Gene expression biomarkers differentiate overall survival of colorectal cancer upon targeted therapies.

While monoclonal antibody-based targeted therapies have substantially improved progression-free survival in cancer patients, the variability in individual responses poses a significant challenge in patient care. Therefore, identifying cancer subtypes and their associated biomarkers is required for assigning effective treatment. In this study, we integrated genotype and pre-treatment tissue RNA-seq data and identified biomarkers causally associated with the overall survival (OS) of colorectal cancer (CRC) patients treated with either cetuximab or bevacizumab. We performed enrichment analysis for specific consensus molecular subtypes (CMS) of colorectal cancer and evaluated differential expression of identified genes using paired tumor and normal tissue from an external cohort. In addition, we replicated the causal effect of these genes on OS using validation cohort and assessed their association with the Cancer Genome Atlas Program data as an external cohort. One of the replicated findings was WDR62, whose overexpression shortened OS of patients treated with cetuximab. Enrichment of its over expression in CMS1 and low expression in CMS4 suggests that patients with CMS4 subtype may drive greater benefit from cetuximab. In summary, this study highlights the importance of integrating different omics data for identifying promising biomarkers specific to a treatment or a cancer subtype.

WITHDRAWN: The effect of ischemia on expression quantitative trait loci (eQTL) in human myocardium and insights into myocardial injury etiology.

26 February, 2024. Research Square has withdrawn this preprint as it was submitted and made public without the full consent of all the authors and without the full consent of the principle investigator of the registered clinical trial. Therefore, this work should not be cited as a reference.

Broadcasters, receivers, functional groups of metabolites and the link to heart failure progression using polygenic factors.

In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.

Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.

Predictive and prognostic gene signatures derived from interconnectivity among genes can tailor clinical care to patients in cancer treatment. We identified gene interconnectivity as the transcriptomic-causal network by integrating germline genotyping and tumor RNA-seq data from 1,165 patients with metastatic colorectal cancer (CRC). The patients were enrolled in a clinical trial with randomized treatment, either cetuximab or bevacizumab in combination with chemotherapy. We linked the network to overall survival (OS) and detected novel biomarkers by controlling for confounding genes. Our data-driven approach discerned sets of genes, each set collectively stratify patients based on OS. Two signatures under the cetuximab treatment were related to wound healing and macrophages. The signature under the bevacizumab treatment was related to cytotoxicity and we replicated its effect on OS using an external cohort. We also showed that the genes influencing OS within the signatures are downregulated in CRC tumor vs. normal tissue using another external cohort. Furthermore, the corresponding proteins encoded by the genes within the signatures interact each other and are functionally related. In conclusion, this study identified a group of genes that collectively stratified patients based on OS and uncovered promising novel prognostic biomarkers for personalized treatment of CRC using transcriptomic causal networks.

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort.

BACKGROUND: The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. RESULTS: We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. CONCLUSION: Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

From classical mendelian randomization to causal networks for systematic integration of multi-omics.

The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.

Triglycerides to high-density lipoprotein cholesterol ratio for diagnosing nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic Fatty Liver Disease (NAFLD) is a widespread disease in the western world. It can develop into more serious pathological conditions (i.e. liver cirrhosis). Therefore, it is important to diagnose it in order to prevent this evolution. For diagnosis it is possible to use both imaging methods and biomarkers, such as the Triglycerides To High-Density Lipoprotein Cholesterol Ratio (TG/HDL-C). Aim of our study is to determine whether TG/HDL-C ratio is significantly associated with NAFLD and Metabolic Syndrome (MetS). METHODS: We recruited 231 patients, 131 with and 100 without NAFLD. The Body Mass Index had been calculated and different laboratory parameters had been obtained. TG/HDL-C ratio was calculated for each. RESULTS: In our sample HDL-C was not significantly reduced in NAFLD group (P=0.49), but higher TG and TG/HDL-C ratio were significantly associated with NAFLD: in both P<0.001. According to receiver operating characteristic curve, the best cut-off of TG/HDL-C in NAFLD population was 1.64 (area under the curve [AUC] 0.675 [95% CI 0.604-0.746], P<0.001). TG/HDL-C higher ratio was significantly associated with MetS (P<0.001). The best cut-off of TG/HDL-C in patients with MetS was 2.48 (AUC 0.871 [95% CI 0.808-0.935], P<0.001). CONCLUSIONS: We demonstrated that higher TG/HDL-C ratio is associated with NAFLD and MetS. Though nowadays TG/HDL-C ratio is not a criteria for NAFLD diagnosis, we believe that in the future it could be used as a reliable non-invasive marker in routine diagnostics of NAFLD.

Serologic and Molecular Evidence of Widespread Infection of Avian Hepatitis E Virus in Poultry Farms of Iran.

Hepatitis-splenomegaly syndrome is caused by avian hepatitis E virus (aHEV), a nonenveloped, single-stranded RNA virus. The economic importance of this disease in the poultry industry is due to the decline in egg production (10%-40%) and the rise in mortality (1%-4%). In the present study, 1540 serum samples from 33 broiler breeder flocks were analyzed by an enzyme-linked immunosorbent assay for the presence of an anti-aHEV antibody. In addition, a diagnostic nested reverse transcriptase-PCR was done on all farm samples. In the serologic study, 66.7% (22/33) of the flocks and 28.5% (439/1540) of the chickens were positive. The molecular study showed that three farms were positive, and PCR products were observed for the conserved regions of the aHEV helicase and capsid virus genes as 386 bp and 242 bp, respectively. It should be noted that clinical and pathologic symptoms including decreased egg production, enlarged livers and spleens, and a slight rise in mortality rate were observed in eight farms. To our knowledge, this is the first documented study on the aHEV identification and its antibody detection in broiler breeder farms in Iran.

Evidencia serológica y molecular de una infección diseminada del virus de la hepatitis E aviar en granjas avícolas en Irán. El síndrome de hepatitis-esplenomegalia es causado por el virus de la hepatitis E aviar (aHEV), un virus de ARN de cadena simple sin envoltura. La importancia económica de esta enfermedad en la industria avícola se debe a la disminución en la producción de huevo (10%-40%) y al aumento de la mortalidad (1%-4%). En el presente estudio, se analizaron 1540 muestras de suero de 33 parvadas de reproductores pesados mediante un ensayo de immunoabsorción con enzimas ligadas para determinar la presencia de anticuerpos contra el virus de la hepatitis E aviar. Además, se realizó un método de transcripción reversa y PCR anidado de diagnóstico en todas las muestras de la granja. En el estudio serológico, el 66.7% (22/33) de las parvadas y el 28.5% (439/1540) de los pollos fueron positivos. El estudio molecular mostró que tres granjas fueron positivas, y se observaron productos de PCR para las regiones conservadas de los genes del virus de la cápside y de la helicasa del virus de la hepatitis E aviar con tamaños de 386 pb y 242 pares de bases, respectivamente. Cabe señalar que en ocho granjas se observaron signos clínicos y patológicos como disminución de la producción de huevos, agrandamiento del hígado y del bazo y un ligero aumento en la tasa de mortalidad. Hasta donde se conoce, este es el primer estudio documentado sobre la identificación del virus de la hepatitis E aviar y la detección de anticuerpos en granjas de pollos de engorde en Irán.

Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic-causal network.

BACKGROUND: Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. METHODS: Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. RESULTS: Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. CONCLUSIONS: Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.

Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics.

Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables us to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with the potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of a healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized.

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.

BACKGROUND: Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. RESULTS: The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate (p-value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. CONCLUSION: Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.

A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function.

Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.

Cutaneous xanthogranuloma associated with Klebsiella pneumoniea in a budgerigar (Melopsittacus undulatus).

Budgerigar is a common name for a colorful Australian native bird belonging to the Melopsittacus undulatus species. It is a very familiar pet around the world and its breeding has been grown in Iran. This study was conducted on a 2-year-old budgerigar with a nodular mass on the left wing. Physical examination revealed a firm, round and well-circumscribed mass approximately 1.70 cm in diameter. Radiographs showed a soft tissue mass with no involvement of bony structures. Fine needle aspiration was performed and the sample was cultured. In cultural examination, Klebsiella spp. were isolated in pure culture. Genus and species of the bacteria were confirmed using multiplex polymerase chain reaction. The mass was surgically excised and it was mainly composed of numerous, large lipid-laden macrophages containing abundant vacuolated cytoplasm, extracellular acicular cholesterol clefts and large number of multinucleated giant cells (especially multinucleated Touton giant cells) in the dermis. Finally, a diagnosis of cutaneous xanthogranuloma was made based on histopathological findings.

Identification, analysis, and interpretation of a human serum metabolomics causal network in an observational study.

Untargeted metabolomics, measurement of large numbers of metabolites irrespective of their chemical or biologic characteristics, has proven useful for identifying novel biomarkers of health and disease. Of particular importance is the analysis of networks of metabolites, as opposed to the level of an individual metabolite. The aim of this study is to achieve causal inference among serum metabolites in an observational setting. A metabolomics causal network is identified using the genome granularity directed acyclic graph (GDAG) algorithm where information across the genome in a deeper level of granularity is extracted to create strong instrumental variables and identify causal relationships among metabolites in an upper level of granularity. Information from 1,034,945 genetic variants distributed across the genome was used to identify a metabolomics causal network among 122 serum metabolites. We introduce individual properties within the network, such as strength of a metabolite. Based on these properties, hypothesized targets for intervention and prediction are identified. Four nodes corresponding to the metabolites leucine, arichidonoyl-glycerophosphocholine, N-acyelyalanine, and glutarylcarnitine had high impact on the entire network by virtue of having multiple arrows pointing out, which propagated long distances. Five modules, largely corresponding to functional metabolite categories (e.g. amino acids), were identified over the network and module boundaries were determined using directionality and causal effect sizes. Two families, each consists of a triangular motif identified in the network had essential roles in the network by virtue of influencing a large number of other nodes. We discuss causal effect measurement while confounders and mediators are identified graphically.

A Causal Network Analysis of the Fatty Acid Metabolome in African-Americans Reveals a Critical Role for Palmitoleate and Margarate.

Fatty acids are important sources of energy and possible predictors and etiologic factors in many common complex pathologies such as cardiovascular disease, diabetes, and certain forms of cancers. While fatty acids are thought to covary with each other, their underlying causal networks have not been fully elucidated. This study reports the identification and analysis of a statistical causal network among 15 mostly long-chain fatty acids. In an African-American population sample and using the Genome granularity-Directed Acyclic Graph (GDAG) algorithm, we determined directions or causal relationships in the fatty acid metabolome. A directed causal network was constructed that revealed 29 significant edges among the 15 nodes (p < 0.001). We report that two fatty acid metabolites, palmitoleate and margarate, which originate from dietary intake, together influence every other fatty acid in the network. On the other hand, despite its high connectivity, dihomo-linoleate did not appear to play an important role over the whole fatty acid network. These findings collectively suggest possible strategic entry points for new treatments or preventive modalities against diseases affected by fatty acid metabolites such as cardiovascular disease, diabetes, and obesity. Further studies examining the embedded substructure of the fatty acid metabolite networks in independent population samples would be timely and warranted as we move toward novel postgenomic diagnostics and therapeutics.

Identification of Rare Variants in Metabolites of the Carnitine Pathway by Whole Genome Sequencing Analysis.

We use whole genome sequence data and rare variant analysis methods to investigate a subset of the human serum metabolome, including 16 carnitine-related metabolites that are important components of mammalian energy metabolism. Medium pass sequence data consisting of 12,820,347 rare variants and serum metabolomics data were available on 1,456 individuals. By applying a penalization method, we identified two genes FGF8 and MDGA2 with significant effects on lysine and cis-4-decenoylcarnitine, respectively, using Δ-AIC and likelihood ratio test statistics. Single variant analyses in these regions did not identify a single low-frequency variant (minor allele count > 3) responsible for the underlying signal. The results demonstrate the utility of whole genome sequence and innovative analyses for identifying candidate regions influencing complex phenotypes.

A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels.

INTRODUCTION: Plasma triglyceride levels are a risk factor for coronary heart disease. Triglyceride metabolism is well characterized, but challenges remain to identify novel paths to lower levels. A metabolomics analysis may help identify such novel pathways and, therefore, provide hints about new drug targets. OBJECTIVES: In an observational study, causal relationships in the metabolomics level of granularity are taken into account to distinguish metabolites and pathways having a direct effect on plasma triglyceride levels from those which are only associated with or have indirect effect on triglyceride. METHOD: The analysis began by leveraging near-complete information from the genome level of granularity using the GDAG algorithm to identify a robust causal network over 122 metabolites in an upper level of granularity. Knowing the metabolomics causal relationships, we enter the triglyceride variable in the model to identify metabolites with direct effect on plasma triglyceride levels. We carried out the same analysis on triglycerides measured over five different visits spanning 24 years. RESULT: Nine metabolites out of 122 metabolites under consideration influenced directly plasma triglyceride levels. Given these nine metabolites, the rest of metabolites in the study do not have a significant effect on triglyceride levels at significance level alpha = 0.001. Therefore, for the further analysis and interpretations about triglyceride levels, the focus should be on these nine metabolites out of 122 metabolites in the study. The metabolites with the strongest effects at the baseline visit were arachidonate and carnitine, followed by 9-hydroxy-octadecadenoic acid and palmitoylglycerophosphoinositol. The influence of arachidonate on triglyceride levels remained significant even at the fourth visit, which was 10 years after the baseline visit. CONCLUSION: These results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels.

Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data.

Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk.

Preparation and Characterization of Three Tilmicosin-loaded Lipid Nanoparticles: Physicochemical Properties and in-vitro Antibacterial Activities.

Tilmicosin (TLM) is an important antibiotic in veterinary medicine with low bioavailability and safety. This study aimed to formulate and evaluate physicochemical properties, storage stability after lyophilization, and antibacterial activity of three TLM-loaded lipid nanoparticles (TLM-LNPs) including solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), and lipid-core nanocapsules (LNCs). Physicochemical parameters such as particle size-mean diameter, polydispersity index, zeta potential, drug encapsulation efficiency (EE), loading capacity, and morphology of the formulations were evaluated and the effects of various cryoprotectants during lyophilization and storage for 8 weeks were also studied. The profiles of TLM release and the antibacterial activities of these TLM-LNPs suspensions (against Escherichia coli and Staphylococcus aureus) were tested in comparison with their corresponding powders. TLM-LNPs suspensions were in nano-scale range with mean diameters of 186.3 ± 1.5, 149.6 ± 3.0, and 85.0 ± 1.0nm, and also EE, 69.1, 86.3, and 94.3% for TLM- SLNs, TLM-NLCs, and TLM- LNCs respectively. TLM-LNCs gave the best results with significantly low particle size and high EE (p<0.05). Mannitol was the most effective cryoprotectant for lyophilization and storage of TLM-LNPs. The drug release profiles were biphasic and the release times were longer at pH 7.4 where TLM-NLCs and TLM-LNCs powders showed longer release times. In microbiological tests, S. aureus was about 4 times more sensitive than E. coli to TLM-LNPs with minimum inhibitory concentration ranges of 0.5-1.0 and 2-4 µg/mL respectively, and TLM-LNCs exhibited the best antibacterial activities. In conclusion, TLM-LNP formulations especially TLM-LNCs and TLM-NLCs are promising carriers for TLM with better drug encapsulation capacity, release behavior, and antibacterial activity.