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Biology subjects

Cruz, D.

Publications and source records attributed to Cruz, D..

5 recordsLinked to original sources

Outbreak of H9N2 avian influenza viruses in lesser rhea in Peru, June-July 2025

Avian influenza viruses (AIVs) are endemic in the Americas and responsible for outbreaks in both domestic and wild birds that occasionally spill over into humans. We report the first known outbreak of AIV H9N2 in lesser rhea (Rhea pennata), also known as Darwins rhea, in the region of Puno-Peru. The animals in this study lived in an isolated conservation center located in remote highlands above 4,000 m.a.s.l. Between June and July 2025, a total of 46/92 animals were recorded sick, with symptoms including greenish diarrhea (100%), hyporexia (24%), dyspnea (76%), nasal discharge (42%), drowsiness (18%) and isolation from the flock (73%), and 94% later died. Gross pathology exams revealed septicemia characterized by severe hepatitis, pneumonia, tracheitis, enteritis, and encephalitis. Swab and necropsy samples tested positive for Influenza A by PCR and were later identified as H9N2 through whole genome sequencing. We generated complete H9N2 genomes for two individuals. No additional pathogens were found. Phylogenetic analysis across all eight segments revealed that the viruses were low pathogenicity H9N2 AIV strains of North American origin, which indicated this outbreak was a new introduction of the virus into South America. We also performed a comparative mutational analysis and identified multiple mutations previously associated with mammalian host adaptation, increased virulence, increased pathogenicity, and increased virus binding to 2-6 receptors, which may explain the high mortality rates observed despite the supposedly low pathogenicity of the strain. We also identified novel mutations specific to rhea viruses that will need to be experimentally validated. This is the first report of a natural H9N2 systemic infection in an avian host, highlighting a need for increased surveillance efforts for zoonotic influenza viruses with pandemic potential. Author SummaryAvian influenza viruses (AIVs) are endemic in the Americas and cause more than 7,600 infections annually in domestic and wild birds worldwide each year. We report detection of AIV H9N2 in lesser rhea during an outbreak that occurred in June-July 2025 in the Andean highlands of Puno in Peru. Multiple sick animals were reported with symptoms of respiratory and gastrointestinal disease and 94% of them later died. Samples collected tested positive for Influenza A and they were subtyped as H9N2 of low pathogenic origin from North America. This is the third time H9N2 enters South America from North America, presumably through wild birds, some of which migrate along the Pacific Flyway. Comparison with other H9N2 sequences revealed a total of 44 mutations of interest that may explain the elevated death rates observed. Surveillance in wild birds remains patchy at best and needs to be strengthened in order to prevent spillover events into other animals, including humans.

evolutionary biology↗

Multi-region spatial transcriptomics reveals region specific differences in response to amyloid beta (Aβ) plaque induced changes in Alzheimer's Disease (AD)

Alzheimers disease (AD) is the leading cause of dementia affecting 55 million people worldwide. The pathological hallmarks of AD, beta-amyloid (A{beta}) plaques and neurofibrillary tangles (NFT), follow distinct stereotypical patterns of progression across brain regions and trigger a multicellular response that ultimately leads to neuronal loss and cognitive decline. Despite the uniform spread of A{beta} plaque across the cortex during AD progression, different regions demonstrate varying levels of vulnerability and resilience to temporal A{beta} plaque induced changes, such as NFT accumulation. There is a critical gap in our understanding of the cell types and molecular mechanisms that underlie these region-specific differences in resilience to A{beta} plaque induced changes. In this study, we hypothesized that brain region and cell type specific transcriptional responses within the A{beta} microenvironment, and more broadly within the grey matter, may contribute to this variation. We carried out matched multi-region spatial transcriptomics and A{beta} immunofluorescence staining from the entorhinal, occipito-temporal, dorsolateral prefrontal and striate cortices from two individuals with Braak III and Thal 4 AD. Spatiotemporal comparisons of cell type proportions, gene expression, and cell-cell communication revealed differences in the vulnerability of somatostatin and somatostatin chondrolectin inhibitory neurons and the expression of endosomal and lysosomal trafficking and metallothionein genes within the A{beta} plaque microenvironment. We also observed variations in blood-brain-barrier dysfunction, fibroblast growth factor signaling, and vascular impairment and repair related cell-cell communication networks within the grey matter. Our results demonstrate the value of simultaneously profiling AD-omic and spatial modalities in multiple regions to elucidate how cortical region-specific differences contribute to selective vulnerability and resilience during neurodegeneration.

genomics↗

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method. It was initially designed to extract latent features shared between two assays by finding the linear combinations of features - referred to as canonical vectors (CVs) - within each assay that achieve maximal across-assay correlation. Sparse multiple CCA (SMCCA), a widely-used derivative of CCA, allows more than two assays but can result in non-orthogonal CVs when applied to high-dimensional data. Here, we incorporated a variation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs. Applying our SMCCA-GS method to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS), we identified strong associations between blood cell counts and protein abundance. This finding suggests that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA, similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We further developed Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits. Author SummaryComprehensive understanding of human complex traits may benefit from incorporation of molecular features from multiple biological layers such as genome, epigenome, transcriptome, proteome, and metabolome. CCA is a correlation-based method for multi-omics data which reduces the dimension of each omic assay to several orthogonal components - commonly referred to as canonical vectors (CVs). The widely-used SMCCA method allows effective dimension reduction and integration of multi-omics data, but suffers from potentially highly correlated CVs when applied to high-dimensional omics data. Here, we improve the statistical independence among the CVs by adopting a variation of the GS algorithm. We applied our SMCCA-GS method to proteomic and methylomic data from two cohort studies, MESA and JHS. Our results reveal a pronounced effect of blood cell counts on protein abundance, strongly suggesting blood cell composition adjustment in protein-based association studies may be necessary. Finally, we present SSMCCA which allows supervised CCA analysis for the association between one phenotype of interest and more than two assays. We anticipate that SMCCA-GS would help reveal meaningful system-level factors from biological processes involving features from multiple assays; and SSMCCA would further empower interrogation of these factors for phenotypic traits related to health and diseases.

genomics↗

PFRED: A computational platform for siRNA and antisense oligonucleotides design.

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred.

bioinformatics↗

Transcriptomic Organization of Human Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) affects approximately 8% of the general population, with higher rates in extreme stress groups, including combat veterans or victims of sexual assault. Despite extensive study of the neurobiological correlates of PTSD, little is known about its molecular substrates. Here differential gene expression and network analyses of 4 prefrontal cortex (PFC) postmortem subregions of male and female PTSD subjects demonstrates extensive remodeling of the transcriptomic landscape. The data revealed a highly connected down-regulated set of interneuron transcripts in the most significant gene network associated with PTSD and integration of this data with genotype data from the largest PTSD GWAS identified the interneuron synaptic gene ELFN1 as conferring significant genetic liability for PTSD. We also identified marked sexual dimorphism in the transcriptomic signatures that could contribute to the higher rates of PTSD in women. Comparison with a matched major depressive disorder (MDD) cohort revealed significant divergence between the molecular profiles of subjects with PTSD and depression despite their high comorbidity. Our analysis provides convergent systems-level evidence of genomic networks within the PFC that contribute to the pathophysiology of PTSD in humans.

genomics↗