and quercetin (Q), was conducted in early-stage symptomatic patients with AD to assess central nervous system (CNS) penetrance, safety, feasibility and efficacy. Five participants (mean age = 76 + 5 years; 40% female) completed the 12-week pilot study. D and Q levels in blood increased in all participants (12.7-73.5 ng ml-1 for D and 3.29-26.3 ng ml-1 for Q). In cerebrospinal fluid (CSF), D levels were detected in four participants (80%) ranging from 0.281 to 0.536 ml-1 with a CSF to plasma ratio of 0.422-0.919%; Q was not detected. The treatment was well-tolerated, with no early discontinuation. Secondary cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment further supporting a favorable safety profile. CSF levels of interleukin-6 (IL-6) and glial fibrillary acidic protein (GFAP) increased (t(4) = 3.913, P = 0.008 and t(4) = 3.354, P = 0.028, respectively) with trending decreases in senescence-related cytokines and chemokines, and a trend toward higher Aß42 levels (t(4) = -2.338, P = 0.079). In summary, CNS penetrance of D was observed with outcomes supporting safety, tolerability and feasibility in patients with AD. Biomarker data provided mechanistic insights of senolytic effects that need to be confirmed in fully powered, placebo-controlled studies. ClinicalTrials.gov identifier: NCT04063124 .

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging.

BACKGROUND: Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults. OBJECTIVE: To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome. METHODS: Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up). RESULTS: Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities. CONCLUSIONS: Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

Rapid-SL identifies synthetic lethal sets with an arbitrary cardinality.

The multidrug resistance of numerous pathogenic microorganisms is a serious challenge that raises global healthcare concerns. Multi-target medications and combinatorial therapeutics are much more effective than single-target drugs due to their synergistic impact on the systematic activities of microorganisms. Designing efficient combinatorial therapeutics can benefit from identification of synthetic lethals (SLs). An SL is a set of non-essential targets (i.e., reactions or genes) that prevent the proliferation of a microorganism when they are "knocked out" simultaneously. To facilitate the identification of SLs, we introduce Rapid-SL, a new multimodal implementation of the Fast-SL method, using the depth-first search algorithm. The advantages of Rapid-SL over Fast-SL include: (a) the enumeration of all SLs that have an arbitrary cardinality, (b) a shorter runtime due to search space reduction, (c) embarrassingly parallel computations, and (d) the targeted identification of SLs. Targeted identification is important because the enumeration of higher order SLs demands the examination of too many reaction sets. Accordingly, we present specific applications of Rapid-SL for the efficient targeted identification of SLs. In particular, we found up to 67% of all quadruple SLs by investigating about 1% of the search space. Furthermore, 307 sextuples, 476 septuples, and over 9000 octuples are found for Escherichia coli genome-scale model, iAF1260.

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes.

BACKGROUND: Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood. METHODS: This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity. RESULTS: Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis. CONCLUSIONS: This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.