Abstract Collagen structure in biological tissues imparts its intrinsic physical properties by the formation of several covalent crosslinks. For the first time, two major crosslinks in the skin dihydroxylysinonorleucine (HLNL) and histidinohydroxymerodesmosine (HHMD), were isotopically labelled and then analysed by liquid-chromatography high-resolution accurate-mass mass spectrometry (LC-HRMS) and small-angle neutron scattering (SANS). The isotopic labelling followed by LC-HRMS confirmed the presence of one imino group in both HLNL and HHMD, making them more susceptible to degrade at low pH. The structural changes in collagen due to extreme changes in the pH and chrome tanning were highlighted by the SANS contrast variation between isotopic labelled and unlabelled crosslinks. This provided a better understanding of the interaction of natural crosslinks with the chromium sulphate in collagen suggesting that the development of a benign crosslinking method can help retain the intrinsic physical properties of the leather. This analytical method can also be applied to study artificial crosslinking in other collagenous tissues for biomedical applications. Graphical abstract <<<

Confocal Raman spectroscopy (CRS) is a powerful tool that has been widely used for biological tissue analysis because of its noninvasive nature, high specificity, and rich biochemical information. However, current commercial CRS systems suffer from limited detection regions (450-1750 cm), bulky sizes, nonflexibilities, slow acquisitions by consecutive excitations, and high costs if using a Fourier transform (FT) Raman spectroscopy with an InGaAs detector, which impede their adoption in clinics. In this study, we developed a portable CRS system with a simultaneous dual-wavelength source and a miniaturized handheld probe (120 mm × 60 mm × 50 mm) that can acquire spectra in both fingerprint (FP, 450-1750 cm) and high wavenumber (HW, 2800-3800 cm) regions simultaneously. An innovative design combining 671 and 785 nm lasers for simultaneous excitation through a compact and high-efficiency (>90%) wavelength combiner was implemented. Moreover, to decouple the fused FP and HW spectra, a first-of-its-kind precise Raman spectra separation algorithm (PRSSA) was developed based on the maximum probability (MAP) estimate. The accuracy of spectra separation was greater than 99%, demonstrated in both phantom experiments and human skin measurements. The total data acquisition time was reduced by greater than 50% compared to other CRS systems. The results proved our proposed CRS system and PRSSA's superior capability in fast and ultrawideband spectra acquisition will significantly improve the integration of CRS in the clinical workflow. <<<

Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang <<<

Abstract Collagen structure in biological tissues imparts its intrinsic physical properties by the formation of several covalent crosslinks. For the first time, two major crosslinks in the skin dihydroxylysinonorleucine (HLNL) and histidinohydroxymerodesmosine (HHMD), were isotopically labelled and then analysed by liquid-chromatography high-resolution accurate-mass mass spectrometry (LC-HRMS) and small-angle neutron scattering (SANS). The isotopic labelling followed by LC-HRMS confirmed the presence of one imino group in both HLNL and HHMD, making them more susceptible to degrade at low pH. The structural changes in collagen due to extreme changes in the pH and chrome tanning were highlighted by the SANS contrast variation between isotopic labelled and unlabelled crosslinks. This provided a better understanding of the interaction of natural crosslinks with the chromium sulphate in collagen suggesting that the development of a benign crosslinking method can help retain the intrinsic physical properties of the leather. This analytical method can also be applied to study artificial crosslinking in other collagenous tissues for biomedical applications. Graphical abstract <<<

Mammalian collagen has been widely used as a biomedical material. Nevertheless, there are still concerns about the variability between preparations, particularly with the possibility that the products may transmit animal-based diseases. Many groups have examined the possible application of bioengineered mammalian collagens. However, translating laboratory studies into large-scale manufacturing has often proved difficult, although certain yeast and plant systems seem effective. Production of full-length mammalian collagens, with the required secondary modification to give proline hydroxylation, has proved difficult in E. coli. However, recently, a new group of collagens, which have the characteristic triple helical structure of collagen, has been identified in bacteria. These proteins are stable without the need for hydroxyproline and are able to be produced and purified from E. coli in high yield. Initial studies indicate that they would be suitable for biomedical applications. <<<

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article. <<<

Wenyi Yang, Pingping Wang, Meng Luo, Yideng Cai, Chang Xu, Guangfu Xue, Xiyun Jin, Rui Cheng, Jinhao Que, Fenglan Pang, Yuexin Yang, Huan Nie, Qinghua Jiang, Zhigang Liu, Zhaochun Xu <<<

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity.RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI. <<<

Daniel Levine, Syed Asad Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, Ruiming Wu, Zihe Zheng, Antonio Oliveira Fonseca, Xingyu Chen, Sina Ghadermarzi, Rahul M. Dhodapkar, David van Dijk <<<

AbstractLarge language models like GPT have shown impressive performance on natural language tasks. Here, we present a novel method to directly adapt these pretrained models to a biological context, specifically single-cell transcriptomics, by representing gene expression data as text. Our Cell2Sentence approach converts each cell’s gene expression profile into a sequence of gene names ordered by expression level. We show that these gene sequences, which we term “cell sentences”, can be used to fine-tune causal language models like GPT-2. Critically, we find that natural language pretraining boosts model performance on cell sentence tasks. When fine-tuned on cell sentences, GPT-2 generates biologically valid cells when prompted with a cell type. Conversely, it can also accurately predict cell type labels when prompted with cell sentences. This demonstrates that language models fine-tuned using Cell2Sentence can gain a biological understanding of single-cell data, while retaining their ability to generate text. Our approach provides a simple, adaptable framework to combine natural language and transcriptomics using existing models and libraries. Our code is available at:https://github.com/vandijklab/cell2sentence-ft. <<<

AbstractThe potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities.De-novoenzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility inE.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications. <<<

Neurodegeneration is a phenomenon that occurs in the central nervous system through the hallmarks associating the loss of neuronal structure and function. Neurodegeneration is observed after viral insult and mostly in various so-called 'neurodegenerative diseases', generally observed in the elderly, such as Alzheimer's disease, multiple sclerosis, Parkinson's disease and amyotrophic lateral sclerosis that negatively affect mental and physical functioning. Causative agents of neurodegeneration have yet to be identified. However, recent data have identified the inflammatory process as being closely linked with multiple neurodegenerative pathways, which are associated with depression, a consequence of neurodegenerative disease. Accordingly, pro‑inflammatory cytokines are important in the pathophysiology of depression and dementia. These data suggest that the role of neuroinflammation in neurodegeneration must be fully elucidated, since pro‑inflammatory agents, which are the causative effects of neuroinflammation, occur widely, particularly in the elderly in whom inflammatory mechanisms are linked to the pathogenesis of functional and mental impairments. In this review, we investigated the role played by the inflammatory process in neurodegenerative diseases. <<<

Ramya Undamatla, Olayemi G Fagunloye, Jeffrey Chen, Lia R Edmunds, Anjana Murali, Amanda Mills, Bingxian Xie, Martha M Pangburn, Ian Sipula, Gregory Gibson, Claudette St Croix, Michael J Jurczak <<<

Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of pathologies that includes steatosis, steatohepatitis (NASH) and fibrosis and is strongly associated with insulin resistance and type 2 diabetes. Changes in mitochondrial function are implicated in the pathogenesis of NAFLD, particularly in the transition from steatosis to NASH. Mitophagy is a mitochondrial quality control mechanism that allows for the selective removal of damaged mitochondria from the cell via the autophagy pathway. While past work demonstrated a negative association between liver fat content and rates of mitophagy, when changes in mitophagy occur during the pathogenesis of NAFLD and whether such changes contribute to the primary endpoints associated with the disease are currently poorly defined. We therefore undertook the studies described here to establish when alterations in mitophagy occur during the pathogenesis of NAFLD, as well as to determine the effects of genetic inhibition of mitophagy via conditional deletion of a key mitophagy regulator, PARKIN, on the development of steatosis, insulin resistance, inflammation and fibrosis. We find that loss of mitophagy occurs early in the pathogenesis of NAFLD and that loss of PARKIN hastens the onset but not severity of key NAFLD disease features. These observations suggest that loss of mitochondrial quality control in response to nutritional stress may contribute to mitochondrial dysfunction and the pathogenesis of NAFLD. <<<

Mingkun Lu , Jiayi Yin , Qi Zhu , Gaole Lin , Minjie Mou , Fuyao Liu , Ziqi Pan , Nanxin You , Xichen Lian , Fengcheng Li , Hongning Zhang , Lingyan Zheng , Wei Zhang , Hanyu Zhang , Zihao Shen , Zhen Gu , Honglin Li , Feng Zhu <<<

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed. <<<

Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F Gao, Elisa Oricchio, Beat Fierz, Didier Trono, Henning Stahlberg, Michael Bronstein, Bruno E Correia <<<

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function. <<<

Rongjie Cheng, Fanying Li, Maolei Zhang, Xin Xia, Jianzhuang Wu, Xinya Gao, Huangkai Zhou, Zhi Zhang, Nunu Huang, Xuesong Yang, Yaliang Zhang, Shunli Shen, Tiebang Kang, Zexian Liu, Feizhe Xiao, Hongwei Yao, Jianbo Xu, Chao Yan, Nu Zhang <<<

Mutations of the RAS oncogene are found in around 30% of all human cancers yet direct targeting of RAS is still considered clinically impractical except for the KRAS mutant. Here we report that RAS-ON (RASON), a novel protein encoded by the long intergenic non-protein coding RNA 00673 (LINC00673), is a positive regulator of oncogenic RAS signaling. RASON is aberrantly overexpressed in pancreatic ductal adenocarcinoma (PDAC) patients, and it promotes proliferation of human PDAC cell lines in vitro and tumor growth in vivo. CRISPR/Cas9-mediated knockout of Rason in mouse embryonic fibroblasts inhibits KRAS-mediated tumor transformation. Genetic deletion of Rason abolishes oncogenic KRAS-driven pancreatic and lung cancer tumorigenesis in LSL-Kras; Trp53 mice. Mechanistically, RASON directly binds to KRAS and inhibits both intrinsic and GTPase activating protein (GAP)-mediated GTP hydrolysis, thus sustaining KRAS in the GTP-bound hyperactive state. Therapeutically, deprivation of RASON sensitizes KRAS mutant pancreatic cancer cells and patient-derived organoids to EGFR inhibitors. Our findings identify RASON as a critical regulator of oncogenic KRAS signaling and a promising therapeutic target for KRAS mutant cancers. <<<

Luis R Lopes, Soledad Garcia-Hernández, Massimiliano Lorenzini, Marta Futema, Olga Chumakova, Dmitry Zateyshchikov, Maria Isidoro-Garcia, Eduardo Villacorta, Luis Escobar-Lopez, Pablo Garcia-Pavia, Raquel Bilbao, David Dobarro, Maria Sandin-Fuentes, Claudio Catalli, Blanca Gener Querol, Ainhoa Mezcua, Jose Garcia Pinilla, Torsten Bloch Rasmussen, Ana Ferreira-Aguar, Pablo Revilla-Martí, Maria Teresa Basurte Elorz, Alicia Bautista Paves, Juan Ramon Gimeno, Ana Virginia Figueroa, Raul Franco-Gutierrez, Maria Eugenia Fuentes-Cañamero, Marina Martinez Moreno, Martin Ortiz-Genga, Jesus Piqueras-Flores, Karina Analia Ramos, Ainars Rudzitis, Luis Ruiz-Guerrero, Ricardo Stein, Mayte Triguero-Bocharán, Luis de la Higuera, Juan Pablo Ochoa, Dad Abu-Bonsrah, Cecilia Y T Kwok, Jacob B Smith, Enzo R Porrello, Mohammed M Akhtar, Joanna Jager, Michael Ashworth, Petros Syrris, David A Elliott, Lorenzo Monserrat, Perry M Elliott <<<

AIMS: The aim of this study was to determine the frequency of heterozygous truncating ALPK3 variants (ALPK3tv) in patients with hypertrophic cardiomyopathy (HCM) and confirm their pathogenicity using burden testing in independent cohorts and family co-segregation studies.METHODS AND RESULTS: In a discovery cohort of 770 index patients with HCM, 12 (1.56%) were heterozygous for ALPK3tv [odds ratio(OR) 16.11, 95% confidence interval (CI) 7.94-30.02, P = 8.05e-11] compared to the Genome Aggregation Database (gnomAD) population. In a validation cohort of 2047 HCM probands, 32 (1.56%) carried heterozygous ALPK3tv (OR 16.17, 95% CI 10.31-24.87, P < 2.2e-16, compared to gnomAD). Combined logarithm of odds score in seven families with ALPK3tv was 2.99. In comparison with a cohort of genotyped patients with HCM (n = 1679) with and without pathogenic sarcomere gene variants (SP+ and SP-), ALPK3tv carriers had a higher prevalence of apical/concentric patterns of hypertrophy (60%, P < 0.001) and of a short PR interval (10%, P = 0.009). Age at diagnosis and maximum left ventricular wall thickness were similar to SP- and left ventricular systolic impairment (6%) and non-sustained ventricular tachycardia (31%) at baseline similar to SP+. After 5.3 ± 5.7 years, 4 (9%) patients with ALPK3tv died of heart failure or had cardiac transplantation (log-rank P = 0.012 vs. SP- and P = 0.425 vs. SP+). Imaging and histopathology showed extensive myocardial fibrosis and myocyte vacuolation.CONCLUSIONS: Heterozygous ALPK3tv are pathogenic and segregate with a characteristic HCM phenotype. <<<

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development. <<<

Natalie I Mazur, Jonne Terstappen, Ranju Baral, Azucena Bardají, Philippe Beutels, Ursula J Buchholz, Cheryl Cohen, James E Crowe, Clare L Cutland, Linda Eckert, Daniel Feikin, Tiffany Fitzpatrick, Youyi Fong, Barney S Graham, Terho Heikkinen, Deborah Higgins, Siddhivinayak Hirve, Keith P Klugman, Leyla Kragten-Tabatabaie, Philippe Lemey, Romina Libster, Yvette Löwensteyn, Asuncion Mejias, Flor M Munoz, Patrick K Munywoki, Lawrence Mwananyanda, Harish Nair, Marta C Nunes, Octavio Ramilo, Peter Richmond, Tracy J Ruckwardt, Charles Sande, Padmini Srikantiah, Naveen Thacker, Kody A Waldstein, Dan Weinberger, Joanne Wildenbeest, Dexter Wiseman, Heather J Zar, Maria Zambon, Louis Bont <<<

Respiratory syncytial virus is the second most common cause of infant mortality and a major cause of morbidity and mortality in older adults (aged >60 years). Efforts to develop a respiratory syncytial virus vaccine or immunoprophylaxis remain highly active. 33 respiratory syncytial virus prevention candidates are in clinical development using six different approaches: recombinant vector, subunit, particle-based, live attenuated, chimeric, and nucleic acid vaccines; and monoclonal antibodies. Nine candidates are in phase 3 clinical trials. Understanding the epitopes targeted by highly neutralising antibodies has resulted in a shift from empirical to rational and structure-based vaccine and monoclonal antibody design. An extended half-life monoclonal antibody for all infants is likely to be within 1 year of regulatory approval (from August, 2022) for high-income countries. Live-attenuated vaccines are in development for older infants (aged >6 months). Subunit vaccines are in late-stage trials for pregnant women to protect infants, whereas vector, subunit, and nucleic acid approaches are being developed for older adults. Urgent next steps include ensuring access and affordability of a respiratory syncytial virus vaccine globally. This review gives an overview of respiratory syncytial virus vaccines and monoclonal antibodies in clinical development highlighting different target populations, antigens, and trial results. <<<

Since the 1990s, a novel third approach to vaccination against a broad array of target antigens and diseases has been in development. This technology involves the direct administration of plasmid deoxyribonucleic acid (DNA) containing the gene encoding the immunogen against which an immune response is sought, leading to the in-situ production of the target immunogen(s) in the vaccine recipient. Such vaccines are referred to as “plasmid DNA vaccines” or simply “DNA vaccines” (with both terms used interchangeably throughout these Guidelines). The value and advantages of plasmid DNA products need to be assessed on a case-by-case basis; their utility will depend upon: (a) the nature of the organism being vaccinated against or the targeted disease; (b) the nature of the immunogen or activity of the gene insert; (c) the type of immune response required for effectiveness; and (d) the delivery system and route of administration. The development and application of DNA vaccines continues to progress. Since the WHO Guidelines for assuring the quality and nonclinical safety evaluation of DNA vaccines were adopted by the Expert Committee for Biological Standardization in 2005, many clinical trials of DNA vaccines have taken place and considerable experience in their manufacture and control has accrued. The current revision reflects this experience, especially in relation to data derived from nonclinical and clinical safety testing, which address many of the concerns expressed in previous versions of these Guidelines. The control of DNA vaccines should continue to be approached in a flexible manner to enable further modifications as more experience is gained in their production and use, and as other components or delivery systems are included. The intention of the current document is to provide a scientifically sound basis for the consistent manufacture and control of DNA vaccines for human use to ensure their continued safety and efficacy following licensure. <<<

It has been suggested that beige fat thermogenesis is tightly controlled by epigenetic regulators that sense environmental cues such as temperature. Here, we report that subcutaneous adipose expression of the DNA demethylase TET1 is suppressed by cold and other stimulators of beige adipocyte thermogenesis. TET1 acts as an autonomous repressor of key thermogenic genes, including Ucp1 and Ppargc1a, in beige adipocytes. Adipose-selective Tet1 knockout mice generated by using Fabp4-Cre improves cold tolerance and increases energy expenditure and protects against diet-induced obesity and insulin resistance. Moreover, the suppressive role of TET1 in the thermogenic gene regulation of beige adipocytes is largely DNA demethylase-independent. Rather, TET1 coordinates with HDAC1 to mediate the epigenetic changes to suppress thermogenic gene transcription. Taken together, TET1 is a potent beige-selective epigenetic breaker of the thermogenic gene program. Our findings may lead to a therapeutic strategy to increase energy expenditure in obesity and related metabolic disorders. <<<