Building momentum through networks: Bioimaging across the Americas

In September 2023, the two largest bioimaging networks in the Americas, Latin America Bioimaging (LABI) and BioImaging North America (BINA), came together during a 1-week meeting in Mexico. This meeting provided opportunities for participants to interact closely with decision-makers from imaging core facilities across the Americas. The meeting was held in a hybrid format and attended in-person by imaging scientists from across the Americas, including Canada, the United States, Mexico, Colombia, Peru, Argentina, Chile, Brazil and Uruguay. The aims of the meeting were to discuss progress achieved over the past year, to foster networking and collaborative efforts among members of both communities, to bring together key members of the international imaging community to promote the exchange of experience and expertise, to engage with industry partners, and to establish future directions within each individual network, as well as common goals. This meeting report summarises the discussions exchanged, the achievements shared, and the goals set during the LABIxBINA2023: Bioimaging across the Americas meeting.

A distributed geometric rewiring model

We propose a distributed rewiring model which starts with a planar graph embedded into the Euclidean space and then behaves as a distributed system, where each node is provided with a set of dynamic links. The proposed rewiring evolves through cycles, where nodes explore the network to identify possible shortcuts and rewire their dynamic links. The rewiring decisions are subject to Euclidean and geodesic distance constrains. The emerging networks were assessed through topological and robustness analyses. We found that the networks display a variety of characteristics observed in complex networks encompassing phenomena such as preferential attachment, the distinctive traits of small-world networks, the presence of community structures, and robustness against degradation process. We consider that our proposal can be applied in the design of those self-managed systems in which there is a limitation on communication resources that can be represented by the Euclidean distance and, however, the components themselves can deploy strategies to optimize the transport of information and develop tolerance before contingencies.

Postnatal hypofunction of N-methyl-D-aspartate receptors alters perforant path synaptic plasticity and filtering and impairs dentate gyrus-mediated spatial discrimination

BACKGROUND AND PURPOSE

Transient hypofunction of the NMDA receptor represents a convergence point for the onset and further development of psychiatric disorders, including schizophrenia. Although the cumulative evidence indicates dysregulation of the hippocampal formation in schizophrenia, the integrity of the synaptic transmission and plasticity conveyed by the somatosensorial inputs to the dentate gyrus, the perforant pathway synapses, have barely been explored in this pathological condition.

EXPERIMENTAL APPROACH

We identified a series of synaptic alterations of the lateral and medial perforant paths in animals postnatally treated with the NMDA antagonist MK-801. This dysregulation suggests decreased cognitive performance, for which the dentate gyrus is critical.

KEY RESULTS

We identified alterations in the synaptic properties of the lateral and medial perforant paths to the dentate gyrus synapses in slices from MK-801-treated animals. Altered glutamate release and decreased synaptic strength precede an impairment in the induction and expression of long-term potentiation (LTP) and CB1 receptor-mediated long-term depression (LTD). Remarkably, by inhibiting the degradation of 2-arachidonoylglycerol (2-AG), an endogenous ligand of the CB1 receptor, we restored the LTD in animals treated with MK-801. Additionally, we showed for the first time, that spatial discrimination, a cognitive task that requires dentate gyrus integrity, is impaired in animals exposed to transient hypofunction of NMDA receptors.

CONCLUSION AND IMPLICATIONS

Dysregulation of glutamatergic transmission and synaptic plasticity from the entorhinal cortex to the dentate gyrus has been demonstrated, which may explain the cellular dysregulations underlying the altered cognitive processing in the dentate gyrus associated with schizophrenia.

Ionic Liquid-Assisted Thermal Evaporation of Bimetallic Ag-Au Nanoparticle Films as a Highly Reproducible SERS Substrate for Sensitive Nanoplastic Detection in Complex Environments

Nanoplastic particles are emerging as an important class of environmental pollutants in the atmosphere that have adverse effects on our ecosystems and human health. While many methods have been developed to quantitatively detect nanoplastics; however, sensitive detection at low concentrations in a complex environment remains elusive. Herein, we demonstrate a greener method to fabricate a surface-enhanced Raman spectroscopy (SERS) substrate consisting of self-assembled plasmonic Ag-Au bimetallic nanoparticle (NP) films for quantitative SERS detection of nanoplastics in complex media. The self-assembly of Ag-Au bimetallic NPs was achieved through thermal evaporation onto a vapor-phase compatible ionic liquid based on deep eutectic solvent over the growth substrate. The finite-difference time-domain simulation revealed that the localized field enhancement is strong in the gaps, which generate uniform SERS "hotspots" in the obtained substrate. Benefiting from highly accessible SERS "hotspots" at the gaps, the SERS substrate exhibits excellent sensitivity for detecting crystal violet with a limit of detection (LOD) as low as 10-14 M and excellent reproducibility (RSD of 5.8%). The SERS substrate is capable of detecting PET nanoplastics with LOD as low as 1 μg/mL and about 100 μg/mL in real samples such as tap water, lake water, diluted milk, and wine. Moreover, we also validated the feasibility of the designed SERS substrate for the practical detection of PET nanoplastics collected from commercial drinking water bottles, and it showed great potential applications for sensitive detection in actual environments.

Laurencia johnstonii extract reverses early lesions in the K14E7HPV16 murine cervical carcinogenesis model

Persistent infection with high-risk human papillomavirus (HR-HPV) is a well-established risk factor to the development of cervical intraepithelial neoplasia (CIN), a condition that can progress to cervical cancer (CC) a major health problem worldwide. Recently, there has been growing interest in exploring alternative therapies utilizing natural products, among which is the algae species Laurencia johnstonii Setchell & Gardner, 1924 (L. johnstonii), proposed for the management of precancerous lesions. The aim of this work was to determine the effect of an organic extract from L. johnstonii (ELj) in early cervical lesions (CIN 1). These CIN 1 lesions were generated in a murine model expressing the HR-HPV16 E7 oncoprotein (K14E7HPV transgenic mice) with a single exogenous hormonal stimulus using 17β-estradiol. The histopathological studies, the determination of cell proliferation and of the apoptotic levels in cervical tissue, showed that, seven doses of ELj (30 mg/kg weight per day diluted in a DMSO-saline solution [1:7]) lead to recovery the architecture of cervical epithelium. Accordingly, in the transgenic mice it was observed a statistically significant decrease of the PCNA expression levels, a marker of cell proliferation, and a statistically significant increase in the apoptosis levels using Caspase 3 as a marker. In addition, we determined the expression levels of the tumor suppressor miR-218 and the oncomiRNA miR-21. Interestingly, our results may suggest that ELj treatment tended to restore the normal expression of both miRNAs as compared with controls being more evident in the non-transgenic induced mice. Differences of p < 0.05 were considered statistically significant through the whole study. Based on these results, we propose that the use of ELj could be an alternative for the treatment of cervical early lesions.

Transport-controlled growth decoupling for self-induced protein expression with a glycerol-repressible genetic circuit

Decoupling cell formation from recombinant protein synthesis is a potent strategy to intensify bioprocesses. Escherichia coli strains with mutations in the glucose uptake components lack catabolite repression, display low growth rate, no overflow metabolism, and high recombinant protein yields. Fast growth rates were promoted by the simultaneous consumption of glucose and glycerol, and this was followed by a phase of slow growth, when only glucose remained in the medium. A glycerol-repressible genetic circuit was designed to autonomously induce recombinant protein expression. The engineered strain bearing the genetic circuit was cultured in 3.9 g L-1 glycerol + 18 g L-1 glucose in microbioreactors with online oxygen transfer rate monitoring. The growth was fast during the simultaneous consumption of both carbon sources (C-sources), while expression of the recombinant protein was low. When glycerol was depleted, the growth rate decreased, and the specific fluorescence reached values 17% higher than those obtained with a strong constitutive promoter. Despite the relatively high amount of C-source used, no oxygen limitation was observed. The proposed approach eliminates the need for the substrate feeding or inducers addition and is set as a simple batch culture while mimicking fed-batch performance.

Hemodialysis Wastewater Treatment via Electrocoagulation and Electro-Oxidation: Modular Pilot-Level Modeling and Simulation

Hemodialysis treatment in specialized clinics within the same hospital significantly impacts environmental water health due to contaminated wastewater. The issues observed included changes in electrical conductivity, the presence of dangerous bacterial loads, toxicity from heavy metals, total cyanide content, and helminth parasite eggs. The level of damage is dependent on the patient's health under treatment. This research will use a modular system that employs electrocoagulation and electro-oxidation processes at the laboratory and pilot levels to treat hemodialysis wastewater using synthetically prepared and real samples extracted from local clinics. The results showed that these hybrid systems improved various physicochemical parameters. Specifically, decreases in electrical conductivity of 49 %, total suspended solids of 27-100 %, chemical oxygen demand of 49 %, biochemical oxygen demand of 49 %, and cation and anion loading were observed at 96-100 % and pH 8.13 UpH in accordance with the established standards. With these results and the experimental conditions used, the proposed treatment system was modeled using the GPS-X program, and it was concluded that the modular system used and the electrocoagulation/electro-oxidation/activated carbon configuration is suitable for treating wastewater from hemodialysis and that scaling up this process to facilities that have dialysate machines more advanced than those considered in this work is possible.

Synergistic effect of antagonists to KRas4B/PDE6 molecular complex in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among all human cancers as it is highly resistant to chemotherapy. K-Ras mutations usually trigger the development and progression of PDAC. We hypothesized that compounds stabilizing the KRas4B/PDE6δ complex could serve as PDAC treatments. Using in silico approaches, we identified the small molecules C14 and P8 that reduced K-Ras activation in primary PDAC cells. Importantly, C14 and P8 significantly prevented tumor growth in patient-derived xenotransplants. Combined treatment with C14 and P8 strongly increased cytotoxicity in PDAC cell lines and primary cultures and showed strong synergistic antineoplastic effects in preclinical murine PDAC models that were superior to conventional therapeutics without causing side effects. Mechanistically, C14 and P8 reduced tumor growth by inhibiting AKT and ERK signaling downstream of K-RAS leading to apoptosis, specifically in PDAC cells. Thus, combined treatment with C14 and P8 may be a superior pharmaceutical strategy to improve the outcome of PDAC.

mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data

MOTIVATION

Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure.

RESULTS

We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails.

AVAILABILITY AND IMPLEMENTATION

The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

PhyEffector, the First Algorithm That Identifies Classical and Non-Classical Effectors in Phytoplasmas

Phytoplasmas are the causal agents of more than 100 plant diseases in economically important crops. Eleven genomes have been fully sequenced and have allowed us to gain a better understanding of the biology and evolution of phytoplasmas. Effectors are key players in pathogenicity and virulence, and their identification and description are becoming an essential practice in the description of phytoplasma genomes. This is of particular importance because effectors are possible candidates for the development of new strategies for the control of plant diseases. To date, the prediction of effectors in phytoplasmas has been a great challenge; the reliable comparison of effectoromes has been hindered because research teams have used the combination of different programs in their predictions. This is not trivial since significant differences in the results can arise, depending on the predictive pipeline used. Here, we tested different predictive pipelines to create the PhyEffector algorithm; the average value of the F1 score for PhyEffector was 0.9761 when applied to different databases or genomes, demonstrating its robustness as a predictive tool. PhyEffector can recover both classical and non-classical phytoplasma effectors, making it an invaluable tool to accelerate effectoromics in phytoplasmas.

Cell-penetrating peptides predictors: A comparative analysis of methods and datasets

Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.

In vitro anti-Trypanosoma cruzi activity of methanolic extract of Bidens pilosa and identification of active compounds by gas chromatography-mass spectrometry analysis

Chagas disease, caused by Trypanosoma cruzi parasite, is a significant but neglected tropical public health issue in Latin America due to the diversity of its genotypes and pathogenic profiles. This complexity is compounded by the adverse effects of current treatments, underscoring the need for new therapeutic options that employ medicinal plant extracts without negative side effects. Our research aimed to evaluate the trypanocidal activity of Bidens pilosa fractions against epimastigote and trypomastigote stages of T. cruzi, specifically targeting the Brener and Nuevo León strains-the latter isolated from Triatoma gerstaeckeri in General Terán, Nuevo León, México. We processed the plant's aerial parts (stems, leaves, and flowers) to obtain a methanolic extract (Bp-mOH) and fractions with varying solvent polarities. These preparations inhibited more than 90% of growth at concentrations as low as 800 μg/ml for both parasite stages. The median lethal concentration (LC50) values for the Bp-mOH extract and its fractions were below 500 μg/ml. Tests for cytotoxicity using Artemia salina and Vero cells and hemolytic activity assays for the extract and its fractions yielded negative results. The methanol fraction (BPFC3MOH1) exhibited superior inhibitory activity. Its functional groups, identified as phenols, enols, alkaloids, carbohydrates, and proteins, include compounds such as 2-hydroxy-3-methylbenzaldehyde (50.9%), pentadecyl prop-2-enoate (22.1%), and linalool (15.4%). Eight compounds were identified, with a match confirmed by the National Institute of Standards and Technology (NIST-MS) software through mass spectrometry analysis.

Impact of Drying Process on the Phenolic Profile and Antioxidant Capacity of Raw and Boiled Leaves and Inflorescences of Chenopodium berlandieri ssp. berlandieri

C. berlandieri ssp. berlandieri (C. berlandieri) is one of the most common members of the group of plants known as quelites, which are dark leafy greens widely consumed in Mexico. This study aimed to evaluate the impact of two drying procedures (oven drying and freeze-drying/lyophilization) on the polyphenolic composition, antioxidant capacity, and proximal chemical analysis of C. berlandieri leaves and inflorescences (raw or boiled). The results indicated that the raw freeze-dried samples had higher amounts (p < 0.05) of total phenolic compounds, total flavonoids, and antioxidant capacity, mainly in the inflorescence. The oven-dried samples showed an increased concentration of polyphenols after boiling, while the lyophilized samples showed a slightly decreased concentration. The drying process was observed to have little impact on the proximal chemical composition. Quantification by UPLC-DAD-ESI-QToF/MS identified up to 23 individual phenolic compounds, with freeze-dried samples showing higher amounts of individual compounds compared with oven-dried. Procyanidin B2 was found exclusively in the inflorescences. The inflorescences have a higher content of phenolic compounds and greater antioxidant capacity than the leaves. Regardless of the drying process, the leaves and inflorescences of C. berlandieri contain an interesting variety of phenolic compounds that may have beneficial effects on health.

Human-modified landscapes driving the global primate extinction crisis

The world's primates have been severely impacted in diverse and profound ways by anthropogenic pressures. Here, we evaluate the impact of various infrastructures and human-modified landscapes on spatial patterns of primate species richness, at both global and regional scales. We overlaid the International Union for the Conservation of Nature (IUCN) range maps of 520 primate species and applied a global 100 km2 grid. We used structural equation modeling and simultaneous autoregressive models to evaluate direct and indirect effects of six human-altered landscapes variables (i.e., human footprint [HFP], croplands [CROP], road density [ROAD], pasture lands [PAST], protected areas [PAs], and Indigenous Peoples' lands [IPLs]) on global primate species richness, threatened and non-threatened species, as well as on species with decreasing and non-decreasing populations. Two-thirds of all primate species are classified as threatened (i.e., Critically Endangered, Endangered, and Vulnerable), with ~86% experiencing population declines, and ~84% impacted by domestic or international trade. We found that the expansion of PAST, HFP, CROP, and road infrastructure had the most direct negative effects on primate richness. In contrast, forested habitat within IPLs and PAs was positively associated in safeguarding primate species diversity globally, with an even stronger effect at the regional level. Our results show that IPLs and PAs play a critical role in primate species conservation, helping to prevent their extinction; in contrast, HFP growth and expansion has a dramatically negative effect on primate species worldwide. Our findings support predictions that the continued negative impact of anthropogenic pressures on natural habitats may lead to a significant decline in global primate species richness, and likely, species extirpations. We advocate for stronger national and international policy frameworks promoting alternative/sustainable livelihoods and reducing persistent anthropogenic pressures to help mitigate the extinction risk of the world's primate species.

Pseudocercospora fijiensis Conidial Germination Is Dominated by Pathogenicity Factors and Effectors

Conidia play a vital role in the survival and rapid spread of fungi. Many biological processes of conidia, such as adhesion, signal transduction, the regulation of oxidative stress, and autophagy, have been well studied. In contrast, the contribution of pathogenicity factors during the development of conidia in fungal phytopathogens has been poorly investigated. To date, few reports have centered on the pathogenicity functions of fungal phytopathogen conidia. Pseudocercospora fijiensis is a hemibiotrophic fungus and the causal agent of the black Sigatoka disease in bananas and plantains. Here, a conidial transcriptome of P. fijiensis was characterized computationally. Carbohydrates, amino acids, and lipid metabolisms presented the highest number of annotations in Gene Ontology. Common conidial functions were found, but interestingly, pathogenicity factors and effectors were also identified. Upon analysis of the resulting proteins against the Pathogen-Host Interaction (PHI) database, 754 hits were identified. WideEffHunter and EffHunter effector predictors identified 618 effectors, 265 of them were shared with the PHI database. A total of 1107 conidial functions devoted to pathogenesis were found after our analysis. Regarding the conidial effectorome, it was found to comprise 40 canonical and 578 non-canonical effectors. Effectorome characterization revealed that RXLR, LysM, and Y/F/WxC are the largest effector families in the P. fijiensis conidial effectorome. Gene Ontology classification suggests that they are involved in many biological processes and metabolisms, expanding our current knowledge of fungal effectors.

Nanomaterial accumulation in boiling brines enhances epithermal bonanzas

Epithermal bonanza-type ores, characterized by weight-percent contents of e.g., gold and silver in a few mm to cm, are generated by mixtures of magmatic-derived hydrothermal brines and external fluids (e.g., meteoric) that transport a variety of metals to the site of deposition. However, the low solubilities of precious metals in hydrothermal fluids cannot justify the high concentrations necessary to produce such type of hyper-enriched metal ore. Here we show that boiling metal-bearing brines can produce, aggregate, and accumulate metal nanomaterials, ultimately leading to focused gold + silver ± copper over-enrichments. We found direct nano-scale evidence of nanoparticulate gold- and/or silver-bearing ores formed via nonclassical growth (i.e., nanomaterial attachment) during boiling in an intermediate-sulfidation epithermal bonanza. The documented processes may explain the generation of bonanzas in metal-rich brines from a range of mineral deposit types.

Low-Cost Sensor for Lycopene Content Measurement in Tomato Based on Raspberry Pi 4

Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R² = 0.99 and a mean error of 1.9 × 10^-5. This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.