MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism

AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides, MA-PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA-PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA-PEP are available at https://github.com/liangxiaodata/MA-PEP.

Fiber optic probe-based ATR-FTIR spectroscopy for rapid breast cancer detection: A pilot study

Breast cancer diagnosis is crucial for timely treatment and improved outcomes. This paper proposes a novel approach for rapid breast cancer diagnosis using optical fiber probe-based attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy from 750 to 4000 cm-1 . The technique enables direct analysis of tissue samples, eliminating the need for microtome sectioning and staining, thus saving time and resources. By capturing molecular fingerprint information, various machine-learning models were used to analyze the spectroscopic data to classify cancerous and non-cancerous tissues accurately. Comparing deparaffinized and paraffinized samples reveals the impact of sample preparation and experimental methods. The study demonstrates a strong correlation between the cancerous nature of a sample and its ATR-FTIR spectrum, suggesting its potential for breast cancer diagnosis (sensitivity of 74.2% and specificity of 78.3%). The proposed approach holds promise for integration into clinical operations, providing a rapid method for preliminary breast cancer diagnosis.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

Crop-Weed Introgression Plays Critical Roles in Genetic Differentiation and Diversity of Weedy Rice: A Case Study of Human-Influenced Weed Evolution

As an important driving force, introgression plays an essential role in shaping the evolution of plant species. However, knowledge concerning how introgression affects plant evolution in agroecosystems with strong human influences is still limited. To generate such knowledge, we used InDel (insertion/deletion) molecular fingerprints to determine the level of introgression from japonica rice cultivars into the indica type of weedy rice. We also analyzed the impact of crop-to-weed introgression on the genetic differentiation and diversity of weedy rice, using InDel (insertion/deletion) and SSR (simple sequence repeat) molecular fingerprints. Results based on the STRUCTURE analysis indicated an evident admixture of some weedy rice samples with indica and japonica components, suggesting different levels of introgression from japonica rice cultivars to the indica type of weedy rice. The principal coordinate analyses indicated indica-japonica genetic differentiation among weedy rice samples, which was positively correlated with the introgression of japonica-specific alleles from the rice cultivars. In addition, increased crop-to-weed introgression formed a parabola pattern of dynamic genetic diversity in weedy rice. Our findings based on this case study provide evidence that human activities, such as the frequent change in crop varieties, can strongly influence weed evolution by altering genetic differentiation and genetic diversity through crop-weed introgression in agroecosystems.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Transcriptome-wide selection and validation of a solid set of reference genes for gene expression studies in the cephalopod mollusk Octopus vulgaris

Octopus vulgaris is a cephalopod mollusk and an active marine predator that has been at the center of a number of studies focused on the understanding of neural and biological plasticity. Studies on the machinery involved in e.g., learning and memory, regeneration, and neuromodulation are required to shed light on the conserved and/or unique mechanisms that these animals have evolved. Analysis of gene expression is one of the most essential means to expand our understanding of biological machinery, and the selection of an appropriate set of reference genes is the prerequisite for the quantitative real-time polymerase chain reaction (qRT-PCR). Here we selected 77 candidate reference genes (RGs) from a pool of stable and relatively high-expressed transcripts identified from the full-length transcriptome of O. vulgaris, and we evaluated their expression stabilities in different tissues through geNorm, NormFinder, Bestkeeper, Delta-CT method, and RefFinder. Although various algorithms provided different assemblages of the most stable reference genes for the different kinds of tissues tested here, a comprehensive ranking revealed RGs specific to the nervous system (Ov-RNF7 and Ov-RIOK2) and Ov-EIF2A and Ov-CUL1 across all considered tissues. Furthermore, we validated RGs by assessing the expression profiles of nine target genes (Ov-Naa15, Ov-Ltv1, Ov-CG9286, Ov-EIF3M, Ov-NOB1, Ov-CSDE1, Ov-Abi2, Ov-Homer2, and Ov-Snx20) in different areas of the octopus nervous system (gastric ganglion, as control). Our study allowed us to identify the most extensive set of stable reference genes currently available for the nervous system and appendages of adult O. vulgaris.

Fast Synthesis of Au Nanoparticles on Metal-Phenolic Network for Sweat SERS Analysis

The biochemical composition of sweat is closely related to the human physiological state, which provides a favorable window for the monitoring of human health status, especially for the athlete. Herein, an ultra-simple strategy based on the surface-enhanced Raman scattering (SERS) technique for sweat analysis is established. Metal-phenolic network (MPN), an outstanding organic-inorganic hybrid material, is adopted as the reductant and platform for the in situ formation of Au-MPN, which displays excellent SERS activity with the limit of detection to 10^-15 M for 4-mercaptobenzoic acid (4-MBA). As an ultrasensitive SERS sensor, Au-MPN is capable of discriminating the molecular fingerprints of sweat components acquired from a volunteer after exercise, such as urea, uric acid, lactic acid, and amino acid. For pH sensing, Au-MPN/4-MBA efficiently presents the pH values of the volunteer's sweat, which can indicate the electrolyte metabolism during exercise. This MPN-based SERS sensing strategy unlocks a new route for the real-time physiological monitoring of human health.

Design and Development of a Bimodal Optical Instrument for Simultaneous Vibrational Spectroscopy Measurements

Vibrational spectroscopy techniques are widely used in analytical chemistry, physics and biology. The most prominent techniques are Raman and Fourier-transform infrared spectroscopy (FTIR). Combining both techniques delivers complementary information of the test sample. We present the design, construction, and calibration of a novel bimodal spectroscopy system featuring both Raman and infrared measurements simultaneously on the same sample without mutual interference. The optomechanical design provides a modular flexible system for solid and liquid samples and different configurations for Raman. As a novel feature, the Raman module can be operated off-axis for optical sectioning. The calibrated system demonstrates high sensitivity, precision, and resolution for simultaneous operation of both techniques and shows excellent calibration curves with coefficients of determination greater than 0.96. We demonstrate the ability to simultaneously measure Raman and infrared spectra of complex biological material using bovine serum albumin. The performance competes with commercial systems; moreover, it presents the additional advantage of simultaneously operating Raman and infrared techniques. To the best of our knowledge, it is the first demonstration of a combined Raman-infrared system that can analyze the same sample volume and obtain optically sectioned Raman signals. Additionally, quantitative comparison of confocality of backscattering micro-Raman and off-axis Raman was performed for the first time.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Simple Summary

Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology.

Abstract

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Representation of molecules for drug response prediction

The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.

De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis

G protein-coupled receptor 40 (GPR40), one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type 2 diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited research focus on developing peptides acting as GPR40 agonists to treat T2DM. Here, we propose a novel strategy for peptide design to generate and determine potential peptide agonists against GPR40 efficiently. A molecular fingerprint similarity (MFS) model combined with a deep neural network (DNN) and convolutional neural network was applied to predict the activity of peptides constructed by unnatural amino acids (UAAs). Site-directed mutagenesis (SDM) further optimized the peptides to form specific favorable interactions, and subsequent flexible docking showed the details of the binding mechanism between peptides and GPR40. Molecular dynamics (MD) simulations further verified the stability of the peptide–protein complex. The R-square of the machine learning model on the training set and the test set reached 0.87 and 0.75, respectively; and the three candidate peptides showed excellent performance. The strategy based on machine learning and SDM successfully searched for an optimal design with desirable activity comparable with the model agonist in phase III clinical trials.

Molecular fingerprint of precancerous lesions in breast atypical hyperplasia

OBJECTIVE

To identify atypical hyperplasia (AH) of the breast by shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), and to explore the molecular fingerprinting characteristics of breast AH.

METHODS

Breast hyperplasia was studied in 11 hospitals across China from January 2015 to December 2016. All patients completed questionnaires on women's health. The differences between patients with and without breast AH were compared. AH breast lesions were detected by Raman spectroscopy followed by the SHINERS technique.

RESULTS

There were no significant differences in clinical features and risk-related factors between patients with breast AH (n = 37) and the control group (n = 2576). Fifteen cases of breast AH lesions were detected by Raman spectroscopy. The main different Raman peaks in patients with AH appeared at 880, 1001, 1086, 1156, 1260, and 1610 cm^-1, attributed to the different vibrational modes of nucleic acids, β-carotene, and proteins. Shell-isolated nanoparticles had different enhancement effects on the nucleic acid, protein, and lipid components in AH.

CONCLUSION

Raman spectroscopy can detect characteristic molecular changes in breast AH lesions, and may thus be useful for the non-invasive early diagnosis and for investigating the mechanism of tumorigenesis in patients with breast AH.

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, T_g, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure-property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

[Application of Raman Spectroscopy in the Diagnosis of Oral Cancer]

Oral cancer is a common and deadly malignancy.While multidisciplinary treatment(mainly surgery)has been applied in the treatment of cancer treatment,early diagnosis and complete removal of the primary lesion are essential for a better prognosis.Raman spectroscopy is an optical technique that detects inelastic scattered light generated by the interaction of light and matter.It can detect the vibrational spectra of biochemical and biomolecular structures and tissue conformations,and can provide the "molecular fingerprint" for cells,tissues,and biological fluids.With the development of related technologies and optical instruments,Raman spectroscopy has been widely applied in medical fields.This article reviews the research advances and application of Raman spectroscopy in the diagnosis of oral cancer.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

BACKGROUND

The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.

RESULTS

In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.

CONCLUSION

This study provides the guide to the development of computational methods for the drug-target interaction prediction.

Nutrigenomics: Opportunities & challenges for public health nutrition

The hierarchical information flow through DNA-RNA-protein-metabolite collectively referred to as ‘molecular fingerprint’ defines both health and disease. Environment and food (quality and quantity) are the key factors known to affect the health of an individual. The fundamental concepts are that the transition from a healthy condition to a disease phenotype must occur by concurrent alterations in the genome expression or by differences in protein synthesis, function and metabolites. In other words, the dietary components directly or indirectly modulate the molecular fingerprint and understanding of which is dealt with nutrigenomics. Although the fundamental principles of nutrigenomics remain similar to that of traditional research, a collection of comprehensive targeted/untargeted data sets in the context of nutrition offers the unique advantage of understanding complex metabolic networks to provide a mechanistic understanding of data from epidemiological and intervention studies. In this review the challenges and opportunities of nutrigenomic tools in addressing the nutritional problems of public health importance are discussed. The application of nutrigenomic tools provided numerous leads on biomarkers of nutrient intake, undernutrition, metabolic syndrome and its complications. Importantly, nutrigenomic studies also led to the discovery of the association of multiple genetic polymorphisms in relation to the variability of micronutrient absorption and metabolism, providing a potential opportunity for further research toward setting personalized dietary recommendations for individuals and population subgroups.

Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints

The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several strategies of performing a structure-activity relationship study for a cytotoxic endpoint in the AID364 dataset were explored to solve the class-imbalance problem. Random forest adaboost was used as the base learners for 10 types of molecular fingerprints and an ensemble method and six data-balancing methods were applied to balance the classes. As a result, the ensemble model using MACCS fingerprint was found to be the best, giving area under the curve of 85.2% ± 0.35%, sensitivity of 81.8% ± 0.65%, and specificity of 76.0% ± 0.12% in fivefold cross-validation and area under the curve of 78.8%, sensitivity of 55.5% and specificity of 78.5% in external validation. Good performance also appeared on other datasets with different sizes/degrees of imbalance. To explore the structural commonality of cytotoxic compounds, several substructures were identified as an important reference for substructure alerts. The convincing results indicate that the proposed models are helpful in predicting the cytotoxicity of chemicals.

Whole Genome Sequencing for Determining the Source of Mycobacterium bovis Infections in Livestock Herds and Wildlife in New Zealand

The ability to DNA fingerprint Mycobacterium bovis isolates helped to define the role of wildlife in the persistence of bovine tuberculosis in New Zealand. DNA fingerprinting results currently help to guide wildlife control measures and also aid in tracing the source of infections that result from movement of livestock. During the last 5 years we have developed the ability to distinguish New Zealand (NZ) M. bovis isolates by comparing the sequences of whole genome sequenced (WGS) M. bovis samples. WGS provides much higher resolution than our other established typing methods and greatly improves the definition of the regional localization of NZ M. bovis types. Three outbreak investigations are described and results demonstrate how WGS analysis has led to the confirmation of epidemiological sourcing of infection, to better definition of new sources of infection by ruling out other possible sources, and has revealed probable wildlife infection in an area considered to be free of infected wildlife. The routine use of WGS analyses for sourcing new M. bovis infections will be an important component of the strategy employed to eradicate bovine TB from NZ livestock and wildlife.

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.

Plasmonic Vertically Coupled Complementary Antennas for Dual-Mode Infrared Molecule Sensing

Here we report an infrared plasmonic nanosensor for label-free, sensitive, specific, and quantitative identification of nanometer-sized molecules. The device design is based on vertically coupled complementary antennas (VCCAs) with densely patterned hot-spots. The elevated metallic nanobars and complementary nanoslits in the substrate strongly couple at vertical nanogaps between them, resulting in dual-mode sensing dependent on the light polarization parallel or perpendicular to the nanobars. We demonstrate experimentally that a monolayer of octadecanethiol (ODT) molecules (thickness 2.5 nm) leads to significant antenna resonance wavelength shift over 136 nm in the parallel mode, corresponding to 7.5 nm for each carbon atom in the molecular chain or 54 nm for each nanometer in analyte thickness. Additionally, all four characteristic vibrational fingerprint signals, including the weak CH₃ modes, are clearly delineated experimentally in both sensing modes. Such a dual-mode sensing with a broad wavelength design range (2.5 to 4.5 μm) is potentially useful for multianalyte detection. Additionally, we create a mathematical algorithm to design gold nanoparticles on VCCA sensors in simulation with their morphologies statistically identical to those in experiments and systematically investigate the impact of the nanoparticle morphology on the nanosensor performance. The nanoparticles form dense hot-spots, promote molecular adsorption, enhance near-field intensity 10³ to 10⁴ times, and improve ODT refractometric and fingerprint sensitivities. Our VCCA sensor structure offers a great design flexibility, dual-mode operation, and high detection sensitivity, making it feasible for broad applications from biomarker detection to environment monitoring and energy harvesting.

Characterization of the catalytic properties of the membrane-anchored metalloproteinase ADAM9 in cell-based assays

ADAM9 (A Disintegrin And Metalloprotease 9) is a membrane-anchored metalloproteinase that has been implicated in pathological retinal neovascularization and in tumor progression. ADAM9 has constitutive catalytic activity in both biochemical and cell-based assays and can cleave several membrane proteins, including epidermal growth factor and Ephrin receptor B4; yet little is currently known about the catalytic properties of ADAM9 and its post-translational regulation and inhibitor profile in cell-based assays. To address this question, we monitored processing of the membrane-anchored Ephrin receptor B4 (EphB4) by co-expressing ADAM9, with the catalytically inactive ADAM9 E > A mutant serving as a negative control. We found that ADAM9-dependent shedding of EphB4 was not stimulated by three commonly employed activators of ADAM-dependent ectodomain shedding: phorbol esters, pervanadate or calcium ionophores. With respect to the inhibitor profile, we found that ADAM9 was inhibited by the hydroxamate-based metalloprotease inhibitors marimastat, TAPI-2, BB94, GM6001 and GW280264X, and by 10 nM of the tissue inhibitor of metalloproteinases (TIMP)-3, but not by up to 20 nM of TIMP-1 or -2. Additionally, we screened a non-hydroxamate small-molecule library for novel ADAM9 inhibitors and identified four compounds that selectively inhibited ADAM9-dependent proteolysis over ADAM10- or ADAM17-dependent processing. Taken together, the present study provides new information about the molecular fingerprint of ADAM9 in cell-based assays by showing that it is not stimulated by strong activators of ectodomain shedding and by defining a characteristic inhibitor profile. The identification of novel non-hydroxamate inhibitors of ADAM9 could provide the basis for designing more selective compounds that block the contribution of ADAM9 to pathological neovascularization and cancer.

Chemoinformatic expedition of the chemical space of fungal products

AIM

Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project.

RESULTS

Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties.

CONCLUSION

Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.

The use of PCR-DGGE to determine bacterial fingerprints for poultry and red meat abattoir effluent

Strict legislation and chemical composition monitoring of effluent may be useful, but the data generated do not allow for source tracking, and enforcing legislation remains problematic in the South African setting. These difficulties emphasize the necessity for effluent source traceability. Denaturing gradient gel electrophoresis (DGGE) targeting the V3 region of the 16S rRNA gene was considered as fingerprinting technique for effluent originating from abattoirs slaughtering different animal species. The influence of treatment to remove excess fat from effluent prior to molecular analyses and different PCR approaches on the detection of bacterial diversity were considered. Use of a treatment option to remove fat and a nested PCR approach resulted in up to 51% difference in inter-sample diversity similarity. A robust approach with no pre-treatment to remove PCR inhibitors, such as fat, and direct amplification from genomic DNA yielded optimal/maximal bacterial diversity fingerprints. Repeatable fingerprints were obtained for poultry abattoir effluent over a 4-month period, but profiles for the red meat abattoir varied with maximum similarity detected only 33·2%. Genetic material from faecal indicators Aeromona spp and Clostridium spp were detected. Genera unique to each effluent were present; Anoxybacillus, Patulibacter and Oleispira in poultry abattoir effluent and Porphyromonas and Peptostreptococcus in red meat abattoir effluent.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study was the first to demonstrate the application of denaturing gradient gel electrophoresis (DGGE) to construct bacterial diversity fingerprints for high-throughput abattoir effluents. Proved redundancy of fat removal as PCR inhibitor and change in diversity similarity introduced by nested PCR approach. The importance of limiting excessive handling/processing which could lead to misrepresented diversity profiles was emphasized.

Microbes on the cliff: alpine cushion plants structure bacterial and fungal communities

Plants affect the spatial distribution of soil microorganisms, but the influence of the local abiotic context is poorly documented. We investigated the effect of a single plant species, the cushion plant Silene acaulis, on habitat conditions, and microbial community. We collected soil from inside (In) and outside (Out) of the cushions on calcareous and siliceous cliffs in the French Alps along an elevation gradient (2,000–3,000 masl). The composition of the microbial communities was assessed by Capillary-Electrophoresis Single Strand Conformation Polymorphism (CE-SSCP). Univariate and multivariate analyses were conducted to characterize the response of the microbial beta-diversity to soil parameters (total C, total N, soil water content, N-NH4+,N-NO3-, and pH). Cushions affected the microbial communities, modifying soil properties. The fungal and bacterial communities did not respond to the same abiotic factors. Outside the cushions, the bacterial communities were strongly influenced by bedrock. Inside the cushions, the bacterial communities from both types of bedrock were highly similar, due to the smaller pH differences than in open areas. By contrast, the fungal communities were equally variable inside and outside of the cushions. Outside the cushions, the fungal communities responded weakly to soil pH. Inside the cushions, the fungal communities varied strongly with bedrock and elevation as well as increases in soil nutrients and water content. Furthermore, the dissimilarities in the microbial communities between the In and Out habitats increased with increasing habitat modification and environmental stress. Our results indicate that cushions act as a selective force that counteracts the influence of the bedrock and the resource limitations on the bacterial and fungal communities by buffering soil pH and enhancing soil nutrients. Cushion plants structure microbial communities, and this effect increases in stressful, acidic and nutrient-limited environments.

Intraspecies variability and conopeptide profiling of the injected venom of Conus ermineus

The venom of cone snails (ssp. Conus), a genus of predatory mollusks, is a vast source of bioactive peptides. Conus venom expression is complex, and venom composition can vary considerably depending upon the method of extraction and the species of cone snail in question. The injected venom from Conus ermineus, the only fish-hunting cone snail species that inhabits the Atlantic Ocean, was characterized using nanoNMR spectroscopy, MALDI-TOF mass spectrometry, RP-HPLC and nanoLC-ESI-MS. These methods allowed us to evaluate the variability of the venom within this species. Single specimens of C. ermineus show unchanged injected venom mass spectra and HPLC profiles over time. However, there was significant variability of the injected venom composition from specimen to specimen, in spite of their common biogeographic origin. Using nanoLC-ESI-MS, we determined that over 800 unique conopeptides are expressed by this reduced set of C. ermineus specimens. This number is considerably larger than previous estimates of the molecular repertoire available to cone snails to immobilize prey. These results support the idea of the existence of a complex regulatory mechanism to express specific venom peptides for injection into prey. These intraspecies differences can be a result of a combination of genetic and environmental factors. The differential expression of venom components represents a neurochemical paradigm that warrants further investigation.

Assessment of genome and proteome profiles in cocaine abuse

Until recently, knowledge of the impact of abuse drugs on gene and protein expression in the brain was limited to less than 100 targets. With the advent of high-throughput genomic and proteomic techniques investigators are now able to evaluate changes across the entire genome and across thousands of proteins in defined brain regions and generate expression profiles of vulnerable neuroanatomical substrates in rodent and non-human primate drug abuse models and in human post-mortem brain tissue from drug abuse victims. The availability of gene and protein expression profiles will continue to expand our understanding of the short- and long-term consequences of drug addiction and other addictive disorders and may provide new approaches or new targets for pharmacotherapeutic intervention. This chapter will review gene expression data from rodent, non-human primate and human post-mortem studies of cocaine abuse and will provide a preliminary proteomic profile of human cocaine abuse and explore how these studies have advanced our understanding of addiction.